Search

Portfolio diversification has long been known and prevalent as a risk minimization method. The goal of this work is to develop a model to use Grey Wolf Optimization (GWO) algorithm to create an investment portfolio with optimal risk-return ratio. The initial set of equities we propose to be selected using Self-Organizing Maps (SOM). We have highlighted the set of factors to use as input variables for SOM. When the equities in portfolio is defined, we need to decide the proportion of our capital distributed among the portfolio equities. For this task we employ the GWO algorithm, which let us find the optimal weights assignment along the portfolio shares based on Mean-Variance portfolio minimization condition. As we know the Variance and Mean respectively express the Risk and Return rating of portfolio. Our research investigates the sensitivity of the GWO algorithm to number of iterations, wolf herd size and shares weight limits. Just knowing these parameters best values, we can expect for optimal portfolio diversification. The model verification was performed on stocks from S&P500 index. The GWO was applied for determining the weights for portfolio equities to get optimal risk-return ratio. The comparison of GWO balanced portfolio performance with the direct investment to S&P500 index, let us conclude the advantage of our proposed model.

This chapter deals with the products and services supplied or potentially offered by financial institutions within the framework of banking 5.0. The chapter analyzes new types of financial institutions: challengers, neo banks, open banking, and especially fintech organizations. The second part of this chapter examines financial institutions’ new services, such as instant payments, mobile wallets, request to pay, and Peer-to-Peer (P2P) banking. The chapter uses a method based on a distinction between new value-adding services based on the technologies used and new services based on better serving the customer. Banking 5.0 draws particular attention to sustainable banking. Sustainable banking integrates environmental, social, and governance (ESG) criteria into banking. It sets ESG as a critical goal also for banking. Banking decisions used to be based on a two-dimension risk and return analysis. In sustainable banking, decisions rely on three dimensions: risk, return, and ESG impacts.

Digital inclusive finance plays an important role in optimization of financing resources. Information extracted from big data provides cost effective instruments for risk and return analysis, which enhanced efficiency in digital inclusive finance. The characteristic of inclusiveness in digital finance promotes high quality development and facilitates innovation. With data at provincial level, based on OLS model, this paper quantitatively analyzed the impact of digital inclusive finance on development and innovation. The variables include digital inclusive finance index, GDP, resident consumption, net export, human resources and number of research patent. Taking time gap of financing to outcome into consideration, one period lag explanatory variables are used in the regression models. The empirical analysis results indicate that digital inclusive finance has positive impact on GDP and consumption. Digital inclusive finance also contributes to innovation, reflected in the number of patents, while economic openness is improved by digital finance and innovation. Digital inclusive finance plays an important role in inclusive development, green development, sustainable growth, innovation and technology upgrading.

This paper aims to solve the problem of optimal portfolio of venture capital investment by intelligent algorithm. Using the actual data, this paper constructs the optimal portfolio models based on different risk measures and termination conditions: unit risk return maximization model, VaR risk control model, risk and return preference model. Genetic algorithm and simulated annealing algorithm are used to optimize the models.

Real estate investments are known for requiring huge capital outlay thereby making prospective investors interested in understanding the risk and possible return from their investment. The risk and return attributed to the investments can only be revealed through conducting project appraisals. Unfortunately, the project appraisals of real estate investment are tainted with numerous inaccuracies and discrepancies. These inaccuracies have been attributed to poor market research conducted by estate surveyors and valuers. Therefore, this study appraises the challenges of conducting market research during project appraisal of real estate investment. The study adopted convenience sampling for administering the questionnaire to estate surveyors and Valuers in Lagos metropolis. A total of 56 questionnaires were obtained from the professionals out of 70 that were distributed. The data were analysed with statistical package for social science (SPSS version 20), using, mean score and Kruskal–Wallis test. The study discovered that the challenges faced by the estate surveyor in conducting market research are pressure from their clients to deliver the appraisal report and difficulty in sourcing information. The study recommends that a data bank should be created to facilitate the ease of information sourcing. The study contributes to improving the quality of project appraisal in the country.

