Narrow Results

Stock market reactions to financial reports have been extensively studied in previous years and for various markets. However, not much research has been conducted regarding the reaction of global financial markets to integrated reporting. This study examines the market reaction to the publication of integrated reports for a sample of 316 global companies for the reporting year 2018 by using an event study methodology. The results of the applied event study indicate significant cumulative average abnormal returns (CAARs) after the publication date. To ensure robust estimation results, we use a modern asset pricing model, namely, the three-factor model according to Fama and French (1993). For comparability purposes, we also estimate the average cumulative abnormal returns using a market-adjusted model, a capital asset pricing model (CAPM), and a Fama-French model taking generalized autoregressive conditional heteroskedasticity (GARCH effects) into account. In addition, a cross-sectional analysis is conducted. We find a significant positive CAAR on days one to four after the publication day of the integrated report compared to a negative CAAR for financial information disclosure. Our results suggest that investors react to information provided in the integrated report and that they react differently to the release of integrated report information than to financial information. Furthermore, our cross-sectional analysis confirms that companies with a significant positive CAAR around the publication date show certain characteristics. It was found that European companies have a higher likelihood to experience a stronger significant positive market reaction to their integrated report publication. It was also found that firms in the consumer defensive, financial, industrial, and real estate sectors are more likely to experience a positive market reaction. No significant differences were found for companies of a larger size or with a higher profit margin. Ultimately, this confirms that integrated reporting affects company value. This is the first event study for a multi-country sample applying multi-factor models for nonfinancial disclosure event studies including a cross-sectional analysis.

This paper investigates whether the risk-return relation varies, depending on changing market volatility and up/down market conditions. Three market regimes based on the level of conditional volatility of market returns are specified — "low", "neutral" and "high". The market model is extended to allow for these three market regimes and a three-beta asset-pricing model is developed. For a set of US industry sector indices using a cross-sectional regression, we find that the beta risk premium in the three market volatility regimes is priced. These significant results are uncovered only in the pricing model that accommodates up/down market conditions.

This paper reviews the extant studies on the equity premium. While paper attempts to make the review comprehensive, describing all of the work in this area is difficult considering the numerous researches that have been done in this area. Essentially, the paper assesses the relationship between the excess return and the equity risk premium and draws attention to their interchangeable use in the finance literature. Existing literature is reviewed around possible theories explaining the equity premium puzzle and followed by the empirical evidence on the theories. Finally, this paper focuses on the problems of attaining consensus value and source of the market risk premium, which makes equity premium puzzle an unresolved issue among the academics and finance practitioners.

We evaluate the initial public offering price of a new DRAM chipmaker in Taiwan in accordance with the compound real call options model of Lin (2002). The worldwide average sales price is the underlying variable, and the average production cost of the new DRAM foundry is the exercise price. The twin security is defined as a portfolio of DRAM manufacturing firms publicly listed in Taiwan stock markets. We estimate the dividend-like yield with two methods, and find that the yield is negative. The negative dividend-like yield results from the negative correlation between the newly constructed DRAM foundry and its twin security, implying the diversification advantage of a new generation of DRAM foundry with a relative low cost of investment opportunity. We solve the critical value for the multivariate normal integral with the secant method, approximating the integral with the lattice method. It has been found that there is only a 4.6% difference between the market IPO price and the estimated one.

This paper investigates the underlying factors that determine share returns on the Dhaka Stock Exchange. The empirical analysis does not support the critical condition of the Capital Asset Pricing Model of a positive relationship between share return and beta. However, it shows that variables such as size, price to book, volume of shares traded, earnings yield and cash flow yield have a significant influence on share returns. The degree and direction of relationship among the variables are similar to other emerging markets, but are not always consistent with developed markets perhaps due to lack of homogeneous expectations regarding risk return characteristics and different market microstructure.

Practitioners and academics have spent the past few decades debating the validity and relevance of the capital asset pricing model (CAPM). One of the attributes of the model is an estimate of risk by beta, which in equilibrium describe the behavior of mean-variance (MV) investors. In the MV framework, risk is measured by the variance of returns which is a questionable and restrictive risk measure. In contrast, the average drawdown risk is a more acceptable risk measure and can be applied to modeling an alternative behavioral hypothesis, namely mean-drawdown behavior with a replacement risk measure for diversified investors, the average drawdown beta leading to an alternative pricing model based on this beta. Our findings clearly support the average drawdown beta and the pricing model of average drawdown CAPM versus the conventional beta and CAPM in a sample of Malaysian mutual funds.

