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COVID-19 and its accompanying lockdowns were arguably the most traumatic events of our times. This paper investigates the impact of COVID-19 on market efficiency.

I analyze all publicly traded U.S. equities for 2014–September 2021, using intraday data from TAQ, TRACE, I/B/E/S and Capital IQ and daily data from CRSP, Thomson Reuters, Compustat, CRSP-Compustat Merged Database and FRED, using a controlled contrast between absolute abnormal returns for relevant halfhours versus absolute abnormal returns in control halfhours, measured by the negative of the coefficient of the fixed effect of the interaction between the indicator variable, and as the case may be, ticker and/or time period of interest, in the regression of halfhour-level absolute abnormal returns on tickers, months and interactions.

Using two separate objective, systematic, independent and ordinal per se measures of market efficiency based upon market reactions separately to key developments and earnings announcements, I find that U.S. equities markets were statistically and economically significantly less efficient during the first two-three months of the COVID-19 lockdowns.

Efficient capital markets provide substantial social benefits and are a sine qua non for the democratization of markets and the protection of investors, and constitute a critical mission of regulatory bodies such as the U.S. Securities and Exchange Commission (SEC) and the U.S. Financial Industry Regulatory Authority (FINRA).

The impact on market efficiency provides one critical input into the social cost-benefit analysis of public health policy and that of government interventions in general.

There has been no previous work done on the systematic and objective characterization of the impact of COVID-19 and associated lockdowns on market efficiency.

It has been argued in the literature that structural changes in the financial markets, such as integration, have the potential to cause herding behavior or correlated behavioral patterns in traders. The purpose of this study is to investigate whether there is any financial herding behavior in the Latin American Integrated Market (MILA), a transnational stock market composed of Chile, Peru, Colombia and Mexico stock exchanges and whether there is any ARCH or GARCH effect in the herding behavior models.

This study uses the modified return dispersion approach on daily index return data. The sample period is from January 03, 2002 to May 07, 2019. The data are obtained from the MILA database. To count time-varying volatilities in herding models, the authors run ARCH family regression with GARCH (1,1) settings. Hwang and Salmon (2004) model is used as a robustness test.

The authors found strong herding behavior under the general market conditions and moderate and partial herding behavior under some specified markets circumstances, such as bull and bear markets and high-low volatility states. Moreover, the pre-MILA period exhibits more herding behavior than the post-MILA period. The empirical results show that most of the ARCH and GARCH effects are statistically significant, implying that the past information of stock returns and market volatility significantly affect the volatility of following periods, which can also explain the formation of herding tendency among investors. Finally, the results of the robustness tests (Hwang and Salmon, 2004) confirm herding in all periods, except full sample period for Mexico and post-MILA period for Mexico and Colombia.

This study investigates the herding behavior in the MILA market in terms of market return, volatility and timing. A limitation of the paper is that the authors have not included other factors on the formation of herding behavior, such as macroeconomic factors, effects of regional or international markets and policy influences. The authors will explore the issue in the extension of the paper.

As MILA is the first virtual integration of stock exchanges without merging, the study provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are useful for academics, investors and policymakers in their investment and decision makings.

The paper provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are not only useful in practical implications, but also in social implications.

This study contributes to the herding literature by examining four different hypotheses in respect of the unique case of transnational stock exchange without fusions or corporate mergers, where each market maintains its independence and regulatory autonomy. The authors also contribute to the literature by including both ARCH and GARCH effects in the herding behavioral models along the Hwang and Salmon (2004) approach.

The aim of this study is to identify environmental, social and governance (ESG)-focused funds that can effectively uphold ethical principles while also delivering competitive financial returns by evaluating the performance of 24 well-established exchange-traded funds (ETFs). The study also compares the performance of four widely recognized ETFs representing NASDAQ (ticker: QQQ), S&P500 (SPY), Dow Jones (DIA) and Russell 2000 (IWM) with the sample of 24 ESG funds.

This paper utilizes four complementary measures, namely Sharpe, Sortino, Omega and Calmar ratios, to assess the risk-adjusted return performance of ETFs, with a particular emphasis on extreme downside risk.

