Handbook of Investment Analysis, Portfolio Management, and Financial Derivatives

The following sections are included:

• Preface
• Advisory Board
• About the Editors
• List of Contributors
• Contents of Volume 1
• Contents of Volume 2
• Contents of Volume 3
• Contents of Volume 4

The main purposes of this introduction chapter are (i) to give an overview of the following 109 papers, which discuss investment analysis, portfolio management, and financial derivatives; (ii) to classify these 109 chapters into nine topics; and (iii) to classify the keywords in terms of chapter numbers.

Consensus earnings forecasts matter to investment practitioners in forming profitable investment portfolios and matter to researchers in studying how earnings inform the market. This study revisits the issue of considering analyst heterogeneity in forming better analyst consensus earnings forecasts (Clement, 1999; Clement and Tse, 2003; Brown and Mohd, 2003). Based on quarterly data for US firms from 1994 to 2017, the study finds that characteristics-based consensus forecasts outperform the simple mean consensus to predict abnormal returns in both the three-day window around and the two-month drift window after earnings announcements. They perform as well as the median consensus does to predict abnormal returns in the three-day window around earnings announcements but outperform the median consensus in the drift window. Investors may find these findings relevant to their investment decisions, and researchers may find them relevant to the studies of earnings informativeness.

Starting from humble beginnings, the use of financial options has substantially increased as an important financial tool for both speculation and hedging over the last 50 years. This chapter discusses both the theoretical and practical applications of financial options and related models. While the content is somewhat technical, we provide illustrations of their applications in simple settings. We address particular stylized features of option pricing models.

In this chapter, we apply various portfolio models to rebalance portfolios and further analyze their realized performance, including mean-variance (MV), conditional value-at-risk (CVaR), and Omega models. To ensure feasibility, we consider the transaction costs and the optimization of short-selling weights. The empirical results using the daily returns of international funds across 21 countries over 20 years show that the risky portfolios realize higher performance than a naïve diversification, particularly the CVaR model with a confidence level of 95% despite their higher trading costs. The CVaR model, mainly focusing on controlling loss, yields higher performance than those that are based on trade-off between return and volatility, such as the mean−variance and Omega models. The Omega model, however, generates lower downside risk. The superiority of risky portfolios over the equally weighted diversification varies intertemporarily across various models. The excess returns of risky portfolios over the equally weighted diversification are larger when the market is volatile, such as the periods of the subprime mortgage financial crisis and the 2020 COVID-19 recession.

Financial markets serve numerous roles, amongst them of course is the uncoerced exchange of securities. In addition to that role, they serve a very useful function of conveying to market observers information about the future, the challenge being our ability to elicit and interpret that information.

This paper addresses that the latter function regarding the option markets which provide the value for the VIX 30-day implied volatility on the S&P 500 Market Index. It demonstrated that the peak value of VIX during Persian Gulf I, 1990/1991, and Persian Gulf II, 2003, was nearly identical.

In this chapter, we employ data from a panel consisting of 28 European Union (EU) member countries over the period 1990–2020 to examine the validity of the famous Feldstein– Horioka (F–H) puzzle. Despite many criticisms, the F–H saving–investment correlation hypothesis is still used in the literature to infer the degree of capital mobility among countries. To this end, we apply a battery of panel unit root and cointegration tests. The finding of the presence of cointegration of the savings and investment ratios and the observed magnitude of the estimated average saving-retention coefficient for the panel reveal that for this panel of EU member countries, the F–H puzzle is not valid and the long-run international solvency condition is maintained in most of these countries. The observed low saving-retention coefficients for these countries imply a moderate degree of capital mobility and the absence of the F–H puzzle. This finding of the prevalence of a moderate degree of capital mobility is consistent with the macroeconomic experience of these countries during the period under investigation.

In this chapter, we apply a three-stage approach using an intermediate classification period between the estimation and test periods. In the intermediate period, we stratify individual firms into deciles based on the predictive power of the Carhart 4-factor model, measured by the out-of-sample R-squared prediction in this period. Our motive for this stratification is that firms with poor out-of-sample predictive power of the estimated model are likely to suffer from coeffcient instability and that these instabilities will result in a mismeasurement of expected returns in the test period. The empirical results show that lower predictive power deciles have larger averaged absolute changes of estimated coeffcients, which is our proxy for coeffcient instability, and lower averaged out-of-sample R-squared in the test period.

The main purpose of this article is to empirically demonstrate the effects of temporal aggregation when applying reverse regression models hypothesizing that spot prices today help predict forward rates in the future. This paper essentially reviews results from earlier research indicating that time-series aggregation will most certainly influence standard errors on parameter estimates. Standard errors are likely to increase with aggregation. The relationships between futures prices and spot oil prices are analyzed along with the importance of the effects of temporal aggregation and alternative model specification for understanding empirical relationships between the markets. Model specification and time-series aggregation over daily, weekly, and monthly aggregations confirm evidence that estimated standard errors are likely to increase with aggregation and t-ratios change as well. While goodness-of-fit measures might increase with aggregation, forecast accuracy with macrolevel aggregation might deteriorate owing to information loss due to the averaging of observations associated with underlying microstructures.

In this chapter, we examine risk spillover between the returns series of oil and stock prices of worst-affected countries due to the COVID-19 outbreak in unconditional and conditional frameworks, where the relationship was conditioned upon the US economic policy uncertainty and financial stress indices. Specifically, we used three different measures of oil prices, namely, WTI, OPEC, and Dubai oil prices. We also examined the risk spillover from US and Chinese stock markets to stock markets of affected countries, such as the UK, France, Germany, Italy, Spain, Switzerland, and Turkey, for the time period from 31st December 2019 to 22nd April 2020. Our results provide evidence that during the COVID-19 outbreak, Dubai and OPEC oil prices have had a strong positive effect on stock price when both of them are at their lower quantiles, which suggests that during extreme markets conditions, oil price affects stock price. Furthermore, evidence of the directional predictability from stock returns of the US/China to all other stock returns shows positive predictability from the US to France, Germany, Italy, Spain, Switzerland, and the UK at lower quantiles. Last but not least, when the relationships were conditioned by the policy uncertainty and financial stress, evidence of directional predictability became stronger and spread to more quantiles, suggesting that the interrelationships between oil price and stock price returns and between stock price returns of the US/China to all other stock price returns were not driven by the systemic risk but rather uncertainties during the COVID-19 outbreak.

We show that the call-put implied volatility spread (IVS) outperforms many well-known predictors of the U.S. equity premium at return horizons up to six months over the period from 1996:1 to 2017:12. The predictive ability of the IVS is unrelated to the dividend yield and is useful in explaining the cross-section of returns. Decomposing the IVS, we find the longer run predictive ability of the IVS operates primarily through a cash flow channel. We also find the IVS is significantly related to indicators of aggregate market direction and expected market conditions. Our results are consistent with the IVS reflecting market sentiment as well as information about informed trading.

This paper uses the change in individual securities accounts as a measure of equity funding supply to examine whether the persistent timing effect on capital structure exists for the Chinese equity market. This new equity timing measure avoids previous criticisms over a timing measure not being independent of a firm’s characteristics of capital structure. Our empirical results show that this new measure is an effective market timing variable for issuing equity in the Chinese equity market, and that a persistent effect of equity market timing on firm capital structure exists for more than 7 years. This paper offers evidence that the market conditions of equity funding supply play an important role in corporate financing decisions in China.

We develop a simultaneous determination model of capital structure and stock returns. Specifically, we incorporate the managerial investment autonomy theory into the structural equation modeling with confirmatory factor analysis to jointly determine the capital structure and stock return. Besides attributes introduced in previous studies, we introduce indicators affecting a firm’s financing decision, such as managerial entrenchment, macroeconomic factors, government financial policy, and pricing factors. Empirical results show that stock returns, asset structure, growth, industry classification, uniqueness, volatility, financial rating, profitability, government financial policy, and managerial entrenchment are major factors of the capital structure.

The growth rate plays an important role in determining a firm’s asset and equity values. The premier model used to price equity is the basic dividend growth model of Gordon and Shapiro (1956). Nevertheless, the basic assumptions of the growth rate estimation model are less well understood. In this paper, we demonstrate that the model makes strong assumptions regarding the financing mix of the firm. In addition, we discuss various estimation methods of the firms’ growth rate. We demonstrate that the arithmetic average method is very sensitive to extreme observations, and the regression methods yield similar but somewhat smaller estimates of the growth rate compared to the compound-sum method. Interestingly, the ex post forecast shows that arithmetic average and compoundsum methods (continuous regression) yield the best (worst) performance with respect to estimating a firm’s future dividend growth rate. Firm characteristics, like size, book-to-market ratio, and systematic risk, have a significant influence on the forecast errors of dividend and sales growth rate estimation.

