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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.

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.

This paper examines whether there are differences in the nature of the price discovery process across established versus emerging stock markets using a twenty-country sample.

The authors analyse security returns for traces of predictability or non-randomness using variance ratio tests, Granger-Causality models and runs tests.

The findings pinpoint at predictabilities which seem inconsistent with market efficiency, and they suggest that the inherent cause of predictability differs across groups.

The authors present empirical evidence which may be used to attain a deeper understanding of the links between predictability and market efficiency, in view of the conflicting evidence in prior literature.

Whilst the pricing process in emerging markets may be hindered by delayed adjustments, in case of established markets it seems that there is a higher tendency for price reversals which could be due to prior over-reactions.

This study presents evidence of substantial differences in predictability across developed and emerging markets which was gleaned through the rigorous application of different empirical tests.

Considering the relationship between the central bank balance sheet and unconventional monetary policy after the 2008 financial crisis, it is crucial to see how the unconventional monetary policy, given near-zero interest rates, affects future stock market performance. This paper analyzes the impact of the Fed's balance sheet size on stock market performance.

To analyze the Fed's balance sheet size's long-term stock market implications, this paper uses the asset pricing framework of market return predictability such as Ordinary least squares (OLS) and Generalized method of moments (GMM) analysis.

Findings in this paper suggest that the Fed's balance sheet size, deflated by asset market wealth, presents evidence of return predictability during 1926–2015 that is robust against standard controls. These results can be explained through the redistribution of risk and the wealth channels of monetary policy transmission. The changing balance sheet size of a central bank (1) affects systemic risk, yields and expectations and (2) signals the future direction of monetary policy and thus economic outlook.

The main implication of these findings is that policymakers should avoid a severe imbalance between a central bank's balance sheet size and assets market wealth.

The empirical evidence in this paper documents a century-old relation between the Fed's balance sheet size and US stock market return using the Fed's balance sheet data for the last 100 years and stock market returns from the Center for research in security prices (CRSP) database.

This paper investigates the effect of the short sale ban by the Korean government on the relationship between the disagreement among investors and the future stock returns. Short selling in Korean stock market was banned twice in 2008 and 2011. The short sale ban provides a natural experiment environment to study the effect of the short sale constraints on the relationship between the disagreement among investors and the future stock returns. Furthermore, it is an exogenous shock in the point of individual stocks. Thus, this paper focus on short sale ban periods to analyzes the stock return predictability of the disagreement among investors’ opinions about analysts’ earnings forecasts. Main results of this paper are as follows: First, the portfolio within the top 30% of the disagreement among investors experiences the significantly higher returns than that within the bottom 30% of the disagreement only during short sale ban periods. However, the two portfolio returns are not significantly different during the other periods excluding the short sale ban periods. These results are robust even after controlling for firm sizes, boot to market ratios, and the momentum effects. Second, a portfolio with higher the disagreement among investors presents significantly positive abnormal returns estimated by Fama-French’s three factor model during short sale ban periods. On the other hand, the abnormal returns of the portfolio with lower the disagreement among investors are not significantly different from zero. Furthermore, those returns of the portfolio with lower disagreement are not affected by the short sale ban. Finally, our findings show that individual stock returns are positively related to disagreement after controlling for the characteristics of individual stocks. Consequentially, the stocks with higher disagreement are overvalued during the short sale ban periods according to our robust empirical analyses with various control variables. According to our findings, we conclude that the short sale constraints are important factors to determine the predictability of disagreement on future stock returns. These are consistent with the results of short sale ban on the U.S. stock market from Autore, Billingsley, and Kovacs (2011).

This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.

This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.

Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.

This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.

This paper investigates the relationship between output to input efficiency and stock return predictability in the Korean stock market. We measure the efficiency using Data Envelopment Analysis with independent outputs of sales and market value data. Sales efficiency measures the operational efficiency whereas market value efficiency measures the efficiency evaluated by the investors. Through our empirical analysis, it is found that low efficiency stocks in either measures tend to have higher future returns. However, if both efficiency measures are employed at the same time there exists a strong tendency that high operation efficient and low market value efficient stocks generate larger future returns. We find that DEA analysis for efficiency can process a cross-sectional stock return predictability in the Korean stock market.

The present research aims to examine a range of momentum trading strategies for the tourism and hospitality sector.

The paper followed the methodology of Jegadeesh and Titman (1993) to construct the portfolios. In this methodology, all portfolios were formed and evaluated by their cumulative stock returns over the past J periods and holding the position for the next K periods. In total, nine formation and holding periods were used, represented by 3, 6 and 12. For example, strategy 3–3 (that is, strategy with J = 3 and K = 3) refers to the strategy that stocks are ranked based on their previous three months and then held for the next three months.

The findings demonstrated that none of these momentum investing strategies was profitable. Most of the results, however, show positive, but insignificant momentum returns. This finding can be interpreted as price reversal over a horizon of three to twelve months in the US hospitality and tourism sector. These results are robust to size, different formation and holding combinations, beta and turnover.

