scholarly journals Another look at the CAPM in South Africa: The influence of bull and bear markets

2014 ◽  
Vol 7 (2) ◽  
pp. 341-360
Author(s):  
Ailie Charteris
Keyword(s):  
South Africa ◽  
Asset Pricing ◽  
South African ◽  
Capital Asset Pricing ◽  
Market Returns ◽  
Risk Premiums ◽  
Bear Market ◽  
Risk Return ◽  
Bear Markets ◽  
Bull And Bear Markets

Several studies of the Capital Asset Pricing Model (CAPM) in South Africa find that beta cannot explain returns. However, these studies do not consider the effect of bull and bear markets, yet over the period 1995-2009, excess market returns were positive in only 98 of 180 months. The influence of market conditions on the risk-return relationship is examined internationally by evaluating the conditional risk-return relationship where risk premiums are allowed to vary in bull and bear markets, and the dual-beta CAPM, which allows for the sensitivity of an asset to the market to vary under the two economic states. In this study, the ability of these two models to explain returns on South African shares is compared to the CAPM using the Fama and MacBeth (1973) and panel data approaches. The dual-beta model is found to be more successful than either the conditional relation or CAPM, as bull- and bear-market betas differ; but the estimates of the risk premiums in this model are significant only after adjusting for market segmentation. The findings thus indicate that asset-pricing models with time-varying risk should be the focus of future asset-pricing tests.

scholarly journals Comparing Entropy and Beta as Measures of Risk in Asset Pricing

2017 ◽  
Vol 65 (6) ◽  
pp. 1889-1894 ◽  
Author(s):  
Galina Deeva
Keyword(s):  
Asset Pricing ◽  
Shannon Entropy ◽  
Explanatory Power ◽  
Kernel Density ◽  
Parametric Estimation ◽  
Capital Asset Pricing ◽  
Risk Premiums ◽  
Risk Return ◽  
Asset Pricing Models ◽  
Non Parametric Estimation

The paper establishes entropy as a measure of risk in asset pricing models by comparing its explanatory power with that of classic capital asset pricing model’s beta to describe the diversity in expected risk premiums. Three different non‑parametric estimation procedures are considered to evaluate financial entropy, namely kernel density estimated Shannon entropy, kernel density estimated Rényi entropy and maximum likelihood Miller‑Madow estimated Shannon entropy. The comparison is provided based on the European stock market data, for which the basic risk‑return trade‑off is generally negative. Kernel density estimated Shannon entropy provides the most efficient results not dependent on the choice of the market benchmark and without imposing any prior model restrictions.

The impact of Mad Money recommendations during bull and bear markets

2016 ◽  
Vol 12 (1) ◽  
pp. 52-70 ◽  
Author(s):  
Adam J. Roszkowski ◽  
Nivine Richie
Keyword(s):  
Prospect Theory ◽  
Behavioral Finance ◽  
Abnormal Returns ◽  
Good News ◽  
Content Type ◽  
Bear Market ◽  
Bear Markets ◽  
The Impact ◽  
Investor Reaction ◽  
Bull And Bear Markets

Purpose – The purpose of this paper is to examine semi-strong market efficiency by observing the behavioral finance implications of Jim Cramer’s recommendations in bull vs bear markets. The authors extend the literature by analyzing investor reaction through the lenses of prospect theory, overreaction, and herding. Design/methodology/approach – The authors test for abnormal returns in response to Mad Money buy and sell recommendations. The authors use a sample of buy and sell recommendations from MadMoneyRecap.com from July 28, 2005 through February 9, 2009. The 3.5-year time period is the most recent and comprehensive set of Mad Money recommendations that has been tested to date. Findings – The results indicate market inefficiency at the semi-strong level. Furthermore, the findings highlight the loss aversion tendencies of investors in regards to prospect theory of Kahneman and Tversky (1979) as well as the disposition effect of Shefrin and Statman (1985). Evidence also exists consistent with the herding and overreaction hypotheses. Practical implications – The evidence suggests contrarian behavior in which investors respond positively to good news in bad times – perhaps, in effort to stay the course and at least break even. This behavior may suggest that losers tend to hold on to losses in hopes of recouping them. Thus, positive information in bad times could further persuade market participants to hang on to or buy more of losers, while also persuading non-shareholders to buy in as well. Originality/value – Though other studies including Kenny and Johnson (2010) have estimated abnormal returns in response to analyst recommendations, to the knowledge, none has examined behavioral implications of investor reaction to buy and sell recommendations in both bull and bear markets. Furthermore, the study captures a longer bull and bear market and covers two definitions of such markets.