This study examines how fundamental accounting information can be used to supplement technical information to separate momentum winners from losers. We first introduce a ratio of liquidity buy volume to liquidity sell volume (BOS ratio) to proxy the level of information asymmetry for stocks and show that the BOS momentum strategy can enhance the profits of momentum strategy. We further propose a unified framework, produced by incorporating two fundamental indicators—the FSCORE (Piotroski, 2000) and the GSCORE (Mohanram, 2005)—into momentum strategy. The empirical results show that the combined investment strategy includes stocks with a larger information content that the market cannot reflect in time, and therefore, the combined investment strategy outperforms momentum strategy by generating significantly higher returns.

The main purposes of this paper are (i) to review three alternative methods for deriving option pricing models (OPM), (ii) to discuss the relationship between binomial OPM and Black–Scholes OPM, (iii) to compare the Cox et al. (1979) method and Rendleman and Bartter method for deriving Black–Scholes OPM, (iv) to discuss the lognormal distribution method to derive Black–Scholes OPM, and (v) to show how the Black–Scholes model can be derived by stochastic calculus.

This chapter shows that the main methodologies used to derive the Black–Scholes model are binomial distribution, lognormal distribution, and differential and integral calculus. If we assume risk neutrality, then we don’t need stochastic calculus to derive the Black– Scholes model. However, the stochastic calculus approach for deriving the Black–Scholes model is still presented in Section 15.6. In sum, this chapter can help statisticians and mathematicians understand how alternative methods can be used to derive the Black– Scholes option model.

In credit risk modeling, factor models, either static or dynamic, are often used to account for correlated defaults among a set of financial assets. Within the realm of factor models, default dependence is due to a set of common systematic risk factors. By coupling with a copula function, e.g., the normal, t-, Clayton, Frank, and Gumbel copula functions, an analytic formulation of the joint distribution of assets’ default times can be derived. On the other hand, factor models fail to account for the contagion mechanism of defaults in which a firm’s default risk increases due to their commercial or financial counterparties’ defaults. This study considers the dynamic factor model of Duffee (1999) coupling with a Hawkes process, a class of counting processes allowing intensities to depend on the timing of previous events (Hawkes, 1971) for the contagious effect. Using the factor- contagious-effect model, Monte Carlo simulation is performed to generate default times of two hypothesized firms. It is demonstrated that as the contagious effect increases, the goodness of fit of the joint distribution of assets’ default times based on copula functions decreases, which highlights the deficiency of the copula function approach.

This research introduces the following to establish a TAIEX prediction model: intervention analysis integrated into the ARIMA–GARCH model, ECM, intervention analysis integrated into the transfer function model, the simple average combination forecasting model, and the minimum error combination forecasting model. The results show that intervention analysis integrated into the transfer function model yields a more accurate prediction model than ECM and intervention analysis integrated into the ARIMA–GARCH model. The minimum error combination forecasting model can improve prediction accuracy much better than non-combination models and also maintain robustness. Intervention analysis integrated into the transfer function model shows that the TAIEX is affected by external factors, the INDU, the exchange rate, and the consumer price index; therefore, facing the different issues of the TAIEX, the government could pursue some macroeconomic policies to reach the goals of policies.

This paper examines the profits of revenue, earnings, and price momentum strategies in an attempt to understand investor reactions when facing multiple information of firm performance in various scenarios. We first offer evidence that there is no dominating momentum strategy among the revenue, earnings, and price momentums, suggesting that revenue surprises, earnings surprises, and prior returns each carry some exclusive unpriced information content. We next show that the profits of momentum driven by firm fundamental performance information (revenue or earnings) depend upon the accompanying firm market performance information (price), and vice versa. The robust monotonicity in multivariate momentum returns is consistent with the argument that the market does not only underestimate the individual information but also the joint implications of multiple information on firm performance, particularly when they point in the same direction. A three-way combined momentum strategy may offer monthly return as high as 1.44%. The information conveyed by revenue surprises and earnings surprises combined account for about 19% of price momentum effects, which finding adds to the large literature on tracing the sources of price momentum.

Due to the continual economic integration and the accumulation of wealth in China, Hong Kong, and Taiwan since early 1990’s, understanding portfolio strategies and the benefits of diversification for these countries is an indispensible element in managing global assets. Using weekly industry-level data, we analyze the culturally home-biased diversification and find that local investors still benefit from regional investments. The time-varying benefits of diversification exist even as the economies of this region have become increasingly integrated. Our analysis suggests that stricter weighting bounds reduce the economic values of diversification but enhance the feasibility of the optimal portfolio allocations. The larger benefits gained by Chinese investors suggest that international diversification is more advantageous to investors in emerging economies than those in developed markets. The robustness tests generate similar findings when we evaluate the out-of-sample effectiveness and the benefits of diversification under various parameter estimation windows.

We investigate how a firm manipulates its real activities in production to meet the earnings target in product market competition against its product-market rivals. We show that the equilibratory way to reach the earnings target is to set a higher first-period output level, reaching a higher short-term profit level. However, once the expected level of demand uncertainty is high, a firm will exploit this effect on its output choice by taking a mixed strategy and raising its short-term output level. This result suggests that one should consider longer-horizon paths of variables to detect opportunistic real activities manipulation. Based on our results, we further argue that competitive strategy is an omitted variable in real activities manipulation estimation models and recommend that capacity utilization, which is related to a firm’s output competitive decisions, should be included in the first-stage models of normal investment levels in Roychowdhury (2006) and Gunny (2010).

The purpose of this chapter is to evaluate the role played by gold in a diversified portfolio comprised of bonds and stocks. The continuous wavelet transform analysis is applied to capture the correlation features between gold and other risky assets at a specific time horizon to determine whether gold should be included in a diversified portfolio. This chapter uses the U.S. stock, bond, and gold data from 1990 until 2020 to investigate the optimal weights of gold obtained from the minimum variance portfolio. Empirical findings suggest that little evidence supports that gold acts as an efficient diversifier in traditional stocks and bond portfolios. Gold typically has been a long-term diversifier in the traditional port-folio comprised of bonds and stocks only before the early 2000s and acts as a short-term diversifier in times of crisis periods. The significant drop in the long-term weight of gold indicates that gold losses much of its long-term role in the diversified portfolio. These findings are useful for portfolio managers to justify the gold’s diversification benefits over different investment horizons.

Investors often need to evaluate the investment strategies according to their own subjective preferences in terms of numerical values based upon various criteria when making investment in mutual funds. This situation can be regarded as a fuzzy multiple criteria decision-making (MCDM) problem. The purpose of this study is to propose an alternative approach, fuzzy multiple criteria decision-making with fuzzy integral. This approach relaxes the independence assumption among criteria for the evaluation of the MCDM problems, which is oftentimes the basic assumption in applying a hierarchical system for evaluating the strategies of selecting the mutual funds investment style. We also employ triangular fuzzy numbers to represent the decision makers’ subjective preferences on the criteria, as well as for the criteria measurements to evaluate mutual funds investment style. To achieve this objective, first, we employ factor analysis to extract four independent common factors from those criteria. Second, we construct the evaluation frame using a hierarchical system composed of the above four common factors with 16 evaluation criteria and then derive the relative weights with respect to the considered criteria. Third, the synthetic utility value corresponding to each mutual fund’s investment style is aggregated by the fuzzy weights with fuzzy performance values. Finally, we compare with empirical data and find that the model of FMCDM predicts the rate of return very accurately in certain ranges of λ, hence the non-additive fuzzy integral technique is an effective method for evaluating mutual funds’ strategy.

Using quarterly ownership data which identify identity codes of mutual funds in Taiwan, we investigate mutual fund herding and its impact on stock price. We show that mutual funds tend to follow their own steps in trading rather than follow trades made by other funds. More importantly, evidence of price continuation following mutual fund herd buying suggests that such herding is based on value-relevant information and is consistent with the investigative herding hypothesis. Alternatively, evidence of return reversal following mutual fund herd selling suggests that such herding is non-informational and is consistent with the characteristic herding hypothesis.

This chapter shows how the legal environment in a country influences performance and risk of stock across countries at different developmental stages and of various rules of jurisdiction. Using data of 4,916 stocks from 37 countries, our empirical findings confirm that equities in countries with English common-law origin have higher risk premiums than those in civil-law countries, particularly for countries of the French/Spanish code. The indicators representing high efficiency in law system, low corruption, strong legal protection of investors’ rights, and reliable political environment are associated with low risk and high performance. The various elements of legal procedural formalism, however, have differing effects on volatility and return.

In several countries in the world, Bitcoin and P2P lending have been accepted and developed strongly. This study aims to evaluate the suitability, pros, and cons of Bitcoin, Fintech, P2P lending, and its platform in emerging markets such as Vietnam. The research used qualitative analysis combined with data collection method published, statistics, analysis, synthesis, comparison, to generate qualitative comments and discussion; evaluate results, the article analyzed and evaluated the impacts of Fintech, P2P lending, and Bitcoin and virtual currency on society of Vietnam, both positive and negative sides. It was found that we need to improve regulations on Fintech and shadow banking to overcome the weaknesses of commercial banks, to reduce risks as many nations in the world accept it. Experiences of other countries such as the United States, Japan, China, or the developed countries of the European Union and consequences for the economy became a lesson for well as developing countries. Hence, we need to implement risk management plans to reduce technological and IT risks. Proper solutions and development orientation as well as risk management for Bitcoin and cryptocurrencies are suggested. Last but not least, the research was limited to the case of Vietnam; hence, we can expand research to other Asian countries or other emerging markets.

