Volatility Risk Premium,Risk Aversion,and the Cross‐Section of Stock Returns

We test if innovations in investor risk aversion are a priced factor in the stock market. Using 25 portfolios sorted on book‐to‐market and size as test assets, our new factor together with the market factor explains 64% of the variation in average returns compared to 60% for the Fama‐French model. The new factor is generally significant with an estimated risk premium close to its time series mean also when industry portfolios and portfolios sorted on previous returns are augmented to the test assets.     

Exchange rate risk in the US stock market

Kolari et al. (2008) show that exchange rate risk measured by contemporaneous exchange rate changes is priced in the US stock market. However, by construction, their exchange rate risk factor has a strong correlation with the size factor, and their exchange rate sensitivity portfolios have a strong factor structure. To test whether their results are spurious, we carry out two sets of tests. The first set is motivated by Lewellen et al. (2010), where the second set is motivated by the voluminous literature which suggests that stock returns are heavy-tailed (e.g. Rachev and Mitnik, 2000). Different from Kolari et al. (2008), we find that exchange rate risk measured by contemporaneous exchange rate changes is not priced in the US stock market if we use industry portfolios which do not have a strong factor structure as the testing assets or if we use more robust methods to estimate firm-specific exchange rate sensitivity. Our findings therefore suggest that researchers take a new perspective on exchange rate risk.     

Accrual quality,skill, and the cross-section of mutual fund returns

We use returns of actively managed mutual funds to document the link between accrual quality (AQ) and systematic (priced) risk. Despite compelling theoretical arguments, prior research finds no evidence that poor AQ commands a risk premium in the cross-section of realized stock returns. We argue that the previously obtained premium estimates are biased downward because, for a large portion of poor AQ stocks, higher expected returns are offset by the news of deteriorating fundamentals. We suggest that skilled mutual fund managers should be able to either avoid investing in stocks with deteriorating fundamentals or assign them lower portfolio weights. As a consequence, returns on their portfolios should better reflect the expected AQ risk premium. Our empirical evidence is consistent with these predictions.     

Constructing and Testing Alternative Versions of the Fama–French and Carhart Models in the UK

This paper constructs and tests alternative versions of the Fama–French and Carhart models for the UK market with the purpose of providing guidance for researchers interested in asset pricing and event studies. We conduct a comprehensive analysis of such models, forming risk factors using approaches advanced in the recent literature including value‐weighted factor components and various decompositions of the risk factors. We also test whether such factor models can at least explain the returns of large firms. We find that versions of the four‐factor model using decomposed and value‐weighted factor components are able to explain the cross‐section of returns in large firms or in portfolios without extreme momentum exposures. However, we do not find that risk factors are consistently and reliably priced.     

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.     

The asset pricing effects of UK market liquidity shocks: Evidence from tick data

Using tick data covering a 12 year period including much of the recent financial crisis we provide an unprecedented examination of the relationship between liquidity and stock returns in the UK market. Previous research on liquidity using high frequency data omits the recent financial crisis and is focused on the US, which has a different market structure to the UK. We first construct several microstructure liquidity measures for FTSE All Share stocks, demonstrating that tick data reveal patterns in intra-day liquidity not observable with lower frequency daily data. Our asymptotic principal component analysis captures commonality in liquidity across stocks to construct systematic market liquidity factors. We find that cross-sectional differences in returns exist across portfolios sorted by liquidity risk. These are strongly robust to market, size and value risk. The inclusion of a momentum factor partially explains some of the liquidity premia but they remain statistically significant. However, during the crisis period a long liquidity risk strategy experiences significantly negative alphas.     

Does the Pricing of Financial Reporting Quality Change Around Dividend Changes?

We examine whether accrual earnings quality is a priced information risk factor in a dividend change setting. We define information risk as the probability that firm‐specific financial statement information pertinent to investor pricing decisions is of low precision, and use the factor‐mimicking portfolio returns formed on the Dechow‐Dichev [2002] accrual quality (AQ) metric to proxy for the information risk (IR) factor returns. We augment the Fama‐French three‐factor model with this IR factor, and find that dividend initiation and increase firms exhibit a decrease in the factor loadings on the IR factor while dividend decrease firms exhibit an increase in the corresponding factor loadings, but such changes in the factor loadings occur months prior to the dividend change announcements. The results are robust to further controls for operating risk and using an alternative measure of information risk. Further analysis on changes in information characteristics such as AQ, the probability of informed trading score (PIN), forecast dispersion, and return volatility surrounding dividend change events are consistent with the asset pricing results. Overall, we interpret our results as being consistent with investors treating the information risk associated with the precision of financial statement information as a priced risk factor, with both the precision and pricing changing in predictable directions around dividend changes. However, while we attempt to control for operating risk changes in additional tests, we cannot completely rule out changes in operating risk as a competing alternative explanation for our observed results.     

