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Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

This paper investigates how the gambling measure captures market bubble events, and how it predicts stock return and option return.

This paper proposes a gambling activity measure by jointly considering open interest and moneyness of out-of-the-money (OTM) individual equity call options.

The new measure, CallMoney, captures excessive optimism during the dot-com bubble, the oil price bubble and the pre-GFC stock market bubble. CallMoney robustly and negatively predicts both OTM and at-the-money call option returns cross-sectionally. The option return predictability of CallMoney is stronger when stock price is further from its 52-weeks high, capital gains overhang is lower, and when information uncertainty of the underlying stock is higher. CallMoney also robustly and negatively predicts cross-sectional stock returns.

The gambling measure has the advantages of being economically intuitive, model-free, easy to measure. The measure performs more robustly than existing lottery measures with respect to option and stock return predictability and more reliably captures the overpricing of options and stocks. The work helps understanding the gambling related anomalies in equity option returns and stock returns.

This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms.

The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model.

The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners.

This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies.

In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

The purpose of this paper is to investigate the role of volatility risk on stock return predictability specified on two global financial crises: the dot-com bubble and recent financial crisis.

Using a broad sample of stock options traded on the American Stock Exchange and the Chicago Board Options Exchange from January 2001 to December 2010, the effect of different idiosyncratic volatility forecasting measures are examined on future stock returns in four different periods (Bear and Bull markets).

First, the authors find clear and robust empirical evidence that the implied idiosyncratic volatility is the best stock return predictor for every sub-period both in Bear and Bull markets. Second, the cross-section firm-specific characteristics are important when it comes to stock returns forecasts, as the latter have mixed positive and negative effects on Bear and Bull markets. Third, the authors provide evidence that short selling constraints impact negatively on stock returns for only a Bull market and that liquidity is meaningless for both Bear and Bull markets after the recent financial crisis.

These results would be helpful to disclose more information on the best idiosyncratic volatility measure to be implemented in global financial crises.

This study empirically analyses the effect of different idiosyncratic volatility measures for a period that involves both the dotcom bubble and the recent financial crisis in four different periods (Bear and Bull markets) and contributes the existing literature on volatility measures, volatility risk and stock return predictability in global financial crises.

This paper aims to investigate the performance of various return forecasting variables and methods in Saudi Arabia and Malaysia. The authors document that market excess returns in Saudi Arabia are predicted by changes in oil prices, the dividend yield and inflation, whereas the equity premium in Malaysia is predicted only by the US market excess returns. In both countries, the authors find that the diffusion index is the best forecasting method and stock return predictability is stronger in expansions than in recessions. To interpret the findings, the authors perform two tests. The empirical results suggest irrational pricing in Malaysia and rationally time-varying expected returns in Saudi Arabia.

The authors apply the state-of-the-art in-sample and out-of-sample forecasting techniques to predict stock returns in Saudi Arabia and Malaysia.

The Saudi equity premium is predicted by oil prices, dividend yield and inflation. The Malaysian equity premium is predicted by the US market excess returns. In both countries, the authors find that the diffusion index is the best forecasting method. In both countries, predictability is stronger in expansions than in recessions. The tests suggest irrational pricing in Malaysia and rationality in Saudi Arabia.

The empirical results have some practical implications. The fact that stock returns are predictable in Saudi Arabia makes it possible for policymakers to better evaluate future business conditions, and thus to take appropriate decisions regarding economic and monetary policy. In Malaysia, the results of this study have interesting implications for portfolio management. The fact that the Malaysian market seems to be inefficient suggests the presence of strong opportunities for sophisticated investors, such as hedge and mutual funds.

First, there are no papers that have studied the return predictability in Saudi Arabia in spite of its importance as an emerging market. Second, the methods that combine all predictive variables such as the diffusion index or the kitchen sink methods have not been implemented in emerging markets. Third, this paper is the first study to deal with time-varying short-horizon predictability in emerging countries.

In this paper, the author examines the role of uncertainty due to pandemic on the predictability of sectoral stock returns in South Africa. This is motivated by the ongoing global pandemic, COVID-19, in predicting sector stock returns.

The study considers estimation of dynamic panel data with dynamic common correlated effects estimator and two pair-wise forecast measures, namely Campbell and Thompson (2008) and Clark and West (2007) tests in dealing with the nested predictive models.

The results show that pandemic uncertainty has a negative and statistically significant effect on the different sector returns, implying that sector stock returns decline as the pandemic outbreak becomes more pronounced. While the single predictor model consistently outperforms the historical average model both for in-sample and out-of-sample, controlling for other macroeconomic variables effect improves the forecast accuracy of infectious diseases uncertainty. These results are consistently robust to both the in-sample and out-of-sample forecast periods, outliers and heterogeneity. These results have implications for portfolio diversification strategies, which we set aside for future research.

