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This thesis consists of three essays investigating the asset pricing implications of technologies and corporate adaptation. The first essay examines the role and impact of technology stocks on the value premium. The second essay shows the value and risk implications of corporate adaptation to the Covid crisis based on information shocks from voluntary work-from-home announcements. The third essay studies firms' stock and operational performances in adopting new technologies.

In this thesis, I present three essays examining how machine learning techniques can be used to enhance traditional approaches to credit default forecasting and pricing. In the first essay, we provide empirical evidence in favour of a widespread non-linear, time-varying relationship between sovereign credit risk and macroeconomic fundamentals across OECD countries. Random forests significantly outperform sparse and dense linear predictive models and explain up to 80% of the out-of-sample variation in CDS spreads by conditioning on macroeconomic fundamentals alone. This suggests that non-linearity may represent a key modelling feature in capturing the cross-country variation in sovereign credit risk. A set of pure out-of-sample implementations also suggest that tree-based methods may enable "shadow" sovereign CDS pricing for countries and periods in which reliable sovereign CDS data might not be available. In the second essay, we utilise a unique peer-to-peer (P2P) loan dataset to compare different machine learning approaches to predicting loan default over 2017-2021, a period that covers the Covid crisis. We find that P2P loan default factors appear stable over time, with total borrowing and account age the most important predictors across both pre-Covid and Covid sample periods. We subsequently show that the out-of-sample default predictability of short-maturity loans is considerably lower than for long-maturity loans, particularly during Covid. Higher loan repayment-to-income ratios render short-maturity loans more susceptible to Covid-driven income shocks not captured at loan origination. Furthermore, we document a structural break in the relation between default risk and payment holiday adoption rates for borrowers that are highly uncertain in their ability to repay a loan, consistent with the hypothesis that high degrees of financial uncertainty led to precautionary borrowing and subsequent precautionary payment holiday behaviour during the Covid crisis. In my final essay, I explore whether loan defaults during Covid were primarily influenced by borrower credit histories or income shocks. Monthly post-origination data captures Covid-driven income shocks unseen in borrower credit histories and results in a significant improvement in the ability to predict defaults relative to credit history data alone. This effect is stronger for shorter default windows and shorter maturity loans and helps to minimise information asymmetry between borrowers and lenders. Crucially, credit history data explains only 25% of the mean default forecast during Covid. Considering these findings, I am the first to explore the concept of an interest rate "reset clause" for P2P loans. I show that such a reset clause reduces the number of mispriced loans during both Covid and non-Covid periods, resulting in significant cost savings for lenders and borrowers alike.

Entrepreneurship has come to serve as a platform for innovation with majority of the world's economies seeking to promote structures, policies and environments that encourage it. For entrepreneurial firms to succeed, availability of finance has been identified as a key factor; however, despite availability of new financial tools, a demand‐side gap still exists. This is quite important as entrepreneurial firms have particularly high hurdles to cross when seeking finance due to the complexity and uncertainty associated with their products/services. Several studies have investigated the access to finance problem without yielding unique results, this study adopts a novel conceptual framework where the intellectual capital of firm and its social media capabilities are examined to investigate the empirical relationship that exists between intellectual capital and access to finance. Entrepreneurial orientation and collaboration act as mediators with environmental munificence acting as the moderator. Theoretical triangulation is applied to this study as two main theories namely the Agency theory which provides a framework to assess the agency problems which are information asymmetry and moral hazard within an entrepreneurial setting. The second theory is the resource dependency theory which highlights dependencies created between firms and financiers when external finance is raised. This research adopts a quantitative methodology using questionnaire instrument to collect 488 valid responses. The responses were collected using both in‐person and online channels. The researcher had to engage in entrepreneurial activities to build their social capital by organising global events online, hosting online fireside chats, volunteering on various entrepreneurship mentoring platforms (Posters can be found in the appendix). Data collected was analysed using covariance based structural equation modelling. A detailed and rigorous analysis of data collected is carried out to test the measurement and structural model. Findings include but are not limited to the following: There is a direct and positive relationship between social capital and access to finance due to its legitimising qualities, social media is found to promote collaboration in the firm and with competitors. Structural capital is seen to have a positive relationship with access to finance as robust processes within a firm enables it mitigate information asymmetry problems. Firms possessing entrepreneurial orientation and collaboration capabilities are seen to leverage on their intellectual capital more efficiently thus aiding them to access finance. There is also a positive and direct relationship between environmental munificence and access to finance whereby firms operating in resource rich environments tend to raise external finance more easily. Several contributions are made to academia, practice and policy making that highlight the paramount role of intellectual capital which has become more pronounced as the world moves towards service‐based economies.

The study seeks to investigate the relationship between Business Ownership Structure (Managerial Ownership, Institutional Ownership, Block Holding Owning 5% or More, and Free Float Ownership) and Financial Performance (ROA, ROE, and Tobin Q) in the top 50 Egyptian stock exchange firms. To accomplish this goal, the literature was reviewed, and thus the research hypotheses were developed. Previous studies identified the Business Ownership Structure dimensions of managerial ownership, institutional ownership, block holding owning 5% or more, and free float ownership. On the other hand, earlier studies looked at metrics for a company's financial performance, including Tobin Q, ROA, and ROE. As a result, the deductive method was applied, and research hypotheses were created and put to the test using statistical methods. Using secondary data, the research hypotheses were evaluated. The relationship between the dimensions of the business ownership structure and each of the financial performance indicators-ROA, ROE, and Tobin Q-is the focus of the research hypotheses. Data was gathered from the official websites of Egypt's stock exchange for the most active fifty companies on the Egyptian stock exchange from 2004 to 2019. The data was then analyzed with SPSS, and the research results and findings were drawn. After using SPSS to analyze the data, conclusions and research findings were generated. The findings showed there was no evidence to support the first hypothesis, which claimed there was a connection between managerial ownership and financial performance (ROA, ROE, and Tobin Q). While the second hypothesis, which claimed that institutional ownership and financial performance (ROA, ROE, and Tobin Q) are significantly correlated, was fully supported. The fourth hypothesis, according to which there is a relationship between free float ownership and financial performance (ROA, ROE, and Tobin Q), was also fully supported. However, due to a multicollinearity issue, the third hypothesis, which claimed a connection between block holder ownership and financial performance (ROA, ROE, and Tobin Q), cannot be tested.

Existing whaling historiography frames the end of the traditional whaling era in the Arctic along established interpretational and methodological paradigms. These perspectives consider the industry almost entirely within the context of ships, men, whales and the sea. This thesis shifts its attention to the industry's historical peripheries to examine the people, connections and spaces which existed within Dundee's widening scope of Arctic commercial activities from 1858 to 1922. As bowhead whale populations diminished, the trade began to diversify its financial base of support, explore additional natural resource to exploit on an industrial scale, and change the dynamics of its labour requirements. Going beyond the men on whaling ships, the thesis seeks to identify what other people and activities defined this enterprise. Research therefore introduces the term 'Arctic trade' to comprehend its investigation into this transitive era more fully. The thesis adopts a transnational/global history approach, enabling it to shift from a national perspective to a changing mix of local, regional and transoceanic historical scales. This makes it possible for research to recognise the various social and spatial boundaries within the Arctic trade and study where they can be found. Research also considers the ways in which the industry became a variable social anchor within the transmaritime communities it engaged.

Equity-based Crowdfunding (EBC) has been playing an increasingly important role in financing entrepreneurial companies (Signori & Vismara, 2018). Project founders raise funds via online platforms, such as Crowdcube, which also gives project founders direct interaction with potential investors. Through their feedback, investors provide opinions of the business idea, which may prove useful in reshaping and improving the products or services. Although current research on equity-based crowdfunding has correlated various campaign and projects' features to success, there is a lack of convincing conceptual framework that has the potential to incorporate the interplay of the different factors in crowdfunding. In particular, the extant literature has so far attempted to explain success in terms of project characteristics, generally ignoring the characteristics of fund structure and their interrelationship with project characteristics in the success (or failure) of equity crowdfunding. This thesis is aiming to address this gap by building upon signalling theory, goal setting theory and social capital theories to develop a comprehensive conceptual framework that incorporate projects characteristics, fundraising structure and their interplay in the EBC campaign. Hypotheses are developed based on the conceptual framework. The data of the thesis were collected from the 850 projects funded between 2011 and 2019 on EBC platform, Crowdcube, in UK. The data was analysed using a quantitative method, with the dataset being analysed using an Ordinary least squares (OLS) regression and a series of robustness tests using a logistic regression to assess the reliability of my results. The result of this study found that higher firm age, more social media channels, and more management team members in project characteristics can contribute to the success of EBC campaigns, whilst lower funding target, less equity offered, and moderate investment threshold value to gain voting rights in fundraising characteristics can also contribute to the success of EBC campaigns. The results also suggest that project founders may establish a higher investment threshold value for investors to obtain voting rights if they are confident in the project's ability to create substantial cash flows or revenue in the future. Moreover, investors may waive their voting rights if project founders provide signals of higher-quality projects. More importantly, the result of this study found significant interaction effect between different factors, including firm age and social media, equity offered and social media, and equity offered and investment. The findings help to depict a complex web of interacting factors that when considered together, are contributing to the success of EBC. Through investigation of the factors of project characters and its fund structure characters in equity crowdfunding, this thesis assists project founders in setting up their crowdfunding campaign and investors screening out projects with growth potential and advances understanding of Crowdfunding, especially for equity crowdfunding.

