Risk,return and mean‐variance efficiency of Islamic and non‐Islamic stocks: evidence from a unique Malaysian data set

We find that Islamic stocks are more mean–variance efficient than non‐Islamic stocks and the market because they reduce risk of the same level of returns. We combine a unique Malaysian data set of individual Islamic stocks (as opposed to aggregate stock indices) since 1997 with a new method where we apply Islamic business activity and financial ratio screens to the universe of Malaysian stocks. Both data sets show that Islamic stocks have an annualised standard deviation that is on average 3.43–3.78 percent points lower compared to non‐Islamic stocks. This lower variance of Islamic stocks is exclusively driven by financial ratio screens.     

Stock market return predictability: Does network topology matter?

This paper provides new evidence for the predictability of excess market portfolio returns using a network approach. In particular, this article introduces a measure of interconnectedness to capture the interrelationship of returns of 100 largest stocks in S&P500 during 1990–2014. In the financial network literature, the interconnection of a stock network is often regarded as a channel through which an idiosyncratic shock propagates. The idiosyncratic risk propagation is crucial to the debate over the relationship between idiosyncratic risk and market returns because the idiosyncratic risk is not always diversified away. Rather, the network can sometimes amplify the effect of the idiosyncratic risk to cause aggregate fluctuation. In accordance with this theoretical argument, I empirically show that the network topology, measured by diameter, works together with the idiosyncratic risk, measured by average stock variance, to affect the market portfolio returns. This relationship persists after controlling for well-known variables known to forecast the stock market returns.     

Which past returns affect trading volume?

Anecdotal evidence suggests and recent theoretical models argue that past stock returns affect subsequent stock trading volume. We study 3,000 individual investors over a 51 month period to test this apparent link between past returns and volume using several different panel regression models (linear panel regressions, negative binomial panel regressions, Tobit panel regressions). We find that both past market returns as well as past portfolio returns affect trading activity of individual investors (as measured by stock portfolio turnover, the number of stock transactions, and the propensity to trade stocks in a given month). After high portfolio returns, investors buy high risk stocks and reduce the number of stocks in their portfolio. High past market returns do not lead to higher risk taking or underdiversification. We argue that the only explanations for our findings are overconfidence theories based on biased self-attribution and differences of opinion explanations for high levels of trading activity.     

What Is the Expected Return on a Stock?

We derive a formula for the expected return on a stock in terms of the risk‐neutral variance of the market and the stock's excess risk‐neutral variance relative to that of the average stock. These quantities can be computed from index and stock option prices; the formula has no free parameters. The theory performs well empirically both in and out of sample. Our results suggest that there is considerably more variation in expected returns, over time and across stocks, than has previously been acknowledged.     

Investor sentiment and stock returns: Some international evidence

We examine whether consumer confidence – as a proxy for individual investor sentiment – affects expected stock returns internationally in 18 industrialized countries. In line with recent evidence for the U.S., we find that sentiment negatively forecasts aggregate stock market returns on average across countries. When sentiment is high, future stock returns tend to be lower and vice versa. This relation also holds for returns of value stocks, growth stocks, small stocks, and for different forecasting horizons. Finally, we employ a cross-sectional perspective and provide evidence that the impact of sentiment on stock returns is higher for countries which have less market integrity and which are culturally more prone to herd-like behavior and overreaction.     

The market price of risk of the variance term structure

In this paper I examine the market price of risk of the variance term structure. To this end, the S&P 500 option implied variance term structure is used as a proxy for aggregate variance risk. Principal component analysis shows that time variation in the variance term structure over the 1996–2012 period can be explained mainly by two factors which capture changes in the level and slope. The market price of risk of each factor is estimated in the cross-section of stock returns. The slope of the variance term structure is the most significant factor in the cross-section of stocks returns and carries a negative risk premium. The slope factor has also some predictive ability over long horizon equity returns.     

The Cross-Section of Volatility and Expected Returns

We examine the pricing of aggregate volatility risk in the cross‐section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book‐to‐market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.     

