Positive Risk-return Relationship Research Articles

The Left-tail Risk and the Cross-section of Stock Returns: Evidence from China

As a matter of fact, left-tail risk is a common risk in stock market. This study examines the linear correlation considering Value-at-Risk (VaR) and future stock returns within context of the Chinese main board stocks, particularly during periods marked by significant left-tail risk. Processing data from March 2014 to March 2024, this study concentrate to daily and monthly cross-section returns, along with other financial statistics. A suitable Fama-Macbeth regression using in this paper to exam the significance of left-tail risk. The key focus is to investigate the effect of VaR concerning future stock returns under downside market conditions, as indicated by the recent 23% drop in China’s benchmark CSI 300 index. The regression analysis indicates a significant negative correlation between VaR and future stock returns, suggesting that higher potential losses as indicated by VaR correspond with lower future returns. This outcome is contrary to traditional asset pricing theories which posit a positive risk-return relationship. The findings will attract attention of investors to consider extreme risk management of public stocks and contributes to the ongoing discussion on risk assessment in financial markets by highlighting the importance of integrating robust risk management tools like VaR in investment strategies, especially in volatile markets.

State-dependent intertemporal risk-return tradeoff: Further evidence

We suggest that the intertemporal risk-return tradeoff is not necessarily positive but rather state dependent. We further explore the state dependent risk-return relation by examining how the positive risk-return relation is distorted in response to various market conditions, including extreme price changes, differing levels of investor sentiment, the introduction of stock options, and throughout business cycles. The tendency for uninformed investors to be optimistic (pessimistic) in response to good (bad) market news cause overpricing (underpricing), and the resulting trade activity of arbitrageurs that distorts the positive risk-return tradeoff, is documented consistently across these environments. We find that the attenuation (reinforcement) of the positive risk-return relation under investors’ optimistic (pessimistic) expectations is stronger in high (low) sentiment periods, in the presence of extreme returns, in the period after stock options became available, and during expansionary periods. We argue that the asymmetric intertemporal risk-return relation is a consequence of rational arbitrageurs’ trading to exploit mispricing through the selling of overpriced stocks conditional on good news and buying underpriced stocks conditional on bad news.

State-dependent volatility feedback effect in the ICAPM

Considering that risk-averse investors revise their expectations in response to changes in both expected and unexpected volatility, we hypothesize and demonstrate that an unexpectedly high (low) volatility shock causes an increase (decrease) in risk premium. Using a novel approach to endogeneity issues, we utilize a state dependent, ICAPM to measure volatility effects on risk-return relationships. Our empirical results show that the volatility feedback effect strengthens (attenuates) the positive risk-return relation under bad (good) news. Furthermore, the volatility feedback effect under the combined conditions of bad news and a high unexpected volatility causes an extremely heightened level of the risk-return tradeoff.

Volatility feedback effect and risk-return tradeoff

Using the two alternative measures of the ex-ante unexpected volatility shock, we show that the volatility feedback effect plays an important role in the intertemporal risk-return tradeoff. The empirical results indicate that the volatility feedback effect reinforces the positive risk-return relation conditional on bad market news but attenuates the relation under good market news. The results provide strong evidence that an extremely heightened risk-return tradeoff caused by a high level of volatility feedback effect might lead to a market crash, even with no macroeconomic uncertainties in the markets. Also, the results show that the asymmetric volatility feedback effect is attributable to the negative correlation between the concurrent volatility and a price change. The state-dependent volatility feedback effect is observed for different market conditions such as high and low market sentiments and business cycles.

Investor sentiment and the risk–return relation: A two‐in‐one approach

AbstractTraditional finance theory posits a positive risk–return relation, but empirical evidence is inconclusive. Retail investor sentiment has long been viewed as a distorting factor, while more recently institutional investor sentiment is thought to play a role. We examine the separate and joint impacts of retail and institutional investor sentiments on the risk‐return relation. We find, at both market and firm levels, the risk‐return relation is more likely to be distorted by the two investor‐type sentiments jointly, rather than separately. We further find a cross‐sectional pattern, with the risk‐return relation being more sensitive to investor sentiment for stocks with specific characteristics.

Risk-return trade-off in international stock returns: Skewness and business cycles

The fundamental risk-return relation is examined with a flexible regime switching model combining the impact of skewness and business cycle regimes in stock returns. Key methodological and empirical findings point out the need for a highly nonlinear and non-Gaussian model to get a reliable picture on the risk-return relationship. With an international dataset of major countries to global financial markets, the empirical results show that accounting especially for skewness patterns leads to the expected positive risk-return relation, which is importantly also maintained over different business cycle conditions.

