Stock Return Predictability Research Articles

Do oil price shocks have any implications for stock return momentum?

The study examines the implications of structural oil price shocks on one of the most widely observed and pervasive momentum anomalies. We use a structural vector autoregression (SVAR) model to find the time-varying responses of momentum payoffs of India, Japan, the UK, and the US to different structural oil shocks—oil supply global aggregate demand shock, speculative shock, and other oil demand shocks. Structural oil shocks, particularly oil demand and speculative shocks, have important implications as conditioning information in predicting stock return momentum behavior according to the conditional asset pricing model. Overall, the results indicate that structural oil shocks have a significant impact on small cap and loser stock portfolios. Our findings have crucial implications to develop effective momentum investment strategies that consider structural oil shocks as conditioning information.

Using Textual Analysis to Diversify Portfolios

Semantic fingerprinting is a leading AI solution that combines recent developments from cognitive neuroscience and psycholinguistics to analyze text with human-level accuracy. As an efficient method of quantifying text, it has already found its application in finance where the semantic fingerprints of company descriptions have been shown to successfully predict stock return correlations of Dow Jones Industrial Average (DJIA) constituents. By extension, it has been suggested that diversified portfolios could be constructed to exploit the fundamental (dis)similarity between companies’ core activities (measured by the semantic overlap of company descriptions). This paper follows the performance of two portfolios made of the same DJIA constituent companies: the “minimum semantic concentration” portfolio (constructed with text-based portfolio weights) and the traditional “minimum variance” portfolio, over a time span of 16 years including two high volatility events: the 2007 − 2009 financial crisis and the COVID pandemic. The results confirm that textual analysis using semantic fingerprinting is consistently successful in predicting stock return correlations and is valuable as a portfolio selection criterion. However, in times of high market volatility the fundamental information given by the companies’ core activities, while still relevant, might carry less weight.

Gone with the Vol: A Decline in Asset Return Predictability During the Great Moderation

We document and examine a significant shift in the comovement of asset returns and macroeconomic volatility during the Great Moderation. Strong U.S. stock and bond return predictability from several macroeconomic volatility series before 1982 was followed by a significant predictability decline during the Great Moderation (1982–2008). These findings are robust to alternative empirical specifications and out-of-sample tests. In a calibrated equilibrium model with time-varying volatility, the predictability decline requires changes in several model elements. Lower return predictability is consistent with stronger policy responses to inflation and output, a larger slope in the New Keynesian Phillips curve, and reduced sensitivity of both macroeconomic and financial variables to a volatility factor. Our results contribute to the examination of macroeconomic volatility as a driver of expected asset returns and the instability in predictive regressions. We further identify sources of the Great Moderation using asset price dynamics. This paper was accepted by Tomasz Piskorski, finance. Supplemental Material: Data files and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4429 .

Stock return prediction: Stacking a variety of models

We employ an ensemble learning approach, “stacking”, to refine and combine a variety of linear and nonlinear individual stock return prediction models. In an application of forecasting U.S. market excess return, stacking with a simple structure can outperform the traditional historical mean benchmark, Mallows model averaging, simple combination forecast, complete subset regression, combination elastic net forecast, and several other models in terms of both in- and out-of-sample performance measures on a consistent basis. More importantly, we find that the out-of-sample gains of stacking are especially evident during extreme downside market movements. Overall, stacking can generate substantive improvements in market excess return predictability.

The cross-section of expected stock returns and components of idiosyncratic volatility

PurposeThe purpose of this paper is to contribute to the existing stock return predictability and idiosyncratic risk literature by examining the relationship between stock returns and components derived from the decomposition of stock returns variance at the portfolio and firm levels.Design/methodology/approachA theoretical model is used to decompose the variance of stock returns into two volatility and two covariance terms by using a conditional Fama-French three-factor model. This study adopts portfolio analysis and Fama-MacBeth cross-sectional regression to examine the relationship between components of idiosyncratic risk and expected stock returns.FindingsThe portfolio analysis results show that volatility terms are negatively related to expected stock returns, and alpha risk has the most significant relationship with stock returns. On the contrary, covariance terms have positive relationships with expected stock returns at the portfolio level. Furthermore, the results of the Fama-MacBeth cross-sectional regression show that only alpha risk can explain variations in stock returns at the firm level. Another finding is that when volatility and covariance terms are excluded from idiosyncratic volatility, the relation between idiosyncratic volatility and stock returns becomes weak at the portfolio level and disappears at the firm level.Originality/valueThis is the first study that examines the relations between all the components of idiosyncratic risk and expected stock returns in equal-weighted and value-weighted portfolios. This research also suggests covariance terms of idiosyncratic volatility as new predictors of stock returns at the portfolio level. Moreover, this paper contributes to the idiosyncratic risk literature by examining whether all the four additional components explain all the systematic patterns included in the unconditional idiosyncratic risk.