The volatility effect of futures trading: Evidence from LSE traded stocks listed as individual equity futures contracts on LIFFE

This study investigates the impact of LIFFE's introduction of individual equity futures contracts on the risk characteristics of the underlying stocks trading on the LSE. We employ the Fama and French three-factor model (TFM) to measure the change in the systematic risk of the underlying stocks which arises subsequent to the introduction of futures contracts. A GJR-GARCH(1,1) specification is used to test whether the futures contract listing affects the permanent and/or the transitory component of the residual variance of returns, and a control sample methodology isolates changes in the risk components that may be caused by factors other than futures contract innovation. The observed increase (decrease) in the impact of current (old) news on the residual variance implies that futures contract listing enhances stock market efficiency. There is no evidence that futures innovation impacts on either the systematic risk or the permanent component of the residual variance of returns.     

Speculative bubbles and the cross-sectional variation in stock returns

Evidence suggests that rational, periodically collapsing speculative bubbles may be pervasive in stock markets globally, but there is no research that considers them at the individual stock level. In this study we develop and test an empirical asset pricing model that allows for speculative bubbles to affect stock returns. We show that stocks incorporating larger bubbles yield higher returns. The bubble deviation, at the stock level as opposed to the industry or market level, is a priced source of risk that is separate from the standard market risk, size and value factors. We demonstrate that much of the common variation in stock returns that can be attributable to market risk is due to the co-movement of bubbles rather than being driven by fundamentals.     

Ambiguity and asset pricing: An empirical investigation for an emerging market

This study explores the impact of ambiguity on returns of both individual stocks and stock portfolios in an emerging market setting. First, an ambiguity index is derived and then the sensitivity of stock returns to ambiguity is analyzed while controlling for the other risk factors commonly cited in the literature. Results show that stocks with a high (low) sensitivity to ambiguity generate higher (lower) excess returns. These results are intuitive in the sense that investors seem to ask for lower returns from those stocks that serve as a natural hedge against ambiguity. Our findings are also in line with the earlier studies that provide similar evidence from the US stock markets.     

Large returns,conditional correlation and portfolio diversification: a value-at-risk approach

This paper, using daily returns on 30 Dow Jones Industrial stocks for the period 1991-1999, investigates the possibility of portfolio diversification when there are negative large movements in the stock returns (i.e. when the market is bearish). We estimate the quantiles of stock return distributions using non-parametric and parametric methods that are widely being used in measuring value-at-risk (VaR). We find that the average conditional correlation of 30 stocks is much higher when the large movements are negative than that when the market is 'usual'. Further, we find that, contrary to the results of previous studies, there is no notable difference between the average conditional correlations when the large movements are positive and when the market is 'usual'. Moreover, it is evident from the results of the conditional CAPM that the portfolio's diversifiable and non-diversifiable risks, as measured by the error variance of the CAPM and beta respectively, are highly unstable when the market is bearish than that when it is 'usual' or bullish. The overall results suggest that the possibility of portfolio diversification would be eroded when the stock market is bearish. These findings have implications for portfolio diversification and risk management in particular and for finance in general. The ideas presented in this paper can be utilized for testing contagion in the international financial markets, a much-researched topic in international finance.     

Time-varying risk The case of the American computer industry

A bivariate GARCH-in-mean model for individual stock returns and the market portfolio is designed to model volatility and to test the conditional Capital Asset Pricing Model versus the conditional Residual Risk Model. We find that a univariate model of volatility for individual stock returns is misspecified. A joint modelling of the market return and the individual stock return shows that a major force driving the conditional variances of individual stocks is the history contained in the market return variance. We find that a conditional residual risk model, where the variance of the individual stock return is used to explain expected returns, is preferred to a conditional CAPM. We propose a partial ordering of securities according to their market risk using first and second order dominance criteria.     

Time Variation in the Covariance between Stock Returns and Consumption Growth

The conditional covariance between aggregate stock returns and aggregate consumption growth varies substantially over time. When stock market wealth is high relative to consumption, both the conditional covariance and correlation are high. This pattern is consistent with the “composition effect,” where agents' consumption growth is more closely tied to stock returns when stock wealth is a larger share of total wealth. This variation can be used to test asset‐pricing models in which the price of consumption risk varies. After accounting for variations in this price, the relation between expected excess stock returns and the conditional covariance is negative.     

