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This study examines the semi-strong market efficiency of the Indian agricultural commodity market in light of market reforms and policies. This study investigates whether the market reforms have boosted the speed of price adjustment and influenced the market quality.

The study used the daily data of nine agricultural commodities. To precisely capture the effects of market microstructure changes, this study split the whole data into pre- and post-ban and pre- and post-reform eras. To ascertain the velocity of price adjustment, the authors used the ARMA (1,1) model, and the ADD VRatio was employed to identify the price movement on a specific day.

This study found that full incorporation of information happens sometimes. The authors noticed no gradual progress in the quickness of price adjustment. Since both methods suggested the same result for the period, the authors confirm that market microstructure changes do not enhance market quality.

This research has implications for academicians, policymakers and market players.

The paper has twofold novelty. First, this is a contemporary topic, and very few studies have been done in the Indian agriculture context. Second, the study has implications for policymakers and government because it highlights the effects of structural changes on market quality and market efficiency.

Momentum returns are considered an anomaly in the finance literature as their existence cannot be fully explained under the asset pricing paradigm. This study attempts to shed more light on this anomaly by investigating the determinants of momentum returns.

The panel data technique is applied to the sample of 40 countries worldwide from 1996 to 2018. The authors use the panel-corrected standard error (PCSE) model to estimate the coefficient of World Governance Indicators (WGI), whereas the fixed effect model is used to determine the coefficient for corporate governance indicators (CGIs). The choice of PCSE estimation methods is guided by the fact that WGI variables are subjected to serial correlation, heteroskedasticity and cross-sectional dependence problems while CGI variables are not. Furthermore, a composite WGI index is constructed using principal component analysis (PCA).

Regression analysis shows a negative and significant relationship between WGI index and momentum returns. The negative coefficient value of WGI supports the prediction of the overreaction hypothesis, which postulates a lower behavioral bias in the market with high governance quality. Breaking down of the WGI by their six indicators reveals that four of the indicators (control over corruption, government effectiveness, stability and avoidance of violence) are negative statistically significant with momentum returns while two indicators are not significant. As for CGIs, only one (strength of investor protection) of the four tested indicators is negative and significantly related to momentum returns.

The study fills the gap in economic literature by highlighting the association between governance quality at the country (WGI) and firm level (CGI) on stock momentum returns.

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.

Investors are inattentive to continuous information as opposed to discrete information, resulting in underreaction to continuous information. This paper aims to examine if the well-documented return predictability of the strategies based on the ratio of short-term to long-term moving averages can be enhanced by conditioning on information discreteness. Anchoring bias has been the popular explanation for the source of underreaction in the context of moving averages-based strategies. This paper proposes and studies another possible source based on investor inattention that can potentially result in superior performance of these strategies.

The paper uses portfolio sorting as well as Fama-MacBeth cross-sectional regressions. For examining the role of information discreteness in the return predictability of the moving average ratio, the sample stocks are double-sorted based on the moving average ratio and information discreteness measure. The returns to these portfolios are computed using standard approaches in the literature. The regression approach controls for various well-known return predictors.

This study finds that the equally-weighted monthly returns to the long-short moving average ratio quintile portfolios increase monotonically from 0.54% for the discrete information portfolio to 1.37% for the continuous information portfolio over the 3-month holding period. This study observes a similar pattern in risk-adjusted returns, value-weighted portfolios, non-January returns, large and small stocks, for alternative holding periods and the ratio of 50-day to 200-day moving average. The results are robust to control for well-known return predictors in cross-sectional regressions.

To the best of the authors’ knowledge, this is the first paper to document the significant role of investor inattention to continuous information in the return predictability of strategies based on the moving average ratios. There are many underreaction anomalies that have been reported in the literature, and the paper's results can be extended to those anomalies in subsequent research.

The findings of this paper have important practical implications. Strategies based on moving averages are an extremely popular component of a technical analyst's toolkit. Their profitability has been well-documented in the prior literature that attributes the performance to investors' anchoring bias. This paper offers a readily implementable approach to enhancing the performance of these strategies by conditioning on a straightforward measure of information discreteness. In doing so, this study extends the literature on the role of investor inattention to continuous information in anomaly profits.

While there is considerable literature on technical analysis, and especially on the performance of moving averages-based strategies, the novelty of this paper is the analysis of the role of information discreteness in strategy performance. Not only does the paper document robust evidence, but the findings suggest that the investor’s inattention to continuous information is a more dominant source of underreaction compared to anchoring. This is an important result, given that anchoring has so far been considered the source of return predictability in the literature.

The study looks into the idea of Circular Economy through the lens of stock market performance. More specifically the authors examine if a company's association with the innovative and environment friendly disposal practices has an implication on its stock market performance.

The study adopts a quantitative approach to derive conclusions. The market efficiency and excess volatility are estimated using OHLC measures for more accuracy. For robustness checks VRatio, bootstrapping and simulation are used as detailed in section 3.2 of the study.

The study estimates show a clear sign of excess volatility in the majority of the stocks under consideration. That confirms that the market is highly fragmented for Circular Economy–focused stocks. These types of stocks are often cyclical in nature and can offer a variety investment opportunity in the short to medium term. In addition to it the study estimates confirm that Circular Economy–focused stocks show limited signs of market efficiency. Thus, unlike ESG investment the concept of Circular Economy investment is a bit more ambiguous to the global investors.