The empirical literature is satiated with studies on how news can predict economic and financial variables, however, the role of uncertainty due to infectious diseases in the stock return predictability especially at the sectoral level is less understudied, this is the main contribution of the study.

To capture the last hour momentum over the intraday session, the authors develop a trading strategy for the exchange-traded fund (ETF) that is effective because of the T+0 trading rule. This strategy generates annualized excess return of 9.673%.

In this study, the authors identify a last hour momentum pattern in which the sixth (seventh) half-hour return predicts the next half-hour return by employing high frequency 2012–2017 data from the China Securities Index (CSI) 300 and its ETF.

Overall, both the predictability and the trading strategy are statistically and economically significant. In addition, the strategy performs more strongly on high volatility days, high trading volume days, high order-imbalance days and days without economic news releases than on other days.

Noise trading, late-information trading, infrequent rebalancing and disposition effects from retail investors may account for this phenomenon.

The purpose of this study is to investigate the relationship between predictability in return and investment strategy performance. Two measures that characterize investment strategies within a mean‐variance framework, an activity measure and a style measure, are developed and the performance of alternative strategies (e.g. contrarian, momentum, etc.) is examined when risky asset returns are mean reverting.

Returns are assumed to follow a multivariate Ornstein‐Uhlenbeck process, where reversion to a time‐varying mean is governed by an additional variable set, similar to that proposed by Lo and Wang (1995). Depending on its parameterization, this process is capable of producing an autocorrelation pattern consistent with empirical evidence, that is, positive autocorrelation in short‐horizon returns and negative autocorrelation in long‐horizon returns.

The results, for four uninformed investment strategies and assuming that returns are generated by a simple univariate Ornstein‐Uhlenbeck process, show that the unadjusted returns from the contrarian (momentum) strategy are greater than those from the other strategies when the mean reversion parameter, α, is greater than (less than) one. The results are expected, given the relationship between α and the first‐order autocorrelation in returns. The risk level (measured by either the standard deviation of returns or beta) of the contrarian strategy is the lowest at essentially all levels of mean reversion and the risk‐adjusted returns from the contrarian strategy, measured by the both the Sharpe and Treynor ratios, dominate those from the other strategies.

In future research, a number of issues not considered in this study may be investigated. The style measure developed here can be used to determine whether the results obtained hold when an informed, mean‐variance efficient active strategy is employed. In addition, the performance of both the informed and uninformed strategies may be examined under the assumption that the risky return process follows a multivariate Ornstein‐Uhlenbeck process. This work should provide findings that facilitate the separation of fund risk due to dynamic strategies from that due to time‐varying expected returns.

The methodology used here may be easily extended to consider a number of important issues, such as the frequency of portfolio rebalancing, transactions costs, and multiple asset portfolios, that are encountered in practice.

The approach used here provides insight into how predictability affects the relative performance of tactical investment strategies and, thus, may serve as a basis for determining the magnitude and persistence in autocorrelation required for active investment strategies to yield profits significantly different from those of passive strategies. In this sense, this study may have appeal for both academics and investment professionals.

This study aims to explore the oil–stock market nexus from a novel angle by examining the predictive role of oil prices over the excess returns associated with the market, size, book-to-market and momentum factors via bivariate cross-quantilograms.

This study makes use of the bivariate cross-quantilogram methodology recently developed by Han et al. (2016) to analyze the predictability patterns across the oil and stock markets by focusing on various quantiles that formally distinguish between normal, bull and bear as well as extreme market states.

The study analysis of systematic risk premia across the four regions shows that crude oil returns indeed capture predictive information regarding excess factor returns in stock markets, particularly those associated with market, size and momentum factors. However, the predictive power of oil return over excess factor returns is asymmetric and primarily concentrated on extreme quantiles, suggesting that large fluctuations in oil prices capture markedly different predictive information over stock market risk premia during up and down states of the oil market.

The findings have significant implications for the profitability of factor- or style-based active portfolio strategies and suggest that the predictive information contained in oil market fluctuations could be used to enhance returns via conditional strategies based on these predictability patterns.

This study contributes to the vast literature on the oil–stock market nexus from a novel perspective by exploring the effect of oil price fluctuations on the risk premia associated with the systematic risk factors including market, size, value and momentum.

The latest financial crisis marks a milestone in the development of financial markets. It was a period when it was possible to observe a booming development in the stock markets.

Faced with such a phenomenon, theorists have agreed on the need to resume the debate on the validity of the predictability of stock market returns, which is considered to be the cornerstone of all financial theories. The purpose of this article is to examine the predictability of the bearish stock market using a number of variables widely used in forecasting stock returns. In particular, we focus on variables related to imperfect credit markets.

We revisit the predictability of the bearish market using variables that measure the External Funding Premium (EFP), such as the Default Yield Spread.As the EFP is the key indicator of the extent of credit market imperfections, it should therefore be linked to stock market dynamics and provide useful predictive content.