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In this paper we propose a novel approach to estimating and testing skewness in a stochastic volatility (SV) model. Our key idea is to replace a normal return error in the standard SV model with a split normal error. We show that this simple variation in the model brings about two large computational advantages. First, the SV can be simulated fast and efficiently using a one-block Gibbs sampling technique. Second, more importantly, this is the first to provide a marginal likelihood calculation method to formally test the skewness and SV in a Bayesian framework. We subsequently demonstrate the efficiency and reliability of our posterior sampling and model comparison methods through a simulation study. The simulation study results also show that neglecting skewness leads to inaccurate SV estimates and conditional expected returns. Our empirical applications to daily stock return data also show strong evidence of negative skewness.

This paper investigates the impact of regional factors on Islamic and conventional stock returns in the member countries of the Gulf Cooperation Council (GCC) from April 2011 to April 2021. This paper employs the quantile regression method to determine the effect of regional factors on GCC markets during different market states, enabling a more detailed investigation of the structure and degree of dependence. Regional stock market returns and regional political risk are used to study the effect of regional factors. The findings of this study reveal that the reaction of market returns to regional factors is heterogeneous across the conditional distribution of the GCC\"s stock returns. More specifically, the results demonstrate that changes in regional factors, with respect to Islamic and conventional markets, have asymmetric effects on stock returns in the majority of the GCC markets. Except in Qatar, the regional geopolitical risk negatively affects the GCC\"s Islamic stock returns during bearish markets. Results for conventional stock returns have the same negative effect, yet only in extreme market states. As for the impact of the regional stock market, we find that the regional Islamic index has a positive impact across almost all quantiles with a stronger effect during bullish markets, except in Bahrain and Oman. As for conventional markets, we observe the same impact throughout the GCC except for in Saudi Arabia. Islamic and conventional markets\" responses to changes in regional factors have similar behavior. Therefore, we conclude that, despite fundamental differences, Islamic and conventional stock markets in the GCC perform comparably, implying that a potential portfolio diversification benefit is not achieved except in limited cases.

Option-based measures can predict underlying stock returns, due to differences in price discovery and price pressure effects between options and underlying stocks. We investigate stock return predictability by various option price-based measures using REITs. REITs are more transparent and efficiently priced than general stocks, but REIT options are less liquid. Consistent with the model of Easley, O’Hara, and Srinivas (1998), most of the option price-based measures do not significantly forecast REIT stock returns, but changes in option implied volatilities are robust and significant return predictors. We provide further evidence supporting the informed trading channel instead of the price pressure effects.

International audience; Exloring the role of different types of investors on stock market is crticial since different types of investors react and behave differently when making investment decision. The role of investor behavior is a very important issue in an immature stock market like Vietnam stock market because the market is characterized by a large number of individual investors and low reporting standard. Institutional and foreign investors however play an influential role due to their large exposure and strong investment expertise. Clearly, examining the role of investor behavior and its impact on the stock market in Vietnam is an important topic in finance. A significant body of empirical research has shown that investor behavior is an essential factor to explain stock price that the classical financial theory cannot explain. This research examines the role of investor behavior in stock market by examining the relationship between investor behavior and stock return using the Vietnamese stock exchange data. We create a sentiment index using the principal components analysis (PCA). Consistent with the sentiment and stock return literature, the research shows a negative contemporaneous relationship between investor sentiment and market return.

Implemented in 2018, MiFID II changed sell-side analyst incentives in Europe, forcing analysts to justify the value they add. While the number of analysts decreases, the average stock return synchronicity with the market also decreases, implying an improvement in price informativeness. The decrease in synchronicity is larger for firms that are more important for the analysts and brokers covering them. It is also asymmetric and substantially larger for downside market movements. Our results suggest that, by changing incentives, MiFID II not only improves the quality of individual analyst work, but also achieves an improvement in the aggregate stock price informativeness.

Material news events can be potentially important sources of jumps in stock returns. We collect 21 million news articles associated with more than 9,000 publicly-traded companies and use textual analyses to derive measures to summarize the news. We find that stock return jumps (including time-variation in jump-size distributions and jump intensity) are significantly related to news flow frequency and content and those effects increase substantially over the last few decades. The sensitivity of jump probability to news is stronger for firms with higher media visibility, analyst coverage, and institutional ownership. This sensitivity also varies across different news categories. The primary working dataset based on Factiva news can be downloaded using this link: https://www.dropbox.com/s/62lt6uq1t4n6gcr/MainData_Factiva_Public.zip?dl=0.