The risk-return relationship and volatility feedback in South Africa: a comparative analysis of the parametric and nonparametric Bayesian approach

<abstract> <p>This study aimed to investigate the risk-return relationship, provided volatility feedback was taken into account, in the South African market. Volatility feedback, a stronger measure of volatility, was treated as an important source of asymmetry in the investigation of the risk-return relationship. This study analyzed the JSE ALSI excess returns and realized variance for the sample period from 15 October 2009 to 15 October 2019. This study modelled the novel and robust Bayesian approach in a parametric and nonparametric framework. A parametric model has modelling assumptions, such as normality, and a finite sample space. A nonparametric approach relaxes modelling assumptions and allows for an infinite sample space; thus, taking into account every possible asymmetric risk-return relationship. Given that South Africa is an emerging market, which is subject to higher levels of volatility, the presence of volatility feedback was expected to be more pronounced. However, contrary to expectations, the test results from both the parametric and nonparametric Bayesian model showed that volatility feedback had an insignificant effect in the South African market. The risk-return relationship was then investigated free from empirical distortions that resulted from volatility feedback. The parametric Bayesian model found a positive risk-return relationship, in line with traditional theoretical expectations. However, the nonparametric Bayesian model found no relationship between risk and return, in line with early South African studies. Since the nonparametric Bayesian approach is more robust than the parametric Bayesian approach, this study concluded that there is no risk-return relationship. Therefore, investors can include South Africa in their investment portfolio with higher risk countries in order to spread their risk and derive diversification benefits. In addition, risk averse investors can find a safe environment within the South African market and earn a return in accordance to their risk tolerance.</p> </abstract>

GARCH-M MODEL AND THE BEHAVIOR OF RISK-RETURN RELATIONSHIP IN INDONESIA STOCK MARKET

This study examines the risk-return trade-off and volatility behaviour in Indonesia stock market. As the analytical tool this study uses GARCH-M model with symmetric GARCH(1,1). To obtain more reliable results, this study takes daily and weekly stock index as well as 5 individual stock returns from January 2004 to November 2020 as a sample. This study also investigates the results with two alternative mean equations, simple regression and AR(1) model. The first finding of this study is that in Indonesia stock market both in stock index and in individual stocks, the volatilities of return are time varying. From investigating the risk-return relationship the results are mixed. This study finds that positive risk-return relationships in stock market index are observed both in daily and weekly data. A positive risk-return relationhip in stock market index is also found either in AR(1) model of mean equation or in simple regression model. The same results are observed in two stocks investigated. There is one stock where a positive risk return relationship is observed only in daily return data not in weekly return data. A negative risk-return relationships is observed in one stock and there is no evidence of risk-return trade-off in one stock. The conclusion is that a positive risk-return relationship as a postulated by investment theory only exists in stock index and does not exist in all stocks
 
 Key words : Indonesia stock market, Risk-return trade-off, GARCH-M, GARCH(1,1), Time-varying volatility.

WITHDRAWN: Risk-Return Trade-off in International Stock Returns: Skewness and Business Cycles

The fundamental risk-return relation is examined with a flexible regime switching model combining the impact of skewness and business cycle regimes in stock returns. Key methodological and empirical findings point out the need for a highly nonlinear and non-Gaussian model to get a reliable picture on the risk-return relationship. With an international dataset of major countries to global financial markets, the empirical results show that accounting especially for skewness patterns leads to the expected positive risk-return relation, which is importantly also maintained over different business cycle conditions.

Revisiting the ICAPM under the distortion of risk–return tradeoff in short‐horizon stock returns

We suggest that the distortion of the positive risk–return relation in the ICAPM is a consequence of trading by informed investors to exploit mispricing. We hypothesize and demonstrate that a non-positive (strongly positive) risk–return relation following positive (negative) market returns is attributed to short-selling (purchasing) of overpriced (underpriced) stocks along with optimistic (pessimistic) expectations conditional on good (bad) market news. We verify this asymmetry in the risk–return relation through the indirect risk–return relation conditional on good (bad) market news. We also find that the attenuation (reinforcement) of the positive risk–return relation is more profound in high- (low-) sentiment periods.

