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This paper presents a Systematic Literature Review (SLR) to investigate the relationship of cost with Business Process Management (BPM). Cost is part of finance, so cost is often addressed in a broader sense related to finance. Previous studies show contradictory views on cost and BPM. Several studies state that finance did not have a direct relationship with business processes and other studies state the lack of theory relating cost to BPM. However, other studies saw a positive relationship and consider cost aspects to perform efficiency and optimization by using BPM. Further and in-depth research needs to investigate, the relationship between cost aspects and BPM particularly related to Information Technology applications. [ABSTRACT FROM AUTHOR]

Blockchain is an emerging technology penetrating various real-time and sensitive domains including financial products. Block chain can deal with complex system of financial transactions with focus to provide security and integrity to both the parties with reliable flow of transactions and asset securitization. Block chain is a distributed technology providing a lot of benefits such as cryptic transactions, smart contracts, distributed ledger system along with decentralization, authentication, authorization, and immutability to enhance the efficiency of the financial system. In this paper, the existing solutions for asset-backed securitization in blockchain are analysed and reviewed to highlight the requirements for change in design and ideas for enterprise applicability of blockchain for asset securitization. Also, the benefits of blockchain to reduce the risk. [ABSTRACT FROM AUTHOR]

The usage of machine learning methods in the financial sector, regarding repayment prediction or forecasting, is quite a new topic, constantly gaining in importance. The concept of the alternative costs in the literature covering machine learning and deep learning occurs most often in connection with the non-financial areas as costs of lost benefits. This empirical paper presents research dedicated to deep learning used in forecasting the alternative costs of leasing represented by the variable KUK_PRC. The study is based on the experimental approach and uses real organization data to solve the forecasting problems in the financial area with AI solutions. This research contributes to the science by identifying and exploration of the research gap in the field of applied economics and finances. The main finding of this paper is the proposed forecasting ACSeq-DNN model that forecasts opportunity costs with smaller deviations from actual values than the forecasting achieved by state-of-the-art models. [ABSTRACT FROM AUTHOR]

The massive increase of data and the existence of several distinct methods in which data is generated, accumulated, stored, and utilized has significantly changed with time. Additionally, the nature of data has changed throughout the years transforming from structured to more unstructured data. This brings about a need for efficient storage and management of such data which cannot be handled by traditional RDBMS methods. Hence, NoSQL databases have gained popularity and have become pivotal in database management. Finance is one domain where dynamic and large amounts of data is produced on a daily basis, thereby making NoSQL databases an ideal choice for data management. This paper compares these types of NoSQL databases based on certain metrics like data model, indexing methods, atomicity, integrity and several more and demonstrate implementation of three NoSQL databases namely, MongoDB, Cassandra and Redis, using financial data. Experiments were performed to compare the performance of the aforementioned databases when using fundamental READ queries to retrieve the complete dataset and complex READ queries to retrieve a specific section. Aggregation operations were also implemented on the data. Fundamental WRITE queries to load the entire dataset and complex WRITE queries to update particular parts of it were also performed. [ABSTRACT FROM AUTHOR]

Financial time series forecasting is crucial in empowering investors to make well-informed decisions, manage risks effectively, and strategically plan their investment activities. However, the non-stationary and non-linear characteristics inherent in time series data pose significant challenges when accurately predicting future forecasts. This paper proposes a novel Recurrent ensemble deep Random Vector Functional Link (RedRVFL) network for financial time series forecasting. The proposed model leverages randomly initialized and fixed weights for the recurrent hidden layers, ensuring stability during training. Furthermore, incorporating stacked hidden layers enables deep representation learning, facilitating the extraction of complex patterns from the data. The proposed model generates the forecast by combining the outputs of each layer through an ensemble approach. A comparative analysis was conducted against several state-of-the-art models over financial time-series datasets, and the results demonstrated the superior performance of our proposed model in terms of forecasting accuracy and predictive capability. [ABSTRACT FROM AUTHOR]

In this contribution, the interest is in three things, arising from a project on 'dark' F-KIBS clusters. This has already resulted in critical papers on management consultancy and accounting 'assemblages' in which such clusters are embedded (Cooke, 2023a; 2003b). Questions posed are: first, why is there so much financial crime being punished with huge fines? Second has there been a significant rise in such prosecutions in recent years? Third, which jurisdictions or clusters display the most prosecuted firms and sectors? These questions are stimulated by an interest in 'assemblage geography' and evolutionary spatial analysis; accordingly, financial 'dark' clusters are proposed for study in this contribution. Assemblage connections may be hypothesised to be found in financial districts that replicate Marshallian proximity, notably Delaware in the US (Weitzman, 2022). That state specifically and its main city, Wilmington, is proposed as an exemplary rising agglomeration of relational interactions specialising in US tax avoidance and bankruptcy. In Switzerland, Bellinzona Federal Criminal Court recently hosted a Swiss Bank's first-ever domestic trial for criminality in the shape of Credit Suisse. This bank had a fine of SFr2mn imposed on the bank, which was also ordered to pay €18.6mn in compensation to the Swiss government. An extra penalty was imposed by confiscation of SFr12 mn in criminal assets from the convicted perpetrators. The case had involved a group of Bulgarian clients laundering cocaine profits between 2004 and 2008 involving huge sums of cash being brought to the bank in suitcases, further associated with two assassinations. These red flags were missed by a bank which had received multiple fines elsewhere (Carrel, 2022). In reference to gang crime, in 2004, a Credit Suisse banker in Tokyo was arrested for allegedly helping launder at least ¥5bn linked to Japan's largest Yakuza gang connected in a Japan, Switzerland and Hong Kong network (Makortoff and Pegg, 2022). Regarding the demise of Switzerland's second global bank, after being badly hit by the huge Greensill and Archegos bankruptcies Credit Suisse was bailed out for £45 billion by the Swiss Central Bank in March 2023. Earlier, in 2015–2017, Disney, the creative economy exemplar in this narrative, is profiled in our third vignette as an imperious tax-dodging 'fiefdom' yet surviving as the global mass-entertainment leader. Finally, in sections 4 and 5, we profile two London-centred banking 'scandals' with 'sustainability' rather than 'creative' overtones. Thus of dubious banking practices London's many categories of financial regulator are served by two 'assemblage' cases that will be analysed subsequent to being advanced as candidates. One recent exemplar is Balli Steel, an Anglo-Iranian sanctions-breaker, while another is Britishvolt, a financially failed EV gigafactory project in the UK. [ABSTRACT FROM AUTHOR]