The Effect of US Stock Market Uncertainty on Emerging Market Returns

We investigate the effects of US stock market uncertainty (VIX) on the stock returns in Latin America and aggregate emerging markets before, during, and after the financial crisis. We find that increases in VIX lead to significant immediate and delayed declines in emerging market returns in all periods. However, changes in VIX explained a greater percentage of changes in emerging market returns during the financial crisis than in other periods. The higher US stock market uncertainty exerts a much stronger depressing effect on emerging market returns than their own-lagged and regional returns. Our risk transmission model suggests that a heightened US stock market uncertainty lowers emerging market returns by both reducing the mean returns and raising the variance of returns. The VIX fears raise the volatility of emerging market returns through generalized autoregressive conditional heteroskedasticity (GARCH)-type volatility transmission processes.     

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.     

Stock Returns in Thinly Traded Markets

We examine the share price behavior of thinly traded NASDAQ National Market System stocks during periods when financial markets are open but the individual stocks do not trade. The absence of trade allows us to isolate the effect of nontrading from that of market closure. We find that nontrading stocks have negative mean returns and lower variances regardless of whether markets are open or closed. Two-day returns that include one nontrading day have a mean daily return of -0.226% compared to +0.164% for two-day returns over consecutive trading days. Two-day returns that include one nontrading day have only 3.8% higher variance than one-day returns. We conclude that the relation between transaction arrival, mean returns, and volatility depends on whether a stock is trading and not simply on whether the market is open.     

Institutional trading and stock returns

In this study, we explore the dynamics of the relation between institutional trading and stock returns. We find that stock returns Granger-cause institutional trading (especially purchases) on a quarterly basis. The robust and significant causality from equity returns to institutional trading can be largely explained by the time-series variation of market returns, that is, institutions buy more popular stocks after market rises. Stock returns appear to be negatively related to lagged institutional trading. A further analysis of the behavior of trading and the returns of the traded stocks reveals evidence that stocks with heavy institutional buying (selling) experience positive (negative) excess returns over the previous 12 months.     

The Role of Beta and Size in the Cross-Section of European Stock Returns

This paper examines the ability of beta and size to explain cross-sectional variation in average returns in 12 European countries. We find that average stock returns are positively related to beta and negatively related to firm size. The beta premium is in part due to the fact that high beta countries outperform low beta countries. Within countries high beta stocks outperform low beta stocks only in January, not in other months. We reject the hypothesis that differences in average returns on size- and beta-sorted portfolios can be explained by market risk and exposure to the excess return of small over large stocks (SMB). Consistent with recent US evidence, we find that after controlling for size, there is no association between average returns and exposure to SMB.     

Cross-sectional dispersion and expected returns

This study investigates whether the cross-sectional dispersion of stock returns, which reflects the aggregate level of idiosyncratic risk in the market, represents a priced state variable. We find that stocks with high sensitivities to dispersion offer low expected returns. Furthermore, a zero-cost spread portfolio that is long (short) in stocks with low (high) dispersion betas produces a statistically and economically significant return. Dispersion is associated with a significantly negative risk premium in the cross section (–1.32% per annum) which is distinct from premia commanded by alternative systematic factors. These results are robust to stock characteristics and market conditions.     

Measuring investor sentiment with mutual fund flows

We investigate a proxy for monthly shifts between bond funds and equity funds in the USA: aggregate net exchanges of equity funds. This measure (which is negatively related to changes in VIX) is positively contemporaneously correlated with aggregate stock market excess returns: One standard deviation of net exchanges is related to 1.95% of market excess return. Our main new finding is that 85% (all) of the contemporaneous relation is reversed within four (ten) months. The effect is stronger in smaller stocks and in growth stocks. These findings support the notion of “noise” in aggregate market prices induced by investor sentiment.