Test of CAPM: A Study of India and US

GIS Business ◽  
2016 ◽  
Vol 11 (5) ◽  
pp. 51-58
Author(s):  
Pankaj Chaudhary
Keyword(s):  
Asset Pricing ◽  
Capital Asset Pricing ◽  
Regression Equations ◽  
Long Distance ◽  
Cross Sectional ◽  
Risk Return ◽  
Research Areas ◽  
Asset Pricing Models ◽  
Using Data ◽  
Capm Model

Asset pricing is one of the most important research areas in the field of finance. The simple CAPM model (capital asset pricing model) relates the return of the stocks and portfolios to the market factor captured by beta. Since the formulation of CAPM in 1960s, asset pricing has covered a long distance. We conduct the test of CAPM for India and US by using data from January 2001 to December 2015. We run 84 second pass cross-sectional regression equations to test the applicability of CAPM. The results of our test find that CAPM is not able to capture the cross section of average returns both in India and US and we should consider the alternative asset pricing models to establish the risk-return relationship.

scholarly journals Cross-sectional Volatility and Stock Returns: Evidence for Emerging Markets

2016 ◽  
Vol 41 (3) ◽  
pp. 234-246 ◽  
Author(s):  
Sanjay Sehgal ◽  
Vidisha Garg
Keyword(s):  
South Africa ◽  
Asset Pricing ◽  
South Korea ◽  
Stock Returns ◽  
Risk Premium ◽  
Emerging Market ◽  
Significant Risk ◽  
Residual Variance ◽  
Cross Sectional ◽  
Risk Premiums

Executive Summary Cross-sectional volatility measures dispersion of security returns at a particular point of time. It has received very little focus in research. This article studies the cross-section of volatility in the context of economies of Brazil, Russia, India, Indonesia, China, South Korea, and South Africa (BRIICKS). The analysis is done in two parts. The first part deals with systematic volatility (SV), that is, cross-sectional variation of stock returns owing to their exposure to market volatility measure ( French, Schwert, & Stambaugh, 1987 ). The second part deals with unsystematic volatility (UV), measured by the residual variance of stocks in a given period by using error terms obtained from Fama–French model. The study finds that high SV portfolios exhibit low returns in case of Brazil, South Korea, and Russia. The risk premium is found to be statistically significantly negative for these countries. This finding is consistent with Ang et al. and is indicative of hedging motive of investors in these markets. Results for other sample countries are somewhat puzzling. No significant risk premiums are reported for India and China. Significantly positive risk premiums are observed for South Africa and Indonesia. Further, capital asset pricing model (CAPM) seems to be a poor descriptor of returns on systematic risk loading sorted portfolios while FF is able to explain returns on all portfolios except high SV loading portfolio (i.e., P1) in case of South Africa which seems to be an asset pricing anomaly. It is further observed that high UV portfolios exhibit high returns in all the sample countries except China. In the Chinese market, the estimated risk premium is statistically significantly negative. This negative risk premium is inconsistent with the theory that predicts that investors demand risk compensation for imperfect diversification. The remaining sample countries show significantly positive risk premium. CAPM does not seem to be a suitable descriptor for returns on UV sorted portfolios. The FF model does a better job but still fails to explain the returns on high UV sorted portfolio in case of Brazil and China and low UV sorted portfolio in South Africa. The findings are relevant for global fund managers who plan to develop emerging market strategies for asset allocation. The study contributes to portfolio management as well as market efficiency literature for emerging economies.

