Robust analysis for downside risk in portfolio management for a volatile stock market

Ayub, Usman; Shah, Syed Zulfiqar Ali; Abbas, Qaisar

doi:10.1016/j.econmod.2014.10.001

Cited by 30 publications

(22 citation statements)

References 63 publications

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“…This is due to the fact that investors dislike downside risk and do not give equal weight to both upside and downside. The CAPM, on the other hand, assumes that the investor is indifferent to upside and downside risk [25]. The proxy of downside risk in the asset pricing model was first developed as a semi-variance.…”

Section: The Three Perspectives Of Capmmentioning

confidence: 99%

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Downside Risk-Based Six-Factor Capital Asset Pricing Model (CAPM): A New Paradigm in Asset Pricing

et al. 2020

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The importance of downside risk cannot be denied. In this study, we have replaced beta in the five-factor model of using downside beta and have added a momentum factor to suggest a new six-factor downside beta capital asset pricing model (CAPM). Two models are tested—a beta- and momentum-based six-factor model and a downside-beta- (proxy of downside risk) and momentum-based six-factor model. Beta and downside beta are highly correlated; therefore, portfolios are double-sorted to disentangle the correlation. Factor loadings, i.e., size, value, momentum, profitability, and investment, are constructed. The standard methodologies are applied. Data for sample stocks from different non-financial sectors listed in the Pakistan Stock Exchange (PSX) are taken from January 2000 to December 2018. The PSX-100 index and three-month T-bills are taken as proxies for market and risk-free returns. The study uses three subsamples for robustness—period of very high volatility, period of stability, and period of stability and growth with volatility. The results show that the value factor is redundant in both models. The momentum factor is rejected in the beta-based six-factor model only. The beta-based six-factor model shows very low R2 in periods of highly volatility. The R2 is high for the other periods. In contrast, the downside beta six-factor model captures the downside trend of the market in an effective manner with a relatively high R2. The risk–return relationship is stronger for the downside beta model. These reasons lead us to believe that, overall, the downside beta six-factor model is a better option for investors as compared to the beta-based six-factor model in the area of asset pricing models.

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Section: The Three Perspectives Of Capmmentioning

confidence: 99%

“…In the area of asset pricing, the major studies comparing the CAPM and DCAPM models go back to the 2000s. The most notable among them are those of [25,31,[33][34][35][36]. All of these studies prefer the DCAPM to the CAPM in a single-factor framework.…”

Section: The Evidence For Capm and Dcapmmentioning

confidence: 99%

Downside Risk-Based Six-Factor Capital Asset Pricing Model (CAPM): A New Paradigm in Asset Pricing

et al. 2020

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“…Hence, the above phenomena have initiated the integration of higher-moments and downside beta in the traditional capital asset pricing model. In Pakistan, various researchers such as Javid and Ahmad (2008), Javid and Ahmad (2011), Tahir, Abbas, Sargana, Ayub and Saeed (2013), Ayub, Shah and Abbas (2015) and Rashid and Hamid (2015) have tested higher co-moment and downside beta CAPM but their analysis is based on dataset of companies from selected sectors which result in a common problem of survivorship bias in portfolio management (see: Nagel, 2001). However, this paper provides more intensive results as it includes all companies of PSX.…”

Section: Introductionmentioning

confidence: 99%

Higher Co-Moments and Downside Beta in Asset Pricing

Chhapra¹,

Kashif²

2019

AAMJAF

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The Capital Asset Pricing Model (CAPM) assumes a linear relationship between an asset's return and financial market. However, empirical invalidity of linearity of returns has given birth to other CAPM models. Therefore, this study aims to examine the implication of preference by a risk-averse investor for higher moments and downside risk as investors are assumed to be prudent, temperate and cautious and prefer firms with negative coskewness, positive co-kurtosis, and downside risk as they yield higher risk premium. To empirically test these theoretical assumptions data of all 901 firms (listed and delisted) in Pakistan Stock Exchange (PSX) from 2000 to 2016 have been used. Decile portfolios are constructed for cross-sectional and time series analysis. Generalized Method of Moments (GMM) and Wald Test are applied to check the robustness of results. The results indicate that co-skewness, co-kurtosis and downside beta are important risk factors but only downside beta is genuinely priced over and above what co-variance risk can explain and CAPM does not significantly capture market risk premium indicating the existence of other risk measures in PSX. The findings can help investors in formulating investment strategies for constructing well-diversified and efficient portfolios and can enable firm managers to take appropriate capital budgeting decisions by appropriately costing equities.

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“…We take the data for all the closing prices of stocks listed on the Pakistan Stock Exchange (PSX). Likewise, a proxy for market portfolio used in CAPM based models is taken as the closing PSX index as the standard procedure following Ayub, Shah, and Abbas (2015). Stock prices and PSX index is converted into returns ignoring the dividends, using log difference returns.…”

Section: Methodsmentioning

confidence: 99%

Do the Fama and French Five-Factor Model Forecast Well Using Ann?

Jan

Ayub

2019

Journal of Business Economics and Management

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Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily, Artificial Neural Networks (ANN) is a flexible machine learning tool which caters both the linear and nonlinear markets. This paper investigates the forecasting ability of ANN by using Fama and French five-factor model. We construct ANN’s based on the composite factors of the FF5F model to predict portfolio returns in two stages; in stage one, the study identifies the best-fit combination of training, testing, and validation along with the number of neurons full sample period. In stage two, the study uses this best combination to forecast the model under 48-months rolling window analysis. In-sample and out-sample comparisons, regression, and goodness of fit test and actual and predicted values of the stock returns of our ANN model reveal that the proposed model accurately predicts the one-month ahead returns. Our findings reinforce the investment concept that the markets compensate the high-risk portfolios more than mid and low beta portfolios and the methodology will significantly improve the return on investment of the investors.

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Robust analysis for downside risk in portfolio management for a volatile stock market

Cited by 30 publications

References 63 publications

Downside Risk-Based Six-Factor Capital Asset Pricing Model (CAPM): A New Paradigm in Asset Pricing

Downside Risk-Based Six-Factor Capital Asset Pricing Model (CAPM): A New Paradigm in Asset Pricing

Higher Co-Moments and Downside Beta in Asset Pricing

Do the Fama and French Five-Factor Model Forecast Well Using Ann?

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