The Great Financial Crisis of '07/'08 highlighted the dangers of instabilities in financial systems. In unstable financial systems, an initially small and localized financial shock may be amplified and spread throughout the system in a process referred to as financial contagion. Various channels of contagion exist, and propagate shocks through a deterioration of the liquidity and solvency of institutions, decreases in the market prices of tradable securities, etc. Contagion channels interact and amplify one another, such that the channels' collective impact may far exceed the sum of the individual channels' contributions. The current state-of-the-art for studying the (endogenous) systemic risk generated by interacting contagion channels are so-called 'system-wide stress tests'. These stress tests subject a financial system to an initial stress scenario and simulate how interacting contagion channels propagate the stress across the system. Stress tests provide valuable insights into systemic risk and the stability of financial systems. These insights are limited in scope, however, as a stress test's results are conditional on the (often subjectively imposed) initial stress scenario. We show that measures of systemic risk due to interacting contagion channels that forego the reliance on an initial stress scenario contribute greatly to the understanding of financial stability. Exposures are an important measure of risk that does not rely on an initial stress scenario. When assessing an institution's exposure to the default of a counterparty, traditional exposure measures focus on direct exposures. Since the Global Financial Crisis, indirect exposures via common asset holdings are increasingly recognised too. Yet direct and indirect exposures fail to capture the losses that result from shock propagation and amplification following the counterparty's default. We refer to those spill-over losses as higher-order exposures and contribute to the literature on financial exposures by proposing a way to formalize and quantify them. Using granular data of the South African financial system and a contagion model that captures the most commonly studied contagion channels and their interactions, we show that higher-order exposures make up a significant part of exposures - particularly during times of financial distress when exposures matter most. We also show that higher-order exposures cannot simply be extrapolated from direct or indirect exposures, since they depend strongly on the network structure and the robustness of individual institutions. As the higher-order exposures we propose capture various contagion channels and their interactions without reliance on an initial stress scenario, they are an objective measure of systemic risks that may go unnoticed when using other measures. Like scenario-based stress tests, however, as a measure of systemic risk higher-order exposures are limited in scope because they only capture the losses that follow from the (idiosyncratic) default of an institution in the system. Yet, financial systems may be subjected to various other kinds of shocks. Eigenvalue-based approaches study the inherent tendency of a financial system to dampen or amplify shocks, providing a holistic measure of systemic risk. Current eigenvalue-based approaches, however, only handle a single contagion mechanism. We develop an eigenvalue-based approach that gives the best of both worlds, allowing analysis of multiple, interacting contagion channels without the need to impose a subjective stress scenario. This allows us to demonstrate that the instability due to interacting contagion channels can far exceed that of the sum of the individual channels acting alone, which highlights the importance of capturing these interactions. We also derive an analytic formula in the limit of a large number of institutions that elucidates the mechanisms through which interactions between contagion channels amplify instabilities. We apply the developed eigenvalue-based approach to the data of the South African financial system to study the risk of liquidity spirals emerging that consist of various contagion channels and/or span multiple sectors. We refer to these as complex liquidity spirals and show that the intensity of these liquidity spirals may be severely underestimated when interactions between contagion channels or sectors are overlooked. We capture the collective stability of the banking sector and investment fund sector and show that the stability of the South African financial system strongly depends on how institutions choose to respond to a liquidity shock, with some choices yielding a robust-yet-fragile system. We also show that liquidity spirals are exacerbated when the liquidity of institutions worsens, and that central bank-provided liquidity can greatly dampen liquidity spirals. We study the banking and investment fund sectors' individual contributions to the liquidity spiral and find that market conditions determine which of the two sectors is the main driver of the spiral. The approach developed here can be used to formulate interventions that specifically target the sector that is causing the liquidity spiral.

This PhD investigates innovative development finance, a celebrated trend in development, arguing that, while there is potential, there are also important economic and political costs. Innovative development finance, which has emerged since the millennium, is situated at the intersection of the turn to private finance in development and the financialisation of development. This PhD develops and adopts a follow the money methodology, using critical financial analysis, to analyse three emblematic case studies of innovative development finance: the International Finance Facility for Immunisation, the Caribbean Catastrophe Risk Insurance Facility, and The Currency Exchange. This PhD argues that innovative development finance initiatives make three core development claims: to leverage private finance, manage risk, and create markets. Conceptually, these interventions represent financial solutions to genuine development challenges. Following the money contributes a detailed empirical understanding of the economic and political consequences of the financialisation of development. In contrast to its core claims, innovative development finance fundamentally relies on public finance with no aid additionality, manages risk in a limited way and creates additional financial risks, supports existing financial markets, and is expensive with significant private profit opportunities. Politically, innovative development finance interventions represent technical financial solutions, which I suggest can be understood through the concept of 'rendering financial?', and are ruled by financial experts. The consequences are to de-politicise, financialise, and limit political control and accountability in development. The implications of these findings are significant. In the light of the economic and political costs of the financialisation of development, this PhD also touches on alternatives which deliver many of the same benefits with fewer costs.

Automated trading systems have been present in the world of finance for a number of years and as a result, these systems have been the subject of academic research. The topics of these studies include, but are not limited to, the ability of these trading robots to generate positive financial results over the long-term as well as their ability to accommodate changes in the market environment. However, the existing studies have not focused upon the disruptive nature of these systems and on their optimal operating conditions within different trading environments. Therefore, integrating the automated systems within a particular trading environment has not been investigated to any depth previously. Automated trading systems are capable of working effectively in clearly defined areas of financial trading and their applicability for implementation across the full spectrum of the financial sector is not possible due to the complexities of trading. As a result, the identification of where it is possible to employ automated systems is critical to financial traders as the incorrect application could lead to ineffective trading. Therefore, following on from the work around assessing the impact of automated systems integration, this study will focus on a detailed primary research program that surveys 300 traders within the industry complemented by comprehensive secondary research. The detailed analysis of the gathered primary and secondary data will allow the author to develop a conceptual model which enables individual and institutional traders to identify the best operating conditions for using automated trading systems within a particular trading environment. The model will consider a wide range of variables including: technological factors, market conditions, cultural plus ethical characteristics, and experiential considerations. The focus of the study will be Bulgaria as this provides a unique setting in that the use of such systems is in its infancy and whilst trading in Bulgaria has developed at a tremendous pace over the last several years, trading robots are now becoming the interest of market participants in the country. Still, there is insufficient information about the application of automated trading systems in countries where these systems are at an early stage of implementation, such as Bulgaria, including their ability to generate profits and ultimately to substitute, or at least lower the use of, traditional trading.

Volatility is an important component of market risk analysis and it plays a key role in many financial activities, such as risk management, asset pricing, hedging, and diversification strategies. This thesis consists of four empirical essays that evaluate the utility of a wide range of econometric models as well as explore and propose the use of further novel methods to enhance the understanding of volatility mechanisms across emerging and developed financial markets of Asia. Specifically, the first empirical essay provides an in-depth analysis on the characteristics of volatility phenomenon by comparing various GARCH models using three different frequencies with 24 years of data. The findings reveal robust empirical evidence that asymmetric GARCH models outperform in daily and weekly return series, while symmetric GARCH models outperform in monthly return series, indicating that different frequencies have their own structure and characteristics. The second empirical chapter investigates the forecast ability of a number of representative econometric models belonging to two main model groups based on recursive and rolling window methods. The obtained results report that frequency of the data and choice of forecast method have strong effects on performance of the models. Furthermore, existence of strong volatility asymmetry has been found in the higher frequencies of data which is also systematically confirmed by the superiority of the asymmetric models in daily and weekly series. On the other hand, it is found that the monthly series of Asian stock markets are less sensitive to the leverage effects, thus the predictive capability of symmetric GARCH genre of models are more superior in lower frequencies. The third empirical chapter extended the volatility forecasting exercise by evaluating the utility of advanced Machine Learning models in comparison to traditional forecasting models. The findings indicate that the neural network prediction models exhibit improved forecasting accuracy across both statistical and economic based metrics, offering new insights for market participants, academics, and policymakers. The obtained results are further evaluated by the risk management settings of Value at Risk (VaR) and Expected Shortfall (ES). The final empirical essay introduced an Early Warning System (EWS) by integrating DCC correlations with state-of-the-art Deep Learning (DL) model. The novel results demonstrate that the bursts in volatility spillovers are successfully verified by the proposed model and EWS signals are generated with high accuracy before the 12-month period of crises, providing supplementary information that contributes to the decision-making process of practitioners, as well as offering indicative evidence that facilitate the assessment of market vulnerability to policymakers.

The data used for the research project is secondary  panel data which has been collected from Reserve bank of India database. The technical efficiency and performance of 26 commercial banks of India have been analysed. The technical efficiency scores of banks have been calculated employing the non-parametric frontier method DEA i.e. Data Envelopment Analysis. The inputs and outputs of banking activity used in the DEA method are determined using the intermediation approach. Loan loss provisions, operating expenses, deposits and borrowings are used as inputs and; investments, advances, net interest income and total other income are considered as outputs of the banking activity. After this, the relationship of technical efficiency with profitability of banks has been tested in the two periods. Return on equity (ROE) has been taken as a measure of profitability of banks. Lastly, micro and macro determinants of ROE have been tested in the pre and post crisis periods using regression methods. The internal determinants researched in the thesis are capital adequacy, market share, bank size, bank age, liquidity, non-performing assets and net interest margin and; the external variables are GDP, Inflation, exchange rate and real interest rate.

The empirical evidence relating to the process of making investment decisions of small and medium sized enterprises (SMEs) clusters submits that most SMEs usually fail within the first 5 years of its establishment. This is majorly due to maladministration of finances which in most cases is ordinarily as a result of unprofitable investments. Inefficient capital investment decision making techniques, poor evaluation of capital investments and the failure to incorporate risks before making decisions on capital investments will surge the likelihood of scarce resources being distributed within projects that are not likely to yield optimum earnings over the actual cost of capital. This will in turn affect the profitability and the organization's value as a whole adversely; thereby rendering the whole investment process inefficient. A cross-sectional study was adopted for the purpose of this research and this comprises a final sample of 252 SMEs from four major SME clusters in Lagos, Nigeria. These SME clusters are Otigba Information and Communication Technology cluster Ikeja, International Music and Video Production cluster Alaba, Auto Parts and Mechanical cluster Ladipo, Agribusiness Cluster, Lagos. The study adopted the Taro Yamane's technique used in determining relevant sizes of various samples. This study used a well-thought-out questionnaire which was created by the researcher based on the objectives of the research for data collection. This well-thought-out questionnaire was alienated into six sections: The dependability of this research instrument was verified making use of two different procedures - the Cronbach's alpha and Split Half Techniques. These were adopted to measure the inner stability of the research apparatus, by analysing the scale reliability of the questionnaire. Descriptive statistics involving percentage scores, bar charts, frequency counts and pie charts were used. Also, inferential statistics such as chi-square and Independent Samples T-test was adopted in testing statistical connotation of the association between the variables. The findings of this research established that ranking of projects based on values to be generated on each unit of venture (profitability index) was mostly used as a financial factor in when deciding on capital investment propositions. This should not be surprising since profit making is the major goal of every business and to remain operational, costs must stay well below inflows. Capital investment appraisal was discovered as being significantly linked to greater financial enactment of SMEs. This study concluded however that there existed a substantial variance in the average financial performance scores amongst the SME clusters who regularly used and those who did not use capital investment appraisal techniques.

It is irrefutable that stock markets provide a barometer that can be relied upon to measure the economic condition of a country. As countries increasingly integrating, there is an urgent need for an ongoing and detailed study of the stock market. The stock market of a country plays a pivotal role in transferring funds from surplus units to deficit units. Any unfavourable news within the stock market may not only impact on market participants but also affect the entire economy. Hence, it is quite essential to recognise the factors that affect market favourably or unfavourably. The empirical literature has provided a set of exhaustive factors with either direct or indirect implications on the stock market. However, this research focusses on domestic Macroeconomic factors, which are projected through the application of the Arbitrage Pricing theory. The key macroeconomic drivers used for this research are Inflation, Interest rates, Treasury Bills, Money supply, GDP and stock returns. The study aims at reflecting upon the exogenous channels through which macroeconomic variables influence the stock market. The study seeks to investigate the relationship between the variables using econometric tests, like ARDL bound test, ARDL short-term and Long-term cointegration tests. Additionally, CUSUM test and Variance decomposition statistical tests were adopted to ensure the stability of the model and to make inferences on the causal relationship among the variables. The analysis began with first differencing the variables, and the results from the tests indicated that all the macroeconomic variables explain the variability in US stock returns in the long run except INDPRO. Whereas LM3 and INDPRO can only explain changes in Indian stock returns in the long term. Both stock markets exhibit the opposite relationship with their own domestic economic variables to some extent. In contrast, the variables behave differently in the short run, and so their relationship with stock returns varies. TB and CPI are the two variables that explain the variation in US stock returns in the short term. Whereas LM3 influences the India stock returns and the rest of the variables demonstrate a weak relationship with stock returns in the short run. The study further extends by investigating the stock returns volatility using ARCH & GARCH techniques which suggested the past stock returns influence the future period stock returns volatility for US and India as well as the results indicated that there is a relationship between the volatility of stock market returns and short-run deviations of the macroeconomic variables for both the countries.