We argue that coinsurance among a firm’s business units changes the properties of reported earnings through less volatile operations (financial synergies) and fewer estimation errors in the accrual process (accounting synergies). Consistent with a coinsurance effect, we find that diversified firms have on average higher earnings quality compared to industry-matched portfolios of focused firms. Specifically, diversification leads to more predictable earnings, superior mapping of accruals to cash flows, and lower absolute abnormal accruals. In addition, we find higher earnings quality for diversified firms with less correlated segment earnings and that the coinsurance effect is stronger for firms that operate in more volatile and uncertain environments. We contribute by identifying the coinsurance effect of diversification as a new determinant of earnings quality. Our findings complement prior literature on agency-related disadvantages of diversification for earnings quality by highlighting coinsurance related benefits of diversification for earnings quality.

This chapter first reviews alternative methods for determining option bounds. This method includes stochastic dominance, linear programming, semi-parametric method, and non-parametric method for European option. Then option bounds for American and Asian options are discussed. Finally, we discuss empirical applications in equities and equity indices, index futures, foreign exchange rates, and real options.

This chapter finds that short-term reversals become more profound in the current month when economic policy uncertainty is larger in the prior month. There is evidence that the economic policy uncertainty influences return reversals through the liquidity channel. Short-term reversal profits are also positively related to the VIX index, the Baker–Wurgler (2007) investor sentiment index, and the Aruoba–Diebold–Scotti (2009) business conditions index in the prior month. Though, the predictability of the latter two indexes is less robust. However, adding these indexes and other variables does not weaken the relationship between economic policy uncertainty and return reversals.

Data for heavily and lightly traded firms are used to evaluate the effects of temporal aggregation on beta estimates, t values, and R²estimates. In addition to our analysis of the standard market model, dynamic market and error correction models are estimated. This study evaluates differences in the short-term and long-term dynamic relationships between the market and each type of firm. It is found that temporal aggregation has important effects on both the specification of a market model and the stability of beta estimates.

Leasing transfers effective ownership of physical assets to specialists. Firms not specializing in asset ownership focus on core competence and scale. In asset ownership including real estate, specialists have low or zero corporate tax rates and use depreciation to shield dividends. With operating leases, non-specialist firms operate the assets with no entry on balance sheets. On income statements, non-specialists deduct rent as an expense. On specialist income statements, the rent is exempt from double taxation of distributed dividends. Accounting reform proposes to undo and upend these arbitrage provisions. All leases have capitalized present values and are reflected as right-to-use assets and liabilities on balance sheets, expensed straight line. Reported liabilities rise, potentially undermining debt covenants. Sale and leaseback provisions are at risk of being reversed, notably if proceeds have been already distributed.

Based upon my experience in research, teaching, writing textbooks, and editing handbooks and journals, this review paper discusses how financial econometrics, mathematics, statistics, and financial technology can be used in research and teaching for students majoring in quantitative finance.

A major portion of this paper discusses essential content of Lee and Lee (2020). Then Lee et al. (2019) and Lee et al. (2017), and Lee and Lee (2015) are used to enhance the content of this paper. In addition, important and relevant papers, which have been published in different journals are also used to support the issues discussed in this paper.

I have found the applications of financial econometrics, mathematics, statistics, and technology have improved drastically over the last five decades. Therefore, both practitioners and academicians need to update their skills in this area to compete in both financial market and academic research.

This chapter extends the Margrabe formula such that it is suitable for accounting for any type jump of stocks. Despite the fact that prices of an exchange option are characterized by jumps, it seems no study has explored those price jumps of an exchange option. The jump in this chapter is illustrated by a Poisson process. Moreover, the Poisson process can be extended into Cox process in case there is more than one jump. The results illustrate that incompleteness in an exchange option leads to a premium which in turn increases an option value while hedging strategies reveal mixed-bag type of results.

It is well known that the optimal hedge ratios derived based on the mean-variance approach, the expected utility-maximizing approach, the mean extended-Gini approach, and the generalized semivariance approach will all converge to the minimum-variance hedge ratio if the futures price follows a pure martingale process and if the spot and futures returns are jointly normal. In this chapter, we perform empirical tests to see if the pure martingale and joint normality hypotheses hold using 25 different futures contracts and five different hedging horizons. Our results indicate that the pure martingale hypothesis holds for all commodities and all hedging horizons except for three stock index futures contracts. As for joint normality, we propose two new tests based on the generalized method of moments, which allow for calculating multivariate test statistics that take account of the contemporaneous correlation across spot and futures returns. Our findings show that the joint normality hypothesis generally does not hold except for a few contracts and relatively long hedging horizons.

In this paper, we derive an equilibrium relationship between the yields on Eurodollar and Treasury bills based on equivalent martingale results derived by Harrison and Kreps (1979) and Harrison and Pliska (1981, 1983), and corporate debt pricing model developed by Merton (1974). The derived equilibrium relationship incorporates the models used by Booth and Tse (1995) and Shrestha and Welch (2001) as special cases. The equilibrium relationship indicates that the conditional volatility of yield on Eurodollar explains the variation in TED spread. We empirically test the equilibrium relationship using a GARCH-M model and the concept of fractional cointegration. We use both the ex ante data implied by the respective futures contracts as well as the ex post spot data with daily, weekly and monthly frequencies. We find empirical support for the Equilibrium relationship.

In this chapter, we investigate how different measures of volatility influence bank’s capital structure beside mandatory capital requirements. We study the relationship between four volatility risk measures (volatility skew and spread, variance risk premia, and realized volatility) and bank’s market leverage and we analyze if banks adjust their capital needs in response to significant increase of risk premia discounting from traders. Among the four volatility measures, volatility skew (defined as the difference between OTM put and ATM call implied volatility and representing the perceived tail risk by traders) affects bank’s leverage the most. As volatility skew increases — hence OTM put became more expensive than ATM call — banks deleverage their assets structure. One plausible explanation relates to the higher costs of equity issuance that a bank will face during a period of distress. As the possibility to incur in expensive equity issuance increases the bank prefers to deleverage its balance sheet and create a capital buffer.

The purpose of this chapter is to analyze the real-time responses of stock prices, volume, and order submission behavior across investor groups to 2,894 on-air stock reports from 9:16 a.m. to 1:15 p.m. during regular trading hours from October 11, 2010 to December 31, 2010 in Taiwan. First, positive (negative) reports move stock prices upward (downward) in real time, accompanied by increasing trading volume. However, the observed price movements are short-lived and vanish 14 days afterward. Second, responding to the reports, individual investors trade more actively and aggressively than institutional investors do. The overreaction of individual investors are responsible for the observed price movements.

This study investigates theoretically and empirically mutual fund managers’ risk-taking behavior due to ranking objectives. We argue that managers can not only choose the riskiness of their portfolio but can also determine how hard to work (their effort). The combination of risk and effort depends on the interim performance gap and the effort cost level. Both interim winner and loser gamble by taking high risk and spending low effort when the interim performance gap is below a certain threshold. Only the interim loser gambles when the interim performance gap is small and the effort cost is sufficiently high. Otherwise, managers adopt the same choice of risk-effort. In many cases, high (low) risk-taking induces higher (lower) effort. Empirically, we find that managerial effort is strongly and positively linked to their risk-shifting level. The worst-performers behave differently from the others but are not necessarily riskier and lazier.

This chapter first presents a review of various theoretical models and six estimation methods to the optimal futures hedge ratios. Then we use data to show how some of the hedge ratios can be applied to estimate hedge ratio in terms of S&P 500 future. We also show the estimation procedure on how to apply OLS, GARCH, and CECM models to estimate optimal hedge ratios through R language. These approaches are theoretically derived in terms of minimum variance, mean-variance, expected utility, and Value-at-Risk. Various ways of estimating these hedge ratios are also discussed, ranging from simple ordinary least squares to complicated heteroskedastic cointegration methods. Under martingale, joint-normality conditions, and some other conditions, different hedge ratios can be shown that this different ratio can be converted to the minimum variance hedge ratio. Otherwise, the optimal hedge ratios based on the different approaches are in general different. Finally, our empirical findings suggest the importance of capturing the heteroskedastic error structures including the long-run equilibrium error term in conventional regression model.

This article provides both statistical analysis and empirical evidence that the dummy variable regression models extensively employed in the market seasonality literature may wind-up misleading results. We show that the estimates of the said model tend to reject the null hypothesis incorrectly once the stock returns exhibit higher volatility for the specified period under examination. Our empirical results suggest that the so-called “January effect” could be attributed to the application of inappropriate statistical method.