Through the effective processing of the financial dataset of listed companies and the improvement of the traditional collaborative filtering algorithm, this paper proposes a recommendation service which can predict the shareholding proportion of the listed companies more accurately in order to help fund companies to make reasonable decisions to invest listed companies. This service includes two important methods. To deal with the sparsity of data, one solution is to integrate similarity of user ratings and similarity of common-selected item to collaborative filtering algorithm. The relationship of common-selected item defined in this paper fully considers user's common scored items. The greater the relationship value between users is, more similar two users are. The other solution is mainly applicable to new companies with very few indicators. The approach proposed in this paper is mainly based on the similarity between items, and generates different filling value for each unrated item based on re-filling with item-based collaborative filtering algorithm. From the perspective of the recommendation method itself, the method proposed in this paper is superior to the traditional standard collaborative filtering algorithm and the SVD algorithm. [ABSTRACT FROM AUTHOR]

This paper analyzes the potential contributions of photovoltaic energy to sustainable development in its three dimensions, environmental, economic, and social. For that purpose, the capacity deployments proposed by the main international institutions in the field are considered, mainly the International Energy Agency. The paper focuses on the monetary valuation of avoided CO 2 emissions, and the assessment of the required financial funds. The risk implied by the uncertain photovoltaic prices are dealt with, and methods to manage it are suggested. It is shown that valuing emission at a conservative price and properly discounted, would be enough to finance, on average, the required investments: thus, a low carbon tax is all that would be required to finance the investments. [ABSTRACT FROM AUTHOR]

The renewable energy industry is one of the fastest growing industries attracting a great amount of foreign direct investment, being one of the top 5 industries in 2015 in terms of the amount of investment allocated. However, the allocation of foreign direct investment in the sector greatly varies between developing countries. Preceding studies have tried to explain the location determinants of foreign direct investment mainly by looking at the effects of institutional and macroeconomic factors. The renewable energy sector has been supported by various economic, regulatory, and political support policies. Considering the importance of these support policies, the paper analyses their effects on foreign direct investment as location determinants in comparison with that of the widely accepted determinants (institutional and macroeconomic determinants), focusing on wind energy in developing countries. The results show that renewable energy support policies have equivalent or greater effect compared to the widely accepted determinants such as corruption level, price stability, access to finance, and GDP growth. The paper demonstrates the importance of analysing determinants of foreign direct investment focusing on a specific sector rather than looking at overall foreign direct investment. The paper also provides important policy implications including the need to improve the regulatory aspect of renewable energy sector such as access to grid infrastructure in order to attract foreign direct investment into the sector. [ABSTRACT FROM AUTHOR]

• The relative speed of implementing energy transition policies has implications on countries' domestic political economy. • Late decarbonising countries face higher risks for economic instability and decreased competitiveness. • Once lagging behind, feedback cycles make it even harder for late decarbonizing countries to catch up. • Uneven transition patterns increase tensions between early and late decarbonising countries. Uneven access to low carbon finance and technology may give rise to energy transition frontrunners and laggards. This article offers a first conceptualization of the risks of an uneven energy transition and its implications for the international political economy and corroborates those with an empirical investigation of elite risk perceptions in the energy industry and finance sector. The multi-method approach combines descriptive survey data with a multinomial logistic regression testing for different expert risk perceptions between sectors, complemented by a set of qualitative interviews. The findings suggest that uneven transition patterns increase the risks of economic instability and decrease the competitiveness of 'late decarbonizers'. Feedback cycles might impede the latter to catch up, with potentially severe consequences for global equity and international tensions. Countries particularly in the Global South are exposed to higher transition risks than technology-leading economies of the Global North. With this, the paper highlights the importance of relative timing for the implementation of energy transition policies. [ABSTRACT FROM AUTHOR]

As the water resource is becoming scarce, conservation of water has a high priority around the globe, study on water management and conservation becomes an important research problem. People are increasingly becoming more individual households, which tend to be less efficient, requiring more resources per capita than larger households. In order to address these challenges, this paper presents the achievements of monitoring domestic water consumption at the appliance level and intervening people’s water usage behavior which have been made in ISS-EWATUS ( http://www.issewatus.eu ), an European Commission funded FP7 project. The water amount consumed by every household appliance is wirelessly recorded with the exact consumption time and stored in a central database. People’s water consumption behavior is likely affected by the real-time water consumption awareness, instant practical advices regarding water-saving activities and classification of water consumption behavior for individuals, all of which are provided by a decision support system deployed as a mobile application in a tablet or any other mobile devices. Only the enhanced water consumption awareness is presented in this paper due to the space limitation. The integrated monitoring and decision support system has been deployed and in use in Sosnowiec in Poland and Skiathos in Greece since March 2015. The domestic water consumption monitoring system at appliance level and the local DSS for affecting people’s water consumption behavior are innovative and have little seen before according to the knowledge of the authors. [ABSTRACT FROM AUTHOR]