The previous chapter introduced the reader to Markowitz mean-variance analysis and the Capital Asset Pricing Model. The cost of capital calculated in chapter “Debt, Equity, the Optimal Financial Structure, and the Cost of Funds” assumes that the cost of equity is derived from the Capital Asset Pricing Model and its corresponding beta, or measure of systematic risk. The Gordon Model, used for equity valuation in chapter “The Equity of the Corporation: Common and Preferred Stock,” assumes that the stock price will fluctuate randomly about its fair market value. The cost of equity is dependent upon the security beta. In this chapter, we address the issues inherent in a multi-beta or multiple factor risk model. The purpose of this chapter is to introduce the reader to multifactor risk models. There are academic multifactor risk models, such as those of Cohen and Pogue (1967), Farrell Jr. (1974), Stone (1974), Ross (1976), Ross and Roll (1980), Dhrymes, Friend, and Gultekin (1984), Dhrymes, Friend, Gultekin, and Gultekin (1985), and Fama and French (1992, 1995, 2008). There are practitioner multifactor risk models, such as Barra, created during the 1973–1979 time period; Advanced Portfolio Technologies (APT), created in 1987; and Axioma, created in the late 1990s and gaining practitioner acceptance in the 2000–2019 time period. The former academicians who created these practitioner models are Barr Rosenberg, Andrew Rudd, John Blin and Steve Bender, and Sebastian Ceria. We will introduce the reader to the practitioner models and their academician creators in this chapter. Which models are best? We, at McKinley Capital Management, MCM, have tested these models. None of the models are perfect, but the models are generally statistically significant when the statistically significant tilt variables of chapter “Risk and Return of Equity and the Capital Asset Pricing Model” are used for portfolio construction. In this chapter, we discuss the MCM Horse Races of the 2010–2019 time period to test stock selection within the commercially available multifactor risk models.

This chapter presents the modern portfolio theory from the theoretical perspective. It is demonstrated how investors may choose the optimal portfolio out of different sets of portfolios, considering risk and return characteristics. This chapter introduces the primary relationship between the rate of return and risk with the first well-known model of market equilibrium: the capital asset pricing model (CAPM). Furthermore, the chapter discusses potential benefits derived from international diversification and the idea of regional diversification. The main aim of the chapter is to provide knowledge of the portfolio theory to help investors cope with this demanding task.

Defining an investment policy at the Swiss National Bank (SNB) has a long history and tradition. Strategy definition is a two-stage process. As a first step, the investment policy framework is drawn up, comprising all aspects of central banking, reputation, and risk policy, and a detailed, feasible long-term asset allocation strategy is formulated. In the second step, an investment strategy for the following 12–15 months is then prepared on the basis of this “neutral” allocation. Any deviations from the LAA are solely attributable to market valuation estimates. The SNB reports in a safe-haven currency, i.e. the Swiss franc. This has far-reaching implications for investment policy: the SNB can hold a higher proportion of risk assets relative to other central banks, while the diversification effect of risk assets is less pronounced and that of bonds stronger. The gradual expansion of the investment universe over the last 20 years has been accompanied by an ongoing improvement in the portfolio’s risk and return profile, even against the backdrop of the significant expansion in the SNB’s balance sheet. Ultimately, it has also alleviated the issue of excessive concentration in certain markets, which had become ever more problematic in light of this substantial balance sheet growth.

Over the recent years, Danmarks Nationalbank has transformed its approach to reserve management. The new framework is organized around two main pillars—a tiered liquidity management framework and an overall risk budget. Together they provide a framework for handling large balance sheet fluctuations—a consequence of the fixed exchange-rate policy—while aligning long-term exposure to investment risk with the primary policy objectives. The anchoring of the risk budget to a “policy portfolio” clarifies the risk and return implications of the bank’s multiple objectives and highlights the rationale for diversification.

Regulations and government policy are key elements in the success and growth of environmental, social, and governance (ESG) investing. Regulation refers to a broad array of mandatory requirements, guidelines, incentives, and other types of policy intended to steer the flow of capital, not just by government, but also by industry. This chapter explores the current state of play of relevant regulations, looking specifically at how targeted regulations and policies enabled clean energy—today’s biggest sector of sustainable investing representing about $300 billion per year since 2010. The chapter concludes with an outlook on where regulations and government policies supporting, and sometimes blocking, ESG-screened investing are likely to proceed in the coming decade.