In literature, a GARCH-jump mixture model, namely, the GARCH-jump model with autoregressive conditional jump intensity (GARJI) model, in which two conditional independent processes, i.e., a diffusion and a compounded Poisson process are used to account for stock price movements caused by normal and extreme event news arrivals, individually, is developed by Chan and Maheu (2002, 2004) to describe the volatility clustering and leverage effect phenomenon. The resulting model is less efficient and provides only ex post filter for the probability of the occurrences of large price movements. A more informative and parsimonious model, however, the VG NGARCH model, is proposed and calibrated in this study. Being an extension of the variance-gamma model developed by Madan et al. (1998), the proposed VG NGARCH model incorporates an autoregressive structure on the conditional shape parameters, which describes the news arrival rates of different impact sizes on the price movements, and an ex ante prediction for the occurrences of large price movements is provided. The performance of the proposed VG NGARCH model is compared to the GARJI model based on daily stock prices of five component financial companies in S&P 500, namely, Bank of America, Wells Fargo, J.P. Morgan Chase, CitiGroup, and AIG, respectively, from January 3, 2006 to December 31, 2009. The goodness of fit of the VG NGARCH model and its ability to predict the probabilities of large price movements are demonstrated by comparing with the benchmark GARJI model.

Using a large panel of UK public firms, we examine the relationship between the financial risk hedging and the cost of equity capital. We hypothesize that firms utilizing financial derivative instruments reduce the stock return volatility which is priced in investors’ expectations. While financial risk hedging serves as a vehicle for firms to alleviate cash flows volatility, it also leads to economic benefits to the firm value in case of the presence of increasing asymmetric information. In addition, we hypothesize and test whether the nature of relation between financial risk hedging and cost of equity capital varies and is more negative or more ambiguous with economic shocks. Our results show that engaging in financial risk hedging enables firms to have a lower cost of capital. Consistent with the extant literature, we control for potential endogeneity problems and sample selection bias using instrumental variables and treatment effects approaches. Thus, our results are robust to a battery of sensitivity checks, including the use of multiple estimation methods and alternative proxies of cost of equity measures. Overall, our findings suggest that the value of financial hedging decisions increases during economic shocks, and if financial constraints become more severe and if cash flows volatility increases.

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China’s four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China’s stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China’s stock markets and the U.S. stock market and between China and Hong Kong. We find that U.S. return helps predict returns of Shanghai A and Shanghai B stocks. U.S. and Hong Kong volumes do not Granger-cause either return or volatility in China’s stock markets. In short, information contained in returns, volatility, and volume from financial markets in the U.S. and Hong Kong has very weak predictive power for Chinese financial market variables.

This chapter provides readers with the skills and insights to enable a fuller understanding of a public company’s underlying financial statements. Financial statement analysis is a form of fundamental analysis that identifies and analyzes the key financial information relevant to a company, for the purpose of determining the company’s intrinsic value. This chapter concentrates on the calculation and application of the principal financial ratios for profitability, efficiency, liquidity, solvency, and investment potential. There is an effort to make the content accessible to readers on a practical level, by applying the various ratios to the real financial statements of Johnson & Johnson. While the chapter outlines in detail the quantitative calculations required to undertake financial statement analysis, there is a firm emphasis on the importance of context in properly applying these techniques. The establishment of trends, the comparison of data to benchmarks, and the consideration of strategic factors all form part of this holistic contextual analysis of financial statement information.

The IFRS 9 on Financial Instruments has made an important contribution to the credit loss recognition process and financial reporting by replacing the existing Incurred Credit Loss (ICL) model with the Expected Credit Losses (ECL) model. The ECL model applies to all financial instruments whether they are recognized at the amortized cost or at fair value. Firms are required to estimate and recognize loan loss allowances based either on the 12-month or lifetime ECL, depending on whether there has been a significant increase in the credit risk since initial recognition. In this chapter, we first briefly explain the scope of IFRS 9 and then discuss the main characteristics of ECL model and also present mathematical models that can be used to estimate credit loan losses. The mathematical models can be based either on the capital market, discounted cash flow, or weighted losses approach. Finally, we discuss ECL disclosures that are expected to provide greater transparency on credit risk and loan loss provisions, and also present economic implications of the ECL model on firm performance.

This chapter estimates and compares the hedge ratios of the conventional and the error correction models using three advanced international stock markets with different time intervals. Comparisons of out-of-sample hedging performance reveal that the error correction model outperforms the conventional model, suggesting that the hedge ratios obtained by using the error correction model do a better job in reducing the risk of the cash position than those from the conventional model. In addition, this chapter evaluates the effects of temporal aggregation on hedge ratios. It is found that temporal aggregation has important effects on the hedge ratio estimates.

Technical analysis helps investors to better time their entry and exit from financial asset positions. This methodology relies solely on past information of the prices and volumes of financial assets to predict the future price trend of a financial asset. Modern research has established that combined with other sentiment measures such as social media, it can outperform the standard buy and hold strategy. Moreover, it has been documented that novice and professional investors apply technical analysis in their investing strategy. An experienced investor should combine fundamental analysis and technical analysis for better trading results. Programmers use technical analysis to create algorithmic trading systems that learns and adopts to the changing trading environments and performs trading accordingly without human involvement. There are hundreds of technical tools offered by known trading platforms. Investors must use specific tools that fits his trading style and risk adoption. Moreover, different financial assets such as stocks, ETFs, cryptocurrency, futures, and commodities demand different set of tools. Furthermore, investors should use these tools according to the time frame they use for trading. This chapter will discuss different technical tools that are used to help traders of different time frames and different financial assets to achieve better returns over the traditional buy and hold strategy.

Based upon comparative analysis, we first discuss different kinds of Greek letters in terms of Black–Scholes option pricing model, then we show how these Greek letters can be applied to perform hedging and risk management. The relationship between delta, theta, and gamma is also explored in detail.

Analyzing the correlation matrix of listed stocks, we identify “singletons” that table minimal cross-sectional correlations. Portfolios comprising 100–500 singletons all have lower betas and standard deviations and, correspondingly, higher average Sharpe and Treynor ratios than the Center for Research in Security Prices (CRSP) universe over the sample time period 1950–2017. Portfolios of singletons chosen from subsets of the CRSP universe, including small-value, low-variability, and momentum stocks, similarly realize lower portfolio standard deviations and higher risk-adjusted returns. These well-diversified portfolios suggest that the positive abnormal returns to low-beta portfolios are driven by their component stocks having low average cross-sectional correlation. One of the authors invested $20,000 of his own money in the algorithm-chosen 240 stock singleton portfolio over a 4-year period (2015–2018) and beat the market year-by-year on a risk-adjusted basis just as our results predicted.

We examine time-series features of stock returns and volatility, as well as the relation between return and volatility in four of China’s stock exchanges. Variance-ratio tests reject the hypothesis that stock return follows a random walk. We find evidence of long memory of returns. Application of GARCH and EGARCH models provides strong evidence of time-varying volatility and shows volatility is highly persistent and predictable. The results of GARCH-M do not show any relation between expected returns and expected risk. Daily trading volume used as a proxy for information arrival time has no significant explanatory power for the conditional volatility of daily returns.

By using a regression relationship, this chapter investigates the relationship between Value Line rank changes and beta changes in an attempt to explain the Value Line enigma.

The main purpose of this chapter is to investigate hedge ratios in terms of the international index futures markets. Instead of looking at hedging in a single market, we here construct a simultaneous equations system to study the index hedging in the light of the cross-country linkage and interaction. The three-stage least squares (3SLS) estimating procedure is then applied to S&P500, FTSE100, and NIKKEI225 indices over the period 1990–2020. The empirical results indicate that the cross-country hedging strategy in both markets is feasible and the investors can bring down the holding position in own futures market. Moreover, the hedging effectiveness of cross-country hedging strategy performs better than the traditional single market hedging strategy in terms of the percentage reduction in variance.

This paper first reviews empirical evidence and estimation methods of structural credit risk models. Next, an empirical investigation of the performance of default prediction under the down-and–out barrier option framework is provided. In the literature review, a brief overview of the structural credit risk models is provided. Empirical investigations in extant literature papers are described in some detail, and their results are summarized in terms of subject and estimation method adopted in each paper. Current estimation methods and their drawbacks are discussed in detail. In our empirical investigation, we adopt the Maximum Likelihood Estimation method proposed by Duan (1994). This method has been shown by Ericsson and Reneby (2005) through simulation experiments to be superior to the volatility restriction approach commonly adopted in the literature. Our empirical results surprisingly show that the simple Merton model outperforms the Brockman and Turtle (2003) model in default prediction. The inferior performance of the Brockman and Turtle model may be the result of its unreasonable assumption of the flat barrier.

This chapter combines the sentiment features of news headlines, stock market data, and macroeconomic factors to predict the direction of stock return movements in one month after the release of the news article. The sentiment features are extracted with Flair, an advanced library for Natural Language Processing (NLP). The stock market data of a company contains a comprehensive collection of 94 variables used by a prior literature (Gu et al., 2020) for the prediction of stock return. To construct the prediction model, this chapter applies seven machine learning algorithms (including Gradient Boosting, XGBoosting, Random Forest, Artificial Neural Networks, Support Vector Machine, Naïve Bayes, and Logistic Regression). The out-of-sample tests show that tree-based ensemble methods (i.e., Random Forest, XGBoosting, and Gradient Boosting) provide the most accurate predictions, with the maximum AUC-ROC of 0.74. Furthermore, this study provides evidence for the effectiveness of the sentiment features of news headline for the prediction of future stock returns as the median of the sentiment score of the news headline is listed as one of the most important predictors in the model.

