Market-wide Risk Factors Research Articles

On the impact of sentiment on stock returns: the case of Dhaka Stock Exchange

Since frontier markets are dominated by less-informed individual investors, the stock price movements of these markets could be influenced by the sentiment of general investors. This paper investigates the effect of sentiment on the returns of the Dhaka Stock Exchange (DSE), the main stock exchange in Bangladesh. The study uses indirect measures of stock market sentiment. Results show that sentiment impacts contemporaneous returns, followed by some corrections in the next month. Contrary to general belief, large firms are more vulnerable to market sentiment. There is a unidirectional (Granger) causality from market turnover to portfolio returns, and a strong bi-directional causal relationship between moving average changes and stock returns. When conditional volatility is considered, a significant impact of sentiment is observed, mainly on small-size portfolios. In the presence of other market-wide risk factors, sentiment factors reasonably explain individual stock returns. Overall, the study concludes that sentiment should be considered as a source of systematic risk for the firms listed in the DSE.

On the impact of sentiment on stock returns: the case of Dhaka Stock Exchange

Since frontier markets are dominated by less-informed individual investors, the stock price movements of these markets could be influenced by the sentiment of general investors. This paper investigates the effect of sentiment on the returns of the Dhaka Stock Exchange (DSE), the main stock exchange in Bangladesh. The study uses indirect measures of stock market sentiment. Results show that sentiment impacts contemporaneous returns, followed by some corrections in the next month. Contrary to general belief, large firms are more vulnerable to market sentiment. There is a unidirectional (Granger) causality from market turnover to portfolio returns, and a strong bi-directional causal relationship between moving average changes and stock returns. When conditional volatility is considered, a significant impact of sentiment is observed, mainly on small-size portfolios. In the presence of other market-wide risk factors, sentiment factors reasonably explain individual stock returns. Overall, the study concludes that sentiment should be considered as a source of systematic risk for the firms listed in the DSE.

Bayesian Analysis of Coefficient Instability in Dynamic Regressions

This paper deals with instability in regression coefficients. We propose a Bayesian regression model with time-varying coefficients (TVC) that allows to jointly estimate the degree of instability and the time-path of the coefficients. Thanks to the computational tractability of the model and to the fact that it is fully automatic, we are able to run Monte Carlo experiments and analyze its finite-sample properties. We find that the estimation precision and the forecasting accuracy of the TVC model compare favorably to those of other methods commonly employed to deal with parameter instability. A distinguishing feature of the TVC model is its robustness to mis-specification: Its performance is also satisfactory when regression coefficients are stable or when they experience discrete structural breaks. As a demonstrative application, we used our TVC model to estimate the exposures of S&P 500 stocks to market-wide risk factors: We found that a vast majority of stocks had time-varying exposures and the TVC model helped to better forecast these exposures.

Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly

Abstract This paper injects factor structure into the estimation of time-varying, large-dimensional covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals in the presence of time-varying conditional heteroskedasticity in large universes. Conversely, rotation-equivariant estimators of large-dimensional time-varying covariance matrices forsake directional information embedded in market-wide risk factors. We introduce a new covariance matrix estimator that blends factor structure with time-varying conditional heteroskedasticity of residuals in large dimensions up to 1000 stocks. It displays superior all-around performance on historical data against a variety of state-of-the-art competitors, including static factor models, exogenous factor models, sparsity-based models, and structure-free dynamic models. This new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of stock returns.

Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly

This paper injects factor structure into the estimation of time-varying, large-dimensional covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals in the presence of time-varying conditional heteroskedasticity in large universes. Conversely, rotation-equivariant estimators of large-dimensional time-varying covariance matrices forsake directional information embedded in market-wide risk factors. We introduce a new covariance matrix estimator that blends factor structure with time-varying conditional heteroskedasticity of residuals in large dimensions up to 1000 stocks. It displays superior all-around performance on historical data against a variety of state-of-the-art competitors, including static factor models, exogenous factor models, sparsity-based models, and structure-free dynamic models. This new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of stock returns.

Common and fundamental risk factors in shareholder returns of Norwegian salmon producing companies

Salmon farming companies are increasingly gaining attention from investors and portfolio managers. The last decade has seen a substantial growth in the securitization of salmon farming assets and prices. A growing literature demonstrates that industry-specific fundamental, as well as market-wide risk factors help explain stock returns. However, very little is known about the pricing of salmon stocks and especially the contribution of industry-specific fundamental risk factors. Using a multifactor model, we find that stock returns for salmon farming firms are significantly associated with both common market-wide risks and industry-specific risk factors.