scholarly journals Volatility dynamics and the risk-return relationship in South Africa: A GARCH approach

2021 ◽  
Vol 18 (2) ◽  
pp. 106-117
Author(s):  
Nitesha Dwarika ◽  
Peter Moores-Pitt ◽  
Retius Chifurira
Keyword(s):  
South Africa ◽  
Parameter Estimation ◽  
South African ◽  
Risk Estimation ◽  
Probability Distributions ◽  
Financial Data ◽  
Test Results ◽  
Sample Period ◽  
The South ◽  
Risk Return

This study is aimed at investigating the volatility dynamics and the risk-return relationship in the South African market, analyzing the FTSE/JSE All Share Index returns for an updated sample period of 2009–2019. The study employed several GARCH type models with different probability distributions governing the model’s innovations. Results have revealed strong persistent levels of volatility and a positive risk-return relationship in the South African market. Given the elaborate use of the GARCH approach of risk estimation in the existing finance literature, this study highlighted several weaknesses of the model. A noteworthy property of the GARCH approach was that the innovation distributions did not affect parameter estimation. Analyzing the GARCH type models, this theory was supported by the majority of the GARCH test results with respect to the volatility dynamics. On the contrary, it was strongly unsupported by the risk-return relationship. More specifically, it was found that while the innovations of the EGARCH (1, 1) model could account for the volatile nature of financial data, asymmetry remained uncaptured. As a result, misestimating of risks occurred, which could lead to inaccurate results. This study highlighted the significance of the innovation distribution of choice and recommended the exploration of different nonnormal innovation distributions to aid with capturing the asymmetry.

scholarly journals Does national sporting performance affect stock market returns in South Africa?

2013 ◽  
Vol 6 (1) ◽  
pp. 67-82 ◽  
Author(s):  
Ferdi Botha ◽  
Carl De Beer
Keyword(s):  
South Africa ◽  
Stock Returns ◽  
South African ◽  
Descriptive Analysis ◽  
Share Price ◽  
Future Research ◽  
Market Returns ◽  
Stock Market Returns ◽  
Study Methodology ◽  
Sporting Performance

This study explores whether South African national sporting performance can influence investors in such a way that it has the ability to impact on market returns. Using standard event study methodology, this study determines the constant mean return using the daily All-Share price index on the JSE for the period of 1 January 1990 to 31 December 2010. This study focuses on three of South Africa’s most popular sports, namely soccer, cricket and rugby, and examines if these three sports have the ability to influence market returns. Although there is some evidence of a relationship between stock returns and sporting performance in the descriptive analysis, the regression results indicate that sporting performance in South Africa does not significantly explain abnormal market returns on the JSE. The study provides a number of possible reasons for this finding and concludes by suggesting areas for future research.

Test of CAPM: A Study of India and US

2016 ◽  
Vol 6 (2) ◽  
Author(s):  
Pankaj Chaudhary
Keyword(s):  
Asset Pricing ◽  
Capital Asset Pricing ◽  
Regression Equations ◽  
Long Distance ◽  
Cross Sectional ◽  
Risk Return ◽  
Research Areas ◽  
Asset Pricing Models ◽  
Using Data ◽  
Capm Model

Asset pricing is one of the most important research areas in the field of finance. The simple CAPM model (capital asset pricing model) relates the return of the stocks and portfolios to the market factor captured by beta. Since the formulation of CAPM in 1960s, asset pricing has covered a long distance. We conduct the test of CAPM for India and US by using data from January 2001 to December 2015. We run 84 second pass cross-sectional regression equations to test the applicability of CAPM. The results of our test find that CAPM is not able to capture the cross section of average returns both in India and US and we should consider the alternative asset pricing models to establish the risk-return relationship.

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