Dependency on fossil fuels is an issue for numerous countries, and in particular, it is an acutely challenging problem for Pacific Small Island Developing States. For the utilization of Renewable Energy in Pacific Small Island Developing States, donor agencies and international organizations play major roles in funding. This study introduces the nature of the past renewable energy projects in Pacific Small Island Developing States and, in the process, clarifies the contribution of international aid to renewable energy deployment. Thereafter, the paper identifies the most important factors related to enhancing an enabling environment for the introduction of Renewable Energy in Pacific Small Island Developing States and the relative importance of these factors with a special focus on donors' perspectives. The paper clarifies that approximately 1.5 billion USD (2011) was received by Pacific Small Island Developing States over the 44 years from 1970 to 2014 as funding for renewable energy projects that included funding for hardware, and the amount of aid allocated for renewable energy in the region greatly varies between countries. The paper also identifies the important factors that can enhance an enabling environment for the introduction of renewable energy and the relative importance of these factors. Some of the highlighted implications that can be made from the findings of the research are: (1) high renewable energy targets need to be supported by well-structured action plans; (2) there is a need for an effective regulatory body responsible for renewable energy; and (3) the financial aspects of utilities are of higher importance than technological aspects of utilities. [ABSTRACT FROM AUTHOR]

Technology development is determined by many factors which have different functions in the socio-economic system. Knowledge and skills in the process of selection of key factors and assign roles to other factors, plays an important part from the perspective of management of technologies. The purpose of the paper is to present the structural analysis as an instrument of identification and classification of factors influencing the development of a particular technology. The paper discusses the theoretical basis of the method and presents its practical implications in the form of a case study of a project entitled “Technological Foresight «NT FOR Podlaskie 2020». Regional Strategy of Nanotechnology Development”. The aim of the described project was to find answers to the following questions: What are the key drivers of nanotechnology development in Podlaskie region (Poland) in the perspective of year 2020? What are the interrelations between these drivers? For the research purposes of the article structural analysis was used. The applied method allowed eventually to the select two main factors that determine the development of the analyzed technologies which formed the basis for creating development scenarios. [ABSTRACT FROM AUTHOR]

Previous studies have emphasized the importance of social capital for disaster recovery. Yet, relatively few studies have paid attention to how bonding and bridging social capital facilitate disaster recovery differently. The paper highlights the specific role of bridging social capital in rebuilding communities, recognizing it as a ‘catalyst’ of co-production. This paper is structured around the theoretical themes of social interaction, emphasizing that reciprocal interaction constitutes strong social capital that enables communities to work together. Although the literature emphasizes the positive effect of social capital on co-production, this paper offers a more nuanced view based on the following two points: (a) bonding social capital, which relies on tightly-knit bonding social networks is crucial for short term disaster recovery as it draws upon the supports from close community members such as family, relatives, neighbors, and friends; and (b) bridging social capital plays a key role in long-term disaster recovery, as individuals expand their social interactions outside their community through the recovery effort. The argument is then put forward that it is bridging social capital that supports the creation of co-production. This leads to greater opportunities for community-oriented development, which emerges due to better integration among various actors. The paper makes its contribution by proposing bridging social capital as a necessary condition for co-production for disaster recovery. [ABSTRACT FROM AUTHOR]

For deteriorating items with seasonal demand, a supplier usually requests that the buyer (retailer) prepays a fraction of the acquisition cost as a deposit. The expiration date of a deteriorating item is an important factor in a buyer's purchase decision. Despite its importance, relatively little attention has been paid to the effects of the expiration date; the versions of economic order quantity models that are available consider fixed deterioration rates. This paper considers a more realistic situation where the deterioration rate of a product gradually increases as the expiration date approaches. In this paper, the optimal cycle time and the cycle fraction of no shortages are the decision variables that minimize the total cost. The total annual relevant cost is shown to be strictly pseudo-convex for each of the decision variables, which simplifies the search for the global solution to a local minimum. This paper provides an improvement on earlier work, as it provides an optimal rather than a near-optimal solution. Several numerical examples are provided to illustrate the behaviour of the model and to highlight some managerial insights. [ABSTRACT FROM AUTHOR]

Many countries, including Colombia, require new financial mechanisms that lead to increased coverage in supplies of drinking water, particularly in rural areas. However, the establishment of such mechanisms demands new public and private policies that promote and increase financial assets in capital markets. This paper examines how green financial mechanisms can lead to increased coverage of potable water through the funding of new water infrastructure systems in Colombia. Achieving this goal requires both private investment and clean financial mechanisms that promote a low-emissions infrastructure. To address this need, this paper proposes a new financing model that involves green financing, capital markets, and the private and public sectors. In addition, it introduces new policies that the Colombian government should plan to implement to foster private participation and the development of infrastructure-related financial assets. As a result, this paper should contribute to increasing the role of the capital markets and private involvement in the development of a sustainable rural drinking water infrastructure. [ABSTRACT FROM AUTHOR]