This study aims to evaluate, critically and rigorously the weak-form market efficiency and forecasting power of technical analysis in different industries in the developed market, the London Stock Exchange. For this purpose, weekly data were collected from the FTSE-all share index, FTSE-350 general industrial index and twenty companies of four different industries, for the period between 3 March 1997 and 16 July 2017. Friedman test detects that data do not have any seasonality. It is found that there is a statistically significant impact of structural breakpoints for all selected series. Thus, their impacts have been ignored for the purpose of analysing and forecasting stock prices through identifying the plain data of sub-sample periods where there is no structural break for each series from the application of Bai-Perron's multiple breaks test. The descriptive statistics table, histograms, and kernel density graphs show that the weekly closing prices of all the selected series are not normally distributed. The runs test and variance ratio tests document that the stock prices of all the series do not change erratically and randomly. Furthermore, Ljung-Box's serial autocorrelation test evidences that a few series do not have serial auto-correlation at the first difference. Moreover, the ADF-unit root test demonstrates that there is no unit root at the first difference for all series. Therefore, the statistical inference was made that the market is not weak-form efficient in the period of the tests for all series and their stock prices are predictable. This study extends the current literature by considering the existence of weak-form inefficiency in different industrial sectors. It is found that industrial sectors impact market efficiency. The ARIMA and GARCH (1, 1) models contribute to evidence better short-term predictability of stock prices for all series in the manufacturing industry. Contrary to that, these models argue that most of the series in the service industry are not predictable. Therefore, this study does not find any support for weak-form efficiency over the periods tested in the London Stock Exchange. The ARIMA model shows superior prediction power to the GARCH (1,1) model and exponential smoothing techniques for most of the series. On the other hand, exponential smoothing techniques perform even better than ARIMA and GARCH (1, 1) models for a few series in the manufacturing industry due to series or industry characteristics. It is found that certain econometric models are better in certain industry sectors.

The rapid changes in the financial intermediation landscape have significantly impacted the sources of funding available for infrastructure projects. While commercial banks used to dominate the funding sources for project finance, their ability to provide the long-term financing needed for infrastructure projects has been restricted due to the new capital and liquidity requirements introduced by Basel III. This was counterbalanced by an increasing appetite for institutional investors to provide such financing. The increasing participation from institutional investors in financing infrastructure projects with a proportionate decrease in the share of commercial banks to finance these projects are not unfolding at a consistent rate worldwide. While institutional investors have been significantly increasing their relative participation in financing infrastructure projects in advanced economies, their participation in financing such projects in emerging markets has been modest, both in absolute terms and relative to the overall private infrastructure investment in these jurisdictions. Funding sources remained concentrated with commercial banks in the emerging markets but have become more diversified in advanced economies. The thesis assesses the impact of the legal frameworks applicable to institutional financing of infrastructure projects on the appetite of institutional investors to invest in these projects, and aims to identify the legal frameworks that can directly attract or deter such institutional investment in infrastructure projects. The analysis is conducted from supply, demand, and financing instruments perspectives. The supply-side analysis comprises the legal requirements impacting investment decisions by the key types of institutional investors from the lens of the regulations applicable to such investors. The demand-side analysis comprises the legal frameworks relating to an investment in the infrastructure asset classes in the country where the infrastructure asset is located. The financial instruments analysis comprises the tools through which institutional investors can channel their funds to infrastructure projects.

This dissertation investigates how the financial risk management practice of catastrophe modelling is redefining the ontology of natural catastrophe. Drawing from and developing the concept of the 'Anthropocene', referring to co-production of the 'social' and the 'natural' on a planetary scale, the dissertation argues that simulation-based risk modelling of future 'natural' disasters in insurance and reinsurance markets is not just affecting how catastrophe is interpreted by economic agents, economised and financialised, but is also driving changes in the realisation of actual disasters. The thesis calls this recursive dynamic the 'financial ontology of Anthropocene catastrophe'. In developing the argument, the thesis extends actor-network theoretical perspectives on the Anthropocene to take fuller account of market devices, performativity and calculative practices in finance. Documentary research, 62 interviews and 14 participant observation episodes serve to reconstruct current practices of catastrophe modelling and its history since it emerged as a boutique risk management practice in the 1980s. Ultimately, it has become embedded in the calculative practices of some of the largest insurance and financial companies in the world and underpinning a specialist disaster securities market. Adding conceptual depth and fine-grained empirical detail to literature on the financialisation-Anthropocene nexus, the dissertation asks us to reconsider the boundaries between economic representations of the world and the meaning and occurrence of catastrophes in market societies. In an age of anthropogenic climate change, the thesis also serves as an analytical and historical underpinning of epistemic practices in climate finance in the emerging, even more encompassing, 'financial ontology of the Anthropocene'.

The banking and financial markets play a critical role in the modern economic world, which has raised great attention in recent years. Source of researches have put great efforts on the driving forces connecting to the relevant financial markets. The main objective of the thesis is to explore the potential research questions around firm financing channels: bank lending and capital markets. The author developed three independent yet related research topics to address the issues around firms and financial markets. In line with the pecking order theory, the indirect financing is via financial intermediary of which firms can access funds from outside credit. Therefore, the first empirical study investigates the bank loans, especially the syndicated lending markets. The direct finance through pecking order theory access to various financial markets, where the equity market is of primary interests. The author therefore examined the factors contributing to the stock market performance, such as the flight-to-safety effect, and stock performance under the global COVID-19 shock to supply chain, respectively.

Finance is an industry that needs to be regulated and supervised continuously to protect the interests of all its stakeholders. To do so, financial regulatory and supervisory organisations have been set up on the regional, national and international levels. On each level, these organisations support and guide the financial industry and protect its stakeholders. In order to deliver regulatory and supervisory services successfully, financial regulatory and supervisory organisations require a stable organisational structure and systematic operations. This research hence aims to deliver three key objectives. The first objective is to critically analyse the factors that have an impact on the structure of a financial organisation. The second is to identify and analyse the factors that influence the operations of a financial regulatory and supervisory organisation. The third and final objective is to investigate how international standards and enhance cross-border cooperation can be harmonising for the purposes of international financial regulatory authorities. To conduct this research, a qualitative approach to data collection was undertaken by conducting semi-structured interviews. The data was collected from a group of 44 participants, all senior financial regulators from across the globe. Of these, 21 participants came from developed economies, while 16 were from developing economies and seven from international regulatory bodies. The data was then analysed using a thematic analysis approach to examine the findings from the information given by the respondents. The data analysis identified five global themes and 13 organisational themes which were used to evaluate the factors affecting the structure and operations of the financial regulatory and supervisory organisations represented by the respondents. In addition, the themes identified were also used to evaluate the factors and barriers that directly impact upon international financial regulations. The study revealed that the structures of both financial regulatory and supervisory organisations are directly affected by external factors such as politicians, public opinions, market trends and market developments. The findings indicate that radical events, such as financial crises, typically lead to structural and operational changes within financial regulatory and supervisory organisations. The research also revealed that human culture and behaviour impact significantly upon the operations of bothnational and international financial regulatory and supervisory organisations. In terms of operation and structure of international financial regulatory organisations, the research found that a lack of communication, competition and accountability are major factors that hinder the effective application of financial regulatory and supervisory policies. Based on the findings of the study, the researcher suggests that before establishing a structure of financial regulatory authority, the founders should establish clear objectives. They should determine how the authority can be integrated into the existing national or international scenario, endeavour to limit the impact of external influences and then work to promote internal development. In terms of the operations of financial regulatory and supervisory organisations, the research here has emphasised the role and importance of human and cultural aspects of each respective country. Here, the study highlights these factors and recommends the policies that should be put in place to protect the cultural beliefs, norms and bias of each group. In order to harmonise operations between international and national financial regulatory authorities, it is further recommended that these organisations take into account the differences between legal and cultural traditions. These differences apply across their respective regions entailing that these organisations should aim to be independent of political influence and power. Overall, the research delivers an understanding of the factors that affect the structure and operations of financial regulatory and supervisory organisations. It proposes recommendations that can be adopted by these organisation, to help reduce the redundancy of work and encourage increasing cooperation and harmony between the various organisations that govern and supervise the financial industry, both at the national and international level.

As the fields of machine learning, natural language processing, and big data become increasingly important throughout various industries, including finance, it becomes crucial to evaluate how they are being utilised and understand the motivations behind the recommendations of algorithms, in order to make sound decisions based on this information. In finance, particularly, being able to create concise and easy to comprehend tools for understanding machine learning models is extremely beneficial. These tools could serve multiple purposes, including allowing managers to better explain to and convince investors of machine learning based trading strategies. Decision-makers themselves might also be able to act according to the information provided by these tools, which could assist in, for example, making more ethically informed decisions. Thus, this type of research not only has implications in the areas of machine learning, Natural Language Processing (NLP), and finance but also in fields like AI explainability, model interpretability and Responsible Research and Innovation. The central problem this thesis addresses is whether machine learning methods can be used to mine representative sentences from financial text that are able to capture the majority of sentiment in a full document with only a few sentences. These mined sentences are referred to as 'justifications', and the process has been called 'justification mining'. The full documents used here are data taken from 10-K filings. Before examining justification mining, however, transfer learning methods suitable for training data annotated at a different level than testing data (sentence- versus document-level) are assessed. The purpose of this is to address a problem that often occurs in NLP research, where no annotated training data perfectly suited to the research is readily available. Therefore, methods must be created to make use of what is available, in this case sentence-annotated training data being employed to train classifiers for prediction on document-level testing data. These transfer learning methods are then employed in the next step, which focuses on justification mining. The process of justification mining itself is first developed using transformer models to encode embeddings and, then, using clustering algorithms and cosine similarity to extract or mine justifications. This process is evaluated by comparing sentiment from mined justifications to sentiment from full documents, in order to assess the ability of justification mining to capture or summarise sentiment. It is also evaluated by correlating predicted sentiment from mined justifications and full documents to future stock returns, to gauge whether justification mining offers any benefit in identifying signals in the data for financial purposes. Little work has been done previously that evaluates the results of financial sentiment analyses in this way. In the NLP domain, the best way of extracting aggregate justifications for sentiment is still an open question. Moreover, few research papers attempt to apply transfer learning from lower-level data to entire documents. Nor are 10-K filings widely studied in this context, as they are difficult to parse. The methods created in this thesis might offer a novel means of providing information that can assess the motivations behind sentiment analyses. In the transfer learning process, feature engineering and preprocessing steps were modified to obtain accuracies up to 0.903. For justification mining, considering statistically significant (p≤0.05) correlations of |r| > 0.2, sentiment from mined justifications more often correlated with future stock returns than did full document sentiment. Although the correlations (r) were somewhat weak overall, they could potentially be combined with traditional alpha signals to enhance these signals (see Section 11 for further discussion). Moreover, high degrees of similarity were found between aggregated sentiment from mined justifications and full documents, with similarity scores up to 0.9999, supporting the efficacy of justification mining in capturing full document sentiment. In these evaluations, transformer models performed better in numericising text for input into ML models than traditional approaches like Bag of Words. In fact, every statistically significant sentiment to stock return correlation bar one, as well as every mined justification to full document similarity and correlation score above 0.7, used transformer models for numericisation. These results imply that justification mining might be successful in eliminating sentiment noise in financial data, as well as in capturing the majority of sentiment from a full document. Moreover, transformer models might provide an advantage over traditional approaches like Bag of Words for numericisation of text. Mined justifications, themselves, provide an easily interpretable and presentable means of explaining the output of sentiment analysis and have numerous uses, including identifying the driving factors behind sentiment in a financial document, which could be helpful for making more ethically informed decisions or building investor trust in the methodology of sentiment analysis algorithms.