Investors and professional money managers typically categorize assets into different styles to facilitate portfolio management and capital allocations. As these market participants move funds among assets of different styles based on their relative performance, correlated trading generates return co-movement and style momentum. This chapter reviews existing theories on style investing and important findings. In particular, it presents new evidence in a large bond market and demonstrates that behavioral finance theory can help explain return co-movement and momentum in the bond market traditionally dominated by institutional and long-term investors who are thought to be less behaviorally biased.

Using an international dataset of 5,861 firm-year observations between 2009 and 2016 obtained from the Carbon Disclosure Project (CDP), we analyze the effect of firms’ Greenhouse Gas (GHG) emissions on stock price performance. To this end, we first discuss former research which finds an equity discount entailed by high levels of GHG emissions. We then focus on additional metrics of stock price performance, namely stock price return and stock price risk. Interestingly, we do not find any significant impact of GHG emissions on these metrics. A possible explanation is that investors are not yet able to quantify the GHG emission risk due to insufficient disclosure.

The main purposes of this chapter are (i) to discuss risk classification and estimation; (ii) to show how to use minimum-variance and Sharpe performance measure approach to estimate optimal weights for a two-security portfolio; (iii) to discuss applications of performance measures; and (iv) to use concepts discussed in this chapter to show how banking lending rate can be estimated.

Extant studies and my own work show that in US listed corporations, the presence of a firm’s founder adds value to the firm. The incremental value increases with the extent of decision rights controlled by the founder. Furthermore, the value addition is higher if the founder CEO is younger at the time of the initial public offering and decreases with the founder’s tenure in the firm. Further investigation reveals that the founders add value by improving operating performance and being more transparent than similar non-founder firms. Moreover, the founders are more focused on the strategic positioning of the firm in improving operating performance — they improve profit margins in differentiated firms while improving efficiency in firms with cost-leadership strategy. Analysts and investors can benefit by incorporating these insights into their analysis.

The main purpose of this chapter is to use Johnson & Johnson’s (JNJ) accounting information and market information to discuss the following three aspects concerning investment analysis: (i) financial ratio analyses, (ii) impact of intangible assets on Tobin Q estimates, and (iii) use of simultaneous model to forecast pro forma financial statements. Peter and Taylor (2017) have theoretically shown that intangible asset is one of the important factors in calculating Tobin Q. We use accounting information to calculate Tobin Q with and without considering intangible assets. Since JNJ’s intangible assets account for about 51% of its total assets, we review the Tobin Q’s alternative estimation and show how the intangible assets affect Tobin Q of JNJ. By using financial ratio and market information, we perform 20 equation models to forecast pro forma balance sheet and pro forma income statement.

Technical analysis is the study of forecasting future asset prices with past data. In this survey, we review and extend studies on not only the time-series predictive power of technical indicators on the aggregated stock market and various portfolios but also the cross-sectional predictability with various firm characteristics. While we focus on reviewing major academic research on using traditional technical indicators, we also discuss briefly recent studies that apply machine learning approaches, such as lasso, neural network, and genetic programming, to forecast returns both in the time series and on the cross section.

This chapter investigates the effect of sovereign debt ratings on credit default swap (CDS) spreads during the Eurozone sovereign debt crisis. The empirical investigation is conducted by means of panel vector autoregressive models which allow the analysis of multidirectional relationships in a dynamic context. Our main findings, based on directional spillover effects, are that credit rating announcements did not have any impact on the CDS spreads of the Eurozone periphery countries. However, when we limit our analysis on the sovereign debt ratings of Portugal, and Ireland to a minor extent, we obtain some evidence for spillover effects on the CDS spreads of other countries. Furthermore, the quantitative contribution of sovereign ratings has been of secondary importance when calculating their spillover impact on other “systemic” risk indicators. Overall, the results indicate that during the recent sovereign debt crisis, credit rating announcements on the Eurozone’s periphery countries did not contribute to the global financial crisis.

Maturity mismatches (MMs) expose banks to interest rate sensitivity, adding to the uncertainty of banks’ performances. Since information regarding MMs is usually not readily available, considering the high correlation between the two, interest rate sensitivities could serve as proxies to these mismatches for short periods. Therefore, we label them as implied MMs. Our analyses of the correlation between banks’ interest rate sensitivity and the trading volume of the banks’ equity reveal positive correlations. The heightened increase in volume suggests that implied MMs increase disagreement among banks’ investors.

With the rapid growth of index investing in wealth management, such as the exchange traded fund (ETF) market, tracking the reference index of ETF regains considerable attention because the corresponding tracking error is one of the key measurements of a fund’s performance. To improve accuracy and efficiency in minimizing tracking errors, we propose a novel approach that combines the generalized singular value decomposition (GSVD) and the constrained Kalman filter method for estimation of portfolio weights and their dynamical updates, respectively. GSVD reveals all feasible weights under a static constrained regression model for the index tracking error minimization problem. The constrained Kalman filter is applied to update the GSVD-solved weights of the optimal port-folio along with the dynamical time series of return data. This two-stage approach is semi-static. Our empirical studies demonstrate that sufficient low tracking error can be obtained under a robust top-down stratified framework that consists of 11 sector indices defined by the Global Industry Classification Standard.

Fundamental analysis is a simple concept which tries to isolate investing choices from sentiment. Moreover, it is a mixture of science and art that breaks down to simple math. Great value investors such as Warren Buffet, Benjamin Graham, and John C. Bogle very vividly state that in order to successfully value a business, it does not take more than common sense and simple math. In this chapter, we approach the very fundamentals in a practical manner, showcasing a simple way to estimate the intrinsic value of a business.

There is often a wide divergence between academic and practitioner views on risk, return, and portfolio construction. For example, academics focus primarily on purely quantitative measures or factors. Initially, the focus was on dividends, free cash flow, standard deviation, and beta. Later, additional factors analyzed by the academic community came into focus, such as size, style, liquidity, momentum, and quality. Practitioners, in contrast, often focus on a company’s products, its history, and the competitive dynamics of its industry. Furthermore, practitioners “discovered” anomalies, such as momentum, decades before they were rigorously analyzed and published by academics. The current distinction between the two groups is not merely quantitative versus qualitative. This chapter summarizes the viewpoints of the two camps — academic and practitioner — and suggest steps that may effectively combine the two schools of thought, at least to a certain degree using the Black–Litterman model and other qualitative techniques, such as stratifying asset pricing models. This analysis may result in a more robust investment, risk management, and portfolio construction process.

We study three widely used liquidity measures and find that they all carry significant premiums beyond the size, book-to-market, and momentum effects. Although liquidity as a risk factor bears a significant return premium, it is better characterized by a characteristic-based model. Further analysis shows that (1) although the premium persists for up to five years following formation, it diminishes over time and becomes insignificant in the post-1960 period; (2) the premium is larger for stocks with higher idiosyncratic risk. Thus, the empirical results provide some evidence that supports the mispricing argument.

Currently, environmental concern, as well as other issues such as corporate social responsibility (CSR) or socially responsible investment, is leading many companies to implement “greenwashing” behaviors. In this chapter, the factors concerning “greenwashing” will be analyzed, and the relationship between those behaviors and deficient sustainability reports will be examined. This study will be approached from the point of view of the Spanish reality, paying special attention to listed companies, specifically to those which are listed on the IBEX 35 Index. In order to achieve that objective, this chapter analyzes the sustainability reports of six companies listed on the IBEX 35 Index belonging to different sectors: energy, oil, consumer goods, financial services, basic materials, industry and construction, and technology and telecommunications. Moreover, greenwashing in plastic industry will be analyzed. Finally, we draw conclusions about the relationship between “greenwashing” and CSR.

This study identifies nonlinear patterns of momentum profits across stocks with different levels of return volatility and skewness. It finds that momentum profits are the largest from mildly volatile and skewed stocks; this phenomenon is consistently observed for different formation/holding periods, types of examined returns, and momentum grouping. Based on the patterns, this chapter proposes a sample filtering criterion for momentum investment; the profits accrued through the sample filtering are economically enlarged. Such an enhancement of momentum profits by the filtering process is documented in various strategies, including the conventional, 52-week high, and risk-managed momentum strategies.

In this study, we document a novel lead–lag relation between historical and implied volatilities based on China’s CSI300 Index options. We show that historical volatilities have incremental information when we predict implied volatilities, and this pattern tends to be more stable for put options than for call options. Moreover, we reveal that this lead– lag relation is relevant to option terms and time horizons of historical volatilities, which means implied volatilities of long-term options are more likely to be properly predicted by long-term historical volatilities on average. Finally, we find that speculative trading might explain our results.