This chapter introduces a discounted cash flow approach to valuation of business ventures and highlights the key elements of growth. The success of a business, sustainable or not, is dependent on its growth. Expansion of a business relies on expanding its markets’ reach (demographics), its innovation or creation of new products, and its capital structure (leverage) within a legal and governmental framework. The interplay of social distance and demographics highlights the markets that a business may expand as it builds customer relationships. Those relationships will be based on the marketing of innovative sustainable products to new markets. The role of leverage on capital structure will be laid out within a risk and return framework.

This chapter aims to identify the difference between conventional and Islamic capital asset pricing model in order to ensure the efficient investment management of products and markets. Based on theoretical literature and critical and empirical analysis, it provides key justifications for why and how Islamic capital asset pricing model outperforms the conventional capital asset pricing model. The data has been extracted from 69 companies in the period 2000 to 2017, listed at Karachi stock exchange. It provides potential contribution towards the existing body of knowledge through incorporating risk and return perspective in capital market theory. Here, it has been found that Islamic investment is a low risk investment as compared to a conventional one. Moreover, Islamic lenders must share some proportional in profit and risk, which trade-off in risk shares, not in risk-return. It is suggested that Islamic investment strategy is more profitable than the other in case of direct sharing contract. Lastly, recommendations and policy reforms have been discussed for implementation of Islamic investment strategy.

In this paper, a collaborative neurodynamic optimization approach is applied for bicriteria portfolio selection in the Markowitz mean-variance framework. The bicriteria portfolio selection problem consists of two objectives (risk and return) which are scalarized using a weighted Chebyshev function. Multiple neurodynamic optimization models are used to generate a set of Pareto-optimal solutions. Particle swarm optimization is used to diversify the Pareto-optimal solutions by optimizing the weights of the scalarized objective functions. Experimental results show the superiority of the applied approach.

In this paper, we propose a new method of algorithmic trading for short term investors in the financial markets, by applying swarm intelligence. We apply a well known meta-heuristic, known as Grey Wolf Optimizer (GWO), and find multi-peak optimisation solutions having different expected risk and return ratios, to propose 3 automated trading strategies. The novelty of our method is how we leverage three best swarm agents to construct multi-peak solutions that are best suited for the stochastic nature of financial markets. We utilise the variance between the positions of swarm agents in GWO to construct different algorithmic approaches to day trading, with an aim to diversify expected portfolio volatility. Our research showcases how the three best swarms of GWO are best suited to predict stochastic time series problems, as we typically find in the field of finance. Our experiments demonstrate the capability of our model compared to industry benchmark indices and evaluates the effectiveness of the proposed strategies.

The aim of this chapter is to use the risk/return relationship to broaden the picture of the financial axis between the London financial market and Africa during our period of study. We use two central concepts in the analysis of financial risk—volatility and equity risk premium—to put our return estimates in context. We move on to analyse the relationship between the companies’ longevity in our sample and the return on investment. Our results show that investing in Africa was somewhat riskier than investing in the United Kingdom, which on an aggregate level corresponds with a somewhat elevated return on investment. Disaggregating the data by region in Africa at the same time shows that many investors were unable to receive a premium for the increased risk they faced.

Portfolio optimization in stock markets has been investigated by many researchers. It looks for a subset of assets able to maintain a good trade-off control between risk and return. Several algorithms have been proposed to portfolio management. These algorithms use known return and correlation data to build subset of recommended assets. Dynamic stock correlation networks, whose vertices represent stocks and edges represent the correlation between them, can also be used as input by these algorithms. This study proposes the definition of constants of the classical mean-variance analysis using machine learning and weighted link prediction in stock networks (method named as MLink). To assess the performance of MLink, experiments were performed using real data from the Brazilian Stock Exchange. In these experiments, MLink was compared with mean-variance analysis (MVA), a popular method to portfolio optimization. According to the experimental results, using weighted link prediction in stock networks as input considerably increases the performance in portfolio optimization task, resulting in a gross capital increase of \(41\%\) in 84 days.