This paper presents a Robust Genetic Programming approach for discovering profitable trading rules which are used to manage a portfolio of stocks from the Spanish market. The investigated method is used to determine potential buy and sell conditions for stocks, aiming to yield robust solutions able to withstand extreme market conditions, while producing high returns at a minimal risk. One of the biggest challenges GP evolved solutions face is over-fitting. GP trading rules need to have similar performance when tested with new data in order to be deployed in a real situation. We explore a random sampling method (RSFGP) which instead of calculating the fitness over the whole dataset, calculates it on randomly selected segments. This method shows improved robustness and out-of-sample results compared to standard genetic programming (SGP) and a volatility adjusted fitness (VAFGP). Trading strategies (TS) are evolved using financial metrics like the volatility, CAPM alpha and beta, and the Sharpe ratio alongside other Technical Indicators (TI) to find the best investment strategy. These strategies are evaluated using 21 of the most liquid stocks of the Spanish market. The achieved results clearly outperform Buy&Hold, SGP and VAFGP. Additionally, the solutions obtained with the training data during the experiments clearly show during testing robustness to step market declines as seen during the European sovereign debt crisis experienced recently in Spain. In this paper the solutions learned were able to operate for prolonged periods, which demonstrated the validity and robustness of the rules learned, which are able to operate continuously and with minimal human intervention. To sum up, the developed method is able to evolve TSs suitable for all market conditions with promising results, which suggests great potential in the method generalization capabilities. The use of financial metrics alongside popular TI enables the system to increase the stock return while proving resilient through time. The RSFGP system is able to cope with different types of markets achieving a portfolio return of 31.81% for the testing period 2009–2013 in the Spanish market, having the IBEX35 index returned 2.67%. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the volatility dynamics of EUR/GBP currency using statistical as well as the neural network approach which is an alternative way for time series modelling and forecasting in economics. The goal of this paper is to provide an alternative and reasonable way in modelling dynamic economic time series. We suggest an alternative approach for forecasting time series with non-constant volatility – we suggest and implement several neural network prediction models; we also use a large number of statistical models as well as different optimization techniques for artificial neural network. After discussing the basics of statistical volatility modelling and the basis of artificial neural networks we perform the experiment on real financial data. We quantify several ARCH and GARCH models; we also implement various RBF neural network prediction models. The comparative analysis of out-of-sample forecasts evaluated using MSE evaluation measures is performed. Finally, we state that suggested neural network models performed almost as good as the standard statistical models and are therefore reasonable and acceptable in economic modelling. [ABSTRACT FROM AUTHOR]

The 16th Annual Conference on Finance and Accounting (ACFA Prague 2015) is organized by the Faculty of Finance and Accounting, University of Economics, Prague ( http://ffu.vse.cz/english/ ). The Faculty highly promotes international research cooperation and the Annual Conference offers an opportunity for researchers to present their work both in early and final stage of research. The second aim of the Conference is to create a broad platform enabling young researchers, especially Ph.D. students, from all over the world to share their thoughts and visions, to discuss contemporary development in finance and accounting, and thus to obtain references for their research and doctoral theses. In 2015 edition, we received 129 submissions of full papers from authors affiliated with universities in 25 countries. Based on the peer-review of the Scientific Committee, 91 papers (acceptance rate 70.5%) qualified for the presentations at scientific sessions. 69 papers are included in this special issue of Procedia Economics and Finance Proceedings, which are the official proceedings of the ACFA Prague 2015. [ABSTRACT FROM AUTHOR]

Credit scoring methods have been widely investigated by researchers; recently, genetic algorithms have attracted particular attention. Many research papers comparing the performance of genetic algorithms and traditional scoring techniques have been published, but most do not provide enough detail about the fitness function used by the genetic algorithm—despite the fact that fitness function has a key influence on the model’s overall performance. The aim of this paper is to evaluate the predictive performance of different fitness functions used by genetic algorithms in credit scoring. An alternative fitness function based on a variable bitmask is proposed, and its performance then compared with fitness functions based on a polynomial equation as well as an estimation of parameter range. The results suggest that the bitmask is superior to the two other methods in both accuracy and sensitivity. The Wilcoxon matched-pairs sign rank test and paired t-Test indicate these results are statistically significant. [ABSTRACT FROM AUTHOR]

The state of economic disposition that Greece has been since 2009 is a focal point in Economic science for further research. Marketing is a function of a great importance for companies’ prosperity. Also Alay Kohli and Bernard Jaworski proved in their paper that the correlation of Marketing and profit is positive and stronger in a “weak” economy. This paper aims to examine the relationship of Marketing Expenses with the Total Sales and with the Gross Profit along with the changes that may happen in the period of Crisis in Greece. The methodology of the study employs the judgmental sampling because not every company provides the needed financial information. The relationship of the variables examined with the linear regression, mainly because this method gives insights in the contribution of the independent variable in the variability of the dependent and also gives the quantitative relationship between the variability among them. The sample of this study consists of 140 from the total of 254 companies of the Athens Exchange Stock. The findings of this paper shows that marketing is the main factor that determines the profitability of the companies and this relationship tends to be stronger in the crisis period in contrast to Return On Marketing Investments ratio that gradually decreased for the same period, this fact reveals that companies failed to change their Marketing Mix so to adapt with all changes that the economic crisis brought in the consumer behavior. For the investors decisions this paper having examined the relationship between Marketing Expenses and Gross Profit and the variance of the ROMI ratio for 2005 to 2011 advices them to be more careful when ROMI changes dramatically from one year to another, marketing demands time for consumers to be acquaint with. [ABSTRACT FROM AUTHOR]

Construction cost estimation, which is normally labor-intensive and error-prone, is one of the most important works in the architecture, engineering, construction, and facilities management industry during a project's lifecycle. With the development of information technology, it is expected that the efficiency and accuracy of cost estimation for tendering of building projects (TBP cost estimation for short hereafter) can be greatly improved if it can be carried out automatically based on the design results using building information modeling (BIM-based design results for short hereafter). One of the major obstacle to realize the automatic TBP cost estimation is the acquisition of the construction information (e.g. construction method, equipment), which are necessary for TBP cost estimation but often not included in the design results. This paper aims to establish an approach to identify and acquire the construction information systematically in order to facilitate the automatic TBP cost estimation based on BIM-based design results. First, the process of TBP cot estimation based on BQ method in China is introduced and the related specifications are classified. Then a typical specification for TBP cost estimation is analyzed to identify the construction information that are required in TBP cost estimation and a typical specification for the requirement on design detail fulfillment is analyzed to identify the construction information that can be obtained from the design result. Next, based on the above results, the construction information that need to be established during TBP cost estimation are highlighted and an approach to acquire them based on BIM-based design results are presented. Finally, the applicability of the approach is discussed. For simplicity, the scope for the analysis is confined to TBP cost estimation of concrete construction of reinforced concrete structures in this paper. [ABSTRACT FROM AUTHOR]