This study investigates the unsymmetrical effect from air quality (AQ) to stock return (SR) in China’s different industries. Depending on quantile-on-quantile (QQ) test, it draws the important results in following aspects. For tourism, iron and steel, and automobile industries, their coefficient values between AQ and SR turn into negative from positive with deteriorating AQ. Conversely, the coefficients in the wind power, hydro power, thermal power, environmental protection, and medical equipment industries turn positive from negative. Some contributions are thus drawn when compared to existing literatures. Government industrial policy is regarded as an important supplement in explaining mechanism from AQ to SR, except investor sentiment. Industrial heterogeneity is seriously treated in this paper due to different industries have different responses to AQ. Besides, the QQ test is able to capture nexus between AQ and SR in specific quantiles through embedding non-parametric estimation into conventional quantile approach. Therefore, investors should avoid biased trading decisions under different air qualities. Meanwhile, government intervention is paid special attention when appearing serious air pollution.

Optimal stock investment decisions rely on assessments of the distribution of expected returns. Using a representative sample, we find over half the US population cannot answer simple questions on expected stock returns. Respondents who are unable to make any return prediction, who cannot answer questions on the distribution of expected returns, or who reveal unlikely distributional beliefs participate less in the stock market and have smaller stock investments. However, overoptimistic investors are more likely to participate in the stock market and have larger stock investments. These results persist after controlling for financial literacy, intelligence, education, and demographics. People who are ignorant about stock return distribution are more likely to invest in equities if they have higher levels of trust. Therefore, trust can substitute for cognition as a factor positively associated with individuals’ propensity to invest.

This paper investigates differences in value relevance of predictive stock return models depending on which firm size proxy (or proxies) is used, these being market value (MV), total book assets (TBA) and market value of total book assets (MVTA). Over the 27 year period of 1989–2015, MV provides higher value relevance in predicting future returns, while TBA provides higher value relevance when limited to large firms. Moreover, results reveal incremental explanatory power of approximately 27% when TBA are added to a one-year-ahead returns model already containing MV. The increase is 60% when examining only the last 10 years of the sample period. The findings of this study will help future accounting and finance research that uses predictive return models and potentially allow investors to make better resource allocation decisions leading to higher risk adjusted returns. In addition, the findings related to TBA will add to the debate on whether standard setters should place more emphasis on the valuation of assets and liabilities relative to earnings.

We compare the effects of corporate social responsibility (CSR) on firms' equity risk under two different (non-)financial reporting regimes: the risk-based U.S. and the content-based EU system. We observe a strongly negative CSR-risk relation in the EU, but hardly any in the U.S. In correspondence with goal-framing theory, we find several moderating effects on this association, depending on the reporting regime: i) A highly volatile market environment strengthens the risk-reducing effect of CSR in the U.S. system, but not in the EU; ii) Rising CSR awareness buttresses the risk-reducing effect of CSR in the EU, but weakens it in the U.S.; iii) Risk reductions are most strongly associated with social and governance rather than environmental activity in the EU regime, while there are no such individual effects in the U.S. Despite these differences, we observe that return-to-risk ratios decrease similarly with CSR activity in both the U.S. and EU system over the period 2003 - 2017.

The present study examines the effects of the steep surge in crude oil prices which has also been considered as an oil price shock on the stock price returns and currency exchange rates of G7 countries, namely Canada, France, Germany, Italy, Japan, the United Kingdom (UK) and the United States (US), in the context of the Russia–Ukraine conflict. Due to the outbreak of the war, the steep surge in Brent crude oil price returns is seen as an exogenous shock to stock price returns and exchange rates during the period from 2 January 2017 to 29 June 2022. The paper applies the Fractionally Integrated GARCH (FIGARCH) model to capture the effect of the crude oil price shock and the Breakpoint unit root test to examine the structural breaks in the dataset. Structural breakpoints in the dataset for the entire stock price returns and exchange rates are observed during the period commencing from the last week of February, 2022, to the last week of March, 2022. Except for TSX, NASDAQ and USD, noteworthy long memory effects running from Brent crude oil price to all the stock price returns along with the currency exchange rates for all G7 countries were also found.

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish–Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model’s results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets’ sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR’s highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH).