For the asset management industry, the last decade is characterized by a strong focus on the creation of “alpha” by various approaches (Gupta et al. 2016) and methodologies. Despite this, we have all heard at one point or another that asset allocation is an important, if not the most important, decision when investing one’s wealth. Various studies1 have attempted to disentangle the magnitude of the contribution of allocation versus security selection as drivers of the risk and return profile of a portfolio. The debate is around a 90% contribution. Regardless of the number, we observe that the majority of the effort by asset managers (capital and resources allocated) focuses on generating alpha, while alpha accounts for only a residual part of portfolio performance.

What started as an arcane movement is now a full-fledged, many trillions of dollars industry that promotes not only environmental change, but changes in the social fabric and corporate governance arenas in which we live and work. Led by women and millenials, investors are now constructing portfolios with their ethics, values, ideals as guides, regardless of risk and return considerations.

While value investing is a well-defined concept among professionals and academics, the definition of high quality companies is much broader and less consistent. Quality often means investing in companies with sustainable and traditional business models—say, elevator technology or healthcare as examples. This chapter discusses how the quality factor is typically measured and designed. It further highlights some key debates, both qualitative and quantitative, and discusses the key drivers of their risk and return. Depending on the choice of characteristics, performance can differ substantially between various quality factor definitions. In general, investors should keep an open mind as to whether a quality factor makes a good investment on its own and whether it should be combined with other factors in the overall portfolio.

This chapter provides an overview of the rapidly evolving state of real estate investments, valuation, and financing of high-performance commercial buildings. High performance is defined as the optimization and integration of building systems—energy and water efficiency, leveraging technology, and human behavior—and the buildings’ ability to enhance the well-being of its occupants. Centered on the financial impact to the property and/or portfolio, it links high-performance attributes to a buildings’ market value. It identifies core messaging to motivate owners, investors, financiers, and others in the real estate sector to appropriately analyze the risk and return of high-performance approaches and deploy high-performance strategies and technologies across new and existing buildings. The allocation of capital and financing remain critical components. The chapter summarizes the range of financial mechanisms that facilitate increased investment in these buildings.

The paper sketches historically the emerging Indian stock market economy from the birth of the Bombay stock exchange and the National stock exchange to the present. It focuses on analyzing the return properties of securities on the stock market using higher moments of the return distribution viz., skewness and kurtosis. This is in recognition of the fact that return distributions are not normally distributed as assumed by Standard Mean-Variance portfolio models. Consequently, it would help the investors in their forecast of market risk and portfolio adjustments with the help of studying these higher moments to access the risk and return trade-off. This particular analysis, which tests for the significance of higher moments, is likely to yield a deeper insight into the dynamics of stock market crises and booms. Moreover, the attempt here is to describe the stylised facts at a fundamental level to comprehend the extent of diversion from the normal and the reason’s causing the difference. Thus, when the fluctuating nature of Indian stock market is correlated with these rudimentary factors it would yield a model of the risk factors of the stock market in different phases of the market.

The capital asset pricing model (CAPM) is the basic theory that links risk and return for all assets—it quantifies the relationship between risk and return. The aim of running the CAPM model is to identify systematic risk. Capital asset pricing model (CAPM) is an attempt to explain and quantify the non-diversifiable type of risk. In other words, it measures how much additional return an investor should expect from taking a little extra risk. Investors demand a premium for bearing risk and therefore the higher the risk of the security, the higher the expected return to encourage investors to buy that security. As investors hold well-diversified portfolios, they are concerned with the non-diversifiable part of the risk of an individual stock. The relevant risk of an individual stock is its contribution to the risk of a well-diversified portfolio. Nondiversifiable risk is the relevant portion of an asset’s risk attributable to market factors that affect all firms and which cannot be eliminated through diversification. Also called systematic risk. Because any investor can create a portfolio of assets that will eliminate virtually all diversifiable risk, the only relevant risk is the one that is non-diversifiable. International agencies use the same approach in order to identify the beta coefficient of different companies. The beta is used as measure to quantify the systematic risk.

This paper aims to investigate whether including agricultural commodities can improve the portfolio performance by comparing the risk and return of multi-asset portfolio with and without an agricultural commodity price. To achieve our goal, we propose fitting a C-Vine copula based AR-GARCH model to interval data which allows us to capture uncertain characteristics that cannot be sometimes fully described with single data series. By using a convex combination method, we can obtain expected marginal distribution and joint density function, respectively. We then evaluate the portfolios’ risk and return using the expected shortfall concept. The results present that the average risk and return of non-agricultural portfolio outperforms agricultural portfolios. However, considering the one step ahead forecasting efficient frontier, the portfolio with soybean futures becomes superior to other portfolios.