This thesis identifies the circumstances under which corporate governance regulation can help gain traction to minimise systemic risk. Systemic risk is the risk that local losses spread through the financial system and badly affect the financial system and the real economy. Excessive risk-taking by financial institutions can contribute to such systemic risk. Prudential regulation and supervision of financial institutions leave corporate decision-makers with room for discretion to increase or decrease systemic risk. The incentives of these decision-makers are not necessarily aligned with minimising systemic risk. The thesis shows that this problem exists across different business models. More specifically, it identifies perverse incentives in the case of systemically important banks, CCPs, mutual funds, and hedge funds. The need for corporate governance regulation therefore lies in the inherent incompleteness of prudential regulation and supervision. Corporate governance regulation can help fill this gap by regulating the environment within which choices are made within these types of institutions. The analysis has three steps. First, it characterises the systemic importance of financial-sector activities carried on outside the context of 'banks'. The governance literature so far has focused on the 'systemic externalities' created by banks. However, our analysis shows that other non-bank SIFIs generate similar systemic externalities with socially harmful consequences. These systemic externalities are not considered by SIFIs when taking business decisions. In a second step, the thesis shows that prudential regulation and supervision are incomplete and leave room for governance regulation to fill in the gaps. The final step shows that a corporate governance framework focused solely on the interests of shareholders will have negative consequences for systemic stability. Given such divergence between the decision-makers' and society's interests, corporate governance regulation can complement the traditional prudential framework.

Interest in agent-based models of financial markets and the wider economy has increased consistently over the last few decades, in no small part due to their ability to reproduce a number of empirically-observed stylised facts that are not easily recovered by more traditional modelling approaches. Nevertheless, the economic agent-based modelling paradigm faces mounting criticism, focused particularly on the rigour of current validation, calibration, and model selection practices, which still, in many cases, remain qualitative and stylised fact-driven. While the literature on quantitative and data-driven approaches has seen significant expansion in recent years, many studies have focused on the introduction of new methods that are neither benchmarked against existing alternatives nor rigorously tested on the large-scale and computationally-expensive variants that now dominate the literature, instead choosing to focus on highly-simplified models that are no longer representative of the current state of the art. This has ultimately led to some doubt regarding the applicability of current approaches to the kinds of problems of interest to most contemporary modellers and policymakers. In response, this thesis attempts to make a number of meaningful contributions, both by providing a more complete and unified perspective on the existing literature and by additionally advancing it in a number of key areas. To elaborate, in Chapters 1 and 2, we review existing contributions and compare a number of prominent agent-based model estimation methods, both established and new, through the use of an experimental protocol that allows for the determination of the respective strengths and weaknesses of each approach and the quality of the resultant parameter estimates. Overall, we find that a simple, likelihood-based approach to Bayesian estimation consistently outperforms several members of the far more popular class of simulated minimum distance methods and results in reasonable parameter estimates in many contexts, with a degradation in performance observed only when considering a large-scale model and attempting to calibrate a substantial number of its parameters. This ultimately leads us to argue for a paradigm shift, suggesting that an increased emphasis on Bayesian methods is much needed and that attempts should be made to improve the performance of existing techniques. Following on from the above recommendations, Chapter 3 proposes a neural network-based approach to Bayesian estimation that addresses a number of the fundamental weaknesses of the best performing methodology identified in Chapter 2. When benchmarking our proposed approach, we find that it consistently results in more accurate parameter estimates in a variety of settings, including the estimation of financial heterogeneous agent models, the identification of changes in dynamics occurring in models incorporating structural breaks, and applications to a large-scale, state-of-the-art model of the UK housing market. Chapter 4 further builds upon these ideas and demonstrates how the proposed Bayesian estimation methodology may be extended to facilitate the application of contemporary Bayesian model selection tools to a variety of inter- and intraday financial agent-based models and to benchmark such models against traditional asset return modelling approaches. Finally, Chapter 5, which concludes the thesis, demonstrates how recent advances in Gaussian process emulation may be applied to the approaches developed in Chapters 3 and 4 to significantly reduce the overall computational costs and thus yield a computationally-efficient and widely-applicable framework for Bayesian inference in simulation models.

This thesis is concerned with young people's higher education (HE) decisions in Scotland, particularly how school leavers make decisions about where to study and where to live while studying. Using a mixed methods approach, it explores how these decisions are made within the context of the family, and considers the role of family background, finance and region in shaping young people's horizons for action (Hodkinson et al., 1996) and how this serves to limit/expand HE options. These factors remain under-researched in Scotland, where free tuition has tended to frame how most people view the funding of HE and issues of fair access, despite the Scottish system being predicated on the idea of student maintenance loan debt, and policy assumptions that parents will contribute to their children's HE costs. Statistical modelling (binary logistic regression) of Higher Education Statistics Agency (HESA) student records data from 2014/15 was used to predict the likelihood of Scottish-domiciled students 1) living at home and 2) attending a local university. This was complemented with 17 qualitative case studies, recruited from two Scottish secondary schools (one located in Edinburgh and the Lothians and the other in the Strathclyde region). Longitudinal semi-structured interviews (71 in total), undertaken with young people and their parents at the end of S6 and again during their second year of HE, tracked the evolution of young people's HE decision making. Family background and region heavily influenced HEI destinations and term-time accommodation decisions at both the macro and micro levels. The statistical models found that even after controlling for personal characteristics, parental education, school factors and type of HEI, students from working class backgrounds were more likely than those from higher managerial and professional backgrounds to live at home and study locally. The effect of region was pronounced among students from Strathclyde region who behaved differently to those from Edinburgh and the Lothians, irrespective of their social class background. Students from all social class groups in Strathclyde were more likely to live at home, and study locally, than those from the same groups in Edinburgh and the Lothians. This evidence confirms the continued existence of a Strathclyde regional effect, demonstrating how strongly Scottish students' HE destinations and accommodation decisions are shaped by where they live. These social class and regional inequalities were replicated in the case studies. All of the young people from West High lived at home and commuted to local institutions, while the East Academy students travelled considerable distances for study (just one lived at home, and two studied locally). The young people's HE decisions were socially and culturally embedded (Hodkinson et al., 1996). The extent to which they felt financially and culturally able to move away from the parental home and how this influenced institutional decisions was bounded by students' horizons for action, that is, their beliefs of what options were available to them. This in turn was shaped by their parents and their own habitus and dispositions. While students benefitted from living at home in terms of reduced costs, local employment options and supportive family relations, there was also evidence of students limiting their HE options by virtue of local study. The findings point to the enduring effect of family background and region in influencing young people's HE decisions as to where to study and where to live. That social class continues to play such a defining role in these decisions directly challenges the narrative of a fair and egalitarian Scotland in which access to HE is based on the 'ability to learn rather than the ability to pay' (Scottish Government, 2010). Instead, these findings show how unequal access to HE remains in Scotland and how family background, region and other aspects of a young person's context broaden and constrain horizons for action. Even in a system of free tuition, finance matters. The rhetoric of the policy focus on free tuition obscures the reality of student debt for most of Scotland's students, whose decisions are further shaped by the nature of the student support system. Without more effective policy designed to counter inequalities, Scotland risks having a two-tiered system whereby only the children of the most affluent families feel able to leave home and attend institutions further afield.

Severe security breaches can be costly for publicly listed firms. In order to deploy scarce resources in the most efficient way, information security decision makers in such firms need to conduct holistic economic cost-benefit analyses that take into account all costs associated with security breaches and strategic investments in countermeasures. In addition to obvious direct costs following security breaches and investments, there are also multiple indirect underlying and consequential costs introduced or elevated by such events. In this dissertation, four novel cost elements affecting firms' financing costs that are prone to be overlooked in basic cost-benefit analyses are discussed. Over the course of four empirical studies, the effect of security investments on firm value (via abnormal returns) as well as the effect of security breaches on cost of equity (via systematic risk), cost of debt (via credit default swap spreads), and information asymmetries (via insider trading-induced abnormal returns) are analysed. These studies reveal four cost elements that can all be characterised as indirect underlying/consequential financial costs, which ought to be considered in holistic economic information security cost analyses. Analysing a US-centric sample of 202 severe security breaches between 2005 and 2019 revealed that severe security breaches are associated with significantly positive increases of insider trading abnormal returns (hence information asymmetries) and systematic risk (hence cost of equity). However, the examination of CDS spread changes following the same severe breach announcements indicated that CDS spreads (hence default spreads and by implication cost of debt) do not increase significantly in the immediate days surrounding the event, suggesting that costs of debt are subject to different dynamics. Furthermore, assessing an international sample of 221 security investments, the dissertation reveals positive and negative effects on firms' market value following upon small-scale and large-scale security certificate investments, respectively. These market reactions constitute secondary benefits and underlying/consequential costs that also ought to be taken into account for the purpose of expected net benefit of information security (ENBIS) analyses. Collectively, these insights suggest highly relevant information security-induced implications for cost of capital that need to be recognised in ENBIS calculations. Given the absence of a formal definition of the cost type underlying/consequential costs and a framework or model to facilitate assessments of such costs, this dissertation also provides a novel nuanced definition of indirect costs of information security and the Iceberg Model of Information Security Costs, which will help guide further academic research and practitioners' investment endeavours.