In this chapter, we show how Excel and R language programming can be used to estimate the European call/put prices based on Black–Scholes model as well as binomial option pricing model. Different underlings are considered, including individual stocks, currency, and stock indices. SAS language programming to estimate the European call/put prices based on binomial option pricing model and Black–Scholes option pricing model are given in the appendices.

When firms increase the size of their net balance sheet, they earn a higher return as value. Value is earned from different holdings. An examination of holdings compares which earn value. Holdings of US traded firms are divided into categories from their balance sheet. As reported on the asset side are cash, receivables, physicals, and intangibles. On the liability side, in order are payables, short- and long-term debt. In value, the sum of book equity is divided by market capitalization. For individual value, each is divided by market capitalization. Then, the return is regressed on the individual values and their interactions. Negative value is when increasing a holding reduces the return. Positive value comes from return increasing with a holding. The rank order of contributors to value, for US firms over 1980–2016, is

Replacing holdings by their risk-weight adjustments and adding up, the resulting book to market has no effect on returns. Returns are fully priced by firms’ balance sheet holdings. The results potentially explain why value or overall book to market fails to outperform growth. Value is a return to holding certain risky holdings. Not all balance sheet holdings are risky.

Computational modeling is being used in more research and applications in accounting and finance. These approaches include simulations and computer-intensive methods that are different from traditional methods in most prior research. This chapter explains the underlying philosophy of these methods and discusses several research studies using them. Inferences of the studies are discussed and both benefits and concerns with the methods are addressed.

Buyback programs are often used by firms for different purposes, including distributing excess cash to shareholders and signal that the stock price is underpriced. The first purpose of this chapter is to review studies of buyback programs and to highlight that fundamentals-based hypotheses are problematic in financial turmoil. We will show how buyback programs add value to shareholders while also identifying some situations in which they can destroy value. The second purpose is to present the pros and cons of buyback programs to shareholders, particularly during financial turmoil.

Investors lacking ample time, professional knowledge, and sufficient ability may find it difficult to understand the implication of complex corporate information and figure out the clear trend of future corporate performance. Financial analysts who provide earnings forecasts and stock recommendations could help investors with investment decision-making. This chapter explores the roles that analysts play in the stock market, the determinants of the effectiveness of their roles, and how well they play the roles.

The CDS Big Bang increased the upfront funding requirements for trading CDS contracts, especially for those with credit spreads further away from 100 and 500 basis points. Exploiting this regulatory change, we document that a higher funding requirement reduces market liquidity and increases the absolute value of the CDS-bond basis. The funding effects are stronger for smaller reference entities. Our findings highlight the importance of the tradeoff between standardization and the funding cost of upfront payments.

During the past couple of months, the pandemic situation raised the need for assessment of the impact on derivatives, particularly weather and freight derivatives, as an innovative financial product. There are several issues and challenges faced by weather and freight derivatives in the financial market. This chapter aims to appreciate innovative financial derivatives and also address issues relating to the functioning of weather and freight derivatives. We have also examined the pricing models of weather derivatives across the globe. In addition, we examine the impact of the COVID-19 pandemic situation on weather derivatives.

Against the backdrop of increasingly fierce industrial competition nowadays, firms tend to have substantive business risk and/or information risk, increasing the estimation risk and limit of arbitrage for investors in their short-term investments in a stock market. It is thus important for investors to hold a long-term horizon for at least part of their investments in the stock market. This chapter aims to introduce a long-term investment strategy that is practically feasible and potentially profitable for investors. To this end, we first develop a parsimonious model in which we identify the major determinants of a firm’s value and long-term growth. This model is used to select high-value firms from each industry for further fundamental analysis and valuation. We next expatiate on how to perform strategy analysis, accounting analysis, financial analysis, and prospective analysis and therein apply the residual operating income valuation model, in the best possible manner, to further value the selected firms and their long-term investment potential. Lastly, we expound the strategy of forming and adjusting a long-term investment portfolio in a way that potentially maximizes long-term portfolio return.

It is well known that both normal and log-normal distributions are important to understand Black & Scholes-type European and American options. Therefore, we first review the basic theory of normal and log-normal distributions and their relationship, then bivariate and multivariate normal density functions are analyzed in detail. Next, we discuss American options in terms of random dividend payment. We then use bivariate normal density function to analyze American options with random dividend payment. Excel programs are used to show how American co-options can be evaluated. Finally, pricing option bounds are analyzed in some detail.

This chapter discusses how to exploit various information on the web to improve stock market prediction. We first discuss the impacts of investors’ social network on the stock market and then propose several information fusion methods, that is the tensor-based model and the multiple-instance learning model, to integrate web information and quantitative information to improve the prediction capability.

This chapter first focuses on the bond strategies of riding the yield curve and structuring the maturity of the bond portfolio in order to generate additional return. This is followed by a discussion on swapping, which is essentially interest-rate swapping. Next is an analysis of duration or the measure of the portfolio sensitivity to changes in interest rates with and without convexity, after which immunization is the focus. The convexity is essentially discussed as a nonlinear relationship between bond price and duration. Finally, a case study of bond-portfolio management is presented in the context of portfolio theory. Overall, this chapter presents how interest rates change affect bond price and how maturity and duration can be used to manage portfolios.

In this chapter, we investigate whether hedge fund managers possessing advanced professional education, as represented by the CFA@ designation, have better managerial ability and fund performance compared to managers lacking such education. We define managerial ability in terms of market, volatility, and liquidity timing ability. In general, we find that CFA designated managers have better volatility and liquidity timing ability and earn better risk-adjusted returns than non-CFA managers. We also find that the CFA designation does not contribute to the fund flow–performance relation. The rise of high frequency and algorithmic trading may partially explain these findings.

This study examines the implications of a bank’s activity mix and funding strategy for its liquidity management as defined in the Basel III accords or when they create more liquidity as measured by Berger and Bouwman (2009). Using an international sample of 624 banks in 65 countries, we find that, at low levels of noninterest income and nondeposit funding, there could be some risk diversification benefits in increasing these shares; however, at higher levels of noninterest income and nondeposit funding shares, additional increases result in higher illiquidity. Finally, better accounting disclosure will improve the effect on bank liquidity by both bank’s activity mix and funding strategy, but at the same time, they worsen the bank liquidity by stricter capital stringency and more market power.

Our chapter aims to elucidate the theoretical relation and empirical observation between accounting information and contemporaneous firm value (measured as a firm’s stock price and/or stock return). Grounded on the intricate links of future expected cash flow and current stock price, future expected cash flow and future earnings, and future earnings and realized earnings, our work provides constitutional understanding of the residual income valuation model (Ohlson, 1995) and the abnormal earnings growth model (Ohlson and Juettner-Nauroth, 2003). The dynamics of the three intricate links and its implications for the valuation models has untangled a long-time myth in the literature of value relevance of financial reporting. Our chapter also presents the mediation effect of market efficiency on the empirical observation between accounting information and contemporaneous firm value.

The credit default swap (CDS) market has experienced tremendous changes over the last two decades. This chapter reviews recent studies on the CDS contract and the CDS market, highlighting recent developments in CDS contracts and market structures. Characteristics of CDS contracts make them outstanding from other derivatives products, and the rapid development of CDS markets shows the popular use and importance of CDS contracts in general. Policies and regulations on CDSs have brought huge changes to the market and the availability of publicly reported data. The relationship between firms and CDS has long been a focus among academics. Moreover, CDSs are also commonly used among mutual funds, hedge funds, and banks, affecting the trading activities of various financial intermediaries. While the sovereign CDS and index CDS are shown to be more and more significant in financial markets, other CDS products, including CDS options and tranches, also experience changes. We summarize the evolution in CDS markets and the structure of CDS studies, aiming to gain a better understanding of CDS markets and provide insights for future studies on the CDS market.

The main aims of this chapter are (i) to use the decision tree approach to derive binomial option pricing model (OPM) in terms of the method used by Rendleman and Barter (RB, 1979) and Cox, Ross, and Rubinstein (CRR, 1979) and (ii) to use Microsoft Excel to show how decision tree model can be converted to Black–Scholes model when the number period increases to infinity. In addition, we develop binomial tree model for American option and trinomial tree model. The efficiency between binomial and trinomial tree is also compared. In sum, this chapter shows how binomial OPM can be converted step by step to Black–Scholes OPM.

Model risk is critical in constructing a portfolio. To avoid model risk, the Black–Litterman model is an approach that allows to adjust the original estimated parameters using the implied market equilibrium returns and investors’ views (Black and Litterman, 1991). This chapter contrasts the standard approach of Markowitz (1952) with the Black–Litterman model and reviews different investment philosophies by Longo (2021). For empirical demonstrations, we consider a predictive regression to form investors’ views, where asset returns are regressed against their lagged values and the market return. Motivated by stylized features of historical returns, we employ heteroscedastic time-series models. Empirical analysis using five industry indexes in the Taiwan stock market shows that the proposed Black–Litterman portfolio outperforms the 1/N portfolio and Markowitz portfolio.

