Peer-selection and valuation: a systematic risk approach

PurposeUsing capital asset pricing model (CAPM) and the Z-risk index based on weekly data, this study aims to estimate yearly unsystematic, total, three systematic and insolvency risks in the Gulf Cooperation Council (GCC) countries for the period 2010–2018. The findings of CAPM show positive systematic market risk exposure in all GCC countries for all years, which support the contribution of stock markets to bank prices and returns. The mixed signs of systematic interest rate and exchange rate risks in GCC countries provide hedging opportunities, diversification strategies and regional cooperation, which help risk managers to hedge and stabilize their portfolios against interest rate and exchange rate fluctuations. Therefore, it is necessary that managers and policymakers develop a monitoring system on factors affecting bank insolvency risks to avoid bankruptcies and insolvencies.Design/methodology/approachThis study uses the three-factor CAPM and Z-risk index to measure six types of risks. The CAPM uses market information to estimate the sensitivity of banks to the fluctuations of equity markets, debt markets and foreign exchange markets. Sharpe (1964), Lintner (1965) and Treynor (1965) developed a single-factor CAPM and the coefficient of the model was called systematic market risk. The single-factor CAPM highlights stock markets as the only non-diversifiable source of systematic risks, whereas Stone (1974) and Jorion (1990) highlighted interest rate and exchange rate fluctuations as the other types of non-diversifiable systematic risks. The following functional form in equation (1) estimates five types of risks using CAPM.FindingsThe findings of CAPM show positive systematic market risk exposure in all GCC countries for all years, which support the contribution of stock markets to bank prices and returns based on CAPM theory. The mixed signs of systematic interest rate and exchange rate risks in GCC countries support hedging opportunities and diversification strategies which may help risk managers to hedge and stabilize their portfolios against the fluctuations of interest rate and exchange rate. Although, this policy may decrease the profits of banking sectors but at the same time it would stabilize the portfolios and prevent bankruptcies and big losses because of the fluctuations of interest rate. Moreover, a bank has a better chance to have more liquidity position during financial crises because of the diversifications into different regional markets.Research limitations/implicationsTherefore, this study contributes to the existing literature by using risk measurement by a three-factor CAPM and the Z-risk index as discussed further in methodology.Originality/valueIt is necessary that managers and policymakers develop a monitoring system on factors affecting bank insolvency risks to avoid bankruptcies and insolvencies.

Objective - Previous research by this author has stated that the market overreaction phenomenon occurs in the Indonesian capital market and the CAPM (Capital Asset Pricing Model) is able to explain portfolio returns. However, CAPM is still debated along with the emergence of the other asset pricing models, such as the multifactor model proposed by Fama and French. The aim of this research is to test the ability of that model to explain the returns of portfolios formed under market overreaction conditions. Methodology/Technique - The data used in this study is the same as that of the previous research, which includes winner and loser portfolio data formed in market overreaction conditions, particularly on the Indonesian Stock Exchange, between July 2005 and December 2015. The multifactor models used include a three-factor model consisting of the factors of market, firm size, firm value, and a five-factor model with the added factors of profitability and investment. To obtain more accurate results, GARCH econometric models were also used in addition to standard test models for obtaining unbiased results. Findings - This research concludes that market factors (Rm-Rf), firm size (SMB), and firm value (HML), are able to explain the winner and loser portfolio returns well. However, when the factors of profitability (RMW) and investment (CMA) are added into the three-factor model, the RMW and CMA explained the returns negatively and inconsistently when the GARCH model is implemented. Novelty – These results imply that the three-factor model is more accurate than the five-factor model, contrary to the previous findings of Fama and French. Type of Paper - Empirical. Keywords: Fama and French Model; Five-factor Model; Market Overreaction; Three-factor Model; Portfolio. JEL Classification: G11, G12, G14

This paper has made the statement that the different systematic risk exposure style affects hedge fund performance and performance persistence. Employing maintain low systematic risk exposure (LSR) style leads superior performance for hedge fund during the full-time period. The outperformance of LSR style in this finding challenges the principle of standard Capital Asset Pricing Model (CAPM) and being supported by the ?Low-volatility anomaly? in the equity market. However, the market timing, one of systematic risk exposure style, is still crucial and should be taken into consideration when managing portfolios systematic risk exposure, especially during the crisis period. Moreover, there is the evidence in support of performance persistence for all systematic risk exposure style. However, in the crisis period, the ability to time the market along with the ability to maintain low systematic risk exposure are proved to be necessary skills of fund managers to have the highest probability of repeating winner performance.