This is a study of the history and development of Primitive Methodism in Hertfordshire from 1838 to 1918. It aims to discover and document, principally by means of socio-economic and demographic analysis, firstly, how Primitive Methodism became established in the county and how it grew and developed, secondly, the particular characteristics of Primitive Methodism in Hertfordshire, and, thirdly, the importance of the position of Primitive Methodism in Hertfordshire society that the movement had managed to create for itself by the early years of the twentieth century. The study finds that, in the 94 years of its existence in Hertfordshire, Primitive Methodism spread across the whole of the county and was to be found both in villages and rural areas as well as in towns. It confirms that the chapel was central to the life of local societies and it also provides evidence that Primitive Methodism attracted a distinctive following mainly, but not exclusively, from the lower social classes. In the early twentieth century, rather later than in other parts of the country, there was a noticeable change in emphasis, with a marked shift from the labouring classes to the semi-skilled and the craftsmen. This occurred at the same time as societies became an increasingly accepted and important part of their local communities, as evidenced by the active support of local politicians and other dignitaries. In addition, there was often close collaboration between Primitive Methodists and other nonconformist denominations. The life of the local society at Anstey is considered in detail. This case study demonstrates how the four themes of 'organisation', 'people', 'property' and 'finance' come together at the grassroots level and it illustrates the part that societies played in the growth and development of the Connexion. It also provides an insight into the lives of grassroots members of the Primitive Methodist Connexion, an aspect which is often overlooked since ordinary members rarely feature in the national record. Whilst each local society offered its members a variety of leadership roles, status and responsibility, the pattern of circuit development in Hertfordshire demonstrates considerable flexibility and fluidity as the Connexion responded both to changes on the ground and to outside influences. The study also finds that finance underpinned the very existence of a local society. Although the main purposes of chapel building were to support a society's aspirations regarding its permanence and status in a particular locality, to function as a 'focus for group identity', and to be the heart of its religious and social activities, nevertheless chapel building meant that a society would be encumbered with a considerable burden of debt. However, the chapel was of central importance to the life of Primitive Methodist societies in Hertfordshire, and the records show just how much money, time and effort was spent over the years by all societies in ensuring that it was looked after and remained fit for purpose. This study addresses a missing element in the Connexional story not only by adding to research at the local level but also by advancing understanding of a previously neglected and under-researched area of Primitive Methodist history. It confirms that the Primitive Methodist story in Hertfordshire, whilst distinctive, is nevertheless generally consistent with the broader national picture of nonconformity in the journey from 'dissent' to 'free church'. This study provides further evidence of the extent to which nonconformity, and religion generally, was an important part of 19th and early 20th century society in England. It also adds to knowledge and understanding of the history and development of nonconformity from a national perspective.

This DPhil is motivated by the impact that public data can have on the practise of sustainable finance. Disclosure guidance for environmental information is increasingly being used to compel companies to better manage their environmental risk exposure and environmental impacts. Data-driven technologies can 'unravel' company information related to environmental risk and are changing the information asymmetries between company managers, their investors, and society. Three novel and rigorous data science papers have been prepared which provide new access to company environmental risk information. The first paper develops a global inventory of photovoltaic solar energy facilties using machine learning and earth observation and then uses this dataset to assess land cover impacts due to renewable energy siting decisions. The second paper develops asset-level data arrangements of the coal, oil, and gas supply chains, and identifies concentrations of environmental transition risk in the complex network topology. The third paper advances earth observation and computer vision technical methods, assessing the power of these techniques for producing company asset-level data even with limited training data. In developing these papers, this DPhil has identified the opportunities and limits that technology might have in the provision of information for public and private governance of environmental risk and impact. These limits add clarity to the role that policy must play in meeting the diverse information needs of both company shareholders and stakeholders. A discussion chapter provides commentary on these limits; on how the needs of both shareholders and stakeholders can be met with company risk disclosure at the asset-level; and how innovation might be fostered in markets for environmental information. Policy recommendations are summarised and an agenda for further research is provided.

This thesis presents methodological contributions for the quantification of systemic risk in financial systems. Clearing algorithms that can be used for computing financial contagion effects are presented and their relation to network centrality measures is discussed. An algorithm that allows computing clearing solutions for networks of financial contracts including debt of multiple seniority classes, equity participations as well as contingent convertible debt and bail-in-able debt is presented. We present a valuation function that allows computing network-based valuations of contingent convertible and bail-in-able debt instruments. An application to systemic risk assessment for bail-in decisions using real-world data is presented, showing that bail-ins have the potential to reduce systemic risk in some crisis situations. An alternative approach to systemic risk assessment based on a metamodel of clearing algorithms is presented. We use quantile panel regression for learning the metamodel and present statistical tests for deciding between different quantile panel estimators as well as goodness-of-fit measures. An application with real-world-data shows how regulators could improve their identification of systemically important banks.

Achieving net zero emissions will require an unprecedented transformation of our infrastructure system. Different economies will face varying challenges, but the transformation of the global economy to be environmentally sustainable and resilient includes decarbonising industry and transport, building smart energy systems, and increasing access to affordable, clean energy. Understanding the role of the private sector and financial markets will be crucial in achieving a decisive transition. At the same time, institutional investors such as pension funds are facing increasingly complex and challenging macro-conditions and financial markets. Investors are searching for organizational innovation and reconfiguration of their investment models to meet their long-term objectives. Following the Covid crisis with an increasing interest in sustainability among investors, there is an opportunity to scale up institutional investment in low carbon infrastructure and to protect and grow the retirement savings of fund members. Reframing finance will help to respond to the urgent need to act on energy transition while at the same time supporting the economy. Objective of this thesis is to examine the of role institutional investors and in particular pension funds in sustainable and low-carbon infrastructure, diversifying sources of finance and fostering the development of infrastructure as an asset class. To illustrate how long term investors can promote a more inclusive and sustainable financial system while contributing to the real economy I have adopted an economic geography and bottom up approach benefitting from the unique access to micro data for infrastructure analysis. Following this approach, this thesis builds on literature on financial intermediation and the theory of finance, design and governance of infrastructure, the geography of finance, pension fund research and emerging debates on "relational geometries." Beyond the lack of data, there is a lack of analysis in academia and policy institutions on the connection between policies affecting investors' portfolios strategies and political decisions for low carbon infrastructure. The institutional investor's investment strategies and the decision to invest in infrastructure are based on strategic asset allocation and asset-liability management considerations. This thesis has analyzed how the changing nature of long-term savings is shaping the current and future demand for long-term assets and the impact of demographic and financial pressure on infrastructure investment. Asset allocation analysis has shown the overall trend to diversify investment portfolios in private markets, impact of ESG investment integration and increasing interest in low carbon infrastructure investment. At the same time government choices about different types of private funding models and forms of financing of infrastructure are based on short-term political priorities and national constraints. Renewable energy investment is growing, being part of infrastructure asset allocations. However the limited overall investment in low carbon infrastructure is showing major disconnections between investors and governments expectations. As seen in the thesis, partnership models are emerging as a way to leverage traditional bank and utility finance in primary markets and share risks as well as organizational capacity and expertise in support of the financing of infrastructure projects. Well-functioning and deep capital markets, supported by pooling assets, securitization, yieldcos and new listed vehicles could widen the access to and improve transparency of infrastructure investment while diversifying risk and lowering the cost of capital.

The thesis presents three empirical chapters on the credit risk and industry potential of the Peer-to-Peer (P2P) market. The first empirical chapter explores default risk which considers macroeconomic factors in addition to traditional determinants such as credit scores, loan-to-income level, employability, and the credit history of the borrower. The importance of the macroeconomic environment on the lending market is highlighted by Gremi (2013). Logistic regression has been applied to one of the biggest P2P platforms operating in the United States (US), Lending Club, on borrowers from 2007 to 2016. The results show the grading system and FICO score are the significant factors associated with the default of loans. This thesis also finds a significant negative relationship between the macroeconomic environment and the performance of the P2P loans. The second empirical chapter explores default dependency in P2P lending with a specific focus on asymmetric tail dependency. Five different copula families are used to capture possible non-linearity and asymmetric properties of the loan portfolios. Using the monthly loan dataset of one of the biggest P2P Small and Medium-sized Enterprise (SME) lenders, Funding Circle, from 2010 to 2018, the study covers six different risky loan segments and, therefore, fifteen loan portfolio pairs. From the estimations of the entire distribution and upper tails of fifteen portfolio pairs, the study finds that the Gumbel copula, which represents right-tail dependence, fits the empirical data best in eight out of fifteen portfolios. In three cases, the joint default distribution shows symmetric tail dependence represented by Student's t copula. The results highlight the importance of the joint default distributions of portfolios, in addition to individual defaults of each borrower. The third empirical chapter investigates the industry potential of P2P loans in the UK. Quarterly growth rates of P2P consumer and business loans are fitted to the Bass, Logistic, and Gompertz models from 2005 to 2019. The goodness of fit measures strongly supports the soundness of the Gompertz model for P2P Business and Consumer Lending. Selected models are used to forecast the diffusion pattern for the next 5 years and predict the industry potential of P2P lending. According to the estimations, total cumulative loan allocation through P2P platforms in the UK will reach nearly £24 billion by the end of 2024. The market potential is expected to be triple the current size and reach £32 billion, while the P2P business lending keeping its dominant position, compared to P2P consumer lending.

Algorithmic trading has gained great popularity and has been heavily used by institutional investors. Such a trading system involves mathematical and statistical modelling that makes decisions to buy or sell financial securities by following automated trading instructions. In this thesis, we research machine learning based data-driven approaches to construct such trading rules. We utilise deep learning models to predict stock price movements and develop trading strategies based on the derived signals. Instead of using hand-crafted features, we propose neural network architectures to extract nonlinear features from limit order books data. We expand this work to study Bayesian Deep Learning models and Quantile Regression, obtaining uncertainty estimates on predictive outputs. These uncertainty estimates can be used for position sizing to improve trading performance. However, a mapping from predictive signals to trade positions is non-trivial to construct. To solve this problem, we research Reinforcement Learning algorithms to optimise a measure of expected utility of final wealth, directly outputting trade positions thus bypassing the explicit forecasting step. Reinforcement Learning algorithms are used to trade individual assets and, subsequently, we extend this end-to-end training framework to portfolio optimisation and directly maximise the Sharpe ratio for portfolios, maximising return per risk.

This thesis contains three essays on household finance focusing on household consumption and savings decisions. The first paper studies household demand for flexibility in debt repayment. Based on evidence from a natural experiment, I find that few households apply for flexibility in debt repayment, although all households weakly prefer flexibility in a standard consumption- savings model. The paper studies how self-control problems and a desire for commitment, as well as inattention and inertia can explain observed household behavior. The second paper untangles reasons for the credit card debt puzzle describing the co-holding of high-cost debt and low-yield assets. I test existing theories for co-holding and provide evidence on the contribution to co-holding of intra-household financial pooling and of anchoring to minimum credit card payments. The final paper studies household heterogeneity in insuring against cohort-level earnings shocks using: (i) investment income flows; (ii) the tax-and-transfer-system; and (iii) credit markets. The insurance mechanisms vary by household socio-economic characteristics captured by housing tenure.