We study the effects of monetary policy surprises on stock returns under low and high monetary policy uncertainty in the U.S. using the Panel Smooth Transition Regression (PSTR) model to identify the uncertainty regimes. Monetary policy surprises are unexpected changes in the Federal Funds Rate (FFR) on Federal Open Market Committee (FOMC) announcement days, where the mimicking portfolio method is used to obtain a regular time series with surprises since the an-nouncements occur on an irregular basis. Using data for the period 1994-2008, we find a negative relationship between monetary policy surprises and stock returns under both uncertainty regimes but a less pronounced relationship between surprises and returns when uncertainty is low. Hence, it is more important to hedge against unexpected stock market volatility when the uncertainty in monetary policy is high compared to when uncertainty is low.

A green and sustainable business environment has gained the attention of recent researchers and policymakers due to environmental and social issues globally. Therefore, the present research investigates the impact of corporate social responsibilities (CSR), green investment, green credit, and assets return on the stock return of the Chinese export industry. This study has taken the ten top export companies from China using the database of the Shanghai stock exchange. This study collected data from financial statements and stock exchange databases from 2009 to 2020. This study has used panel data analysis techniques such as robust standard error and fixed effect model (FEM) to examine the relations among the variables. The results revealed that CSR, green investment, green credit, and return on assets have a significant and positive association with the stock return of selected industries. These results imply that CSR instigates higher financial performance in the export industry; thus, improving CSR and sustainable financing promote socio-economic and societal development.

We develop a dynamic model of corporate investment and financing, in which shocks to the value of collateralizable assets generate variation in firms’ debt capacity. We show that the degree of similarity among firms’ financial flexibility forecasts cross-sectional variation in return correlation. We test the implications of the model with firm-level data in two empirical analyses using i) an instrumental variable approach based on shocks to the value of collateralizable corporate assets and ii) the outbreak of the COVID-19 crisis as an event study. We find that firms in the same percentile of the cross-sectional distribution of financial flexibility have 62% higher correlation in stock-return residuals than firms 50 percentiles apart.

Whether a stock market should matter or not when monetary policy is concerned seems to be a controversial issue. The purpose of this study is to indicate whether the central bank should use monetary policy to help the stock market or not. Based on macroeconomic data such as interest rate and the stock market, we adopt a novel Bayesian time-varying regression model and determine that the impact of interest rate changes on stock returns varies over time in China, after controlling various macroeconomic factors. Although on average interest rates negatively impact stock price returns, they tend to have an abnormal positive effect at market high points, following a time-varying dynamic pattern. Surprisingly, during periods of overheated economic development, an increase in interest rates cannot suppress the rise in stock prices. Therefore, policymakers need to pay attention when accelerating the marketisation of interest rates and initiating the preventive role of timely and strategic adjustment of interest rates.

The study investigates the dynamic relationship between trading volume and stock returns on the JSE. The results show prima facie evidence of causality from returns to trading volume in the middle ...

This paper investigates the impact of the coronavirus disease 2019 pandemic on investors' trading behaviors around ex‐dividend dates in Europe. The sudden decrease in the number of companies paying dividends reduced the opportunities to capture dividends. Thus, the firms that maintained dividend payments during the pandemic attracted more interest than before. This led to a doubling in the magnitude of stock return patterns usually observed around ex‐dividend days. Our evidence indicates that dividend‐seeking investors are likely to be the main driver of the price patterns observed around ex‐dividend dates.

The active share of mutual funds drops significantly when investor sentiment is high, indicating that fund managers reduce their active stock selection and stay closer to their benchmarks during su...

This paper evaluates the predictability of monthly stock return using out-of-sample interval forecasts. Past studies exclusively use point forecasts, which are of limited value since they carry no information about intrinsic predictive uncertainty. We compare the empirical performance of alternative interval forecasts for stock return generated from a naïve model, univariate autoregressive model, and multivariate model (predictive regression and VAR), using U.S. data from 1926. It is found that neither univariate nor multivariate interval forecasts outperform naïve forecasts. This strongly suggests that the U.S. stock market has been informationally efficient in the weak-form as well as in the semi-strong form.