This paper tests the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model, and the Fama-French five-factor model using 25 size and book-to-market sorted portfolios over the period July 1963 to November 2013 to examine the comparative performance of these well-established asset pricing models in explaining cross-sectional variation of returns. I employ the Fama-MacBeth (1973) methodology to address cross-sectional correlation issues inherent in asset pricing tests. Empirical investigation reveals that market beta alone fails to capture the variation in average returns. In contrast, the Fama-French three-factor model, which incorporates size (SMB) and value (HML) factors alongside market risk, substantially improves explanatory power. Moreover, incorporating two more additional factors, profitability (RMW) and investment (CMA), - known as the Fama-French five-factor model - enhances explanatory power but the improvement is marginal. However, both models have lower root mean square alpha and higher average R-squared compared to CAPM. Fama-MacBeth approach of estimating coefficients confirms that book-to-market equity, profitability, and investment factors exhibit strong explanatory power for stock returns while market beta, and size factors do not sufficiently capture the heterogeneity in stock returns. The diminishing power of the size effect has significant implications for portfolio management strategies based on the size effect. This study corroborates the superiority of the five-factor model over three-factor model and single-factor CAPM in describing the cross-sectional pattern of stock returns for this extended sample period. Keywords: Asset pricing, CAPM, Fama-French three-factor model, Fama-French five-factor model, Fama-MacBeth, Cross-sectional returns, Size effect.

Asset pricing has always been a hot issue in the financial industry. The cutting-edge research achievements of Capital Asset Pricing Models are the Fama-French three-factor model and the Fama-French five-factor model. Although many scholars have studied the performance of Fama-French three-factor model and five-factor model in China's A-share market, there is still controversy about the explanatory power of these two model in the A-share market. This thesis discusses the applicability of Fama-French three-factor and five-factor models adopted in various industries. This thesis chooses A shares in terms of performances, with 14 years commencing from August 2007 to August 2021 as the samples, and utilizes the data of monthly transactions of listed companies in the market for calculation. The thesis has divided the samples into 18 industries. The Fama-French three-factor and five-factor models are used for regression to verify the model's applicability in China's stock market. Through the empirical test, this thesis found that the Fama-French three-factor and five-factor models have strong explanatory powers regarding the excess returns of 15 industries. The research obtained in the thesis has enriched and broadened the study of the Asset Pricing Theory in China, providing theoretical guidance for various investment entities in acts conducted in the market.

The Correlations and Volatilities of Stock Returns: The CAPM Beta and the Fama-French Factors

In the world of investment, there is a lot of model being used to calculate a return which will be compared with the available risk. The model that being compared in this investigation is the capital asset pricing model and the Fama French three factor model. The Capital Asset Pricing model is one of the model that calculate return using the market risk factor only, while the fama French three factor model uses the factor of market risk, size and book to market equity. The investigation uses 12 data of stock market companies that have been chosen from 45 companies’ data. This data have met the requirement in the investigation. The period of the data is taken from 2005 until 2015. Every data will be processed based on several portfolios and the result will be entered to both of the model to calculate the return estimation. Each of this return estimation will be compared to know the accuracy to calculate the return. With this comparison then investor able to evaluate which model to be used in the situation and condition at the present. Keyword: Fama-French Three Factor Model, Capital Asset Pricing Model, Investment