Out-of- Sample Stock Return Predictability of Alternative COVID-19 Indices

ABSTRACT We explore the predictive value of the various indices developed to capture COVID-19 pandemic for daily stock return predictability of 24 Emerging Market economies (based on data availability). We identify eight measures of COVID-19 indices, namely, the uncertainty due to pandemics and epidemics (UPE) index, Global Fear Index (GFI), COVID index, vaccine index, medical index, travel index, uncertainty index and aggregate COVID-19 sentiment index. We find that, out of the considered measures, the GFI consistently offers the best out-of-sample forecast gains followed by the aggregate COVID-19 sentiment index while the UPE index offers the least predictability gains. The outcome generally improves after controlling for oil price but the ranking of forecast performance remains the same and robust to multiple forecast horizons and alternative forecast evaluation methods. We infer that the relative predictive powers of the indices are proportional to the extent to which the indices truly measure the pandemic.

Informed trading of out‐of‐the‐money options and market efficiency

AbstractWe examine the stock return predictability of out‐of‐the‐money (OTM) put‐to‐call trading volume ratio (OTMPC). Our numerical analysis predicts that in the US equity option market, informed investors rarely write OTM options because the leverage effect is not sufficient to compensate for transaction costs. OTMPC thus captures the informed investors’ OTM put purchase volume relative to their OTM call purchase volume. After controlling for existing empirical proxies for informed option trading, we find that OTMPC predicts future stock returns and corporate news. The return predictability offers implementable stock portfolio strategies. Our findings suggest that market inefficiency can emerge from uninformed investors’ limited knowledge about how transaction costs influence the trading strategies of informed investors.

EPU spillovers and stock return predictability: A cross-country study

Inspired by interconnectedness of economic policy uncertainty (EPU) across countries, this paper applies the high-dimensional factor-copula model to estimate EPU spillovers as expected probability in distress (EPD) and examines its effect on stock returns across 23 global markets. We find that EPD significantly and negatively predicts country-level returns in global markets. The predictability is significant for both countries in developed markets and countries in emerging markets by EPD that primarily originates from developed markets. Moreover, return predictability through the spillover channel is highly significant under various market conditions and dominates that through the domestic channel. The spillover effect remains strongly significant after the financial crisis, reflecting increased interconnectedness of the post-crisis global markets. Our results are robust to various model specifications and alternative measures of EPU spillovers.

Asymptotic properties of Bayesian inference in linear regression with a structural break

This paper studies large sample properties of a Bayesian approach to inference about slope parameters γ in linear regression models with a structural break. In contrast to the conventional approach to inference about γ that does not take into account the uncertainty of the unknown break date, the Bayesian approach that we consider incorporates such uncertainty. Our main theoretical contribution is a Bernstein–von Mises type theorem (Bayesian asymptotic normality) for γ under a wide class of priors, which essentially indicates an asymptotic equivalence between the conventional frequentist and Bayesian inference. Consequently, a frequentist researcher could look at credible intervals of γ to check robustness with respect to the uncertainty of the break date. Simulation studies show that the conventional confidence intervals of γ tend to undercover in finite samples whereas the credible intervals offer more reasonable coverages in general. As the sample size increases, the two methods coincide, as predicted from our theoretical conclusion. Using data from Paye and Timmermann (2006) on stock return prediction, we illustrate that the traditional confidence intervals on γ might underrepresent the true sampling uncertainty.

Using machine learning to analyze the impact of coronavirus pandemic news on the stock markets in GCC countries

COVID-19 has resulted in high volatility in financial markets across the world. The goal of this study is to investigate the impact of COVID-19-related news on the stock markets in Gulf Cooperation Council (GCC) countries. The study utilizes machine learning approaches to assess the role of COVID-19 news in stock return predictability in these markets. The results reveal that the stock markets in the United Arab Emirates (UAE), Qatar, Saudi Arabia, and Oman were impacted by coronavirus-related news; however, this news had no impact on the stocks in Bahrain. Moreover, the results indicate that the impacted markets were influenced differently in terms of the quantities and types of news.