The present study is first of its kind that looks into the idea of Circular Economy through the lens of stock market performance.

Stock market performance is paramount to every country, as it signifies economic growth, business performance, wealth maximization, savings deployment and consumer confidence. This study investigates the disparities in the market performance of listed firms in Romania. This study also examines whether the COVID-19 crisis affected market performance.

The data were collected from 69 firms listed on the Bucharest Stock Exchange (BSE) from 2018 to 2022, belonging to 11 sectors. This study used several methods to achieve its objectives. Difference tests were considered to analyze the performance of Romanian companies before and during the COVID-19 crisis, as well as across sectors. Regression analysis was also conducted to estimate the effect of the COVID-19 crisis and classification type on Romanian companies' performance. Additional analyses were performed to verify the findings of the present study.

The study’s findings indicate a clear difference in market performance between the pre-crisis and crisis periods. The COVID-19 pandemic had an adverse and significant impact on market performance. However, after the market contraction in the early stage of the COVID-19 pandemic outbreak, the stock market outperformed the pre-pandemic capitalization levels and the regional and global indices evolution. Furthermore, there was a difference in market performance across sectors. In particular, the communication services sector has specifically demonstrated accelerated growth.

This research examines the variation in the market performance of companies before and during the COVID-19 pandemic and across different sectors. It also provides evidence of the potential impact of COVID-19 on firms' market performance. This research contributes to a better understanding of how sectors perform during times of crisis.

The authors compare sentiment level with sentiment shock from different angles to determine which measure better captures the relationship between sentiment and stock returns.

This paper examines the relationship between investor sentiment and contemporaneous stock returns. It also proposes a model of systems science to explain the empirical findings.

The authors find that sentiment shock has a higher explanatory power on stock returns than sentiment itself, and sentiment shock beta exhibits a much higher statistical significance than sentiment beta. Compared with sentiment level, sentiment shock has a more robust linkage to the market factors and the sentiment shock is more responsive to stock returns.

This is the first study to compare sentiment level and sentiment shock. It concludes that sentiment shock is a better indicator of the relationship between investor sentiment and contemporary stock returns.

This study aims to analyse the effects of Economic Policy Uncertainty (EPU) on the return of growth/value and small/large-cap stocks during expansionary and recessionary periods across a conditional distribution.

The authors selected a sample covering the period between 01/1995–05/2021. Quantile regressions were applied to the EPU and Russell indices. Business cycles were established following the NBER.

The results show that EPU has a negative effect on stocks with the intensity of the effect depending on the stock's profile. Small-cap and growth stocks were found to be most sensitive to EPU, especially during recessions. The negative effect is moderated by the economic cycle but is progressively diluted at the lower tail of the stock return distribution.

The findings shed more light on investment strategies for growth/value investors that pursue opportunities arising from a changing economic cycle.

This study makes the following contributions: (1) explores the impact of EPU on the return of different stocks across a conditional distribution, and (2) provides evidence on how the economic cycle influences EPU impact on growth/value stocks and small/large stocks.

:本研究擬分析跨條件分佈、以及於擴張期和衰退期,經濟政策不確定性對成長型股票/價值股和小盤股/大型股的收益的影響。

我們選擇了涵蓋1995年1月與2021年5月期間的樣本進行研究。我們於經濟政策不確定性指數和羅素指數上採用分位數迴歸法進行研究; 並跟隨著美國國家經濟研究局,建立了多個經濟週期。

研究結果顯示,經濟政策不確定性對股票是有負面影響的,而影響的強度則視乎股票的投資組合而定。我們發現,小盤股和成長型股票對經濟政策不確定性是非常敏感的,尤其是在經濟衰退期間。這負面影響會被經濟週期緩和,唯這緩和作用卻會在股票收益的低尾處逐漸減輕。

研究結果使我們更容易理解為尋找因經濟週期改變而衍生的機會的增長/價值投資者所提供的投資策略。

本研究有以下的貢獻:(一) 、 探究了經濟政策不確定性對跨條件分佈、不同的股票收益的影響; (二) 、為經濟週期會如何左右經濟政策不確定性對成長型股票/價值股和小盤股/大型股的影響,提供了證據。

Motivated by the rapid spread of the COVID-19 outbreak in the world, this study aims to explore the stock markets’ response toward it in the Middle East and North Africa (MENA) countries.

Ordinary least squares (OLS) regressions were used to analyze the association between the COVID-19 outbreak and stock market returns. The author made use of a panel data set, including 4,195 observations from 13 countries in MENA for the period January 29, 2020, to April 30, 2021. The dependent variable was stock market returns. The explanatory variable, i.e. COVID-19, was proxied by daily growing confirmed infected cases and daily growing confirmed death cases caused by the outbreak.

The author finds that stock markets have responded negatively to the growth in COVID-19 deaths. Meanwhile, stock markets show no reaction to the daily growth of confirmed infected COVID-19 cases.

This study presents worthy information to regulators and policymakers in MENA countries, whose responsibility is to govern regulations at the macro and micro levels based on a comprehensive route that leaves no one behind. This study also offers significant insights to policymakers, managers, investors and society by showing how the stock markets quickly react to outbreaks.

This study is, to the best of the author’s knowledge, among those exploring the impact of the COVID-19 outbreak on stock market returns in the MENA countries.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.