In this paper, we compare the performance of Islamic stock indices (ISI) and conventional stock indices (CSI) from FTSE, DJ, MSCI, S&Ps and Jakarta series using common risk-return metrics. The sample consists of 64 ISI and CSI, and covers the period from 2002 to 2017. The majority of the stock indices are from the Pacific Rim countries’ stock markets. Additionally, we employ the GARCH-M model to examine the impact of past volatility on spot returns. Findings suggest that the ISI are less sensitive to the average market movements compared to the CSI, but surprisingly offer similar raw returns suggesting primary support for the low risk-high return paradox. On further examination, results reveal that M², Omega, Sharpe and Treynor measures indicate that ISI underperform CSI while Jensen’s alpha and Sortino ratio put ISI ahead of CSI. Moreover, findings show that pre-crisis winners (CSI) were losers during the 2008 crisis but subsequently recovered and ended up with higher returns than ISI. Findings also show that the previous volatility of stock returns can be potentially used for predicting future returns.

Current asset pricing models require mean-variance efficient benchmarks, which are generally unavailable because of partial securitization and free float restrictions. We provide a pricing model that uses inefficient benchmarks, a two-beta model, one induced by the benchmark and one adjusting for its inefficiency. While efficient benchmarks induce zero-beta portfolios of the same expected return, any inefficient benchmark induces infinitely many zero-beta portfolios at all expected returns. These make market risk premiums empirically unidentifiable and explain empirically found dead betas and negative market risk premiums. We characterize other misspecifications that arise when using inefficient benchmarks with models that require efficient ones. We provide a space geometry description and analysis of the specifications and misspecifications. We enhance Roll (1980), Roll and Ross's (1994), and Kandel and Stambaugh's (1995) results by offering a "Two Fund Theorem," and by showing the existence of strict theoretical "zero relations" everywhere inside the portfolio frontier.

This study examines the relative systematic risks of 10 industries in China and ASEAN-3, including Malaysia, Singapore, and Thailand. We use four different approaches (ordinary least squares, least absolute deviations, MM-estimator and Theil–Sen estimator) and the weekly data from 2004 to 2016 to determine the sectoral systematic risk. The data are also divided into four sub-periods: the pre-crisis, crisis, post-crisis and normal periods. We find that the rankings of systematic risk, and the risk–return framework, for 10 industries vary from one country to another. The pairwise correlation analysis shows that significant correlation of sectoral ranks between estimation methods is found in China and Thailand, but not in Malaysia and Singapore. However, no correlations of industry rankings between China and ASEAN-3 countries for all the estimation methods for the full research periods and sub-periods are found. The sub-periods analysis also suggests that the rankings of systematic risk for industries in four countries across different economic periods are unstable.

This study aims to model arbitrageur behavior in a sentiment-driven capital asset-pricing model under the premise of reflecting a more detailed decomposition of investor types in the equity markets. We explore the behavior and the impact of arbitrageur behavior, particularly, on pricing and on key financial ratios. We observe that the prevalence of the arbitrageur counteracts the effects of unsophisticated investors, resulting in a lower volatility of the price–dividend ratio, lower predictive power of changes in consumption for future price changes and lower equity premium. Thus, the results of our research allow us to conjecture that the extrapolation bias in the prices is lowered.

This paper investigates the Indian equity market for the presence of a beta anomaly. A beta anomaly occurs when the additional market risk taken by an investor is not rewarded. Academic literature shows mixed evidence on whether the market rewards risk-takers or not for the additional risk taken. Using a sample of monthly returns of 265 companies during a period of 240 months from January 2000 to December 2019, the authors test the Indian equity market for the presence of an anomaly. A decile descriptive analysis shows a positive relationship between market risk and returns, and a negative relationship between company-specific risk and returns. A two-stage Fama–MacBeth (FMB) regression procedure is employed to empirically test for the relationship between beta and expected returns. The findings refute the presence of a beta anomaly in the Indian capital market. Also, the study concludes that a linear model of slope-intercept form is enough to explain the beta and expected returns’ relationship. The findings benefit investment managers and wealth advisors by explaining the market risk and expected returns relationship.

The aim of this paper is to show how the existence of equilibrium in CAPM may be obtained when individuals/investors are risk friendly. This assumption is closer to the real world, since risk aversion is rare and the portfolios implying a greater payoff are the ones which increase the variance of the payoff itself. Specifically, we assume that the wage vectors of the individuals/investors do not lie in the market space, which is the usual assumption for CAPM, namely, the payoff of any portfolio is replicated by the wage of the individuals/investors. The interpretation of such an assumption is that the wealth of the individuals/investors may not invest in financial markets as a whole. The second main result is that the perception of risk by the objective probabilities for the states of the world does not affect the equilibrium existence. This is a consequence of the No-Arbitrage assumption. This condition may be stated in terms of the existence of the objective probabilities for the states of the world. Since CAPM is usually considered as a way to compare the return of the so-called “market portfolio” and the return of any other portfolio, in the final section of this paper, we do provide the regression form of this altered form of CAPM.