The Rotterdam Opslag en Afvang Demonstratieproject (Rotterdam Storage and Capture Demonstration project), or ROAD, aims to build and operate a 250 MWe equivalent CCS chain using post-combustion capture technology and off-shore storage in a depleted gas field under the North Sea. The capture plant would be retrofitted to a new 1 070 MWe coal-fired unit (Maasvakte Power Plant 3) in Rotterdam, Netherlands. It was originally intended to reach a final investment decision at the end of 2010, but the project has faced a series delay associated with permitting, complex commercial negotiations and, most seriously, funding. At the moment (September 2014), the European Commission and the Government of the Netherlands are engaged in a renewed push to solve the funding problems and allow construction to finally start. The project has financial support from the European Energy Programme for Recovery (EEPR) and the Government of the Netherlands and is now the only one of the six projects originally supported that still has any realistic prospect of being realized in the short term. This paper presents an update of the overall project development as at the time of GHGT-12 and the accomplished milestones and issues met in the permitting process. In addition, it will focus on the technical and economic aspects of integrating the capture plant with the power plant. Furthermore, it will provide an outlook on future CCS/CCU developments in Rotterdam, describing the steps under development to create a full CO 2 hub in the port including options for ship transport and bio-CCS. The paper concludes with the management of the project delays and the implications for the project economics. [ABSTRACT FROM AUTHOR]

Nonlinear chaotic finance systems are represented by nonlinear ordinary differential equations and play a significant role in micro-and macroeconomics. In general, these systems do not have exact solutions. As a result, one has to resort to numerical solutions to study their dynamics. However, numerical solutions to these problems are sensitive to initial conditions, and a careful choice of the suitable parameters and numerical method is required. In this paper, we propose a robust spectral method to numerically solve nonlinear chaotic financial systems. The method relies on spectral integration diagonal matrices coupled with a domain decomposition method to preserve the high accuracy of our methodology on a long time period. In addition, we investigate stability of chaotic finance systems using the Lyapunov theory, and a two sliding controller mode synchronisation to regulate the synchronisation of these systems. Numerical experiments reveal the high accuracy and the robustness of our method and validate the synchronisation of chaotic finance systems. [ABSTRACT FROM AUTHOR]

Systems supporting financial dealings at an enterprise scale must be able to handle large volumes of transactions in a secure manner while maintaining high level of operational availability. These systems must also have the flexibility to interface with many third-party applications with ease. In this paper we explore these architectural considerations for systems supporting financial transactions using the blockchain technology. We identify the strengths of this technology in achieving the qualities associated with these architectural considerations and suggest architectural tactics that can be used to overcome its weaknesses. A reference architecture is presented along with a prototype that implements this architecture using Iroha, a commercially available financial ledger for blockchain systems. [ABSTRACT FROM AUTHOR]

Pareto distributions are the most popular models in economics and finance. Hence, it is essential to have a wide spectrum of tools for checking their goodness of fit to a given data set. This paper provides the first review of known goodness of fit tests for Pareto distributions. Over twenty tests are reviewed. Their powers are compared by simulation. [ABSTRACT FROM AUTHOR]

Abstract: Against the background of the recognized fact that man's activities in the environment have impact on the life of human beings, this paper presents an overview of the factors militating against the achievement of Sustainable Environmental Management in Nigeria making use of both Secondary data and personal observations. The paper notes that Sustainable Environmental Management is far from being achieved in Nigeria because the activities of man still degrade the environment. The country can only be sustainably developed if it can pay attention to environmental sanitation and conservation. However, the hindrances to this were traced to poor funding, environmental management seen as the responsibility of government and non inclusion of environmental management in national development plan. The paper concludes by suggesting possible sources of sustainable fund for the country to move forward. [Copyright &y& Elsevier]

In the economic activities, the central bank has an important role to cover payments of banks, when they are short of funds to clear their debts. For this purpose, the central bank timely puts funds so that the economic activities go smooth. Since payments in this mechanism are processed sequentially, the total amount of funds put by the central bank critically depends on the order of the payments. Then an interest goes to the amount to prepare if the order of the payments can be controlled by the central bank, or if it is determined under the worst case scenario. This motivates us to introduce a brand-new problem, which we call the settlement fund circulation problem. The problems are formulated as follows: Let G = (V , A) be a directed multigraph with a vertex set V and an arc set A. Each arc a ∈ A is endowed debt d (a) ≥ 0 , and the debts are settled sequentially under a sequence π of arcs. Each vertex v ∈ V is put fund in the amount of p π (v) ≥ 0 under the sequence. The minimum/maximum settlement fund circulation problem (Min-SFC / Max-SFC) in a given graph G with debts d : A → R + ∪ { 0 } asks to find a bijection π : A → { 1 , 2 , ... , | A | } that minimizes/maximizes the total funds ∑ v ∈ V p π (v). In this paper, we show that both Min-SFC and Max-SFC are NP-hard; in particular, Min-SFC is (I) strongly NP-hard even if G is (i) a multigraph with | V | = 2 or (ii) a simple graph with treewidth at most two, and is (II) (not necessarily strongly) NP-hard for simple trees of diameter four, while it is solvable in polynomial time for stars. Also, we identify several polynomial time solvable cases for both problems. [ABSTRACT FROM AUTHOR]

Research on the credit strategy of green supply chain is getting more and more attention. However, there are only few quantitative studies regarding it. In this paper, we first considered a two-echelon green supply chain with one manufacturer and two capital-limited retailers, then the impact of credit strategy on the green supply chain performance was analyzed. Three scenarios of credit strategy were examined respectively: the manufacturer provides two retailers with trade credit at the same time; the manufacturer provides one of the retailers with trade credit; and the retailer using external financing to return to the supply chain. A revenue sharing contract had also been established to coordinate the profits allocation in the green supply chain. Furthermore, the green level of the green products was studied as well, and we also considered the consumer sensitivity to the green product prices and green level. Finally, we modeled and identified the conditions under which the manufacturer may prefer to adopt. The related conclusions could provide reference for the green supply chain enterprises to formulate corresponding management strategies. Image 1 [ABSTRACT FROM AUTHOR]