This dissertation presents three empirical analyses on private equity (PE) investments. The first chapter, The accuracy of net asset values in private equity: Evidence from the secondary market, examines pricing in the secondary market for PE fund stakes. The average transaction occurs at a discount, which is commonly attributed to compensation for liquidity provision. As a quarter of bids come in at a par or a premium, this cannot be the full explanation. I document that cross-sectional variation in discounts predicts future fund returns. The second chapter explores The dynamics of pay-for-performance sensitivity in private equity funds arising from carried interest. As carried interest is conditional on beating a hurdle rate, pay-for-performance sensitivity is increasing in fund performance, and increasingly so as the fund matures. I document that within-fund variation in pay-for-performance sensitivity is positively related to growth in portfolio companies acquired in buyouts. The effect is stronger when the importance of the direct component is high relative to the indirect component of pay for performance. To rule out confounding factors, I utilize public market movements to generate exogenous variation in the measure. My findings provide evidence of the importance of incentives for value creation. The third chapter, Managerial ownership and operational improvements in buyouts, investigates the relevance of CEO ownership stakes in companies acquired in buyouts. I find that post-buyout CEO equity ownership stakes are positively associated with profitability improvements. Consistent with an effect of incentives, the results are stronger when the ownership stake increases, and are driven entirely by the subsample in which the CEO is retained. Taken together, these three studies contribute to the growing body of work on private equity investments and the impact of private equity ownership on companies acquired in buyouts. They open the door to further theoretical and empirical research in the field.

Trading in modern capital markets is currently dominated by algorithms and concerns over the impact of algorithmic trading on market quality have led a number of jurisdictions to regulate this new type of trading. The European Union ('EU') has been no exception to this trend. This thesis offers a critical analysis of the EU algorithmic trading regime, arguing that it rests on three fundamental misconceptions: that simpler execution algorithms carry less risk than other algorithms; that algorithmic trading has meaningfully added to the risk of market manipulation; and that the benefits and risks of different high-frequency trading ('HFT') strategies can be considered and discussed as if HFT were a monolithic category of trading. As a result, the EU algorithmic trading regime mistakenly leaves unregulated a type of algorithmic trading that carries significant risk for the quality of its capital markets, creates unnecessary suspicion that certain algorithmic trading behaviour carries an abnormally high risk of market manipulation, and unwarrantedly imposes onerous requirements on all high-frequency traders, regardless of the particulars of their strategies. In response to these errors, this thesis proposes three main solutions: amending the EU definition of algorithmic trading to include all execution algorithms; eliminating the add-ons introduced by the EU regime to the EU definition of market manipulation; and removing 'HFT' as a regulatory category-which should be accompanied by extending the rules needed to govern the activity of algorithmic traders (and only those rules) to all proprietary investment firms with disintermediated access to EU markets. Ultimately, the EU algorithmic trading regime should nonetheless be praised for its structure, comprehensiveness, proportionality and ability to effectively address many of the risks created by algorithmic trading and by the strategies inspired by this trading. As such, this thesis proposes small incremental changes to the EU regime, rather than its complete overhaul.

My PhD thesis explores American Gothic literature as having always been concerned with finance, and finance as having always been a gothic phenomenon, from 1880 to the present day. The project is split into 4 chapters, each focusing on a different historical financial mechanism as represented by a different American gothic monster that reached peak popularity in the era surrounding that financial mechanism. My research also considers the pre-existing consensus on racial readings of American gothic, and how these interpretations of the slave trade can be expanded upon in conversation with their financial contexts. Drawing on contemporary insights into financialized understandings of economics within the humanities, new analysis of finance as an inherently gothic phenomenon, and archival work completed on the Library of Congress's Black History Collection, the project aims to highlight an as yet unrecognized dimension of haunting and monstrosity within American gothic literature. Through a combination of literary critique, historical analysis, and explorations of key financial mechanisms across the twentieth and twenty-first centuries, the thesis also reads gothic literature, contemporary finance, and the slave trade, as intimately connected and in communication with each other, to the point where a more complete understanding of one is greatly advanced by critical analyses of the other two. My research reveals a diagnostic, rather than causal, correlation between particular American gothic monsters and specific moments of financialization across the long twentieth century. The thesis also highlights how the contemporary financial system is a direct product of the slave trade, and the ways in which the slave trade itself was also highly financialized. Finally, the project counters the recognition of finance as a key influence on gothic literature with close readings that reveal the inherent, yet previously unrecognized, gothic nature of finance capitalism.

This thesis uses information theory to study policy questions in banking, finance, and industrial organization. It consists of four independent papers. The first paper, “Time Inconsistency in Stress Test Design”, shows that central banks face a time inconsistency problem when publishing bank stress test results. Before a stress test, they want to appear tough as the threat of letting banks fail the stress test incentivizes prudent behaviour. After the stress test, they want to act soft by releasing only partial information in order to reassure financial markets about bank health. The paper characterises an institutional design solution to this commitment problem: a social planner sets the framework within which the central bank communicates. We find that a hurdle rate framework, where all banks are judged to pass or fail relative to a common threshold, is optimal in many settings as it generates intermediate levels of both incentives and reassurance. We show that, in such a hurdle rate framework, stress tests become an informational contagion channel. The second paper proposes a model of imperfect competition in markets with selection and investigates whether imperfect competition mitigates the inefficiency which, in these markets, arises under perfect competition. Focusing on markets with advantageous selection, such as consumer credit markets, we find that no degree of competition can restore the first best efficient allocation. We discuss policy alternatives. The third paper shows that, in markets with adverse selection, tying contracts can prevent unravelling and thus have a substantial positive effect on welfare. We illustrate this effect for two insurance markets. When one characteristic (such as life-expectancy) is hidden information and causes agents to have high expected cost for one product (life insurance) but low expected cost for another (pension annuity), then for tying contracts cost differences partially cancel out. This can prevent unravelling. In the fourth paper, which is joined work, we build on a field experiment, find that some agents increase effort when it becomes more costly, and rationalize this in a signalling model. In the field setting, agents also choose whether to truthfully disclose their effort. The data on this form of strategic communication adds further support to the interpretation that effort choices are driven by signalling motives.

My aim in this thesis is to investigate the nature of the relationship between Modern Monetary Theory (MMT) and heterodox economics in general and then to explore the potential for MMT to contribute to an alternative heterodox paradigm. First, I examine the key principles and antecedents of MMT and compare its explanatory power with that of mainstream macroeconomic theory under different exchange rate systems. Second, I consider the most influential literature in relation to the methodology of economics and analyse the methodology employed by mainstream economists and their attitude towards academic freedom in order to establish the reasons why mainstream economics was apparently able to retain its hegemonic status in the aftermath of the global financial crisis (GFC). This is despite its inability to predict its occurrence or provide an adequate explanation for it. Such an analysis is a critical pre-cursor to the specification of an alternative paradigm. Third, I assess the applicability of critical realism both to the study of economics and to the behaviour of the economics profession itself. Following on from this, I utilise a qualitative primary research programme, explicitly based upon critical realism, to examine the possibility of constructing a methodologically-based heterodox paradigm. The nature of such a paradigm and the potential role for Modern Monetary Theory as a key contributor are then evaluated. Finally, I consider whether such a structure might be considered as a feasible alternative to the mainstream or, alternatively, should the advocates of MMT operate primarily as stand-alone school.

Developed to give meaning to differential equations driven by rough signals, rough path theory has opened in recent years a new approach to tackle certain problems in other fields such as mathematical finance and machine learning. This is due to certain algebraic and analytical properties of an object called the rough path signature. This thesis aims to make a contribution in this direction by introducing signature-based methods to study various problems arising in finance and machine learning. In the context of finance, we consider two problems: the pricing and hedging of financial derivatives, and the optimal execution of financial securities. We propose numerical methods based on signatures to solve both problems. Finally, in the context of machine learning we begin by introducing a new neural network architecture called the deep signature model, based on the deep signature transform. The thesis concludes with a practical application of the signature transform on a real-world problem, where we aim to make predictions from psychiatric data using signatures.

Model-independent mathematical finance is a relatively young research field, which aims to quantify Knightian uncertainty on the choice of pricing models. This thesis extends the existing framework to a setting, which is dynamic in time and takes new sources of information (e.g. new statistical estimates or changed market prices) into account as soon as they emerge. In this context, new connections to the fields of of optimal transport on the one hand and statistics on the other are established, with the ultimate goal to develop a universal toolbox for the implementation of robust and time-consistent trading strategies and risk assessment.

Since 2004, the Government of Nepal has sought to increase availability of comprehensive emergency obstetric and neonatal care (CEONC) services at district hospitals. However, significant and persistent bottlenecks exist in the health system which prevent effective scale-up. A “quick fix” strategy to address bottlenecks and ensure that hospitals had resources to deliver CEONC 24/7 was the creation of a “CEONC Fund” to “top-up” routine hospital budgets. The intention was the CEONC fund should only be used for CEONC services, not for any other hospital purpose. In 2015, the government decided to include the fund as a long-term policy and expand its availability to all district hospitals. However, routine data on fund utilisation and service data is limited. What is available indicates that utilisation varies substantially by hospital and actual provision of CEONC services also varies widely. This study aimed to understand why some district hospitals achieve a better provision of CEONC services than others. It employed a multiple case study design, purposefully selecting four district hospitals using an extreme case selection framework. It triangulated data from the districts with semi-structured interviews from central level key informants. The study results show provision of the fund to hospitals has limited impact on CEONC availability. Several other factors in hospitals and the health system have greater influence on uptake of funding and service availability. The conclusion is, not only is a CEONC fund grant alone insufficient to ensure service availability, several other critical factors need to be taken into consideration to ensure that CEONC services are consistently and universally available. CEONC availability is heavily influenced by the quality of leadership and management of the hospitals and their capacity to maintain or develop the service as financial resources permit. Therefore, the fund’s utility as a sole mechanism to improve CEONC services is limited.