The findings indicate that ESG-focused ETFs can predominantly outperform DIA and IWM in the last five years (1 November 2018–22 March 2023). However, when compared to QQQ and SPY, only ICLN, SUSA and DSI consistently delivered competitive risk-adjusted returns. The performance of DSI and SUSA is almost equivalent to QQQ and SPY even during the last ten years.

The paper conducts a risk-return analysis of alternative ESG investment funds, suggesting that not all ETFs are created equal and that careful selection is vital for achieving different investment objectives. It is imperative to recognize that past performance is not a reliable indicator of future outcomes, requiring consideration of other factors in the post-evaluation phase.

The study provides evidence to support the “doing well while doing good” hypothesis, indicating that competitive returns are achievable while also engaging in socially responsible investment.

This study fills a vital gap in the literature on ESG investment by highlighting that the choice of funds stands as the primary factor responsible for the conflicting findings by previous studies.

In the present study, we investigate whether enhanced momentum strategies outperform price momentum strategies and if they show greater resilience and stability under adverse market conditions. We also examine if such strategies are explained by prominent asset pricing models or are a result of behavioral mispricing.

Data consist of the equity shares of all companies listed on National Stock Exchange over the study period. To check the efficacy of enhanced momentum over price momentum, six momentum strategies have been designed and their raw as well as risk-adjusted returns using multi-factor models have been observed. Behavioral mispricing has been examined by constructing an investor attention index. Finally, few robustness tests have been performed to confirm the results.

We find that an enhanced momentum strategy which combines relative and absolute strength momentum outperforms conventional price momentum strategy in India. We also demonstrate that rational pricing models are not able to explain momentum profits for any of the strategies. Finally, we observe that investor overreaction is the possible explanation of momentum profits in India. Thus, our results confirm the role of behavioral mispricing in explaining momentum returns.

Our research is the first major attempt to study enhanced momentum strategies in the Indian context. We experiment with several new enhanced momentum strategies which have not been explored in prior literature. The findings have strong implications for global portfolio managers who wish to design profitable trading strategies.

Investor sentiment (optimism or pessimism) may influence investors to follow others (herding) while taking their investment decisions. Herding may result in bubbles and crashes in the financial markets. The purpose of the study is to examine the presence of herding and the effects of investor sentiment on herding in China and Pakistan.

The investor sentiment is captured by five variables (trading volume, advance/decline ratio, weighted price-to-earnings ratio, relative strength index and interest rates) and a sentiment index developed through principal component analysis (PCA). The study uses daily prices of 2,184 firms from China and 568 firms from Pakistan for the period 2005 to 2018.

The study finds that herding prevails in China while reverse herding prevails in Pakistan. Interestingly, as investors become optimistic, herding in China and reverse herding in Pakistan decrease. This indicates that herding and reverse herding are greater during pessimistic periods. Further, the increase in herding in one market reduces herding in the other market. Moreover, optimistic sentiment in the Chinese market increases herding in the Pakistani market but the reverse is not true.

Considering the greater global financial liberalization, and better opportunities for emotion sharing, this study has important implications for regulators and investors. Market participants need to understand the prevalent irrational behavior before trading in the markets.

Since individual proxies may depict different picture of the relationship between sentiment and herding therefore the study also develops a sentiment index through PCA and incorporates this index in the analysis. Further, this study examines cross-country effects of herding and investor sentiment.

The study examines the herd behaviour in Association of Southeast Asian Nations (ASEAN)-5 foreign exchange markets: Indonesia, Malaysia, Philippines, Singapore and Thailand.

We use the daily data as a sample. Cross-sectional absolute deviation (CSAD) and quantile regression are employed to identify the herding behaviour in these markets.

The findings report that all the foreign exchange markets in ASEAN-5 do not exhibit herding behaviour. Diversity of the fundamental economic, such as economic growth rate, rate of inflation and economy structure, produce monetary and exchange rate policies, is among the reasons for the absence of herd behaviour.

Maintaining macroeconomic stability and promoting market resilience to outside shocks should remain a priority for policymakers. As for investors, diversification is still a vital risk-management strategy.