Highlights • Mean reversion, seasonality, memory, spike, and stochasticity are altogether captured. • Filtering algorithms harness the combined power of OU, Poisson, and HMMs. • Our approach automatically updates parameter estimates as new data arrive. • Model implementation and validation were performed using observed data. Abstract Over the last three decades, the electricity sector worldwide underwent massive deregulation. Power market participants have encountered a growing number of challenges due to competition and other pertinent factors. For most markets at present, electricity is a non-storable commodity and its price is extremely sensitive to changes in supply and demand. The evolution of electricity prices exhibits pronounced mean reversion and cyclical patterns, possesses extreme volatility and relatively frequently occurring spikes, and manifests presence of memory property. These observed features necessitate the development of models aimed to simultaneously capture such price characteristics for forecasting, risk management, and valuation of electricity-driven derivatives. Majority of current research studies examining electricity spot prices are based on one-state models. Such models though may not accurately capture the stylised behaviours of price evolution, especially during periods of sudden spikes driven by the abrupt changes of market sentiments. This paper tackles the modelling and estimation problems under a new paradigm that integrates the deterministic calendar seasons and stochastic factors governing electricity prices with a regime-switching feature. The de-seasonalised component of our proposed model has both the jump and mean-reverting properties to account for spikes and periodic cycles alternating between lower price returns and compensating periods of higher price returns. The parameters of the de-seasonalised model components are also modulated by a higher-order hidden Markov chain (HOHMC) in discrete time. This provides a mechanism to extract latent information from historical data. The HOHMC's state is interpreted as the "state of the world" resulting from the interaction of various forces impacting the electricity market. Filters are developed to generate optimal estimates of HOHMC-relevant quantities using the observation process, and these provide online estimates of model parameters. Empirical demonstrations using the daily electricity spot prices, compiled by the Alberta Electric System Operator (AESO), show that our HOHMM approach has considerable merits in terms of price data fitting and forecasting metrics. Our proposed framework driven by a hidden Markov chain with a state memory-capturing mechanism provides a powerful alternative to support the pricing of contracts in the electricity markets. [ABSTRACT FROM AUTHOR]

Abstract: From the securities investment funds style index theoretical basis, this paper analyses its securities investment funds invest in the market. By using comparative analysis of the domestic and foreign investment style index for research and studies, it showed that the domestic and foreign investment style index were obvious differences in the categorized style, selection and classification standards of model style. In conclusion, this paper has made some suggestion for our country''s securities investment market from the application of investment in style from the financial derivatives [Copyright &y& Elsevier]

Abstract: This paper analyzes the relevance between economic growth and carbon emission in China and finds that the two parts are highly related and, in the long run, they are in steady equilibrium and two-way cause-and-effect relationship. In this way, China should further shift its economic growth mode, develop low-carbon economy and realize sound and fast development for economy and society. There are various ways to develop low-carbon economy. This paper raises measures for pushing the development of low-carbon economy from financial perspective. [Copyright &y& Elsevier]

Abstract: In this paper we propose a theoretical model of evaluating the economic costs and benefits of physician assisted suicide (euthanasia). The contemplation of euthanasia is modeled akin to the valuation of a real option. Our modeling of the decision shows that euthanasia is optimal when certain conditions are satisfied. The findings in this paper suggest that if more money is spent on medical research (such as pain management), the demand for Euthanasia could be reduced. [Copyright &y& Elsevier]

Payments for Ecosystem Services (PES) are generating a lot of attention among conservationists because they have the potential to create new funding opportunities for biodiversity protection and other ecosystem services that contribute to human well-being. A number of recent publications have suggested ways to target and implement PES projects in order to maximize their cost-effectiveness and efficiency, and the Heredia Declaration (this issue) sets forth a list of agreed-upon principles concerning the use of PES schemes. One of those principles concerns the “bundling” of joint products of intact ecosystems in PES schemes in order to maximize the benefits to society. There have been several recent studies focusing on the degree of overlap between biodiversity and other ecosystem services and therefore the opportunities and constraints to bundling these services. Building on this idea, the bulk of this paper focuses on developing a method for selecting sites for PES where the main interest is to bundle biodiversity with other ecosystem services. We focus our analysis on Madagascar, a country with globally important biodiversity that is also beginning to explore the utility of PES as a conservation mechanism. Specifically, we assess the opportunities for bundling biodiversity conservation with carbon and water services at the national scale and identify where using PES to protect these areas of multiple benefits would be most cost-effective and efficient. This analysis identifies almost 30,000 km2 — out of 134,301 km2 — of natural habitat that could potentially meet biodiversity conservation goals and protect additional ecosystem services through a PES scheme. One of the places identified by our methodology corresponds to an ongoing conservation project that has already begun using payments from carbon emission reductions to protect standing forests and restore important biodiversity corridors — the Ankeniheny-Mantadia-Zahamena Biodiversity Conservation and Restoration Project. This project site was selected for its high biodiversity and carbon values, lending credibility to our spatial targeting methodology and providing a case study to draw insights on how multiple-benefit PES schemes can be implemented in biodiversity “hotspots”. In the discussion section of this paper we draw on experiences from this project to consider how many of the principles outlined in the Heredia Declaration affect implementation of PES schemes in Madagascar, providing lessons for similar countries experimenting with PES for biodiversity conservation. [Copyright &y& Elsevier]