Commodity Market Heterogeneity and Cross-Market Integration

We evaluate the recent levels of heterogeneity and cross-market integration for fluctuations in commodity futures returns for a post-financial-crisis data sample. We find that a single commodity-market risk factor explains 30.6% of the total variation in commodity futures returns. The commodity-market risk factor is significantly correlated with the dominant market-wide risk factors from other asset classes: +66.7% with a market risk factor for the US equity market; -74.2% with a US dollar risk factor for the FX market; and -27.8% with an interest-rate level risk factor for the US interest rate market. Thus, a part of the systematic variation in the commodity market is integrated with other asset classes.

Common and Fundamental Risk Factors in Shareholder Returns of Norwegian Salmon Producing Companies

Salmon farming companies are increasingly gaining attention from investors and portfolio managers. The last decade has seen a substantial growth in the securitization of salmon farming assets and prices. A growing literature demonstrates that industry-specific fundamental, as well as market-wide risk factors help explain stock returns. However, very little is known about the pricing of salmon stocks and especially the contribution of industry-specific fundamental risk factors. Using a multifactor model, we find that stock returns for salmon farming firms are significantly associated with both common market-wide risks and industry-specific risk factors.

Time-varying systematic and idiosyncratic risk exposures of US bank holding companies

We study the time-varying risk exposures of US bank holding companies for the 1986–2012 period by decomposing total bank risk into systematic banking-industry risk, systematic market-wide risk, and idiosyncratic bank risk. Banking-industry risk factors directly relate to the banks’ financial intermediation functions while market-wide risk factors affect all stocks. In contrast, idiosyncratic risk is driven by individual bank risk characteristics. We analyze the systematic bank risk factors in time series regressions, determine their importance with a democratic orthogonalization technique, and explore the idiosyncratic risk in a panel regression framework. Our results suggest that corporate credit risk and real estate risk are most detrimental in crisis periods, while banks’ interest rate risk sensitivity has changed over the last decade. The banks’ equity ratio, loan-loss provisions, fraction of real estate loans, and proportion of non-interest income relate to the differences in individual bank risk. Moreover, banks’ idiosyncratic risk contains a strong state-level business cycle component. Our results are robust to alternative risk factor specifications. Overall, our study contributes to understanding the structure and time-variation of banks’ systematic and idiosyncratic risks.

Time-Varying Systematic and Idiosyncratic Risk Exposures of US Bank Holding Companies

We study the time-varying risk exposures of US bank holding companies for the 1986 to 2012 period by decomposing total bank risk into systematic banking-industry risk, systematic market-wide risk, and idiosyncratic bank risk. Banking-industry risk factors directly relate to the banks’ financial intermediation functions while market-wide risk factors affect all stocks. In contrast, idiosyncratic risk reflects individual bank risk characteristics. We analyze the systematic bank risk factors in time series regressions, determine their importance with a democratic orthogonalization technique, and explore the idiosyncratic risk in a panel regression framework. Our results suggest that corporate credit risk and real estate risk are most detrimental in crisis periods, while banks’ interest-rate-risk sensitivity changes over the last decade. The banks’ leverage, loan-loss provisions, fraction of real estate loans, and proportion of non-interest income relate to the differences in individual bank risk. Moreover, banks’ idiosyncratic risk contains a strong state-level business cycle component. Our results are robust to alternative risk factor specifications. Overall, our study contributes to understanding the structure and time-variation of banks’ systematic and idiosyncratic risks.

Effect of Sentiment on the Bangladesh Stock Market Returns

This paper looks at Bangladesh stock market completely from behavioral perspective by introducing behavioral factors in the empirical asset pricing models. Lack of data of sentiment proxies constrains authors to use only few proxies, namely, TRIN (Trading Index), trade volume (turnover), number of IPOs per month and change in four-month moving average. We have found that TRIN and moving average significantly affect the residual market returns. This paper also investigates the impact of sentiment on returns of different size portfolios. Results show that the impact of TRIN and trade volume is strong for large and medium size portfolios. However, the effect of TRIN is either low or insignificant for small size portfolio, indicating less interest of investors for neglected stocks. Granger causality tests show that there is unidirectional causality from TRIN to stock returns and strong bi-directional causal relationship between moving average change and stock returns. When conditional volatility is taken into account, effect of sentiment on returns from market and size portfolios decreases and we mostly observe significant effect of sentiment on small size portfolios. Finally, in the presence of other market-wide risk factors, sentiment factors weakly explain individual stock returns. Overall, we conclude that sentiment is a non-negligible consideration for portfolio investors in Bangladesh.