RISK-RETURN RELATIONSHIP IN THE NIGERIAN STOCK MARKET DURING PANDEMIC COVID-19: SECTORAL PANEL GARCH APPROACH

This study examines how the Nigerian Stock Exchange (NSE) is responding to the COVID-19 pandemic in the form of risk-return relationship and volatility. Panel data analyses of GARCH-in-mean and Threshold GARCH were estimated on three error distributional assumptions. All Share Index (ASI) from January 2020 to December 2020 for ten stock market indices on the NSE. Findings indicate that the cross-section return of the ten stock market indices returns exhibit a positive risk-return relationship during COVID-19 and the impact of bad news was found to have no significant impact on return volatility on the NSE. This indicates that the policy response during the pandemic is adequate to cushion the negative impact of COVID-19, which should be sustained.

Dividend policy, risk and the cross-section of stock returns: Evidence from India

We invoke theories of dividend policy and asset pricing to investigate the impact of dividend changes on stock price risk, stock returns, and the cross-sectional risk-return tradeoff. We use a large sample of Indian firms during the period from 1999 to 2018. We find that favorable dividend changes trigger higher stock returns because they convey new information about firm's future profitability, as posited by the dividend signaling theory. Our analysis also shows that the higher (lower) stock returns following dividend increases (decreases) are more pronounced among stocks with greater information asymmetry. The results reveal a new empirical evidence of the heterogeneity of the risk–return trade-off across firms with different dividend changes. We show that the positive risk–return relation is much more pronounced among dividend-increasing firms, but the risk–return relation is negative among dividend-decreasing firms.

Investor attention and the risk-return trade-off

Previous empirical studies find a negative and significant relation between risk measures and expected future stock returns. Using four risk measures, we document that the negative risk-return relation is more pronounced among firms that receive high levels of attention from investors, while a standard positive risk-return relation holds among stocks to which investors pay little attention. Regardless of our proxy for risk, we find that the magnitude and statistical significance of the risk-related puzzle monotonically decreases as we move from high to low levels of investor attention. These findings suggest that investor attention may play a central role in risk-related anomalies.

Conditional Relation between Beta and Returns: Evidence from a Frontier Market

Capital Asset Pricing Model (CAPM) is one of the most significant finance literature models, which assumes a positive linear relationship between the required rate of return and systematic risk on stocks. The model is frequently used in the business world, but empirical tests repeatedly reject the model's validity in its unconditional form. Pettengill et al. have developed an alternative conditional CAPM approach where the unconditional test procedure developed by Fama & MacBeth, (1973) is improved by taking up and down market conditions. This paper investigates both the conditional and unconditional versions of CAPM in both individual and portfolio stock returns between January 2008 and December 2019 on the stocks listed in the Colombo Stock Exchange (CSE). Population of this research includes all the companies listed on CSE and the top 50 stocks with large market capitalization has been selected as the sample. The results of unconditional test procedure show that there is no statistically significant risk-return relationship is found in any test period in both individual and portfolio stock returns. Thus, this result is similar with the previous literature findings. The results of the conditional tests show that there is no significant positive (negative) risk-return relationship in portfolio stock returns and individual stock returns in CSE during up (down) market months. But findings indicate a significant positive risk-return relationship in individual stock returns in upmarket periods; whereas, a significant inverse risk-return relationship is not provided in down market periods.
 Keywords: Colombo Stock Exchange, CAPM, Conditional Relation, Unconditional Relation

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

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.

The Risk-Return Relationship in Crude Oil Markets during COVID-19 Pandemic: Evidence from Time-Varying Coefficient GARCH-in-Mean Model

In this paper, we propose the new time-varying coefficient GARCH-in-Mean model. The benefit of our model is to allow the risk-return parameter in the mean equation to vary over time. At the end of 2019 to the beginning of 2020, the world witnessed two shocking events: COVID-19 pandemic and 2020 oil price war. So, we decide to use the daily data from December 2, 2019 to May 29, 2020, which cover these two major events. The purpose of this study is to find the dynamic movement between risk and return in four major oil markets: Brent, West Texas Intermediate, Dubai, and Singapore Exchange, during COVID-19 pandemic and 2020 oil price war. For the European oil market, our model found a significant and positive risk-return relationship in Brent during March 26-April 21, 2020. For the North America oil market, our model found a significant positive risk return relationship in West Texas Intermediate (WTI) during March 12-May 8, 2020. For the Middle East oil market, we found a significant and positive risk-return relationship in Dubai during March 12-April 14, 2020. Lastly, for the South East Asia oil market, we found a significant positive risk return relationship in Singapore Exchange (SGX) from March 9-May 29, 2020.