One of the reasons why a firm chooses to go private is the significant amount of cash flows which can exacerbate agency costs: this can indicate poor growth opportuni- ties and make external resources less required. In line with both knowledge economy and financial literature, the paper aims to indagate the relationship between agency costs, firm’s performance, and delisting. Despite there is evidence in finance that highlights the role of exchange regulators in the going private processes, just few studies focus on the voluntary delisting and its antecedents. Starting from the knowl- edge economy setting, this paper aims to define how firm’s financial performance moderates the relationship between agency costs and delisting choices. Based on a sample of non-financial firms delisted from the most important European stock mar- kets between 1997 and 2017, we applied an OLS regression to analyze the main var- iables that influence the going private processes. We find that a high level of stock market performance, associated with high level of free cash flows, promotes going private as a means of anticipating and preventing future value decreases. Finally, the paper provides practical implications for the development of new approach of delist- ing choice.

We use the Gordon (Rev Econ Stat 41(2):99-105, 1959) constant growth model to gauge the effects from innovations in implied growth versus discount rates. During the COVID-19 downturn and the Global Financial Crisis (GFC), stock returns were largely affected by a change in the long-run implied growth rate and only to a lesser extent by a change in discount rate, the latter typically used to explain stock returns in the classical asset pricing literature. We reach this conclusion by using ordinary least-squares (OLS) regressions of stock returns on the unobservable Gordon factors, which we estimate from firm-level valuation ratios D/P, P/E, and P/B. The effects from a decrease in implied growth outweigh those from an increase in discount rate by a factor of approximately 1.6 to 1.7. Also, firms with a decrease in implied growth show a stock return that is approximately 6.6% more negative than that of firms with no decrease in implied growth. Investors can infer valuable information from the joint interpretation of underlying market fundamentals as derived from the Gordon model.

There is a great deal of variation in how countries regulate the relationships between politicians and public-traded firms, but little evidence about how such policies affect firm performance. In 2013, China passed a new regulation that banned politicians from serving on the boards of directors of companies. Using a novel data set that links board members, government officials, and forced resignations, I estimate the effect of the policy on firm performance and stock returns. I find that the loss of a high-level politician significantly reduces a firm’s cumulative stock return and future profits. The effect is driven by officials from government-controlled public sectors and is larger when the firm is in low-marketized areas. The analysis provides important evidence about the efficacy of a commonly used policy tool for reducing political influence in the private sector.

I propose a new risk-based explanation of momentum. I derive a simple two-factor asset pricing model, which contains the market return and stock return dispersion (RD) as risk factors. This model offers a large fit for portfolios sorted on momentum and industry momentum, with cross-sectional R^2 estimates around 80%. Past losers are more positively correlated with return dispersion, which earns a negative price of risk, and hence the lower risk premia earned by those stocks. The model compares favorably with some of the most popular multifactor models in the literature, and also helps explaining the earnings momentum and profitability anomalies.

This paper examines whether insider trade clustering is associated with large stock return variations (i.e. crash or jump risk) in Korea. To investigate private information of insider trade cluster...

Firms with higher R&D intensity subsequently experience higher stock returns in international stock markets, highlighting the role of intangible investments in international asset pricing. The R&D effect is stronger in countries where growth option risk is more likely priced, but is unrelated to country characteristics representing market sentiments and limits-of-arbitrage. Moreover, we find that R&D intensity is associated with higher future operating performance, return volatility, and default likelihood. Our evidence suggests that the cross sectional relation between R&D intensity and stock returns is more likely attributable to risk premium than to mispricing.

The historical skewness of stock return distributions could potentially affect future stock returns. Previous studies in developed markets have shown that investors prefer shares exhibiting positiv...

This paper provides a novel perspective on the predictive ability of credit rating announcements over stock market returns and volatility using a novel methodology that formally distinguishes between different market states that can be characterized as bull, bear and normal market conditions. Using data on the credit rating announcements published by the three well-established credit rating agencies and data on BRICS and PIIGS stock markets, we show that the stock markets react heterogeneously, and in quantile-specific patterns, to rating announcements with more persistent and widespread effects observed for PIIGS stock markets. The effect of rating announcements is generally stronger and more widespread in the case of the volatility of returns, implying significant risk effects of these announcements. Finally, we show that the effect of the aggregate ratings is driven mostly by rating upgrades rather than downgrades, implying asymmetry in the predictive ability of rating announcements during good and bad times. Overall, our findings show that predictive models can be greatly enhanced by disaggregating the overall rating announcements and taking into account nonlinearity in the relationship between rating announcements and stock return dynamics.

