Idiosyncratic risk and the cross-section of expected stock returns

Theories such as Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] predict a positive relation between idiosyncratic risk and expected return when investors do not diversify their portfolio. Ang, Hodrick, Xing, and Zhang [2006. The cross-section of volatility and expected returns. Journal of Finance 61, 259–299], however, find that monthly stock returns are negatively related to the one-month lagged idiosyncratic volatilities. I show that idiosyncratic volatilities are time-varying and thus, their findings should not be used to imply the relation between idiosyncratic risk and expected return. Using the exponential GARCH models to estimate expected idiosyncratic volatilities, I find a significantly positive relation between the estimated conditional idiosyncratic volatilities and expected returns. Further evidence suggests that Ang et al.'s findings are largely explained by the return reversal of a subset of small stocks with high idiosyncratic volatilities.     

Idiosyncratic risk and expected returns in frontier markets: Evidence from GCC

We investigate the pricing of idiosyncratic volatility of seven frontier markets in six GCC countries. We find a significant (marginal) negative relationship between expected returns and lagged idiosyncratic volatility for individual stocks in Saudi Arabia (Qatar) but none in Kuwait and Abu Dhabi. However, when we estimate conditional idiosyncratic volatility either by EGARCH or AR Models, the relationship turns positive. Introducing unexpected idiosyncratic volatility as an explanatory variable to control for any unexpected returns uncovers the true relationship between expected idiosyncratic volatility and expected returns. The evidence turns out to be robust for return reversals and other control variables. Moreover, the pricing of idiosyncratic risk is less evident in higher country governance and seems to be unrelated to the degree of financial development.     

Idiosyncratic risk,costly arbitrage,and the cross-section of stock returns

We test a new cross-sectional relation between expected stock return and idiosyncratic risk implied by the theory of costly arbitrage. If arbitrageurs find it more difficult to correct the mispricing of stocks with high idiosyncratic risk, there should be a positive (negative) relation between expected return and idiosyncratic risk for undervalued (overvalued) stocks. We combine several well-known anomalies to measure stock mispricing and proxy stock idiosyncratic risk using an exponential GARCH model for stock returns. We confirm that average stock returns monotonically increase (decrease) with idiosyncratic risk for undervalued (overvalued) stocks. Overall, our results support the importance of idiosyncratic risk as an arbitrage cost.     

Idiosyncratic volatility and equity returns: UK evidence

The proposition that idiosyncratic volatility may matter in asset pricing is currently a topic of research and controversy. Using data from the UK market we examine the predictive ability of various measures of idiosyncratic risk and provide evidence which suggests that: (a) it is the idiosyncratic volatility of small capitalization stocks that matters for asset pricing and (b) that small stocks idiosyncratic volatility predicts the small capitalization premium component of market returns and is unrelated to either the market or the value premium. The predictive power of the aggregate idiosyncratic volatility of small stocks remains intact even after we control for the possible proxying effects of business cycle fluctuations and liquidity and is robust across time and different econometric specifications.     

Idiosyncratic risk and the cross-section of stock returns: Merton (1987) meets Miller (1977)

Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] predicts that idiosyncratic risk should be priced when investors hold sub-optimally diversified portfolios, and cross-sectional stock returns should be positively related to their idiosyncratic risk. However, the literature generally finds a negative relationship between returns and idiosyncratic risk, which is more consistent with Miller's [1977. Risk, uncertainty, and divergence of opinion. Journal of Finance 32, 1151–1168] analysis of asset pricing under short-sale constraints. We examine the cross-sectional effects of idiosyncratic risk while explicitly recognizing the confounding effects that dispersion of beliefs and short-sale constraints produce in the Merton framework. We find strong support for Merton's [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] model among stocks that have low levels of investor recognition and for which short selling is limited. For these stocks, the relation between idiosyncratic risk and expected returns is positive, as predicted by Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510].     

The joint cross-sectional variation of equity returns and volatilities

This paper analyzes the determinants of the simultaneous cross-sectional variation of return and volatility risk premia. Independently of the model specification employed, the estimated risk premium associated with the default premium beta is always positive and statistically different from zero. Moreover, the risk premium of the market volatility risk premium beta is negative and statistically significant. However, both risk factors are priced economically and statistically differently in the volatility and return segments of the market. On average, common factors in both segments explain 90% of the variability of volatility risk premium portfolios, but only 65% of the variability of equity return portfolios.     

Idiosyncratic skewness and cross-section of stock returns: Evidence from Taiwan

In this study, we examine whether idiosyncratic skewness (IS) affects the returns of the Taiwan stock market. We find that speculative retail investors prefer positive skewness in stocks that leads them to overprice these stocks. As a result, the IS, that reflects gambling, has a negative relation with future returns. Gambling preferences vary with time, mainly occur during recessions and down markets. Moreover, the negative IS-return relation exists only among firms with prior capital gains. We use a difference-in-difference (DID) framework to mitigate the endogeneity concern and find that this IS effect is more significant among stocks with lower arbitrage limits. The IS effect remains significant even after controlling for the IS risk factor. Overall, the IS effect cannot be explained by either arbitrage limits or risk exposure.     

