Asset pricing: A tale of two days

We show that asset prices behave very differently on days when important macroeconomic news is scheduled for announcement. In addition to significantly higher average returns for risky assets on announcement days, return patterns are much easier to reconcile with standard asset pricing theories, both cross-sectionally and over time. On such days, stock market beta is strongly related to average returns. This positive relation holds for individual stocks, for various test portfolios, and even for bonds and currencies, suggesting that beta is after all an important measure of systematic risk. Furthermore, a robust risk–return trade-off exists on announcement days. Expected variance is positively related to future aggregated quarterly announcement day returns, but not to aggregated non-announcement day returns. We explore the implications of our findings in the context of various asset pricing models.     

There is a risk-return trade-off after all

This paper studies the intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns, the mixed data sampling (or MIDAS) approach. Using MIDAS, we find a significantly positive relation between risk and return in the stock market. This finding is robust in subsamples, to asymmetric specifications of the variance process and to controlling for variables associated with the business cycle. We compare the MIDAS results with tests of the intertemporal capital asset pricing model based on alternative conditional variance specifications and explain the conflicting results in the literature. Finally, we offer new insights about the dynamics of conditional variance.     

Momentum strategies based on reward–risk stock selection criteria

In this paper, we analyze momentum strategies that are based on reward–risk stock selection criteria in contrast to ordinary momentum strategies based on a cumulative return criterion. Reward–risk stock selection criteria include the standard Sharpe ratio with variance as a risk measure, and alternative reward–risk ratios with the expected shortfall as a risk measure. We investigate momentum strategies using 517 stocks in the S&P 500 universe in the period 1996–2003. Although the cumulative return criterion provides the highest average monthly momentum profits of 1.3% compared to the monthly profit of 0.86% for the best alternative criterion, the alternative ratios provide better risk-adjusted returns measured on an independent risk-adjusted performance measure. We also provide evidence on unique distributional properties of extreme momentum portfolios analyzed within the framework of general non-normal stable Paretian distributions. Specifically, for every stock selection criterion, loser portfolios have the lowest tail index and tail index of winner portfolios is lower than that of middle deciles. The lower tail index is associated with a lower mean strategy. The lowest tail index is obtained for the cumulative return strategy. Given our data-set, these findings indicate that the cumulative return strategy obtains higher profits with the acceptance of higher tail risk, while strategies based on reward–risk criteria obtain better risk-adjusted performance with the acceptance of the lower tail risk.     

The empirical risk–return relation: A factor analysis approach

Existing empirical literature on the risk–return relation uses relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large data sets, to summarize a large amount of economic information by few estimated factors, and find that three new factors—termed “volatility,” “risk premium,” and “real” factors—contain important information about one-quarter-ahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 16–20% of the one-quarter-ahead variation in excess stock market returns, and exhibit stable and statistically significant out-of-sample forecasting power. We also find a positive conditional risk–return correlation.     

Risk,return, and dividends

Using only the definition of returns, together with a transversality assumption, we demonstrate that given a dividend process, any one of three variables—expected return, return volatility, and the price–dividend ratio—completely determines the other two. By parameterizing only one of these processes, common empirical specifications place strong, and sometimes counter-factual, restrictions on the dynamics of the other variables. Our findings lend insight into the nature of the risk–return relation and the predictability of stock returns.     

Relation between time-series and cross-sectional effects of idiosyncratic variance on stock returns

Consistent with the post-1962 US evidence by Ang et al. [Ang, A., Hodrick, R., Xing Y., Zhang, X., 2006. The cross-section of volatility and expected returns. Journal of Finance 51, 259–299], we find that stocks with high idiosyncratic variance (IV) have low CAPM-adjusted expected returns in both pre-1962 US and modern G7 data. We also test in three ways the conjecture that IV is a proxy of systematic risk. First, the return difference between low and high IV stocks – that we dub as IVF – is a priced factor in the cross-section of stock returns. Second, loadings on lagged market variance and lagged average IV account for a significant portion of variation in average returns on portfolios sorted by IV. Third, the variance of IVF correlates closely with average IV, and the two variables have similar explanatory power for the time-series and cross-sectional stock returns.     

Does the stock market value bank diversification?

This paper investigates whether or not functionally diversified banks have a comparative advantage in terms of long-term performance/risk profile compared to their specialized competitors. To that end, this study uses market-based measures of return potential and bank risk. We calculate the franchise value over time of European banks as a measure of their long-run performance potential. In addition, we measure risk as both the systematic and the idiosyncratic risk components derived from a bank stock return model. Finally, we analyze the return/risk trade-off implied in different functional diversification strategies using a panel data analysis over the period 1989–2004. A higher share of non-interest income in total income affects banks’ franchise values positively. Diversification of revenue streams from distinct financial activities increases the systematic risk of banks while the effect on the idiosyncratic risk component is non-linear and predominantly downward-sloping. These findings have conflicting implications for different stakeholders, such as investors, bank shareholders, bank managers and bank supervisors.     

The role of exchange rates in intertemporal risk–return relations

This paper investigates the significance of an intertemporal relation between expected returns on countries’ stock market portfolios and their risk exposures to the world market portfolio. We find that the intertemporal risk–return relation differs significantly under different currency denominations. The slope coefficient is the largest at around seven when the returns are denominated in Japanese yen, moderate at about five when the returns are denominated in the Canadian or US dollars, and the smallest at around three when the returns are denominated in pound or euro and its predecessors. The ranking of the risk–return coefficients across different currency denominations remains the same when we replace country equity indices with global industry portfolios in estimating the intertemporal relations, when we change the return frequency from monthly to daily, and when we consider different specifications for the conditional covariance process.     