The relationship between risk and return in finance seems controversial. While the crucial implication of the standard models dictates—the higher the risk, the higher the expected return—the empirical evidence seems to contradict this expectation. Emerging evidence has proven that the standard measures of realized risk, including volatility or systematic risk, negatively predict abnormal returns. Surprisingly, some other measures related to tail risk or downside risk have proven as positive predictors of performance. Importantly, all the measures might help investors to predict abnormal returns. In this chapter, the authors carefully reviewed the risk-based return-predictive signals along with their theoretical and empirical evidence and conducted thorough tests of example strategies across 24 international equity markets.

Determination of the delivery methods that favor investors’ interests is of the utmost importance. Undoubtedly, learning styles in pedagogy are different from andragogy and each one requires different delivery methods. In addition, this chapter aims to review some basic financial concepts such as market organization, that are essential for investors. More sophisticated concepts like risk and return are also introduced.

In the previous chapter, we discussed different approaches to forming expectations about future returns of the main asset classes but made only casual reference to the question of risk. In this chapter, we examine the issue of integrating risk and return, that is, optimizing the SAA. The mechanics of standard mean-variance optimization (MVO) are outlined in the Appendix to the book. Here we will focus on some of the challenges when applying MVO, possible remedies and alternative approaches.

The importance of saving and investing is discovered in this chapter. Within the literature, several savings and investment options are presented and the author delves briefly into the risk and return aspect of saving and investment. Insight into the operations of stocks, IPOs, and bonds, as well as the stock market is also explored in this chapter. Various options are also presented for individuals who are interested in making investment decisions. The chapter concludes with the demonstration of financial ratios and their importance when making investment decisions as well as some useful tips on being financially literate.

The paper aims to measure the risk and find the optimal weights of portfolio containing three instruments: Stock Exchange of Thailand, Thai Baht gold, and Treasury 10-year bond yield. The study employs the C-D vine copulas approach to construct the dependency of each pair instruments and uses the Monte Carlo simulation technique to generate the simulated data to compute Value at Risk (VaR) and Expected Shortfall (ES). Our results show that there exists a weak significant dependency between Stock Exchange of Thailand index and Thai Baht gold and dependency between Treasury 10-year bond yield and Thai Baht gold. Moreover, we find that the desired portfolio allocation is 49.8% of SET, 18.8% of Bond, and 31.4% of Gold where risk and return of the portfolio are 2.7% and 0.05%, respectively.

Lending is at the core of banking, and has been determining its fortunes and misfortunes—over the cycle and across Countries. Digitization could however even change this more traditional of paradigms, as banks could be successful (or not) in the future, on the basis of their decision on what digital lending model (if any) to have, and on what risk screening and underwriting process will determine their future origination, pricing and management strategies. Whilst impacts on non-banks originated lending volumes has been so far limited, the impacts introduced by alternative credit risk scoring and rating models are already impacting the credit market, potentially bringing to a better management of the inherent trade-off between risk and return.

Financial literacy is shown to influence investment decisions in different ways. One possible influence is that a lack of understanding of financial risk causes a negative risk attitude with the consequence for optimal investment behaviour that the positive relation between risk and return is not properly taken into account. The authors analyse data (n = 1150) collected in 2015 from three European countries (Italy, Sweden, and Spain) to test whether people with a low financial literacy have a negative risk attitude. The results confirm that survey respondents low in financial literacy on average rate their risk attitude more negative than more financial literate respondents do.

A direct relationship between risk and return—in the sense that return should vary positively and proportionately to volatility—is so essential that this should be designated as the first law of finance. Investors otherwise would never be induced to commit capital to risky assets. In reality, low-volatility stocks offer abnormally high returns relative to their riskier counterparts. By undermining conventional assumptions about the relationship between risk and return, the low-volatility anomaly arguably represents the most significant challenge to mathematical finance. The value premium in the Fama and French’s three-factor model is arguably a special case of the low-volatility premium, attributable to the costly reversibility of physical investments and human capital during recessions and economic downturns. The literature of strategic management has identified a similar effect, called Bowman’s paradox. That discipline identifies managerial culture, especially corporate and social responsibility, as a possible explanation for abnormal returns on low-volatility stocks.