This study investigates the impact of different risk factors on stock returns in the Bangladesh capital market by empirically testing the Capital Asset Pricing Model (CAPM) and the Fama-French Three-Factor Model. The aim is to conduct a comparative analysis to determine which model better explains stock returns in an emerging market like Bangladesh, known for its volatility, inefficiency, and instability. While CAPM and Fama-French models are extensively tested in developed markets, their application in Bangladesh remains underexplored. This research fills that gap by analyzing monthly returns from 170 securities listed on the Dhaka Stock Exchange between 2009 and 2023. The study forms ten portfolios based on a wide spread of estimated betas and examines whether the relationship between expected return and risk is linear. While the CAPM showed significant results across all portfolios, the relationship between mean excess return and beta was linear but negative, attributed to negative average market returns during the study period. The Fama-French Three-Factor Model, tested on 110 companies from 2014 to 2023, utilized a 3x3 sort methodology based on size and book-to-market equity factors. This model demonstrated higher explanatory power, with a 60.58% improvement over CAPM and only 0.7% of excess returns unexplained by the factors. GRS test statistics further indicated that, while both models rejected the null hypothesis over the entire period, the Fama-French model performed better in sub-period analyses, suggesting superior accuracy in capturing stock returns in the Bangladesh market. Overall, the findings highlight the Fama-French Three-Factor Model as more effective than the CAPM in explaining portfolio excess returns in this emerging market context.

This thesis consists of three empirical studies on what drives stock market dynamics. The first empirical study explores the effect of crude oil price changes on the stock market returns of oil-exporting countries and oil-importing countries as well as those of a number of global stock indices. Using the Ordinary Least Squares (OLS) approach as well as the more robust Quantile Regression (QR) approach to explore the relationship between crude oil and stock market dynamics. The empirical findings suggest that the QR approach provides further insights compared to the OLS approach. For instance, the QR approach is able to identify specific quantiles where a significant relation exists. In particular crude oil price increases tend to have a negative impact on the stock market returns for some oil-exporting countries (such as Mexico, Iraq, Ecuador, and Venezuela) and a positive effect for other oil-exporting countries (such as Brazil and Algeria). However, the OLS approach suggests that these relationships are insignificant at the level of the mean. Overall, the empirical findings confirm that the QR approach can reveal more information about the relationship between crude oil price changes and stock market return across different quantiles of their distribution.The second study explores the extent to which implied volatility extracted from commodity markets and developed stock markets can predict the implied volatility of stock markets in BRICS countries. Using daily data from 2011 to 2016 and employing the newly developed Bayesian Graphical Vector Autoregressive (BGVAR) model of Ahelegbey et al. (2016) which does not suffer from over-parameterization and the identification problems associated with traditional VAR frameworks, this study finds that implied volatilities extracted from global and regional stock markets have a significant predictive power over the implied volatilities in BRICS stock markets. However, the predictive power of implied volatility from commodity markets are significant only in the case of South Africa.The third empirical study analyses the relationship between illiquidity and stock market returns in the G7 and BRICS countries. More specifically, this study explore the extent to which the Amihud (2002) illiquidity measure can improve the explanatory power of three commonly used asset pricing models, namely the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model and the Carhart four-factor model. The empirical analysis is based on 15 years of monthly data on the returns of seven stock portfolios: 100 largest companies (Largest100), small value (S/V), small neutral (S/N), small growth (S/G) stocks, big value (B/V) stocks, big neutral (B/N) stocks, and big growth (B/G) stocks. The findings suggest that incorporating illiquidity as an additional factor results in a significant improvement in the explanatory power of these asset pricing models across several of the sample countries (8 countries in the case of the CAPM and Carhart four-factor model, and 6 countries in the case of the Fama-French three-factor model). For example, in the US adding illiquidity to the CAPM leads to an increase of the goodness of fit by 2.6% in the B/V portfolio, and for the Fama-French three-factor model the goodness of fit increases by up to 3% in all portfolios Moreover, the goodness of fit increases in all portfolios in the US by adding illiquidity to the Carhart four-factor model, with an up to 36% increase in the B/N portfolio.