On the relationship between exchange rates and equity returns: A new approach

This paper employs a new approach in order to investigate the underlying relationship between stock markets and exchange rates. Current approaches suggest that the relative equity market performance of two countries is linked to their exchange rate. In contrast, this study proposes an alternative approach where one global variable – global equity market returns – is believed to have an effect on exchange rates, with the relative interest rate level of a currency determining the sign of the relationship. Our empirical findings suggest that exchange rates and global stock market returns are strongly linked. The value of currencies with higher interest rates is positively related with global equity returns, whereas the value of currencies with lower interest rates is negatively related with global equity returns.     

A re-examination of firm's attributes and share returns: Evidence from the Chinese A-shares market

It is widely accepted that some firms' attributes or characteristics, such as a firm's size or book-to-market ratio, attract premiums in terms of average returns, which is a pervasive phenomenon not restricted to just a few individual markets. However, the way to derive these premiums by sorting firms based on their characteristics that are known to be associated with share returns, is not without controversy. This study takes a totally different approach by adopting a novel Self-Organizing Maps approach to cluster share returns first and identify the attributable factors afterwards. It finds eminent presence of the value effect in the Chinese A-shares market. It also finds that two other firm attributes, ROA and cash-flow-to-price, also have explanatory power over share returns. Liquidity and the ratio of tradable to total shares, which are often used to proxy corporate governance, show little evidence of explaining share return dispersion. Surprisingly, a “reversed” size effect is reported in this study.     

The value premium,aggregate risk innovations,and average stock returns

We test whether innovations in aggregate risk, interpolated from a vector autoregressive system that contains the Chen et al. (1986) five factors as in Petkova (2006), are common factors in cross-sectional stock returns. We provide direct evidence that innovation in industrial production growth, a classical business-cycle variable that summarizes the state of the economy, is associated with the cross-sectional return predictability of individual stocks. We conclude that the role of innovation in aggregate risk is not random, and furthermore that it provides guidance concerning an important source of nonfinancial market-based risk in asset returns.     

Idiosyncratic momentum and the cross‐section of stock returns: Further evidence

In this article, we evaluate the profitability and economic source of the predictive power of the idiosyncratic momentum effect, by using five popular asset pricing models to construct the idiosyncratic momentum. We show that all five idiosyncratic momentum strategies produce similar return predictability and consistently outperform the conventional momentum strategy in the cross‐sectional pricing of equity portfolios and individual stocks. This positive effect of idiosyncratic momentum on returns is consistent with the investment capital asset pricing model (CAPM). Further analysis reveals that the firm‐level idiosyncratic momentum effect cannot extend to the aggregate stock market.     

The relationship between returns and unexpected earnings: A global analysis by accounting regimes

Numerous studies have documented a long-term association between earnings and returns. Surprisingly, few attempts have been made to internationally examine market reactions to earnings releases over return windows less than 12 months. This paper globally explores the market reaction to unexpected earnings defined by both the change in earnings per share (EPS) and analyst forecast errors (AFE) using a 1-month return window. First, the existence of the earnings-returns relationship is examined using a sample of firms from 32 countries grouped into accounting regimes. Accounting regimes represent groups of countries that exhibit similarities in accounting standards, stock market characteristics, corporate governance mechanisms, and economic conditions. Thus, similar reactions to earnings are expected within regimes. Next, the incremental information content of analyst forecasts, a proxy for investors’ earnings expectations, is examined. Finally, changes in the structure of the earnings-returns relationship over time are investigated. Results support the existence of a relationship between earnings and returns in all accounting regimes. In addition, analyst forecast errors appear to be incorporated into earnings expectations in most developed countries. Finally, evidence suggests that the significance and explanatory power in the earnings-returns relationship has increased in recent years.     

A careful re-examination of seasonality in international stock markets: Comment on sentiment and stock returns

In questioning Kamstra, Kramer, and Levi’s (2003) finding of an economically and statistically significant seasonal affective disorder (SAD) effect, Kelly and Meschke (2010) make errors of commission and omission. They misrepresent their empirical results, claiming that the SAD effect arises due to a “mechanically induced” effect that is non-existent, labeling the SAD effect a “turn-of-year” effect (when in fact their models and ours separately control for turn-of-year effects), and ignoring coefficient-estimate patterns that strongly support the SAD effect. Our analysis of their data shows, even using their low-power statistical tests, there is significant international evidence supporting the SAD effect. Employing modern, panel/time-series statistical methods strengthens the case dramatically. Additionally, Kelly and Meschke represent the finance, psychology, and medical literatures in misleading ways, describing some findings as opposite to those reported by the researchers themselves, and choosing selective quotes that could easily lead readers to a distorted understanding of these findings.     