In this essay, we empirically test the Constant–Elasticity-of-Variance (CEV) option pricing model by Cox (1975, 1996) and Cox and Ross (1976), and compare the performances of the CEV and alternative option pricing models, mainly the stochastic volatility model, in terms of European option pricing and cost-accuracy based analysis of their numerical procedures.

In European-style option pricing, we have tested the empirical pricing performance of the CEV model and compared the results with those by Bakshi, Cao and Chen (1997). The CEV model, introducing only one more parameter compared with Black-Scholes formula, improves the performance notably in all of the tests of in-sample, out-of-sample and the stability of implied volatility. Furthermore, with a much simpler model, the CEV model can still perform better than the stochastic volatility model in short term and out-of-the-money categories. When applied to American option pricing, high-dimensional lattice models are prohibitively expensive. Our numerical experiments clearly show that the CEV model performs much better in terms of the speed of convergence to its closed form solution, while the implementation cost of the stochastic volatility model is too high and practically infeasible for empirical work.

In summary, with a much less implementation cost and faster computational speed, the CEV option pricing model could be a better candidate than more complex option pricing models, especially when one wants to apply the CEV process for pricing more complicated path-dependent options or credit risk models.

This research discusses the role of cryptocurrencies in portfolio investment and observes the timing within which the cryptos provide benefit to investors in a traditional financial market. We first use a mean-variance spanning test to check for any improvement that cryptos bring to a well-diversified portfolio and find a significant difference between port-folios with and without cryptos. Second, we analyze the weight dynamics of cryptos in the minimum-variance portfolio and the tangent portfolio to examine if cryptos present a hedging property in the mean-variance viewpoint. The finding shows that the optimal weights of cryptos increase distinctly in a market distress period, which shows their hedging property in a mean-variance view. Finally, we include cryptos in a well-diversified portfolio composed of common assets to check their weight dynamics in both tangent portfolio and minimum-variance portfolio. Consequently, we found that the cryptos take more weights in the tangent portfolio rather than in the minimum-variance portfolio, while the weights of cryptos increased in both portfolios during the COVID-19 pandemic; we thus conclude that cryptocurrencies can bring some hedging effect even in a portfolio with very common traditional assets. We also compare gold and cryptos and find that they have a similar pattern of weight dynamics, although gold has a slightly better effect in eliminating the downside risk of a minimum-variance portfolio.

This chapter uses the concepts of basic portfolio analysis and the dominance principle to derive the CAPM. A graphical approach is first utilized to derive the CAPM, after which a mathematical approach to the derivation is developed that illustrates how the market model can be used to decompose total risk into two components. This is followed by a discussion of the importance of beta in security analysis and further exploration of the determination and the forecasting of beta. The discussion closes with the applications and implications of the CAPM, and the appendix offers empirical evidence of the risk–return relationship.

In this chapter, we define both market beta and accounting beta and how they are determined by different accounting and economic information. Then, we forecast both market beta and accounting beta. Finally, we propose a composite method to forecast beta.

In this chapter, we first discuss utility theory and utility function in detail, then we show how asset allocation can be done in terms of quadratic utility function. Based upon these concepts, we show that Markowitz’s portfolio selection model can be executed by the constrained maximization approach. Real-world examples in terms of three securities are also demonstrated. In the Markowitz selection model, we consider that short sale is both allowed and not allowed.

This chapter offers some simplifying assumptions that reduce the overall number of calculations of Markowitz models through the use of the Sharpe single-index and multiple-index models. Besides the single-index model, we also discuss how the multiple-index model can be applied to portfolio selection. We have theoretically demonstrated how single-index and multiple-index portfolio selection model can be used to replace the Markowitz portfolio selection model. An Excel example of how to apply the single-index model approach is also demonstrated.

The main points of this chapter show how Markowitz’s portfolio selection method can be simplified by either the Sharpe performance measure or the Treynor performance measure. These two approaches do not need to use constrained optimization procedures; however, these methods do require the existence of a risk-free rate. Overall, this chapter has mathematically demonstrated how the Sharpe measure and Treynor measure can be used to determine optimal portfolio weights.

The accuracy and appropriateness of discounted cash flows (DCFs) have been debated for years in academia and industry. Broadly, for most valuations, appraisers use accounting cash flows (ACFs) in determining present values (PVs) of firms. Some unique industries such as the real estate investment trusts (REITs) require customized DCFs to account for their capital structure among other factors. Fernández (2004, 2007) suggested that customized DCFs include debt cash flows, equity cash flows, free cash flows, and capital cash flows. The customized cash flows reveal different PVs for REIT firms. Furthermore, robustness results illustrate that customized DCFs are sensitive to selected macroeconomic (debt, equity, and funds from operations) and management (board and executive) variables. Fundamentally, the results are generalizable to the global REIT industry.

This study proposes a Bayesian test for a test portfolio p’s mean-variance efficiency that takes into account the sampling errors associated with the ex-post Sharpe ratio ŜR of the test portfolio p. The test is based on the Bayes factor that compares the joint likelihoods under the null hypothesis H₀ and the alternative H₁. Using historical monthly return data of ten industrial portfolios and a test portfolio, namely, the CRSP value-weighted index, from January 1941 to December 1973 and January 1980 to December 2012, the power function of the proposed Bayesian test is compared to the conditional multivariate F test by Gibbons et al. (1989) and the Bayesian test by Shanken (1987). In an independent simulation study, the performance of the proposed Bayesian test is also demonstrated.

This chapter discusses methods and applications of fundamental analysis and technical analysis. In addition, it investigates the ranking performance of the Value Line and the timing and selectivity of mutual funds. A detailed investigation of technical versus fundamental analysis is first presented. This is followed by an analysis of regression time series and composite methods for forecasting security rates of return. Value Line ranking methods and their performance are then discussed, leading finally into a study of the classification of mutual funds and the mutual fund managers’ timing and selectivity ability. In addition, the hedging ability is also briefly discussed. Sharpe measure, Treynor measure, and Jensen measure are defined and analyzed. All of these topics can help improve performance in security analysis and portfolio management.

This chapter discusses how futures, options, and futures options can be used in portfolio insurance (dynamic hedging). Four alternative portfolio insurance strategies are discussed in this chapter. These strategies are as follows: (i) stop-loss orders, (ii) portfolio insurance with listed put options, (iii) portfolio insurance with synthetic options, and (iv) portfolio insurance with dynamic hedging. In addition, the techniques of combining stocks and futures to derive synthetic options are explored in detail. Finally, important literature related to portfolio insurance is also reviewed.

We construct liquidity and earnings-based factors and combine with the Market to describe stock returns. Liquidity and Liquidity Growth are significant factors across markets. Intercept tests show that the IELM (International Earnings, Liquidity, and Market) model fits the cross section in various country groupings. As previous research showed, a Liquidity Growth factor subsumes momentum in the U.S., and we test this across international markets. From 2001 through 2019, the momentum factor has a high mean and is significant in Europe and in the Asia-Pacific, except Japan. For this time period, however, momentum is not significant in North American and Japan. While the IELM model reduces the momentum intercept in North America, both IELM and Fama and French (2017) have trouble explaining momentum in Europe and Asia where momentum is pervasive.

The main purpose of this chapter is to show how Excel and SAS language can be used to estimate European options and American options. In this chapter, we use bivariate normal distribution to derive American options with one dividend payment. Both Excel program and SAS program are presented in the appendices.

Bond default and rank transition are often modeled as a Markov chain with an absorbing state. However, recent studies have shown that the theory does not match the empirical data. We suggest that this mismatch possibly arises from unobserved heterogeneity and we examine via a numerical example whether increased heterogeneity reduces, as expected, the accuracy of the estimated defaults. The extent to which this reduction is economically significant is also considered. We then suggest a methodology for identifying the heterogeneity parameters and for testing their explanatory power.

Graph data have become an important channel in exploring relations among a large number of subjects in the big data era. In past decades, community structures have been found in many complex real-world networks and play a key role in the modeling of graph data, for example, the stochastic block model (SBM) and its extensions. However, recent studies have unveiled more sophisticated modules, and typical examples include star and bipartite structures. In most graph models, these link-pattern modules are piled up in terms of multiple communities. This paper proposes a graph factor model in which each node is endowed with several (latent) features. Factors are channels for edge connections and can be characterized by link functions that map features of pairs of nodes to the edge probabilities. This model may naturally incorporate different kinds of link-pattern modules including communities, stars, and bipartite structures. The inference for the model can be carried out through an Markov Chain Monte Carlo (MCMC) procedure. Both synthetic data and real-world networks are used for numerical illustrations.

Breeden (1979), Grinols (1984 Cox et al. [Cox, J. C., Ingersoll, J. E., Jr., & Ross, S. A. (1985). An intertemporal general equilibrium model of asset prices. Econometrica 53, 363–384] have described the importance of supply side for the capital asset pricing. Black [Black, S. W. (1976). Rational response to shocks in adynamic model of capital asset pricing. American Economic Review66, 767–779] derives a dynamic, multiperiod CAPM, integrating endogenous demand and supply. However, Black’s theoretically elegant model has never been empirically tested for its implications in dynamic asset pricing. We first theoretically extend Black’s CAPM. Then we use price, dividend per share and earnings per share to test the existence of supply effect with U.S. equity data. We find the supply effect is important in U.S. domestic stock markets. This finding holds as we break the companies listed in the S&P 500 into ten portfolios by different level of payout ratio. It also holds consistently if we use individual stock data.