This study is based on positivism research philosophy and utilizes deductive approach. The study uses a dataset of 117 firms listed on KSE-100 Index from 2005 to 2012 to analyze the predictability of capital asset pricing model (CAPM) under different data frequencies and time frames. Six months daily data, in contradiction to the recommended five years monthly data, provides the best estimates. However, the performance of model can be regarded poor as it only explains 7.39% difference in returns.

This study empirically analyzes the three models of CAPM in order to get the best determinants, and superlative model of CAPM in the context of Pakistan’s Financial Sector. This study used fixed Effect model and Hausman test are used in this study to investigate the single-, three- and five-factor CAPM. First we analyzed the single factor CAPM, and results explain 52% variations in the dependent variable — stock returns. Next, the three-factors CAPM is analyzed, which elucidates 69% variations in the dependent variable — stock returns — on the addition of two more factors (size and value). Lastly, five factor CAPM is estimated, which provides 76% variations in the dependent variable — stock returns — by adding two more factors (investment and profitability) in the three factor CAPM. This shows that the addition of more factors in the CAPM bestows suitable results in the financial sector of Pakistan.

This chapter attempts to explore the puzzle of post-earnings-announcement drifts by focusing on the revision of systematic risk subsequent to the release of earnings information. This chapter proposes a market model with time-varying systematic risk by incorporating ARCH into the CAPM. The Kalman filter is then employed to estimate how the market revises its risk assessment subsequent to earnings announcement. This chapter also conducts empirical analysis based on a sample of US publicly held companies during the five-fiscal year sample period, 2010–2014. After controlling for the revision of risk and isolating potential confounding effect, this chapter finds that the phenomenon of post-earnings announcement drifts, so well documented in accounting literature, no longer exists.

This chapter uses the concepts of basic portfolio analysis and 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 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.

It is well known that in simple linear regression, measurement errors in the explanatory variable lead to a downward bias in the OLS slope estimator. In two-pass regression tests of asset-pricing models, one is confronted with such measurement errors as the second-pass cross-sectional regression uses as explanatory variables imprecise estimates of asset betas extracted from the first-pass time-series regression. The slope estimator of the second-pass regression is used to get an estimate of the pricing-model’s factor risk-premium. Since the significance of this estimate is decisive for the validity of the model, knowledge of the properties of the slope estimator, in particular, its bias, is crucial. First, we show that cross-sectional correlations in the idiosyncratic errors of the first-pass time-series regression lead to correlated measurement errors in the betas used in the second-pass cross-sectional regression. We then study the effect of correlated measurement errors on the bias of the OLS slope estimator. Using Taylor approximation, we develop an analytic expression for the bias in the slope estimator of the second-pass regression with a finite number of test assets N and a finite time-series sample size T. The bias is found to depend in a non-trivial way not only on the size and correlations of the measurement errors but also on the distribution of the true values of the explanatory variable (the betas). In fact, while the bias increases with the size of the errors, it decreases the more the errors are correlated. We illustrate and validate our result using a simulation approach based on empirical return data commonly used in asset-pricing tests. In particular, we show that correlations seen in empirical returns (e.g., due to industry effects in sorted portfolios) substantially suppress the bias.

Breeden [An intertemporal asset pricing model with stochastic consumption and investment opportunities. Journal of Financial Economics 7, (1979) 265–296], Grinols [Production and risk leveling in the intertemporal capital asset pricing model. Journal of Finance 39, 5, (1984) 1571–1595] and Cox et al. [An intertemporal general equilibrium model of asset prices. Econometrica53, (1985) 363–384] have described the importance of supply side for the capital asset pricing. Black [Rational response to shocks in a dynamic model of capital asset pricing. American Economic Review 66, (1976) 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 US equity data. We find the supply effect is important in US domestic stock markets. This finding holds as we break the companies listed in the S&P 500 into 10 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 multiperiod equation to represent the dynamic relationship between supply and demand for capital assets. The equation system is exactly identified under our specification. Then, two hypothesis 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 allows one to estimate the presented system simultaneously while accounting for the correlated errors.

Many economic theories and hypotheses have implications on and only on a moment condition or a set of moment conditions. A popular method to estimate model parameters contained in the moment condition is the Generalized Method of Moments (GMM). In this chapter, we first provide some economic examples for the moment condition, and define the GMM estimator. We then establish the consistency and asymptotic normality of the GMM estimator. Since the asymptotic variance-covariance matrix of a GMM estimator depends on the choice of a weighting matrix, we introduce an asymptotically optimal two-stage GMM estimator with a suitable choice of a weighting matrix. With the construction of a consistent asymptotic variance estimator, we then propose an asymptotically χ² Wald test statistic for the hypothesis of interest, and a model specification test for the moment condition.

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.