Probability of information-based trading and the January effect

We investigate the seasonality in the probability of information-based trading (PIN)–return relationship, the ‘January PIN effect’. We find that on average stock returns decrease with PIN in January, in contrast to other calendar months. This pattern is more apparent for small stocks. We argue that this seasonality is related to the January effect. According to the analysis, the December selling pressure associated with the January effect decreases in PIN, especially for small stocks. This suggests that when the price bounces back in January, low-PIN stocks will exhibit a larger return within a small stock group, leading to the negative PIN–return pattern. Furthermore, this seasonality is not the same as other January anomalies associated with momentum and idiosyncratic risk.     

Time-varying market integration and stock and bond return concordance in emerging markets

We investigate the extent to which emerging stock market integration affects the joint behavior of stock and bond returns using a two-stage semi-parametric approach. Using a sample of 18 emerging markets, we find an unambiguous and robust link between emerging stock market integration and stock–bond return decoupling. We explain this with a decline in the segmentation risk premia in equities modeled by De Jong and De Roon [De Jong, F., De Roon, F.A., 2005. Time-varying market integration and expected returns in emerging markets. Journal of Financial Economics 78, 583–613] that leads to increased demand for stocks and reduced or unchanged demand for bonds. Our findings deliver new insights into the financial liberalization and stock–bond comovement literatures.     

Reference-dependent preferences and the risk–return trade-off

This paper studies the cross-sectional risk–return trade-off in the stock market. A fundamental principle in finance is the positive relation between risk and expected return. However, recent empirical evidence suggests the opposite. Using several intuitive risk measures, we show that the negative risk–return relation is much more pronounced among firms in which investors face prior losses, but the risk–return relation is positive among firms in which investors face prior gains. We consider a number of possible explanations for this new empirical finding and conclude that reference-dependent preference is the most promising explanation.     

The Risk‐Return Relation in International Stock Markets

We investigate the risk‐return relation in international stock markets using realized variance constructed from MSCI (Morgan Stanley Capital International) daily stock price indices. In contrast with the capital asset pricing model, realized variance by itself provides negligible information about future excess stock market returns; however, we uncover a positive and significant risk‐return tradeoff in many countries after controlling for the (U.S.) consumption‐wealth ratio. U.S. realized variance is also significantly related to future international stock market returns; more importantly, it always subsumes the information content of its local counterparts. Our results indicate that stock market variance is an important determinant of the equity premium.     

International diversification strategies: Revisited from the risk perspective

Following Roll [Roll, R., 1992. Industrial structure and comparative behaviour of international stock market indices. Journal of Finance 47, 3–42] and Heston and Rouwenhorst [Heston, S.L., Rouwenhorst, G.K., 1994. Does industrial structure explain the benefits of international diversification. Journal of Financial Economics 36, 3–27], researchers have decomposed stock returns into country and industry components. Evidence suggests that industry components have become more important in recent years, but the reasons for this are unclear. Existing research concentrated mainly on stock returns in industrial countries. In this paper we consider instead the decomposition of stock risks within emerging equity markets. We provide a rationale for this procedure and its relationship to return decompositions. The results provide new firm-specific evidence on the debate over country and industry components, their stability over time, and the implications for portfolio diversification.     

Re-examining the risk–return relationship in Europe: Linear or non-linear trade-off?

This paper analyzes the risk–return trade-off in Europe using recent data from 11 European stock markets. After relaxing the linear assumptions in the risk–return relationship by introducing a new approach that considers the current state of the market, we obtain significant evidence for a positive risk–return trade-off for low volatility states. However, this finding is reduced or even non-significant during periods of high volatility. Maintaining the linear assumption over the risk–return trade-off leads to non-significant estimations for all cases. These results are robust across countries despite the conditional volatility model used. These results also demonstrate that the inconclusive results in previous studies may be due to strong linear assumptions when modeling the risk–return trade-off. This previous research fails to uncover the global behavior of the relationship between return and risk.     

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 role of jump dynamics in the risk–return relationship

Surprisingly, a positive risk–return relationship has not been consistently observed for the traditional GARCH in the mean model in other studies. In this paper, we employ a combination of the jump diffusion and GARCH model in the mean equation to test the risk–return relationship for U.S. stock returns. The results suggest a statistically significant relationship between risk and return if the risk measure includes components of smoothly changing variance and jump events.     

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.     

Implied volatility and future portfolio returns

Prior studies find that the CBOE volatility index (VIX) predicts returns on stock market indices, suggesting implied volatilities measured by VIX are a risk factor affecting security returns or an indicator of market inefficiency. We extend prior work in three important ways. First, we investigate the relationship between future returns and current implied volatility levels and innovations. Second, we examine portfolios sorted on book-to-market equity, size, and beta. Third, we control for the four Fama and French [Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56.] and Carhart [Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance, 52, 57–82.] factors. We find that VIX-related variables have strong predictive ability.     

Endogenous leverage and expected stock returns

This note clarifies conditions under which endogenous choice of debt induces a negative relation between leverage or default risk and expected stock returns. In the context of the model of George and Hwang [2009. Journal of Financial Economics 96, 56–79], we correct the contention that variation in bankruptcy costs across firms is sufficient. Variation in asset risk parameters can lead to the desired relation, but may not when also controlling for variation in book-to-market ratios. A simple parameterization of cross-sectional heterogeneity in risk and profitability implies a negative association of expected return with leverage and distress risk and a positive association with book-to-market.     

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.