An intertemporal capital asset pricing model with bank credit growth as a state variable

An ICAPM which includes bank credit growth as a state variable explains 94% of the cross-sectional variation in the average returns on the 25 Fama–French portfolios. We find compelling evidence that bank credit growth is priced in the cross-section of expected stock returns, even after controlling for well-documented asset pricing factors. These results are robust to the inclusion of industry portfolios in the set of test assets. They are also robust to the addition of firm characteristics and lagged instruments in the factor model. Bank credit growth is important because of its ability to predict business cycle variables as well as future labor income growth. These findings underscore the relevance of bank credit growth in stock pricing.     

A closer look at return predictability of the US stock market: evidence from new panel variance ratio tests

This paper examines the return predictability of the US stock market using portfolios sorted by size, book-to-market ratio and industry. We use novel panel variance ratio tests, based on the wild bootstrap proposed in this paper, which exhibit desirable size and power properties in small samples. We have found evidence that stock returns have been highly predictable from 1964 to 1996, except for a period leading to the 1987 crash and its aftermath. After 1997, stock returns have been unpredictable overall. At a disaggregated level, we find evidence that large-cap portfolios have been priced more efficiently than small- or medium-cap portfolios; and that the stock returns from high-tech industries are far less predictable than those from non-high-tech industries.     

Risk evaluations with robust approximate factor models

Approximate factor models and their extensions are widely used in economic analysis and forecasting due to their ability to extracting useful information from a large number of relevant variables. In these models, candidate predictors are typically subject to some common components. In this paper we propose a new method for robustly estimating the approximate factor models and use it in risk assessments. We consider a class of approximate factor models in which the candidate predictors are additionally subject to idiosyncratic large uncommon components such as jumps or outliers. By assuming that occurrences of the uncommon components are rare, we develop an estimation procedure to simultaneously disentangle and estimate the common and uncommon components. We then use the proposed method to investigate whether risks from the latent factors are priced for expected returns of Fama and French 100 size and book-to-market ratio portfolios. We find that while the risk from the common factor is priced for the 100 portfolios, the risks from the idiosyncratic factors are not. However, we find that model uncertainty risks of the idiosyncratic factors are priced, suggesting that with effective diversifications, only the predictable idiosyncratic risks can be reduced, but the unpredictable ones may still exist. We also illustrate how the proposed method can be adopted on evaluating value at risk (VaR) and find it can delivery comparable results as the conventional methods on VaR evaluations.     

Do Low‐Priced Stocks Drive Long‐Term Contrarian Performance on the London Stock Exchange?

We investigate whether low‐priced stocks drive long‐term contrarian performance on the U.K. market. We find that contrarian performance at low, middle, and high price levels is positive. On the Fama‐French risk adjusted basis, we find both low‐priced and middle‐priced losers have significantly positive returns. When we adjust returns by market and liquidity risk, only middle‐priced losers maintain their positive returns. Our results reveal that low‐priced stocks are not fully responsible for contrarian performance. Our empirical evidence is generally consistent with the overreaction hypothesis and behavioral models of value investing.     

Investor sentiment and economic forces

Economic theory suggests that pervasive factors should be priced in the cross-section of stock returns. However, our evidence shows that portfolios with higher risk exposure do not earn higher returns. More importantly, our evidence shows a striking two-regime pattern for all 10 macro-related factors: high-risk portfolios earn significantly higher returns than low-risk portfolios following low-sentiment periods, whereas the exact opposite occurs following high-sentiment periods. These findings are consistent with a setting in which market-wide sentiment is combined with short-sale impediments and sentiment-driven investors undermine the traditional risk-return tradeoff, especially during high-sentiment periods.     

Productivity Risk and Industry Momentum

A productivity shock identified through a vector autoregression model is a priced risk factor for one‐month industry momentum portfolios and commands a positive risk premium. Stocks in winning industries have greater sensitivity to productivity news, thereby earning higher average returns than stocks in losing industries. This evidence lends support to an Intertemporal Capital Asset Pricing Model (ICAPM) with human wealth. In many specifications, exposure to productivity risk captures more than half of the observed industry momentum profits. This paper studies the sources of profits and attributes the risks of industry momentum portfolios to the behavior of their underlying cash flows.     