The idiosyncratic volatility (IVOL) anomaly, documented in Ang, et al. (2006), has garnered a great deal of attention in the literature. Yet questions remain regarding the robustness and pervasiveness of the IVOL anomaly, with a particular concern that the IVOL anomaly might simply be the manifestation of market microstructure effect. In this paper, we show that the IVOL anomaly is strong and pervasive after we exclude stocks most susceptible to market microstructure noise — such as microcap stocks, penny stocks, and stocks with strong short-term return reversal. These results are robust to equal-weighting or value-weighting stocks in the IVOL portfolios. Our findings suggest that rather than being the cause of the anomaly, market microstructure noise actually weakens the IVOL anomaly.

Smooth transition exponential smoothing (STES) uses a logistic function of a user-specified transition variable as adaptive time varying smoothing parameter. This paper empirically addresses three aspects of the use of STES for volatility forecasting. Previous empirical results showed the method performing well in comparison with fixed parameter exponential smoothing and a variety of GARCH models. However, those results related only to forecasting weekly volatility. In this paper, we address the use of STES for forecasting daily volatility. A second issue that we evaluate is the robustness of STES in the presence of extreme outlying observations. The third aspect that we consider is the use of trading volume within a transition variable in the STES method. Our simulation results suggest that STES performs well in terms of robustness, when compared with standard methods and several alternative robust methods. Analysis using stock return data shows that STES has the potential to outperform standard and robust forms of fixed parameter exponential smoothing and GARCH models. The results suggest the use of the sign and size of past shocks as STES transition variables, and provide no clear support for the incorporation of trading volume in a transition variable.

The objective of this study is to investigate how investors in the Stock Exchange of Thailand practically trade in response to a magnitude of profits and losses, given a discussion of the widely well-known behavioral explanation, so called as the disposition effect. We provide empirical evidence of an existence of the V-shaped disposition effect, which has been recently found in several advanced equity markets. By adopting the methodology suggested by An’s (2016) and Fama and Macbeth (1973), we document that stock return patterns in relation to aggregate unrealized gains and losses of investors are consistent with the V-shaped selling schedule, given an increase in unrealized gains and losses over the period of January 1996 to December 2015. The effect of unrealized gains is stronger than that of unrealized losses and this asymmetry underlies the existence of the V-shaped disposition effect in the Thai equity market. Interestingly, the effect of the V-shaped selling schedule is strongest over the short-term holding time horizon. Last but not the least, stocks for which investors have large unrealized gains and losses outperform in the following month and the long-short trading strategy, based on this premise, generates the average 1.7% monthly (equivalent to 20.0% per year) abnormal return.

We test the time‐varying stock return responses to both expected and unexpected market illiquidity. Using a Markov‐switching methodology on Tunisian market data, we find that expected and unexpected market illiquidity effects on Tunisian small‐cap returns are always negative and stable. But for large caps, unexpected market illiquidity (illiquidity shock) effect on stock returns varies over time across two distinct regimes. In the first regime, illiquidity shocks have small effect on stock returns, whereas in the second regime, this effect rises threefold compared to its magnitude in first regime. The second regime (stress scenario) is short lived and characterized by high volatility. We also find that effect of expected market illiquidity on large cap returns is insignificant. In addition, we investigate the factors that lead to the stress scenario on stock exchange of Tunis. We find that stress scenario is contingent to funding illiquidity and economic distress in Tunisia. Results prove that Tunisian economic distress occurred in the context of subprime crisis and Tunisian political crisis of 2010.

Pastor and Stambaugh (2012) demonstrate that from a forward-looking perspective, stocks are more volatile in the long run than they are in the short run. We investigate how the economic constraint of non-negative equity premia aspects predictive variance. When investors expect non-negative returns in the market and thus impose the constraint on predictive regressions, they find that stocks are less volatile in the long run, even after taking account of estimation risk and uncertainties on current and future expected stock returns because the constraint provides additional parameter identification condition and prior information for future returns. Thus, it substantially reduces uncertainty on future stock returns. This fact, combined with the mean reversion property of stock return dynamics, leads to lower predictive variance in the long run.

This study explores the impact of geopolitical events on the stock return behavior of inter-Korean economic cooperation-related firms depending on the North Korean regime. We document empirical evi...

We develop a simulation-based procedure to test for stock return predictability with multiple regressors. The process governing the regressors is left completely free and the test procedure remains valid in small samples even in the presence of non-normalities and GARCH-type effects in the stock returns. The usefulness of the new procedure is demonstrated in a simulation study and by examining the ability of a group of financial variables to predict excess stock returns. We find robust evidence of predictability during the period 1948-2014, driven entirely by the term spread. This empirical evidence, however, is much weaker over subsamples.