The cash conversion cycle spread: International evidence

The cash conversion cycle (CCC) is important for fundamental analysis as an indicator of management effectiveness in cash and financing. However, there is a lack of empirical evidence for its implications on asset pricing except for the very recent findings that high CCCs negatively predict stock returns in the U.S. By investigating 47 developed and emerging markets from 1993 to 2018, we find a mild CCC effect across the globe. The Low-minus-High equal-weighted hedge portfolios sorted by components of CCC yield significant Fama-French five-factor alphas ranging from 0.277 to 0.730% per month. Our results are consistent with a mispricing explanation by analyzing earnings prediction, announcement returns around future earnings, and limits of arbitrage although there is also some evidence for a risk-based explanation. Moreover, the CCC effect is stronger in emerging markets than developed markets and for markets with more political risk and less integrated with the global market.

The Role of Economic Policy Uncertainty in Predicting Stock Return Volatility in the Banking Industry: A Big Data Analysis

The research aims to study the effects of economic policy uncertainty on the return volatility of stocks with data from the largest banking institutions in Greece. Volatility is constructed using intraday data, whereas the research period extends over a period of about thirteen years, more specifically from January 5, 2001, to June 30, 2014. This period includes various phases of the market, such as stock market crashes along with stock market booms (e.g. the financial crisis of 2007 and 2008 in the United States and the European sovereign debt crisis). The estimated regressions were used to indicate the direct effects of economic policy uncertainty on the return volatility of the stocks of the four large Greek banks. The volatility index is constructed based on intraday data, whereas four different estimators of volatility were used. There is a statistically significant "direct" effect of economic policy uncertainty on the volatility of stock returns of the largest Greek banks, which are (a) Alpha Bank, (b) Eurobank, (c) National Bank of Greece, and (d) Piraeus Bank. Such findings are highly important for specific groups of people, such as investors, policymakers, and regulators. This study is the first research that seeks to identify the effect of economic policy uncertainty on the stock return volatility of the Greek banking system, constructed from intraday data. Doi: 10.28991/ESJ-2022-06-03-011 Full Text: PDF

Good oil volatility, bad oil volatility, and stock return predictability

This paper aims to investigate the predictability of good and bad volatilities of oil prices for stock returns. Our empirical results show that bad volatility of oil prices, rather than good volatility of oil prices, can predict stock returns after the oil financialization. Especially, bad oil volatility negatively predicts stock returns because it leads to falling economic activity and greater financial market uncertainty. Further analysis finds that the association between bad oil volatility and stock return prediction is stronger with the increase in funding constraints and financial regulation policy uncertainty in the post-financialization period. Also, the predictability of bad oil volatility for stock returns can be enhanced by the retail investor attention.

Firm-specific sentiment and individual option's implied volatility slope

PurposePrevious literature shows that market sentiment and the steepness of index option's implied volatility slope have a negative relation. This paper investigates the relation between firm-specific sentiment and individual option's implied volatility slope both theoretically and empirically.Design/methodology/approachThe authors develop a simple model with option traders' sentiment heterogeneity to show that sentiment and the steepness of individual option's implied volatility slope have a positive relation.FindingsWhen firm-specific sentiment is higher (more bullish), individual option's implied volatility slope becomes steeper. The positive relation is stronger when option traders' beliefs on risk are more dispersed. Empirical results support the theoretical model predictions.Originality/valueAlthough both firm-specific sentiment and individual options implied volatility slope predict future stock returns, there is no research exploring the relation between them. In particular, none of previous studies associates implied volatility slope's stock return predictability to investor behavior such as sentiment. The authors’ findings provide a behavior-based explanation on why steep implied volatility slope negatively predicts cross-sectional stock returns.

Harnessing Neuroscientific Insights to Generate Alpha

Building on evidence from neuroscience and psychology, we predict that prolonged exposure to high volatility causes market participants to subsequently underestimate volatility (and vice versa), leading to predictability in stock returns. We find VIX distortions consistent with this prediction and construct a trading strategy that exploits it. Applied to SPY ETFs and VIX futures contracts, the strategy significantly outperforms a buy-and-hold index portfolio, with higher annualized performance, lower volatility, and alphas exceeding 4%.