PurposeThis paper aims to study the cross section of expected returns on Shari’ah-compliant stocks in Pakistan by using single- and multi-factor asset pricing models.Design/methodology/approachTo estimate cross section of expected returns of Shari’ah-compliant stocks, the study uses capital asset pricing model (CAPM), Fama-French three-factor model and Fama-French five-factor model. Data for the period 2001-2015 on 217 companies are used. For the market portfolio, PSX-100 and Dow Jones Islamic Index for Pakistan are used.FindingsThe study could not find empirical support for CAPM using Lintner (1965), Black et al. (1972) and Fama and Macbeth (1973) approach. Nonetheless, the relation between beta and returns is positive in up-market and negative in down-market. The results of Fama-French three-factor and five-factor models suggest that size premium is positive and significant for explaining the cross section of stock returns of small size stocks, whereas value premium is positive and significant for explaining the cross section of returns of high value stocks.Practical implicationsThe results suggest that fund managers can use Shari’ah-compliant stocks for portfolio diversification and for offering specialized investments given the positive market excess returns and the existence of size and value premium on Shari’ah-compliant stocks.Originality/valueThis is the first study on Fama-French (2015) five-factor model for Islamic capital markets in Pakistan.

In competitive capital markets, risky debt claims that offer high yields in good times have high systematic risk exposure in bad times.We apply this idea to bank risk measurement.We find that banks with high accounting return on equity (ROE) prior to a crisis have higher systematic tail risk exposure during the crisis.Proximate causes of crises differ, but the predictive power of ROE is pervasive, including during the financial crisis of 2007-2010 and the recent crisis triggered by the collapse of Silicon Valley Bank.ROE predicts systematic tail risk much better than conventional measures based on risk-weighted assets.

Purpose Asset pricing revolves around the core aspects of risk and expected return. The main objective of the study is to test different asset pricing models for the Indian securities market. This paper aims to analyse whether leverage and liquidity augmented five-factor model performs better than Capital Asset Pricing Model (CAPM), Fama and French three-factor model, leverage augmented four-factor model and liquidity augmented four-factor model. Design/methodology/approach The data for the current study comprises records on prices of securities that are part of the Nifty 500 index for a time frame of 14 years, that is, from October 2004 to September 2017 consisting of 183 companies using time series regression. Findings The results indicate that the five-factor model performs better than CAPM and the three-factor model. The model outperforms leverage augmented and liquidity augmented four-factor models. The empirical evidence shows that the five-factor model has the highest explanatory power among the entire asset pricing models considered. Practical implications The present study bears certain useful implications for various stakeholders including fund managers, investors and academicians. Originality/value This study presents a five-factor model containing two additional factors, that is, leverage and liquidity risk along with the Fama-French three-factor model. These factors are expected to give more value to the model in comparison to the Fama-French three-factor model.

Return on Equity (ROE), Return on Capital Employed (ROCE), Economic Profit (EP) and Economic Value Added (EVA) (Presentation Slides)

Different strokes by different folks: The dynamics of hedge fund systematic risk exposure and performance

Based on the analysis of the A-share markets in Shanghai and Shenzhen Stock Exchange from 2000 to 2023, this paper systematically evaluates the applicability and explanatory power of the Capital Asset Pricing Model (CAPM), the Fama-French Three-Factor Model (FF3) and the Fama-French Five-Factor Model (FF5) in the Chinese market. The study finds that although CAPM has some explanatory power in the early stage, the three-factor and five-factor models can better explain the cross-sectional changes in stock returns as the market develops. In the descriptive statistics, each factor's volatility and correlation reveal the Chinese market's unique characteristics, such as the relative strength of small-cap and growth stocks. In the regression analysis, the results of the Fama-MacBeth regression showed that the significance and explanatory power of the market factor, size factor and value factor were different in different models. Further robustness analysis reveals the COVID-19's impact on the model's performance through the sub-period analysis. This paper provides a new perspective on understanding the asset pricing of China's A-share market and a theoretical basis for investors' investment decisions. It also points out the limitations of existing models in explaining emerging market equity returns. It suggests that future research can deeply explore the performance of more factors in different market environments.