This study provides a novel investigation into herd behaviour in ASEAN-5 foreign exchange market.

Based on the theoretical framework, this paper analyzes the sentiment-herding relationship in emerging stock markets (ESMs). First, it aims to examine the effect of investor sentiment on herding. Second, it seeks the direction of causality between sentiment and herding time series.

The present study applies the Exponential Generalized Auto_Regressive Conditional Heteroskedasticity (EGARCH) model to capture the volatility clustering of herding on the financial market and to investigate the role of the investor sentiment on herding behaviour. Then the vector autoregression (VAR) estimation uses the Granger causality test to determine the direction of causality between the investor sentiment and herding. This study uses a sample consisting of stocks listed on the Shanghai Composite index (SSE) (348 stocks), the Jakarta composite index (JKSE) (118 stocks), the Mexico IPC index (14 stocks), the Russian Trading System index (RTS) (12 stocks), the Warsaw stock exchange General index (WGI) (106 stocks) and the FTSE/JSE Africa all-share index (76 stocks). The sample includes 5,020 daily observations from February 1, 2002, to March 31, 2021.

The research findings show that the sentiment has a significant negative impact on the herding behaviour pointing out that the higher the investor sentiment, the lower the herding. However, the results of the present study indicate that a higher investor sentiment conducts a higher herding behaviour during market downturns. Then the outcomes suggest that during the crisis period, the direction is one-way, from the investor sentiment to the herding behaviour.

The findings may have implications for universal policies of financial regulators in EMs. We have found evidence that the Emerging investor sentiment contributes to the investor herding behaviour. Therefore, the irrational investor herding behaviour can increase the stock market volatility, and in extreme cases, it may lead to bubbles and crashes. Market regulators could implement mechanisms that can supervise the investor sentiment and predict the investor herding behaviour, so they make policies helping stabilise stock markets.

The originality of this paper lies in investigate the sentiment-herding relationship during the Surprime crisis and the Covid-19 epidemic in the EMs.

This study aims to examine empirically the nature of supply chain disruptions caused by the COVID-19 pandemic, particularly on the Indian automobile sector.

The authors evaluate the stock market performance of individual company and its quantitative relationship to certain variables related to company’s supply chain.

The authors analysed the company’s operations considering several ratios like asset intensity, company size, labour intensity and inventory to revenue.

The results of analysis can help the companies to understand how disruptions in the supply chain can affect the company’s operations and how it is perceived by the investors in the stock market.

Also, investors are benefitted, as they can understand how different companies with different operational characteristics react to global disruptions in supply chains, which in turn would help them to find better investment opportunities.

Although there is some literature available on the qualitative as well as quantitative analysis, the authors go further to analyse the impact of supply chain disruption on the stocks of the automobile sector.

This study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.

This study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.

This study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.

These findings can potentially help market participants, policymakers, stock exchanges and regulators.

Unlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

This study aims to investigate the relationship among liquidity, information asymmetry and political risk for non-US stocks listed on the NYSE. Additionally, the study aims to explore the impact of political tension on market quality.

This research adopts a quantitative methodology to examine the interplay between liquidity, information asymmetry and political risk in non-US stocks on the NYSE. A comprehensive analysis encompasses stocks from countries with varying political risk levels, demonstrating a correlation between lower political risk and improved market quality. In assessing the impact of US–China trade conflicts on Chinese stocks, political shocks are scrutinized. Results indicate that heightened political tension exacerbates information asymmetry and diminishes market liquidity, underscoring the susceptibility of stocks in politically strained environments to adverse shocks.

Non-US stocks from countries with lower political risk show higher liquidity and market efficiency, with narrower bid-ask spreads and smaller price impacts of trades. These stocks also demonstrate a higher market quality index, indicating improved overall market performance. In addition, during periods of escalated US –China political tension over trade policy, the liquidity of non-US stocks from China worsens, leading to wider bid-ask spreads and increased information asymmetry.

This study provides novel insights into the impact of political risk on stock market dynamics for non-US stocks listed on the NYSE, with a particular emphasis on the US –China trade conflict's effect on Chinese stocks.