Abstract: Credit scoring has been widely investigated in the area of finance, in general, and banking sectors, in particular. Recently, genetic programming (GP) has attracted attention in both academic and empirical fields, especially for credit problems. The primary aim of this paper is to investigate the ability of GP, which was proposed as an extension of genetic algorithms and was inspired by the Darwinian evolution theory, in the analysis of credit scoring models in Egyptian public sector banks. The secondary aim is to compare GP with probit analysis (PA), a successful alternative to logistic regression, and weight of evidence (WOE) measure, the later a neglected technique in published research. Two evaluation criteria are used in this paper, namely, average correct classification (ACC) rate criterion and estimated misclassification cost (EMC) criterion with different misclassification cost (MC) ratios, in order to evaluate the capabilities of the credit scoring models. Results so far revealed that GP has the highest ACC rate and the lowest EMC. However, surprisingly, there is a clear rule for the WOE measure under EMC with higher MC ratios. In addition, an analysis of the dataset using Kohonen maps is undertaken to provide additional visual insights into cluster groupings. [Copyright &y& Elsevier]

Abstract: In this paper we attempt to distinguish the direct effect of financial development on poverty reduction from its indirect effect through economic growth. Using an efficient estimator called fixed effect vector decomposition (FEVD) we employ a set of panel data from 54 developing countries for the period 1993–2004. Our results indicate that on average financial development is conducive for poverty reduction but the instability accompanying financial development is detrimental to the poor. The major policy recommendations suggested by the paper indicate that financial sector reforms should be directed at easing credit restrictions while taking into consideration the effects of financial instability on the poor. [Copyright &y& Elsevier]

Abstract: In those coastal communities where the most seaward strip of mainland consists of dunes, these dunes often serve as a flexible sea defence. In addition, this strip offers large potential for housing and commercial enterprises. Unfortunately, due to severe storm surges part of this strip (the erosion zone) is subject to erosion, and as a result of which any buildings or infrastructure located here, are destroyed. Therefore, as we will illustrate in this paper, a building policy for this zone should reflect a compromise between two opposite interests: exploitation of the existing potential and, prevention of an unacceptable high risk due to erosion. Accordingly, the authors have developed a framework for such a building policy on the basis of which the desirability of various different types of investments and the location within the erosion zone of such investments can be determined. The examples that are used to illustrate this framework in this paper are limited to experiences in The Netherlands as relevant data and experiences are available and relatively easy accessible here. Nevertheless, the approach as is described is generic and applicable worldwide suggesting that the discovered unused potential for exploitation is not just limited to The Netherlands. [Copyright &y& Elsevier]

Abstract: This paper analyses a situation where the survival of an endangered species depends on certain types of conservation measures being carried out regularly, yet there is financial uncertainty in the future periodical availability of a budget to finance the conservation measures. One option to insure against future budget uncertainty is to save money. To maximise the long-term survival of the endangered species, it has to be decided in each period whether to spend the available money now or to allocate it to future use. The paper provides an ecological–economic model for this stochastic dynamic optimisation problem. The findings include that the available money should be allocated as evenly as possible among periods, which may require some precautionary saving in early periods to take uncertainty into account. Under certain conditions, however, increasing uncertainty may at least temporarily increase the optimal payment. Among other parameters, the amount of precautionary actions depends on the magnitude of natural variation in the species population. [Copyright &y& Elsevier]

Abstract: This paper deals with a class of real-valued random-walk processes, observed over random epochs of time, that forms a delayed renewal process. The present model does not restrict this class to a merely monotone random walk, which is easier to analyze and find explicit form functional. The objective is to find the first passage of the process exiting a rectangular set and registering the value of the process at this time, thus generalizing past models where either the observed process was monotone or the first passage time reduced to the moment of the first drop. The joint transformation of the named random characteristics of the process are derived in a closed form. The paper concludes with examples, including numerical examples, demonstrating the use of the results as well as practical applications to finance. [Copyright &y& Elsevier]

Abstract: The topic of this paper is how to implement optimal decisions in an uncertain world. A study of how this is done in real systems—from the nationwide optimization of the economy by the Central Bank to the optimal use of resources in a single cell—shows that a common approach is to use feedback strategies where selected controlled variables are kept at constant values. For example, in order to optimize the wealth of a country (overall objective), the Central Bank may to attempt to keep the inflation constant (selected controlled variable) by adjusting the interest rate (independent input variable). The underlying idea is that the system behavior is indirectly optimized by keeping selected controlled variables at given constant values (setpoints). In the paper this idea of “self-optimizing control” is explained and illustrated on a large number of examples. [Copyright &y& Elsevier]

This paper addresses the question of why, in spite of its recent success, behavioral economics does not influence most discussions about how economic policy ought to be made. Failing to penetrate into contemporary discourse on leading policy issues is a serious problem, because behavioral techniques often point to policy prescriptions that are at odds with the prescriptions which follow from models using more standard behavioral assumptions. By comparing how the universe of possible policy implications changes when different methodological approaches are used, this paper demonstrates a systematic link between methodology and the range of policy prescriptions that can be socially desirable. Because of this link, the methodological multiplicity of behavioral economics, and the ideological pluralism which it supports, favor the use of normative behavioral economics. This follows from the basic economic principle of diversification: a policy prescription that reflects averaging over a number of distinct kinds of errors (one for each methodology) is less likely to wander far off target than one generated by a single method. [Copyright &y& Elsevier]

It has been claimed that Asia lags behind the rest of the world in the privatization of airports. At the same time, the air transport sector has been growing quickly and this has placed enormous pressure on airport infrastructure. This paper reviews the situation and finds that the private sector is involved extensively with new airports and the upgrading of existing airports in Asia. Although the models used to accommodate the private sector appear similar to those used elsewhere, governments in Asia have retained majority control in every case. Despite the fact that some governments say that efficiency is important to them, the most common and important motive in “privatization” in Asia is to mobilize a new source of finance. Airports remain high on the agenda of public policy. Governments are concerned about abuse of monopoly powers and they want to cross-subsidize regional airports, but they lack the institutional strengths to regulate effectively. By retaining majority control, however, governments risk losing the efficiency benefits of privatization. The paper explores the reasons why airports are such difficult cases to deal with and concludes that there is a greater need than ever to be able to compare the performances of airports. [Copyright &y& Elsevier]