A simultaneous equation system is constructed through a standard structural form of a multi-period equation to represent the dynamic relationship between supply and demand for capital assets. The equation system is exactly identified under our specification. Then, two hypotheses related to supply effect are tested regarding the parameters in the reduced-form system. The equation system is estimated by the Seemingly Unrelated Regression (SUR) method, since SUR allow one to estimate the presented system simultaneously while accounting for the correlated errors.

The literature on indices herding behavior among bonds, equities, and real estate is very scant. When one compares developed and emerging markets, specifically the United States, the United Kingdom, Taiwan, and South Africa, such studies are hard to find. This study uses principal component analysis to extract and illustrate parameters driving herding investment behavior for the indices of the mentioned countries. Thereafter, the vector autoregressive model is used for robustness tests. The results reveal the following: First, governmental relationships and similarities among countries influenced herding behavior in the selected capital markets indices. Second, most of the herding occurs in the bond indices for the four countries. Finally, the robustness results reveal spillover opportunities in between and across countries irrespective of the index analyzed. The results are generalizable as they are consistent with prior studies such as Zaremba et al. (2021).

Financial anomalies have been studied in the U.S. However, recent evidence suggests that what were initially identified as return anomalies have diminished in U.S. data. Have the identified regularities changed or are they persistent? Have historical and earnings forecasting data been a consistent and highly statistically significant source of excess returns? We test a number of financial anomalies of the 1980s–1990s and report that several models and strategies continue to produce statistically significant excess returns not absorbed by then-known factor models. We report that earnings forecasts, revisions, and breadth and price momentum have maintained their statistical significance during the May 1995–December 2017 time period. More importantly, we use expected return models and multi-factor models that are estimated and known at the start of our current analysis, assuring our readers of out-of-sample and post-publication verification of the models.

This paper presents the advancement of several widely applied portfolio models to ensure flexibility in their applications: Mean-variance (MV), Mean-absolute deviation (MAD), Linearized value-at-risk (LVaR), Conditional value-at-risk (CVaR), and Omega models. We include short sales and transaction costs in modeling portfolios and further investigate their effectiveness. Using the daily data of international ETFs over 15 years, we generate the results of the rebalancing portfolios. The empirical findings show that the MV, MAD, and Omega models yield a higher realized return with lower portfolio diversity than the LVaR and CVaR models. The outperformance of these risk–return-based models over the downside-risk-focused models comes from efficient asset allocation and not only the saving of transaction costs.

The main purpose of this chapter is to demonstrate how to estimate implied variance for both the Black–Scholes option pricing model (OPM) and the constant elasticity of variance (CEV) OPM. For the Black–Scholes OPM model, we classify them into two different estimation routines: numerical search methods and closed-form derivation approaches. Both the MATLAB approach and approximation method are used to empirically estimate implied variance for American and Chinese options. For the CEV model, we present the theory and demonstrate how to use a related Excel program in detail.

We survey the literature on anomalies in accounting research with a specific focus on whether and how they account for the momentum effect (Jegadeesh and Titman, 1993, 2001). Even though accounting academics recognize treatment of the momentum effect via inclusion in an extended Fama-French model to be appropriate, most extant empirical studies of accounting anomalies either do not account for the momentum effect or do so as a robustness check. Where included in the analysis, the momentum factor substantially reduces returns to portfolio strategies which exploit market underreaction. We argue that this treatment is in part due to the normal lag in the incorporation of research innovations but also likely due to persisting differences of opinion in the finance and accounting literature on whether to treat momentum as an anomaly or an asset pricing factor. More recent studies in accounting, however, seem to account for and treat the momentum effect more uniformly.

In this chapter we review the renowned constant elasticity of variance (CEV) option pricing model and give the detailed derivations. There are two purposes of this article. First, we show the details of the formulae needed in deriving the option pricing and bridge the gaps in deriving the necessary formulae for the model. Second, we use a result by Feller to obtain the transition probability density function of the stock price at time T given its price at time t with. In addition, some computational considerations are given for the facilitation of computing the CEV option pricing formula.

This chapter aims to establish basic knowledge of options and the markets in which they are traded. It begins with the most common types of options, calls, and puts, explaining their general characteristics, and discussing the institutions where they are traded. In addition, the concepts relevant to the new types of options on indexes and futures are introduced. The next focus is the basic pricing relationship between puts and calls, known as put–call parity. The final section concerns how options can be used as investment tools. An alternative option strategies theory has been presented. Excel is used to demonstrate how different option strategies can be executed.

I find that downward bias of the estimated coefficient of betas in the Fama-MacBeth cross-sectional regression is caused by endogeneity of the estimated betas due to measurement errors. I propose an instrumental variable methodology that purges the endogeneity. The purged betas have a 95% correlation with the original betas and retain the relation with firm size. I document that controlling for the purged betas in the Fama-MacBeth cross-sectional regression has higher statistical power to correctly reject the null hypothesis of nonexistence of size premium and also has higher R-squared and higher total sum of squares than the Brennan-Chordia-Subrahmanyam (1998) methodology.

Marsh and Merton (1987) and Garrett and Priestley (2000) have used aggregated permanent instead of current earnings to estimate aggregated dividend behavior models which were developed by Lintner (1956). Lee and Primeaux (1991) used permanent instead of current EPS to estimate Lintner’s dividend payment behavior model for individual companies. Most recently, Lambrecht and Myer (2012) have theoretically shown that permanent, instead of current, EPS should be used to estimate the dividend payment behavior model for individual companies to avoid measurement error and misspecification of the model.

The main purposes of this paper are as follows: (1) theoretically explain why firms generally allocate permanent earnings and transitory earnings between dividend payments and retained earnings; (2) develop alternative methods for decomposing current earnings into permanent and transitory components; (3) empirically estimate alternative dividend payment behavior models by using two alternative permanent EPS estimates for both individual firms and pooled data; and (4) test Lambrecht and Myer’s (2012) theoretical results related to alternative dividend payment behavior models. We find that the average long-term payout ratio is downward biased and the average estimated intercept is generally upward biased when current instead of permanent EPS is used. We also find that the combined model performs well to deal with both measurement errors and specification errors in describing the dividend payment behavior model.

The main purposes of this paper are to study (1) a differential effect of inside debt on components of firm risk, and (2) how it relates to CEO portfolio diversification to reduce firm risk exposure. We find that compensating CEOs with inside debt (e.g., pensions and other deferred compensation plans) leads to reductions in firms’ systematic risk and idiosyncratic risk, but to disproportionate degrees. CEOs with larger inside debt draft and implement policies, which lead to a significantly larger reduction in the idiosyncratic firm risk and investment. We then show that the differential effect is the result of an asymmetry in CEOs’ perceived benefits of diversifying exposures to individual firm risk components. We further show that granting excessive debt-based pay may divert executives from firm specific but productive activities (e.g., R&D investments), therefore may compromise longrun corporate success.

Following the dividend flexibility hypothesis used by DeAngelo and DeAngelo (2006), Blau and Fuller (2008), and others, we theoretically extend the proposition of DeAngelo and DeAngelo’s (2006) optimal payout policy in terms of the flexibility dividend hypothesis. We also introduce growth rate, systematic risk, and total risk variables into the theoretical model. In addition, based upon Lee and Alice (2021), we discuss the implication of the existence of optimal payout ratio in financial analysis and decision for a company.

To test the theoretical results derived in this chapter, we use data collected in the US from 1969 to 2009 to investigate the impact of growth rate, systematic risk, and total risk on the optimal payout ratio in terms of the fixed-effect model. We find that based on flexibility considerations, a company will reduce its payout when the growth rate increases. In addition, we find that a nonlinear relationship exists between the payout ratio and the risk. In other words, the relationship between the payout ratio and risk is negative (or positive) when the growth rate is higher (or lower) than the rate of return on total assets. Our theoretical model and empirical results can therefore be used to identify whether flexibility or the free cash flow hypothesis should be used to determine the dividend policy.

A large number of studies have examined issues of dividend policy, while they rarely consider the investment decision and dividend policy jointly from a non-steady state to a steady state. We extend Higgins’ (1977, 1981, and 2008) sustainable growth rate model and develops a dynamic model which jointly optimizes the growth rate and payout ratio. We optimize the firm value to obtain the optimal growth rate in terms of a logistic equation and find that the steady state growth rate can be used as the benchmark for the mean-reverting process of the optimal growth rate. We also investigate the specification error of the mean and variance of dividend per share when introducing the stochastic growth rate. Empirical results support the mean-reverting process of the growth rate and the importance of covariance between the profitability and the growth rate in determining dividend payout policy. In addition, the intertemporal behavior of the covariance may shed some light on the fact of disappearing dividends over decades.

The following sections are included:

• Author Index
• Subject Index