The intertemporal relation between expected returns and risk

This paper explores the time-series relation between expected returns and risk for a large cross section of industry and size/book-to-market portfolios. I use a bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model to estimate a portfolio's conditional covariance with the market and then test whether the conditional covariance predicts time–variation in the portfolio's expected return. Restricting the slope to be the same across assets, the risk-return coefficient is highly significant with a risk–aversion coefficient (slope) between one and five. The results are robust to different portfolio formations, alternative GARCH specifications, additional state variables, and small sample biases. When conditional covariances are replaced by conditional betas, the risk premium on beta is estimated to be in the range of 3% to 5% per annum and is statistically significant.     

The relationship between asset growth and the cross-section of stock returns

There is a large body of literature examining the association between stock characteristics and the cross-section of stock returns in international markets. Recently, Cooper et al. (2008) reported a strong association between total asset growth and stock returns in the US. In this paper, we show that an asset-growth effect also exists in the Australian equity market. Of particular interest, it is present amongst the largest Australian stocks. Over the 1983-2007 period, an equally-weighted portfolio of low-growth Big stocks outperforms a portfolio of high-growth Big stocks by an average 1% per month, equating to nearly 13% per annum. At an individual stock level of analysis, the asset-growth effect remains even after controlling for other variables whose association with the cross-section of returns is well known. Finally, we explicitly test whether asset growth is a priced risk factor using the common two-stage cross-sectional regression methodology. We find no evidence to support a risk-based explanation, thereby lending credence to Cooper et al.’s (2008) suggestion that the asset-growth effect is attributable to mispricing.     

Non-linear relationship between oil and cryptocurrencies: Evidence from returns and shocks

The price instabilities between oil prices and cryptocurrencies have motivated the current study to examine the nonlinear relationship between oil returns/shocks and cryptocurrencies during March 3, 2018 to October 10, 2021. We employed a novel methodology of cross-quantilogram to unveil the nonlinearity and asymmetry between oil shocks and cryptocurrencies. We find that when markets are normal and bullish, there is a positive correlation between oil returns and cryptocurrency returns at first lag; however, there is a negative correlation between oil returns and cryptocurrencies in all market conditions. Moreover, rising fluctuations in oil demand shocks brings significant movement in cryptocurrency returns in bearish market conditions and it is unlikely that oil demand shocks and cryptocurrencies returns move in same directions. Given these results, we proposed useful implications for policymakers, strategists, regulators, financial market participants, and investors to hedge/diversify their risk.     

Predicting stock returns: A risk measurement perspective

This paper proposes a new and efficient model selection strategy to obtain significant stock returns predictability from a risk measurement perspective. The risk interval is defined as the distance between the current actual return and the returns' historical average. The model selection strategy involves switching stock return forecasting models according to different risk intervals from the mean reversion and extreme value theory. This new strategy generates encouraging results in the empirical analysis. A mean-variance investor can realize sizeable economic gains by allocating assets through this new approach relative to competing forecasting models. Furthermore, the strategy performs robustly under alternative settings from both statistical and economic perspectives.     

An examination of the relation between asymmetric risk measures,prior returns and expected daily stock returns

We use a sample of individual firm stock returns over the 1988–2009 time period to determine whether: (1) expected daily returns are related to asymmetric risk measures, (2) expected daily returns are related to the directional change of the prior day's price, and (3) our results are robust to the addition of firm size, book-to-market equity and liquidity. We find that investors are compensated for asymmetric risk; however, the positive risk–return relation is present only for our smallest firm quintile. We find a short-term return reversal present in all subgroups, except for the largest firms in our sample. We also document that the low volatility anomaly may be related to firm size and liquidity.     

Empirical analysis of real and financial volatilities on stock excess returns: evidence from Taiwan industrial data

This paper tests the relation between stock excess returns and risk factors measured by volatility. The sources of the volatility are based on the volatility of macroeconomic factors and time-series volatility. To model the macroeconomic fundamentals, we divide the risk into real and financial volatilities pertinent to Taiwan's economic environment. By examining the data of indusry excess returns and market excess returns, we find evidence to reject the hypothesis that the stock excess returns are independent of the real and financial volatilities.     

The relationship between stock returns and inflation: new evidence from wavelet analysis

This paper presents a new perspective on the Fisher hypothesis, which states a positive relationship between nominal stock returns and inflation. The new approach is based on a wavelet multiscaling method that decomposes a given time series on a scale-by-scale basis. Empirical results show that there is a positive relationship between stock returns and inflation at the shortest scale (1-month period) and at the longest scale (128-month period), while a negative relationship is shown at the intermediate scales. This indicates that the nominal return results are supportive of the Fisher hypothesis for risky assets in d1 and s7 of the wavelet domain, while the stock returns do not play a role as an inflation hedge at the intermediate scales. The key empirical results show that time-scale decomposition provides a valuable means of testing the Fisher hypothesis, since a number of stock returns and inflation puzzles previously noted in the literature are resolved and explained by the wavelet analysis.