Predicting the equity market with option-implied variables

We comprehensively analyze the predictive power of several option-implied variables for monthly S&P 500 excess returns and realized variance. The correlation risk premium (CRP) and the variance risk premium (VRP) emerge as strong predictors of both excess returns and realized variance. This is true both in- and out-of-sample. Our results also reveal that statistical evidence of predictability does not necessarily lead to economic gains. However, a timing strategy based on the CRP leads to utility gains of more than 5.03% per annum. Forecast combinations provide stable forecasts for both excess returns and realized variance, and add economic value.     

Reflections on the Efficient Market Hypothesis: 30 Years Later

In recent years financial economists have increasingly questioned the efficient market hypothesis. But surely if market prices were often irrational and if market returns were as predictable as some critics have claimed, then professionally managed investment funds should easily be able to outdistance a passive index fund. This paper shows that professional investment managers, both in The U.S. and abroad, do not outperform their index benchmarks and provides evidence that by and large market prices do seem to reflect all available information.     

Out‐of‐sample stock return predictability in emerging markets

This article builds on the widely debated issue of stock return predictability by applying a broad range of predictor variables and comprehensively considering the in‐sample and out‐of‐sample stock return predictability of ten advanced emerging markets. It compares forecasts from models with a single predictor variable, multiple predictor variables and a combination forecast approach. The results confirm the findings of Welch and Goyal (2008) for US data that only a limited number of individual predictor variables are able to deliver significant out‐of‐sample forecasts. However, a combination forecast approach provides statistically and economically significant out‐of‐sample forecast results.     

Brand capital and firm value

We study the role of brand capital – a primary form of intangible capital – for firm valuation and risk in the cross section of publicly traded firms. Using an empirical measure of brand capital stock constructed from advertising expenditures accounting data, we show that: (i) firms with low brand capital investment rates have higher average stock returns than firms with high brand capital investment rates, a difference of 5.2% per annum; (ii) more brand capital intensive firms have higher average stock returns than less brand capital intensive firms, a difference of 5.1% per annum; and (iii) investment in both brand capital and physical capital is volatile and procyclical. A neoclassical investment-based model in which brand capital is a factor of production subject to adjustment costs matches the data well. The model also provides a novel explanation for the empirical links between advertising expenditures and stock returns around seasoned equity offerings (SEO) documented in previous studies.     

Structural breaks in the mean of dividend-price ratios: Implications of learning on stock return predictability

In their out-of-sample predictions of stock returns in the presence of structural breaks, Lettau and Van Nieuwerburgh (2008) implicitly assume that economic agents’ perception of the regime-specific mean for the dividend-price ratio is time-invariant within a regime. In this paper, we challenge this assumption and employ least squares learning with constant gain (or constant-gain learning) in estimating economic agents’ time-varying perception for the mean of dividend-price ratio. We obtain better out-of-sample predictions of stock returns than in Lettau and Van Nieuwerburgh (2008) for both the U.S. and Japanese stock markets. Our empirical results suggest that economic agents’ learning plays an important role in the dynamics of stock returns.     

S&P 500 Affiliation and Stock Price Informativeness

Abstract

When firms are added to a stock index, more information should be discovered, traded on, and incorporated into their stock prices, making them more informative. We test this hypothesis using a large sample of additions to the S&P 500 index. Using two alternative statistical tests, we find that the stocks added experience more random, less predictable return and, thus, appear to be priced more efficiently information-wise. We further find concurrent increases in institutional ownership and investor awareness, which tend to contribute to the higher pricing efficiency, adding to the literature. These findings should be of interest to academics and practitioners.     

The long-run reversal in the long run: Insights from two centuries of international equity returns

We perform the most comprehensive test of long-term reversal in national equity indices ever done. Having examined data from 71 countries for the years 1830 through 2019, we demonstrate a strong reversal pattern: the past long-term return negatively predicts future performance. The phenomenon is not subsumed by other established cross-sectional return patterns, including the value effect. The long-term reversal is robust to many considerations but highly unstable through time. Finally, our findings support the overreaction explanation of this anomaly.     

Quantile spillovers and dependence between Bitcoin,equities and strategic commodities

This paper has two aims. We first examine the dynamic spillovers between Bitcoin and 12 developed equities, gold, and crude oil for different market conditions using a Bayesian Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with daily spot prices. Our econometric approach enables us to capture the left and right tails as well as the shoulders of the return distribution corresponding to volatility spillovers under the bear, normal, and bull market states among these financial assets. We quantify and trace the dependence and directional predictability from Bitcoin to other assets using the sample cross-quantilogram. Our key findings offer convincing evidence of time variation in the level of volatility. Spillovers between Bitcoin and other financial assets intensify during extreme global market conditions. Secondly, results from the cross-quantilogram indicate strong dependence and positive directional predictability between Bitcoin and most equities and crude oil when market returns are bullish. However, during the bearish market period, there is negative dependence and predictability from Bitcoin to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market only. This implies that Bitcoin can act as a hedge to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market. However, insignificant dependence and directional predictability from Bitcoin to the remaining assets indicate that Bitcoin may act as a safe-haven to these assets during bearish markets. Our findings hold important implications for both international investors and portfolio managers who consider Bitcoin as part of their portfolio diversification and other investment strategies.     

Option trading after the opening bell and intraday stock return predictability

Prior literature finds that information is reflected in option markets before stock markets, but no study has explored whether option volume soon after market open has predictive power for intraday stock returns. Using novel intraday signed option-to-stock volume data, we find that a composite option trading score (OTS) in the first 30 min of market open predicts stock returns during the rest of the trading day. Such return predictability is greater for smaller stocks, stocks with higher idiosyncratic volatility, and stocks with higher bid–ask spreads relative to their options’ bid–ask spreads. Moreover, OTS is a significantly stronger predictor of intraday stock returns after overnight earnings announcements. The evidence suggests that option trading in the 30 min after the opening bell has predictive power for intraday stock returns.     

Oil price dynamics and airline earnings predictability

This study examines the effect of oil price dynamics on quarterly earnings and their predictability with a sample of 30 airlines for 1994–2017. First, we document a significantly positive impact of demand-driven oil shocks on airline earnings, suggesting that the revenue effect from shifting air travel demands dominates the cost effect of aviation fuels. Regarding earnings predictability, we find evidence of deterioration in oil-volatile quarters as indicated by both the earnings variability based on common benchmarks and the quality of analyst forecasts issued early in the quarter. We further show that supply-driven oil shocks have a more detrimental impact on earning predictability than demand-driven oil shocks. Finally, we do not find supporting evidence for the possible moderating effect of hedging.