This research examined whether the beta anomaly exists in the REIT market. By analysing a low-minus-high beta strategy and a betting-against-beta strategy in the REIT market, we find that high-beta REITs earn significantly lower risk-adjusted returns than low-beta REITs. This beta anomaly is only significant in the New REIT Era after 1993. The negative relationship between beta and REIT stock return does not disappear after taking into account some firm characteristics, suggesting that the beta anomaly in the REIT market is not driven by beta’s correlation with profitability, asset growth, lottery-like return or the skewness of stock returns. We find that institutional investors, whose portfolios increasingly contain a significant proportion of REITs, prefer the high-beta REITs. The exposure of institutional investors to high-beta REITs could explain the beta anomaly in the REIT market.

This article examines the behavior of the small-capitalization stock return cycle. The authors compare the period 1980–2020 with a study for the period 1960–1995. They find that in the earlier period small-cap stocks earned a return premium when the economy was rising, long-term rates were rising, the US dollar was rising, and market volatility was falling. During the last decade, a strong move in large-cap stocks dwarfed the small-cap premium. The authors conjecture that a change in the character of economic growth, rising long-term rates, and oversold small-cap conditions may reverse this. TOPICS:Portfolio theory, portfolio construction, wealth management Key Findings • The small-cap premium appears to have waned in the last 20 years. • The premium is, however, still related to economic cycles. • Economic conditions and valuations now favor a small-stock premium.

Model averaging for forecasting and mixed estimation is a recognized improved statistical approach. This paper is a first report on: (1). aggregate information from different conditional quantiles ...

This paper applies the BEKK-GARCH model to construct a comparative analysis of the heterogeneous relationship between the oil prices and stock prices of oil-related firms in the US and China. We find the following results. First, the effects of oil prices on stock returns depend heavily on the subsector category of an oil firm. Second, the effect from stock returns to oil prices displays distinguished country-specific patterns. The stock returns of oil firms in the US affect oil price, whereas stock returns of oil companies in China have limited influence on the oil market. Third, compared to the US, fewer Chinese oil company stocks are subject to risk spillover from oil market, but more stocks transmit their risks to oil market.

The objectives of this study are threefold: 1) to identify the concepts of earnings, stock return and liquidity risks on public shareholding industrial companies listed in the Amman Stock Exchange, 2) to investigate the relationship between earnings, stock return, strength and direction of this relationship, and 3) to find out the effect of liquidity risks at stock return and the effect of liquidity risks on the relationship between earnings and stock return on Jordanian public shareholding industrial companies. To achieve the objectives, an analytical descriptive approach was used. As the data on the public shareholding industrial companies listed in the Amman Stock Exchange were accredited by 52 companies for the period between 2014-2019, data validation tests and their suitability for analysis were considered. A linear regression test was used to test the study hypotheses on the statistical analysis program. The results show that there is a positive and significant correlation at significance level between the earnings and stock return. The results of the study also showed that there is a statistically significant negative effect at significance level of liquidity risk on stock return. In addition, it was demonstrated that liquidity risks have significant negative effects on the relationship between earnings and stock returns.

This study examines stock return predictability in business cycle fluctuations across 17 developed countries and 26 developing countries over the period from January 1970 to December 2019. We uncove...

We construct a robust stochastic discount factor (SDF) summarizing the joint explanatory power of a large number of cross-sectional stock return predictors. Our method achieves robust out-of-sample performance in this high-dimensional setting by imposing an economically motivated prior on SDF coefficients that shrinks contributions of low-variance principal components of the candidate characteristics-based factors. We find that characteristics-sparse SDFs formed from a few such factors—e.g., the four- or five-factor models in the recent literature—cannot adequately summarize the cross-section of expected stock returns. However, an SDF formed from a small number of principal components performs well.

We investigate the informational content of a huge assortment of NASDAQ articles about a joint cross-section of S&P 500 stock return data and related single-stock option data. Splitting the articles into a trading-time and an overnight archive, we distill tone from each of them. We show that media-expressed tone is informative about option markets and that both option data and tone predict stock returns. The predictive power of option variables is robust to partialling out tone, but varies depending on whether tone is from the overnight or the trading-time archive. A potential reason is that the archives differ in terms of their thematic content. Overall, we conclude that the informational content of option data for predicting single-stock returns extends beyond the information summarized in tone and traditional market factors.