This paper briefly surveys the literature on finance, growth, and policy in China and emerging markets. We introduce the papers that comprise this special issue of Emerging Markets Review. The papers comprise themes in corporate social responsibility, innovation, insider trading, financial reporting behaviours, and financial openness. Also, we discuss research trends and offer suggestions for future research on corporate governance and finance in China and emerging markets. • We survey the literature on finance, growth, and policy in China and emerging markets. • We discuss themes that include corporate social responsibility, innovation, insider trading, financial reporting and openness. • We discuss research trends on corporate finance in China. • We offer suggestions for future research on corporate governance and finance in China and emerging markets. [ABSTRACT FROM AUTHOR]

Due to the growing volume of electronic payments, the monetary strain of credit-card fraud is turning into a substantial challenge for financial institutions and service providers, thus forcing them to continuously improve their fraud detection systems. However, modern data-driven and learning-based methods, despite their popularity in other domains, only slowly find their way into business applications. In this paper, we phrase the fraud detection problem as a sequence classification task and employ Long Short-Term Memory (LSTM) networks to incorporate transaction sequences. We also integrate state-of-the-art feature aggregation strategies and report our results by means of traditional retrieval metrics. A comparison to a baseline random forest (RF) classifier showed that the LSTM improves detection accuracy on offline transactions where the card-holder is physically present at a merchant. Both the sequential and non-sequential learning approaches benefit strongly from manual feature aggregation strategies. A subsequent analysis of true positives revealed that both approaches tend to detect different frauds, which suggests a combination of the two. We conclude our study with a discussion on both practical and scientific challenges that remain unsolved. [ABSTRACT FROM AUTHOR]

This paper studies the interaction between housing market sentiment and government interventions. With a unique micro-level data from China, we construct a housing market sentiment index by applying the techniques in the finance literature. This index is significantly correlated with the confidence indexes from official sources. We find that housing market returns increase with contemporaneous sentiment, and high sentiment is followed by low returns. Tightening policies cannot reduce optimism, and high sentiment negatively impacts the effectiveness of tightening policies. This negative impact is especially significant in the zones where housing prices are sensitive to increasing sentiment. [ABSTRACT FROM AUTHOR]

In this paper, two hybrid models are used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick by Support Vector Machine (SVM) and Heuristic Algorithms of Imperialist Competition and Genetic. In the first model, SVM and Imperialist Competition Algorithm (ICA) are developed for stock market timing in which ICA is used to optimize the SVM parameters. In the second model, SVM is used with Genetic Algorithm (GA) where GA is used for feature selection in addition to SVM parameters optimization. Here the two approaches, Raw-based and Signal-based are devised on the basis of the literature to generate the input data of the model. For a comparison, the Hit Rate is considered as the percentage of correct predictions for periods of 1–6 day. The results show that SVM-ICA performance is better than SVM-GA and most importantly the feed-forward static neural network of the literature as the standard one. [ABSTRACT FROM AUTHOR]

We present in this paper a hierarchical Deep Neural Network for stock returns prediction. This DNN is trained in a high frequency context, we use 5 minutes returns of TUNINDEX stocks in a period of 4 years. The designed network aims to predict the next 5 minutes return of a given stock. The predictive power of our network is improved by the hierarchical design and stocks classification while the training process is simplified by dimensionality reduction techniques. Experimental study shows an accuracy up to 71% and a considerable improvement comparing to recent related works. [ABSTRACT FROM AUTHOR]

Microgrids are receiving increasing attention from power systems planners as a means to integrate distributed energy resources (DER) including renewable energy resources into the grid, and as a means of balancing the variability of renewable resources and loads with flexible generation. A key to justifying microgrids is establishing their business case. This helps to set the structure and configuration of the microgrid, including defining the DER required to feed part or all of the loads and the level of control required from the microgrid controller. This paper proposes a systematic approach and methodology for formulating and quantifying a microgrid business case. The framework adapts the use case approach to the microgrid context. It defines stakeholders, benefits and beneficiaries and determines the dependency of the business case on microgrid technologies. A method to quantify and allocate benefits is proposed. Applications to practical microgrids are discussed, including remote communities, remote mining sites and grid connected critical distribution grids. [ABSTRACT FROM AUTHOR]

Market risk exposure plays a key role in risk management. A way to measure risk exposure is to evaluate the losses likely to incur when the assets prices of a portfolio decline. Most financial institutions rely on Value-at-Risk (VaR) estimates to measure downside market risk. This paper suggests an evolving possibilistic fuzzy modeling (ePFM) approach to estimate VaR. The approach is an extension of the possibilistic fuzzy c-means clustering and functional fuzzy rule-based modeling within the framework of incremental learning. Evolving possibilistic modeling employs memberships and typicalities to update the cluster structure and corresponding fuzzy rules using a statistical control distance-based criterion. A utility measure evaluates the quality of the current cluster structure and associated model. Data from the main global equity market indexes of United States, United Kingdom, Germany, Spain, and Brazil from January 2000 to December 2012 are used to estimate VaR using ePFM. The performance of ePFM is evaluated and compared with traditional VaR benchmarks such as Historical Simulation, GARCH, EWMA, and Extreme Value Theory based VaR, as well as with state of the art evolving approaches. The results suggest that ePFM is a potential candidate for VaR modeling because it achieves better results than the alternative approaches. [ABSTRACT FROM AUTHOR]