Dynamic correlation structure and security risk

We investigate the relationship between changing correlation structure of returns, security risk, and mean return. According to our results, securities that were highly correlated with the market-wide risk factors in the past are likely to have high systematic and idiosyncratic risk at present. Correlations with the risk factors, however, are not directly related to the mean return of securities, nor can they consistently explain the puzzling relationship between idiosyncratic risk and return. We demonstrate further that the effect of past correlations on security risk is more likely among less transparent securities.

Investor Sentiment and Stock Return: Do Industries Matter?

The basic objective of this article is to evaluate the pricing implications of market-wide investor sentiment risk for cross-sectional return variations of Indian listed companies across industry groups. A multivariate time-series regression approach has been used to examine the impact of sentiment risk on stock return behaviour in the presence of other market-wide systematic risk factors. Our results suggest that the role of sentiment risk in the determination of a cross-section of stock returns is not uniform across the test asset portfolios formed on the basis of size, book-to-market equity, liquidity and momentum characteristics. For all portfolios, the impact of sentiment risk on the cross-section of stock returns behaviour has been disproportionately negative. The effect holds even after controlling for systematic market-wide risk factors. Although the impact of sentiment risk on industry-shorted portfolio returns persists in accordance with the theoretical argument, the cross-sectional variation with respect to different industries has been heavily dependent on the availability of stocks in that particular industry. The commonality of the sentiment effect across industry is not similar, as it is for the aggregate market. The results suggest that generalisation of the hard-to-value and difficult-to-arbitrage argument must be judged with caution, keeping the industry effects in mind. JEL Classification: GI,G12,G14

Does Sentiment Risk Persist as Priced Risk Factor? A Multifactor Approach for Stock Return

Much of the academic debate has gone in to exploring the better asset pricing model that can explain the quantification of the trade-off between risk and expected return for the cross section of stock return. Nevertheless this intimidating task becomes more pragmatic given the market structure of emerging markets, liquidity effects, market inefficiency, and more significantly the presence of inherent imperfect rationality or sentiment. The basic objective of this paper is to evaluate the pricing implication of unconditional five factor model to explain the cross sectional stock return behaviour in the context of Indian stock market. Our analysis also aims to examine the pricing nature of aggregate market wide sentiment risk in the presence of other risk factors. We employ Fama and French time series regression approach to examine the impact of market risk premium, size, book-to-market equity, momentum and liquidity as risk factors on stock return. Our empirical results show that given the multidimensional nature of risk the choice of a five factor model is apparently persuasive for consideration in investment decisions. However, inconsistent with prior literature book-to-market equity fails to explain the average return in case of large stocks, and pricing evidence of momentum profits fades in case of winner stocks and momentum strategy retains its value only for the sell side transactions i.e., loser portfolios. With the presence of liquidity factor in the five factor model specification, the results suggest that liquidity is priced and explains a cross sectional variation in stock returns. Our results also suggest that it is naive to argue for the universal pricing implication of sentiment risk in a multifactor model framework, and market wide risk factors resume their importance as rational source of priced risk.

Bayesian Analysis of Coefficient Instability in Dynamic Regressions

This paper proposes a Bayesian regression model with time-varying coefficients (TVC) that makes it possible to estimate jointly the degree of instability and the time-path of regression coefficients. Thanks to its computational tractability, the model proves suitable to perform the first (to our knowledge) Monte Carlo study of the finite-sample properties of a TVC model. Under several specifications of the data generating process, the proposed model’s estimation precision and forecasting accuracy compare favourably with those of other methods commonly used to deal with parameter instability. Furthermore, the TVC model leads to small losses of efficiency under the null of stability and it is robust to mis-specification, providing a satisfactory performance also when regression coefficients experience discrete structural breaks. As a demonstrative application, we use our TVC model to estimate the exposures of S&P 500 stocks to market-wide risk factors: we find that a vast majority of stocks have time-varying risk exposures and that the TVC model helps to forecast these exposures more accurately.