The economic models that underlie the previous chapters built on the strong assumption that investors are rational agents who aim at maximizing their wealth while minimizing risk. These investors are able to assess the risk and return of all possible investment opportunities and hold a portfolio that satisfies their level of risk aversion. In contrast, the recent empirical evidence shows that real individual investors behave differently from investors in these models. In what follows, this chapter reviews the most recent findings on individual investor trading and the trade performance.

The epistemological beliefs of investment theory are rooted in the philosophy of probability. Markowitz’s arguments for using statistics in investment theory stem from Savage’s theory of subjective probabilities. The CAPM and the empirical investment theory are founded on the idea of objective probabilities. Markowitz has put investment theory in the category of Knightian risk, instead of in the category of Knightian uncertainty. The common denominator between the views of Knight, Keynes, Von Mises, McCloskey, and Taleb is that stochastical predictability is a rare case for economic phenomena, so Knightian uncertainty is relevant for economics. A thought experiment demonstrates that a rational financial firm would never market a synthetic stock with a specified risk and return like investment theory assumes, because it cannot be hedged risk-free.

We investigate the risk and return characteristics of 59 emerging and developed stock market indices for the period 1990–2015. The main focus of our analysis is the convergence of emerging capital markets. We apply the standard Capital Asset Pricing Model (CAPM) framework to investigate the positive relationship between risk and return, and also examine the evolution of the CAPM beta. We show that CAPM betas of different stock markets converge to the world average over time. In settings applying different risk measures from beta (volatility, Expected Downside Risk, and Shannon entropy), empirical results confirm the positive relationship between risk and expected return; however, we find that international capital markets still have significant individual characteristics.

In this chapter, one considers finance at its very foundations, namely, at the place where assumptions are being made about the ways to measure the two key ingredients of finance: risk and return. It is well known that returns for a large class of assets display a number of stylized facts that cannot be squared with the traditional views of 1960s financial economics (normality and continuity assumptions, i.e. Brownian representation of market dynamics). Despite the empirical counterevidence, normality and continuity assumptions were part and parcel of financial theory and practice, embedded in all financial practices and beliefs. Our aim is to build on this puzzle for extracting some clues revealing the use of one research strategy in academic community, model tinkering defined as a particular research habit. We choose to focus on one specific moment of the scientific controversies in academic finance: the ‘leptokurtic crisis’ opened by Mandelbrot in 1962. The profoundness of the crisis came from the angle of the Mandelbrot’s attack: not only he emphasized an empirical inadequacy of the Brownian representation, but also he argued for an inadequate grounding of this representation. We give some insights in this crisis and display the model tinkering strategies of the financial academic community in the 1970s and the 1980s.

This paper aims to expand a Systematic for the Analysis of the Economic Viability of Investment Projects (SAEVIP) proposed by Lima et al. (Joint conference CIO-ICIEOM-IIIE 2014—XX international conference on industrial engineering and operations management 2014, p. 87, 2014), (Int J Eng Manage Econ, 2015) using methods of Engineering Economics. The SAEVIP received the “Research Award 2014” of the joint conference CIO-ICIEOM-IIIE. For this purpose, adopts a stochastic treatment on principal variables that affect the expected economic performance of an investment project. The method of Monte Carlo simulation along with some distributions of probabilities, such as: uniform, triangular and normal are used to achieve the proposed objective. An example is used to illustrate the use of this approach in SAEVIP. The results extend the quality and reliability of the information generated and better substantiate the decision-making process on investment in fixed assets.

There is a dramatic shift in the investment preference of investors from conventional to Shariah-complaint companies in the last decade. This study provides evidence on the performance measurement of Karachi Meezan Index which represents Shariah-complaint stocks in comparison to KSE 30 which represents conventional stocks registered under Pakistan Stock Exchange formerly known as Karachi Stock Exchange. Daily index prices are incorporated in the analysis from 21 March 2012 to 7 September 2015. Independent sample t-test model is employed to measure the difference in the risk and return of each index. Regression analysis is used to study the relationship between stock return and financial ratios. Results showed that KMI stocks generate higher average return and lower risk as compared to KSE 100 index. Moreover, KMI 30 index shows higher risk-adjusted return of Treynor ratio as compared to conventional counterpart. In regression analysis, net income margin shows a significant negative relationship with KSE 30 index return. However, DER shows negative relationship with KMI 30 index but not significant.