Targeted principal components regression

We propose a principal components regression method based on maximizing a joint pseudo-likelihood for responses and predictors. Our method uses both responses and predictors to select linear combinations of the predictors relevant for the regression, thereby addressing an oft-cited deficiency of conventional principal components regression. The proposed estimator is shown to be consistent in a wide range of settings, including ones with non-normal and dependent observations; conditions on the first and second moments suffice if the number of predictors (p) is fixed, the number of observations (n) tends to infinity, and dependence is weak, while stronger distributional assumptions are needed when p→∞ with n. We obtain the estimator’s asymptotic distribution as the projection of a multivariate normal random vector onto a tangent cone of the parameter set at the true parameter, and find the estimator is asymptotically more efficient than competing ones. In simulations our method is substantially more accurate than conventional principal components regression and compares favorably to partial least squares and predictor envelopes. The method’s practical usefulness is illustrated in a data example with cross-sectional prediction of stock returns.

Stock return predictability of the cumulative abnormal returns around the earnings announcement date: Evidence from China

AbstractWe find that cumulative abnormal returns adjusted by size, book‐to‐market, and momentum around the earnings announcement date (DGTW_CAR3 hereafter) significantly and positively predict stock returns in the 6‐month period from May 2005 to October 2020 in the China's A‐shares market. The monthly equally‐weighted DGTW_CAR3 premiums are 0.47% and 0.67% after risk adjustment. Although stock price delay fails to fully account for the DGTW_CAR3 premium, we find that the DGTW_CAR3 premium is more significant for illiquid stocks and during periods with high investor sentiment. This result suggests that market inefficiency explains the DGTW_CAR3 premium. Further analysis shows that, in addition to earnings information, the optimism reflected in the management discussion and analysis section of the annual or half‐year report also contributes to the DGTW_CAR3 premium. This finding implies that DGTW_CAR3 may contain new fundamental information that correlates significantly and positively with future stock performance. Finally, we find that the institutional ownership change of a stock associated with DGTW_CAR3 also significantly and positively predicts the stock's return, suggesting that institutional investors adjust their holdings according to DGTW_CAR3 and consequently influence the demand for the stock in the China's A‐shares market.

Predicting the equity market risk premium: A model selection approach

We present a novel approach to investigate US stock return predictability. Our analysis utilizes the best subset method to construct a single predictive regression from a set of fundamental factors and hence, it is robust to data snooping. We consider models with non-Gaussian distributions as a first in the literature. We find that our selected predictive regression is Student’s t-distributed and has both in sample and out of sample forecasting power, with a high degree of economic significance.

Does gold–platinum price ratio predict stock returns? International evidence

PurposeThe purpose of this study is to investigate the relationship between gold–platinum price ratio (GP) and stock returns in international stock markets. The study addresses three empirical questions: (1) Does GP have robust predictive power in international stock markets? (2) Does GP outperform other macroeconomic variables in international stock markets? (3) What is the relationship between GP and stock market returns during economic recessions?Design/methodology/approachThe study mainly uses OLS regressions to perform empirical tests for a comprehensive set of 17 advanced international stock markets and overall world market. The monthly data is used for the period January 1978 to July 2019, 499 observations for each market.FindingsThe study finds that the first-difference of GP (ΔGP), not the initial-level of GP, has strong predictive power for stock returns, both in short- and long-time horizons. The results remain robust after controlling for a number of macroeconomic predictors. The out-of-sample test results are significant, confirming the robustness of the predictive power of ΔGP.Originality/valueThis study is the first to examine the ability of the ΔGP to predict stock returns, and provide novel evidence on the relationship between ΔGP and international stock markets. The study draws on behavioral finance theory, specifically the myopic loss aversion, the herd effect and the limited attention theory, to explain the predictability of stock returns in international stock markets.

What factors drive Saudi stock markets? – Firm characteristics that attract retail trades

As Saudi Arabia recently opened its stock market to international investors, there are strong interests in the uniqueness of Saudi stock markets. Unique features of Saudi markets include the dominance of retail trading and many limits-to-arbitrage. Saudi investors tend to pay strong attention to stocks with recent extreme price appreciations. Among these lottery-like stocks, investors tend to bid up (down) stock prices of profitable (unprofitable) firms gradually, generating a stock return predictability by firm profitability. The evidence suggests that investors’ short-term attentions have significant effects on Saudi stock returns, providing both opportunities and risks for informed investors in Saudi markets.