The financial crisis of 2007-2009, which brought the entire system at the brink of collapse, renewed efforts to guard against financial instability. A key pillar of the post-crisis regulatory toolkit is "stress testing". Stress tests provide a forward-looking examination of firms' potential losses during severely ad- verse conditions. And enable timely action to recapitalise those firms who experience capital shortfalls in such crisis scenarios. Today's regulatory stress tests do not heed the key lesson of the financial crisis: amplifications in the networked financial system must be taken into account to be able to assess systemic risk. Because of this, these tests are unable to assess systemic risk and ergo to address it - defeating their raison d'ˆetre. The overarching research question in this thesis is whether new building blocks - expressing the heterogeneity of institutions, contracts, markets, constraints and behaviour in the interconnected financial system - can be supplied for system-wide stress tests to better capture the endogenous amplification of shocks in order to improve the assessment of systemic risk and the evaluation of prudential policies to address financial fragility. The cornerstone of my thesis is the development of a generic network-based method, comprised of these five building blocks (i.e. institutions, contracts, markets, constraints and behaviour), for system-wide stress testing - which has gained traction from leading central banks, including the Bank of England and the European Central Bank. Using this method, I implement two data-driven models to address some of the most salient financial stability questions of today. First, we ask how the regulatory buffer size and its usability under Basel III affect systemic risk? We find that financial resilience decreases if regulatory buffers are seen to be less usable by banks. If regulatory buffers are not treated as usable, then regulatory buffers de facto act as capital requirements. In such case, if an adverse shock threatens an institution to breach its capital buffers constraints, it is forced to delever, which tends to have a destabilising effect on the financial markets. We show that the size of usable regulatory buffers that is required to maintain stability is underestimated if the interaction between exposure loss contagion, funding contagion, overlapping portfolio contagion and margin call contagion is not taken into account. Second, we inquire what the systemic implications are of the bail-in design to resolve systemically important banks? First of all, we find that the bail-in design tremendously matters for whether bail-ins can be credibly executed in system-wide financial crises and cases of large systemically impor- tant bank failures, without significantly exacerbating financial distress. Our results demonstrate that an early bail-in, strong recapitalisation and fair distribution of equity compensation by means of debt-to-equity conversion rates makes bail-in a feasible option on the table for idiosyncratic cases of bank failure and limits - but not eliminates - contagion in cases of system-wide distress. We further show that excluding run-prone, short-term debt from the application of the bail-in tool, increasing the requirements on loss absorbing debt and providing investors with certainty about the bail-in design lowers contagion in system-wide crises to manageable levels. Our findings highlight that while well-designed bail-ins could be credibly administered in system-wide crises, it is not clear that the current bail-in design is in the regime of stability. Altogether, the methods and findings of this thesis emphasise the promise that system-wide stress tests hold for regulators to efficaciously assess systemic risk and calibrate prudential policies constituting the financial architecture.

One of the most important objectives of any firm is maximizing firm value. In order to achieve that goal, many different aspects come into play. One of those aspects is financial decisions. Financial decisions are mostly determined by managers, therefore, it can be stated that managers are people who play the main role in determining the success of firms. Stemming from this statement, this study attempts to investigate the impact of managerial overconfidence on some financial management aspects, including cash holdings, debt maturity and firm performance. Furthermore, the thesis also expands the research to examine the impact of managerial overconfidence on the deviation between the actual level of cash holdings and the optimal level of cash holdings, and the deviation between the actual level of debt maturity and the optimal level of debt maturity. By mainly focusing on data from 123 face-to-face interviews with top-line managers of Vietnamese non-financial listed firms, the thesis uses data on voice pitch to measure managerial overconfidence and test the effect of managerial overconfidence on the research topics. Besides using voice pitch, to strengthen the empirical results, the study also uses some other measurements of managerial overconfidence, including psychometric testing, bias in earnings forecast, manager's gender, the visibility of CEOs' photographs in firms' annual reports and, a comprehensive index from multiple measurements of the research. The findings indicate that, firstly, firms with overconfident managers tend to hold less cash but the actual level of cash holdings is not far from the optimal level of cash holdings. Secondly, overconfident managers prefer using long-term debt over short-term debt, and they tend to make a higher deviation between the actual level of debt maturity and the target level of debt maturity. Finally, managerial overconfidence is found to be associated with a better result of firm performance.

Research in socially responsible investing (SRI), corporate social responsibility (CSR), environmental, social, and governance (ESG) factors, and social impact investing focuses on how social issues are introduced into finance after an organization, institution or investor has accepted the integration of the social issue (post-acceptance perspective). However, advocates start the introduction of these issues into finance decades before a post-acceptance perspective can be studied. Through a case study of investing in women or gender lens investing, this study explores the process, motivations and resistance experienced by advocates introducing social issues into finance. A multi-method approach was used to triangulate findings through participatory ethnography, desk research, attendance at public conferences, roles in intimate gatherings of advocates, participation in projects, and a series of 42 formal interviews with advocates, both men and women, in Australia, Canada, the UK, and the US. Findings show that the process for introducing social issues into finance from an early advocate perspective is challenging, non-linear, fraught with resistance, and requires an understanding and acceptance of different frames of reference and subgroups amongst individual advocates. In the case of investing in women it was found that individuals advocate from three distinct frames of reference: a development champion perspective focused on measures, impacts and counts; an integrated financial risk perspective focused on financial indices and alpha returns; and a visionary value perspective focused on experimentation, innovation, paradigm-shifts, and seeing value or worth in things previously undervalued or not valued. Study findings provide an understanding of the motivations and process that is used by early advocates to introduce social issues into finance and provides a framework for future research of gender lens investing (or the introduction of gender issues into finance).

China began its economic reforms in 1978 and have experienced strong economic growth. However, economic growth is a complex phenomenon, as China's provinces experienced regional disparity, where its coastal provinces grew faster compared to its underdeveloped interior provinces. Previous provincial-level growth studies in China do not account for spatial interactions, as many economic models are dimensionless in nature. This is important to consider, as regional disparity experienced in China can be attributed to its diverse geography and initial reforms that favoured the coastal provinces. Thus, this thesis examines the determinants of economic growth in China's provinces, using an updated panel dataset from 1996 to 2015, employing economic models that accounts for spatial effects. The thesis begins by reviewing the evolution of China's modern economy and the determinants of economic growth followed by three empirical studies. Chapter 4 examines the role of financial development and institutional reforms on economic growth using dynamic panel models. This study does not account for spatial spillovers as banks and financial institutions in China are restricted to operate in their respective provinces. Although the relationship between finance and growth is well established in the empirical literature, there is growing evidence to suggest that the effects of financial development on growth is weakening and, in some cases, negative. More importantly, previous studies that examine finance-growth in China's provinces have found both positive and negative effects of finance. Furthermore, financial development across China's provinces is uneven, as the coastal provinces have benefited from the development of stock markets. Consequently, we examine the nonlinear effects of finance and statistically establish nonlinearity using a stringent U-test, providing new empirical evidence as previous studies do not formally account for nonlinearity. The estimated results support the literature that postulates the positive effects of finance on growth. However, the results also find that the effects of finance are nonlinear suggesting that further reforms are needed for China's financial system. Chapter 5 investigates the effects of health and education expenditure on economic growth using a spatial autoregression model. Endogenous growth theories highlight the positive effects of health and education investment on growth through development human capital and productivity growth. There are various studies that examine the effects of human capital formation on growth in China's provinces, however, most studies overlook the role of spatial spillovers. The spatial autoregression model accounts for spatial interaction between provinces and provide empirical evidence on spatial spillover effects. China's Central government have delegated health and education expenditure responsibility to provincial governments; as a result, spatial interactions are potentially present. This study contributes new empirical evidence with policy implications. We find evidence that positive spatial strategic interaction occurs between provinces supporting the yardstick theory of competition. Furthermore, the results show that the effects of education expenditure are positive to economic growth and exerts a spatial spillover effect. However, the results on the effects of health expenditure is mixed, which suggests further reforms are needed in China's healthcare system. Finally, Chapter 6 examines the efficiency of China's provinces using a spatial Durbin production frontier with an extended dataset from 1985 to 2013. China's geography is vast and diverse across the country, with the Eastern provinces that benefit from flat land and access to coastal seaports while its landlocked provinces are mountainous and lack infrastructure to international markets. Thus, it is important to consider the spatial efficiency spillover effects of the respective provinces, as they face varying production opportunities. This study contributes to the literature as it is the first study that examines the provincial-level transient and persistent efficiency and spatial efficiency spillovers in China, providing important policy implications. Previous studies that examine the productivity and efficiency in China have suggested that efficiency gains have been exhausted. However, we find evidence of persistent inefficiency and show that a one-fifth increase in output can be realised, if inefficiency issues are addressed. Policymakers should identify sectors that require further reforms, such as the healthcare system, in order to maximise the efficiency of resources. We also show that spatial efficiency spillovers are positive, which allows provinces to benefit from its neighbouring provinces.

In the global financial architecture, British Overseas Territories in the Caribbean and North Atlantic are of material significance. Post-colonialism, these relatively homogeneous, archipelagic territories with financial centres have been the recipients of soft-domination by metropolitan interests. Through their inalienable right to self-determination and pursuit of autonomous governance and financial independence, many developed offshore financial centres to achieve sustainable development, with UK encouragement. Recently, and exacerbated by the Panama and Paradise papers, these jurisdictions are subject to increased pressure and ongoing perception that their financial centres facilitate criminality by harbouring suspect wealth, due to lack of transparency. This doctoral thesis concerns suspect wealth as a product of economic misconduct like corruption, money laundering, tax evasion, fraud, and the increasingly controversial tax avoidance. It concerns suspect wealth derived from overseas or domestic misconduct, given law enforcement's response is typically the same irrespective of origin. It focuses on three Overseas Territories: Bermuda, the Turks and Caicos Islands, and Anguilla. Through secondary research into their legal, regulatory and compliance regimes, it sets out to answer three main questions: How viable are international standards on suspect wealth in the context of these three jurisdictions? How willing and able are these jurisdictions to comply with international standards on suspect wealth? And, how can they better prevent their financial centres accepting suspect wealth? Given these standards are envisaged at international levels, the dissertation also considers whether a one-size-fits-all approach to the territories is appropriate. Proceeding on the basis that receipt of suspect wealth is inimical to development, it also discusses its impact on development for both countries from which wealth transits, as well as the overseas territories themselves, many having fundamental development concerns. The research finds that universalism is a desirable aspect of the modern approach to tackling suspect wealth. However, a one-size approach is generally inappropriate for these jurisdictions. Contextual issues pertaining to capacity, resources and underlying considerations like the offshore confidentiality norm, mean that some standards are unviable - or that existing frameworks may be viable alternatives. On a critical evaluation of their legislation, international cooperation and reviews, it is suggested all territories demonstrate willingness to comply with international standards. However, their ability and levels of compliance vary. In summary, Bermuda demonstrates the greatest level of compliance and adherence. Turks and Caicos Islands' compliance is a product of the last decade's legislative and institutional reforms following constitutional crises. It is compliant in many ways, but aspects are still under development. Anguilla's response is the least developed vis-à-vis legislation and institutions, however key anti-money laundering areas have received positive review internationally. This work also shows that fundamental legal protections, like privacy, are often eschewed in favour of transparency standards, some of which remain undefined. The research suggests recommendations to legislators and policymakers aimed at enabling them to better prevent suspect wealth entering their financial centres. In acknowledging the facilitatively harmful role that can be played by these territories, this work draws upon evidence of implication in international cases which indicate a less positive view of the territories. Notwithstanding this, a purpose of this work is to question whether the degree of criticism that these, and other, small jurisdictions have encountered is warranted in light of their apparent willingness to engage in the enactment and administration of internationally accepted standards and legislation, and cooperate with international mechanisms and institutions. In this regard, the dissertation approaches a series of important issues for development and hopes to facilitate a more constructive, meaningful discussion.