Regarding the research limitations, this paper only considers the US tourism and hospitality sector. Therefore, the extension of results to other developed and developing markets should be taken carefully. Also, this paper relies only on the methodology of Jegadeesh and Titman (1993). Other methodologies could be suitable avenues for future research.

Investors and portfolio managers who seek for earning abnormal returns by investing in the US HT stocks can attain their hopes by constructing portfolios based on existing guidelines in the literature and adopting a short-term reversal trading strategy or by buying past losers and selling past winners of the US tourism and hospitality stocks.

This research contributes to the hospitality finance literature by offering the investors who are interested in the US hospitality and tourism sector an uncomplicated trading rule that uses real return data and is expected to generate actual returns. Moreover, the momentum strategy of Jegadeesh and Titman (1993) is never used in the hospitality finance literature.

本研究旨在探討各種可應用於旅遊及酒店業的動量交易策略。

本文按照 Jegadeesh 和 Titman（1993）的研究方法來建造投資組合。使用這研究方法時，所有投資組合均以它們在過去J 時期的累積股票收益和在未來K 時期的持倉來建立及評價的。九個組成方式及持有期被使用，以3、6、12來表示。例如，策略3-3（那就是說，該策略以J = 3和 K = 3）指的策略是以有關的股票基於過去三個月而被分等級，繼而在未來三個月被持有。

研究結果顯示，這些投資策略全沒帶來利潤；唯大部分結果顯示正動能策略報酬，雖報酬是微不足道的。這研究結果或許可理解為在美國酒店及旅遊業為期三至十二個月的價格逆轉。這些結果就規模、不同組成方式和持有組合、beta 和成交量而言是強而有力的。

就研究的局限而言，本文只是考慮美國的酒店及旅遊業；因此，如把研究結果伸延至其它已開發或發展中的市場，則需小心處理。另外，本文只依賴 Jegadeesh 和 Titman（1993）的研究方法，就此，使用其它研究方法會是日後相關研究的適當途徑。

欲透過投資於美國酒店及旅遊股票而尋求賺取異常收益的投資者和投資組合經理可如願以償，方法是基於文獻內現存的準則建造投資組合，以及採用短期的逆轉交易策略，或買入美國酒店及旅遊業過去輸家及賣出過去贏家。

本研究為酒店金融文獻作出貢獻，因研究為對美國酒店及旅遊業有興趣的投資者提供了使用實際收益數據及預期可創造實際回報的簡單交易規則；而且， Jegadeesh 和 Titman（1993）的動量策略從未在酒店金融文獻內被使用過。

The purpose of this paper is to consider the economic information content within several popular stock market factors and to the extent to which their movements are both explained by economic variables and can explain future output growth.

Using US stock portfolios from 1964 to 2019, the authors undertake three related exercises: whether a set of common factors contain independent predictive ability for stock returns, what economic and market variables explain movements in the factors and whether stock market factors have predictive power for future output growth.

The results show that several of the considered factors do not contain independent information for stock returns. Further, most of these factors are neither explained by economic conditions nor they provide any predictive power for future output growth. Thus, they appear to contain very little economic content. However, the results suggest that the impact of these factors is more prominent with higher macroeconomic risk (contractionary regime).

The stock market factors are more likely to reflect existing market conditions and exhibit a weaker relation with economic conditions and do not act as a window on future behavior.

Fama and French three-factor model still have better explanations for stock returns and economic information more than any other models.

This paper contributes to the literature by examining whether a selection of factors provides unique information when modelling stock returns data. It also investigates what variables can predict movements in the stock market factors. Third, it examines whether the factors exhibit a link with subsequent economic output. This should establish whether the stock market factors contain useful information for stock returns and the macroeconomy or whether the significance of the factor is a result of chance. The results in this paper should advance our understanding of asset price movement and the links between the macroeconomy and financial markets and, thus, be of interest to academics, investors and policy-makers.

The National Pension Service (NPS) of Korea is one of the largest institutional investors in the world and it has been known as the market stabilizer in the Korean stock market. Nevertheless, it is hard to find the research about the impact of the NPS on the futures market. We investigated the effect of the NPS’s trading KOSPI200 futures on the returns, the liquidity and the volatility of the market using the recent ten years’ transaction data. The main findings are as follows. First, the NPS’s net investment flow (NIF) in the KOSPI200 futures market shows the predictability about the returns of both KOSPI200 futures and KOSPI200 spot index. Second, the NPS’s NIF in the KOSPI200 futures market improves the liquidity of the KOSPI market, where the transactions involved in both the spot market and the futures market occur. Third, the NPS’s NIF in the KOSPI200 futures market reduces the volatility of both the KOSPI200 futures market and the KOSPI market. Unlike the prior studies showing that our futures market tends to increase the volatility of the stock market through the volatility transfer, our finding suggests that the NPS’s trading KOSPI200 futures contributes to decreasing the volatility in both markets. To the best of the authors’ knowledge, this paper is the first study that investigates the impact of the NPS’s trading KOSPI200 futures on the KOSPI200 futures market and the stock market. It shows that the NPS plays a role of the market stabilizer in the futures market. In addition, the NPS’s trading KOSPI200 futures also affects the KOSPI stock market, stabilizing it in terms of both the liquidity and the volatility.