MACROECONOMIC FACTORS, THE APT AND THE UK STOCKMARKET

This paper examines the macroeconomic sources of risk priced in the UK stockmarket between 1983 and 1990 using monthly data on 840 stocks to form both beta-sorted and market value sorted portfolios using the methodology proposed by Chen, Roll and Ross (1986) and Chan, Chen and Hsieh (1985) for the US. We find that several intuitively plausible macroeconomic variables were priced over this period using the beta sorted portfolios and that once these variables are included there is little role for the return on the market. However, when the market value sorted portfolios were used only inflation and a measure of equity market 'expense' relative to gilts was priced; furthermore with the market value sorted portfolios a role for the market return was found.     

The Price of Correlation Risk: Evidence from Equity Options

We study whether exposure to marketwide correlation shocks affects expected option returns, using data on S&P100 index options, options on all components, and stock returns. We find evidence of priced correlation risk based on prices of index and individual variance risk. A trading strategy exploiting priced correlation risk generates a high alpha and is attractive for CRRA investors without frictions. Correlation risk exposure explains the cross-section of index and individual option returns well. The correlation risk premium cannot be exploited with realistic trading frictions, providing a limits-to-arbitrage interpretation of our finding of a high price of correlation risk.     

Portfolio performance of linear SDF models: an out-of-sample assessment

We evaluate linear stochastic discount factor models using an ex-post portfolio metric: the realized out-of-sample Sharpe ratio of mean–variance portfolios backed by alternative linear factor models. Using a sample of monthly US portfolio returns spanning the period 1968–2016, we find evidence that multifactor linear models have better empirical properties than the CAPM, not only when the cross-section of expected returns is evaluated in-sample, but also when they are used to inform one-month ahead portfolio selection. When we compare portfolios associated to multifactor models with mean–variance decisions implied by the single-factor CAPM, we document statistically significant differences in Sharpe ratios of up to 10 percent. Linear multifactor models that provide the best in-sample fit also yield the highest realized Sharpe ratios.     

Asymmetric mean-reversion and contrarian profits: ANST-GARCH approach

This paper investigates the time-series evidence of asymmetric reverting patterns in stock returns that is attributable to “contrarian profitability.” Using asymmetric nonlinear smooth-transition (ANST) GARCH(M) models, we find that, for monthly excess returns of US market indexes over the period of 1926:01–1997:12, negative returns on average reverted more quickly, with a greater reverting magnitude, to positive returns than positive returns revert to negative returns. The results are quite consistent when the models are implemented not only for the different sample periods, such as 1926:01–1987:09 and 1947:01–1997:12, but also for portfolios with different characteristics, such as different firm-size portfolios and Fama–French risk-adjusted factor portfolios. We interpret the asymmetrical reversion as evidence of stock market overreaction.     

Pairs trading in the UK equity market: risk and return

In this paper, we provide the first comprehensive UK evidence on the profitability of the pairs trading strategy. Evidence suggests that the strategy performs well in crisis periods, so we control for both risk and liquidity to assess performance. To evaluate the effect of market frictions on the strategy, we use several estimates of transaction costs. We also present evidence on the performance of the strategy in different economic and market states. Our results show that pairs trading portfolios typically have little exposure to known equity risk factors such as market, size, value, momentum and reversal. However, a model controlling for risk and liquidity explains a far larger proportion of returns. Incorporating different assumptions about bid-ask spreads leads to reductions in performance estimates. When we allow for time-varying risk exposures, conditioned on the contemporaneous equity market return, risk-adjusted returns are generally not significantly different from zero.     

Taxes,dividend yields and returns in the UK equity market

The higher rate of taxation on dividend income relative to capital gains has been offered as an explanation for the positive relation between stock returns and dividend yields among US firms. In the UK the relative tax rates are the reverse of those in the US. Thus, UK data provides an independent test of the tax-based approach to explaining the relation between stock returns and dividend yields. We find that high yielding stocks earn positive risk adjusted returns, whereas low yielding stocks earn negative risk adjusted returns. We also detect evidence of non-linearity in the performance of zero-dividend stocks. Controlling for firm size, seasonality and market risk we find a significant positive relation between dividend yields and returns. We conclude that the evidence is inconsistent with a tax-based explanation.     

The world price of jump and volatility risk

We study international integration of markets for jump and volatility risk, using index option data for the main global markets. To explain the cross-section of expected option returns we focus on return-based multi-factor models. For each market separately, we provide evidence that volatility and jump risk are priced risk factors. There is little evidence, however, of global unconditional pricing of these risks. We show that UK and US option markets have become increasingly interrelated, and using conditional pricing models generates some evidence of international pricing. Finally, the benefits of diversifying jump and volatility risk internationally are substantial, but declining.