The commercial forest sector manages a significant proportion of the global forest resource, just under a third being classified as 'production forest'. This sector faces a number of threats, including those from natural disturbances, such as wind, fire, pest and disease, and drought. Climate change adds further complexity and uncertainty, as it is likely to lead to an increase in losses from these disturbances. Risk management can help reduce the likelihood and impact of natural disturbances and increase resilience, but needs adequate information to quantify risk and inform decisions. The premise of this PhD is therefore to investigate whether approaches from the finance sector for dealing with risk can be adapted to the forest sector and the risk of natural disturbances. Key financial concepts are introduced in Chapter One, and the results are presented in the three following core chapters. In the introduction, I briefly introduce how uncertainty is intrinsic to the finance sector. Risk management has always been present in the finance sector, but it rose in importance after a series of high-profile losses from the end of the 20th century. Hard lessons learned from this, led to revision and innovation of both external regulatory and internal processes to address risk. I focus on adapting and applying a capital adequacy approach (which quantifies the amount of capital to set aside against losses), and a Risk Adjusted Performance Management (RAPM) approach (which evaluates return against risk) to the forest sector. To better understand the commercial forest stakeholders and their information needs, Chapter Two presents a global survey of these stakeholders to understand their current level of awareness and usage of information on natural disturbance risks. Previous surveys being location-specific and focusing on private forest owners, this global sectoral examination addressed a significant gap in the underpinning knowledge base. It revealed that the main natural disturbances perceived to be threatening global forests and causing the most losses on commercial forest projects to date were fire, pests and diseases, wind, and drought (in descending order of impact). Other disturbances were of less significance. The sector also reported an upward trend in impacts from these and an expectation for a continued increase in such losses. The survey revealed a perceived absence of global information on disturbance losses other than for fire and wind. Likewise, no standardised global information is available on losses experienced purely by the commercial sector. The survey also identified that approaches to assess risk from pests and diseases and drought were either non-existent or ill-suited to the current needs of the sector. This explains why insurance products are more widely available for fire and wind relative to those for pest and diseases and drought. Suitable methods to assess risk and underpin risk management are therefore required. Building on the review presented in Chapter One, approaches from the finance sector are therefore translated to address commercial forest sector needs. Chapter Three presents the application of capital adequacy approaches to pests and diseases. Pests and diseases pose a growing threat to woodlands from both endemic sources, and increasingly, from interregional transmission. Analyses of this risk should include estimates of potential worst-case loss from relevant pest and disease threats to key tree species. Existing approaches tend to focus on single pest or disease assessments or overall trends. I present a capital adequacy based approach for the systematic, risk-based assessment of the future threat to a given woodland where all known individual pests and diseases to constituent individual tree species are considered. The Woodland Carbon Code (WCC) serves as a case study but the approach is adaptable to any woodland resource worldwide. Its novelty lies in the simplification of complex threats, from numerous pests and diseases, to measures that can be used by a range of forest stakeholders. Chapter Four presents the application of a risk versus reward (RAPM) approach to drought risk. Climate change will alter precipitation patterns across the world increasing drought risk to forests in some areas. Choosing tree species resilient to drought would support continued timber supply. The chapter presents a locationspecific risk versus return analysis for twenty commercial timber species in Scotland. The impact of drought risk on potential return, modelled as change in volume of timber production (yield in m3 ha-1) under a high emission climate change scenario, is quantified. The results show that drought tolerant species offer good alternatives to Sitka spruce, which accounts for 58% of the stocked area in Scotland. Whilst Sitka still dominates, these alternatives would deliver only relatively minor volume penalties in areas of the highest drought risk. Diversification is a key strategy to increase forest resilience against multiple risks. This analysis provides strategic regionally coherent information on suitable alternatives to Sitka spruce. I conclude the thesis by presenting views on the next stage of development for these approaches, and offer my perspectives on additional areas of research needed to enhance risk management approaches adapted from the finance sector at a later stage.

This dissertation examines the evolution of UK life insurance arrangements by investigating how the ways in which life insurers evaluate the economic worth of insurance contracts have changed since the early 1970s. It draws on a set of 44 oral-history interviews, supplemented by an extensive set of documents, to describe how, in addition to traditional forms of 'diversifiable' insurance risk, insurers have increasingly sought to quantify forms of 'non-diversifiable' risk such as financial market risk. The central question is how changes in insurers' evaluation practices shaped and were shaped by broader developments in UK life insurance. In addressing this question, the dissertation combines insights from field-theoretical perspectives in 'conventional' economic sociology, the recent literature on the performativity of economics, the sociology of insurance and the sociology of scientific knowledge. Field theory is a useful tool for understanding how meso-level social orders emerge as a function of the strategic behaviour of actors in social domains such as markets. The assumption of 'technological determinism' prevalent in field-theoretical perspectives, however, conflicts with insights from the sociology of scientific knowledge and recent literature on the performativity of economics. This tension may be alleviated, I argue, by conceptualising both the market for life insurance and actuarial science as fields (a 'market field' and an 'epistemic field') and by investigating the interrelations between the two. In deploying this field-theoretical perspective, the dissertation finds, on the one hand, that developments in the market field may lead to new opportunities and challenges in the epistemic field. Particularly important in the epistemic field, for instance, was the ascendancy of modern finance theory's no-arbitrage models as key exemplars for the modelling of insurance liabilities in actuarial science. However, only when the jurisdictional claims of the actuarial profession were threatened and when supervisors required insurers to evaluate their liabilities using techniques already used in banking did these models become dominant in the actuarial field. x On the other hand, I argue that the ways in which life insurers evaluate the economic worth of insurance contracts matters for what life insurance is and does. Evaluations of 'value' and 'risk' inform decision making about the distribution of financial surplus and risk across different groups of policyholders and shareholders, the types of products that life insurers choose to underwrite, and the way in which they invest their assets in capital markets. Since the 1970s, the emergence of novel evaluation practices has contributed to the individualisation of financial risk in insurance arrangements, a shift in insurers' asset allocations from equities to fixed-income investments, and a declining willingness from insurers to underwrite traditional mortality-related risks. The business of life insurance, in other words, increasingly revolves around investment intermediation rather than protection. The findings of this dissertation draw attention to the politics of seemingly technical issues such as the discounting of future cash flows to present values. Overall, I suggest that the evolution of UK life insurance can be fully understood only by paying attention to tensions and conflicts in the epistemic field of actuarial science, attempts to influence the 'rules of the game' in the market field and the interrelations between the two.

The objective of this thesis is to implement metaheuristic approaches to solve di erent types of combinatorial problems. The thesis is focused on neighborhood heuristic optimisation techniques such as Variable Neighborhood Search (VNS) and Ant Colony Optimisation (ACO) algorithms. The thesis will focus on two diverse combinatorial problems. A job shop scheduling problem, and a nancial derivative matching problem. The rst is a NP-hard 2-stage assembly problem, where we will be focussing on the rst stage. It consists of sequencing a set of jobs having multiple components to be processed. Each job has to be worked on independently on a speci c machine. We consider these jobs to form a vector of tasks. Our objective is to schedule jobs on the particular machines in order to minimise the completion time before the second stage starts. In this thesis, we have constructed a new hybrid metaheuristic approach to solve this unique job shop scheduling problem. The second problem has arisen in the nancial sector, where the nancial regulators collects transaction data across regulated assets classes. Our focus is to identify any unhedged derivative, Contract for Di erence (CFD), with its corresponding underlying asset that has been reported to the corresponding component authorities. The underlying asset and CFD transaction contain di erent variables, like volume and price. Therefore, we are looking for a combination of underlying asset variables that may hedge the equivalent CFD variables. Our aim is to identify unhedged or unmatched CFD's with their corresponding underlying asset. This problem closely relates to the goal programming problem with variable parameters. We have developed two new local search methods and embedded the newly constructed local search methods with basic VNS, to attain a new modi ed variant of the VNS algorithm. We then used these newly constructed VNS variants to solve this nancial matching problem. In tackling the Vector Job Scheduling problem, we developed a new hybrid optimisation heuristic algorithm by combining VNS and ACO. We then compared the results of this hybrid algorithm with VNS and ACO on their own. We found that the hybrid algorithm performance is better than the other two independent heuristic algorithms. In tackling the nancial derivative problem, our objective is to match the CFD trades with their corresponding underlying equity trades. Our goal is to identify the mismatched CFD trades while optimising the search process. We have developed two new local search techniques and we have implemented a VNS algorithm with the newly developed local search techniques to attain better solutions.

This thesis studies two subareas within the forecasting literature: volatility forecasting and risk-neutral density estimation and asks the question of how accurate volatility forecasts and risk-neutral density estimates can be made based on the given information. Two sources of information are employed to make those forecasts: historical information contained in time series of asset prices, and forward-looking information embedded in prices of traded options. Chapter 2 tests the comparative performance of two volatility scaling laws - the square-root-of-time (√T) and an empirical law, TH, characterized by the Hurst exponent (H) - where volatility is measured by sample standard deviation of returns, for forecasting the volatility term structure of crude oil price changes and ten foreign currency changes. We find that the empirical law is overall superior for crude oil, whereas the selection of a superior model is currency-specific and relative performance substantially differs across currencies. Our results are particularly important for regulatory risk management using Value-at-Risk and suggest the use of empirical law for volatility and quantile scaling. Chapter 3 studies the predictive ability of corridor implied volatility (CIV) measure. By adding CIV measures to the modified GARCH specifications, we show that narrow and mid-range CIVs outperform the wide CIVs, market volatility index and the BlackScholes implied volatility for horizons up to 21 days under various market conditions. Results of simulated trading reinforce our statistical findings. Chapter 4 compares six estimation methods for extracting risk-neutral densities (RND) from option prices. By using a pseudo-price based simulation, we find that the positive convolution approximation method provides the best performance, while mixture of two lognormals is the worst; In addition, we show that both price and volatility jumps are important components for option pricing. Our results have practical applications for policymakers as RNDs are important indicators to gauge market sentiment and expectations.

This thesis presents three empirical analyses that contribute to our understanding of how financial securities and intermediaries facilitate the allocation of resources between firms, consumers, and the government. Each paper investigates a distinct asset class and aspect of the resource allocation problem. The first paper explores the role of information in financial markets and the wisdom of crowds. The analysis uses a recently developed statistical technique to infer overlap in information sets of security analysts and develops a profitable trading strategy using analyst stock ratings. This finding highlights the prevalence of information diversity. The second paper focuses on government debt managers in national treasuries and finds that they actively choose the maturity structure of government bonds to minimize the expected costs of debt service while responding to demographic factors so as to enable risk-sharing across generations. The third paper leverages the new phenomenon of equity crowdfunding campaigns to disentangle entrepreneurial choices from investor preferences, differentiating the fundraising strategies that entrepreneurs pursue based on their experience and gender, and the funding outcomes they achieve. Taken together, these three studies contribute to our understanding of how intermediaries and securities allocate resources in various financial settings.