We present additional evidence on the risk and return of stocks in the USA and globally in the 1997–2009 period. We use a stock selection model incorporating fundamental data, momentum, and analysts’ expectations and create portfolios using fundamental and statistically based risk models. We find additional evidence to support the use of multifactor models for portfolio construction and risk control. We created portfolios for the January 1997 to December 2009 period. We report three conclusions: (1) a stock selection model incorporating reported fundamental data, such as earnings, book value, cash flow, and sales, and analysts’ earnings forecasts and revisions and momentum can identify mispriced securities; (2) statistically based risk models produce a more effective return-to-risk portfolio measures than fundamentally based risk models; and (3) the global portfolio returns of the multifactor risk-controlled portfolio returns dominate USA-only portfolios.

This paper proposes a risk-aware B2B service pricing approach. The approach characterizes relevant cost positions according to their quantity and adaptiveness towards changes in the quantities sold. Based on the achieved transparency about the cost structure, cost niveau and cost adaptiveness, the approach allows to configure a risk-aware pricing scheme with an arbitrary number of different price components. It also allows for several different pricing schemes and provides analysis functionality for comparing these schemes with respect to risk and return criteria. The approach contributes to the domain of service science, which historically has not been discussing risk-based pricing approach in-depth.

The relationship between risk and return on the financial market is an issue of primary importance in finance, and it spans all the fields of specialization, including corporate finance.

Islamic banks are highly incorporated with social issues because of their rules and regulations. Profit not only depends on its own return and investment but also on trust, moral issues which may be more related to banking profitability. To test these gaps, this chapter attempts to investigate the socioeconomic factors along with bank-specific factors of global Islamic banks using dynamic GMM and Quantile regression. The dataset used in this study involves 55 full-fledged Islamic Banks from 24 countries across the globe. The results suggest that Return on Assets (ROA) is significantly positive to bank-specific factors such as credit risk has and statistically negative to cost-to-income ratio. It is also suggested that the relationship between risk and return is heterogeneous or dissimilar across different quantiles. Findings of the study tend to unravel that the socioeconomic factors especially political stability and investment freedom have positive and significant relation to the Islamic bank performance.

Entry method strategies and operational control strategies are explored, considering the latter strategy independently of the former, isolating its unique risks. These risks are quantified in a small sample with analysis of how the same methodology could be employed in future research.

In this study, we analyze the international portfolio with respect to risk and return aspects. We applied entropy methods to find the optimal portfolio weights. In this method, we used entropy as the objective function and we also compared our results with the conventional method. Moreover, we use the linear belief function to build a portfolio, which can represent market information and financial knowledge and then we use matrix sweepings to integrate the knowledge for evaluating portfolio performance. Overall, our empirical analysis indicates that all entropy methods performed better than Markowitz method, and the finding also suggests that the investor should take the benefit from ASEAN market.

The portfolio-analytic framework, developed in this book, examines exposure, risk, and performance from a high-dimensional perspective. Using detailed information about the individual positions in our portfolios and strategic benchmarks, we can gain substantial insight into the risk and return characteristics of our portfolios. This is our central thesis. Such a detailed level of analysis is, unfortunately, not always possible. This chapter, therefore, changes tactics somewhat and considers a different set of portfolio-analytic techniques that may be used to compare the relative risk and performance of portfolios. We collectively refer to these techniques as ex-post analysis.

It is generally difficult to read any finance article without stumbling, at some point, across the notions of risk and return. Modern portfolio theory has been built on the underlying premise that increased expected return cannot be attained without exposing oneself to greater risk. Strategic asset allocation (SAA)—the process of determining one’s medium- to long-term portfolio given constraints, objectives, and assumptions on the distributional characteristics of asset returns—concerns itself principally with finding an optimal trade-off between risk and return. A broad range of ratios—some well-known examples including the Sharpe, information, Treynor, Calmar and the appraisal ratios—combine historical return observations in such a manner as to approximate the amount of risk earned per unit of risk assumed.