The Halloween indicator, “Sell in May and Go Away”: Everywhere and all the time

To answer the sceptics, we use all historical data (62962 observations) on all stock market indices worldwide to verify the robustness of the so-called Halloween Indicator or Sell in May effect. The effect seems remarkably robust with returns on average 4% higher during November-April period than during May-October. A new test for the effect offers some additional insights. Worldwide excess returns during summer seem negative (around −1%) and often significantly so suggesting a flat or negative risk return relation. Only for Mauritius do we find a significantly positive risk return relation during May-October. Our dataset also allows for a new (upper bound) estimate for the equity premium of around 4%.

Low-risk effect: evidence, explanations and approaches to enhancing the performance of low-risk investment strategies

The authors offer evidence for low-risk effect from the Indian stock market using the top-500 liquid stocks listed on the National Stock Exchange (NSE) of India for the period from January 2004 to December 2018. Finance theory predicts a positive risk-return relationship. However, empirical studies show that low-risk stocks outperform high-risk stocks on a risk-adjusted basis, and it is called low-risk anomaly or low-risk effect. Persistence of such an anomaly is one of the biggest mysteries in modern finance. The authors find strong evidence in favor of a low-risk effect with a flat (negative) risk-return relationship based on the simple average (compounded) returns. It is documented that low-risk effect is independent of size, value, and momentum effects, and it is robust after controlling for variables like liquidity and ticket-size of stocks. It is further documented that low-risk effect is a combination of stock and sector level effects, and it cannot be captured fully by concentrated sector exposure. By integrating the momentum effect with the low-volatility effect, the performance of a low-risk investment strategy can be improved both in absolute and risk-adjusted terms. The paper contributed to the body of knowledge by offering evidence for: a) robustness of low-risk effect for liquidity and ticket-size of stocks and sector exposure, b) how one can benefit from combining momentum and low-volatility effects to create a long-only investment strategy that offers higher risk-adjusted and absolute returns than plain vanilla, long-only, low-risk investment strategy.

Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff

This paper studies nonparametric estimation of the infinite order regression E(Ytk|Ft−1), k∈Z with stationary and weakly dependent data. We propose a Nadaraya–Watson type estimator that operates with an infinite number of conditioning variables. We propose a bandwidth sequence that shrinks the effects of long lags, so the influence of all conditioning information is modelled in a natural and flexible way, and the issues of omitted information bias and specification error are effectively handled. We establish the asymptotic properties of the estimator under a wide range of static and dynamic regressions frameworks, thereby allowing various kinds of conditioning variables to be used. We establish pointwise/uniform consistency and CLTs. We show that the convergence rates are at best logarithmic, and depend on the smoothness of the regression, the distribution of the marginal regressors and their dependence structure in a non-trivial way via the Lambert W function. We apply our methodology to examine the intertemporal risk-return relation for the aggregate stock market, and some new empirical evidence is reported. For the S&P 500 daily data from 1950 to 2017 using our estimator we report an overall positive risk-return relation. We also find evidence of strong time variation and counter-cyclical behaviour in risk aversion. These conclusions are possibly attributable to the allowance of further flexibility and the inclusion of otherwise neglected information in our method.

Are South African equity investors rewarded for taking on more risk?

Orientation: It is rational for investors to expect additional compensation for an increased risk exposure. This positive risk–return relationship is in line with traditional financial theory; however, this relationship does not always hold in empirical research.Research purpose: The aim of this article was to investigate the prevalence of the low-risk anomaly in the South African equity market.Motivation for the study: If there is evidence of a low-risk anomaly, where low-risk shares outperform high-risk shares, then the additional return expectation of investors may be misplaced.Research design/approach and method: A unique sampling procedure and an extended time frame were employed in a quintile portfolio analysis methodology.Main findings: The article presents evidence that South African listed shares with low historical volatility earned higher risk-adjusted returns over the period July 2004 to September 2018. Low-volatility shares delivered a Sharpe ratio of 1.10 compared to 0.65 produced by the Financial Times Stock Exchange / Johannesburg Stock Exchange Shareholder Weighted Index over the same period.Practical/managerial implications: The assumption that return in an investment portfolio could be enhanced by taking on more risk could be wrong. It seems that fund managers could potentially enhance returns and decrease risk in their portfolios by focussing on shares with low historical volatility.Contribution/value-add: The negative relationship observed between volatility and return is inconsistent with theoretical expectations. Therefore, the results of this article suggest that investors are not rewarded for assuming higher levels of risk.