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Although the solid empirical proof of momentum is documented in various stock markets, there are many debates among academics with respect to the source of momentum profit. The first aim of this paper is intensively re-examine the momentum profit in Vietnam, an important emerging market. Secondly, the authors study the return predictability of a measure of investors’ overreaction, then examine whether the momentum effect in Vietnam is explained by overreaction.

Using the weekly data of more than 300 non-financial Vietnamese stocks during 2009–2019, the authors build a measure of investors’ overreaction, which is based on trading volume and the sign of stock returns. Consequently, to investigate whether momentum exits after controlling for overreaction, the authors carefully compare trading strategies based on overreaction with price momentum strategies using adjusted returns and double sorts on past returns and levels of overreaction.

The article makes three main findings. Firstly, the authors discover the empirical evidence of momentum in the Vietnamese equity market. Secondly, the measure of overreaction could be a predictor of Vietnamese stock returns. Stocks that have experienced a stronger upward overreaction provide a higher average return. Finally, returns on trading strategies based on overreaction are robust after adjusting for momentum, while returns on momentum portfolios become insignificant after adjusting for overreaction. By double-sorting, the authors document that holding past returns constant, the average returns of portfolios rise monotonically with their measure of overreaction. Hence, the momentum profit in Vietnam arises from investors’ overreaction.

The paper extends previous research on the behavioral explanation of momentum in emerging stock markets, which has not been fully exploited in the literature.

This study examines the impact of firm-specific information and macroeconomic variables on market overreaction of US and Chinese winner and loser portfolio before and during COVID-19.

The firm-specific information includes firm size, volume, volatility, return of asset (ROA), return of equity (ROE), earning per share (EPS) and quick ratio while the macroeconomic variables are export rate, import rate, real GDP, nominal GDP, FDI, IPI and unemployment rate. Besides, one-third of the top performance stocks are categorized as winner portfolio while one-third of lowest performance stocks are categorized as loser portfolio. This study uses AECR to indicate stock return and measure market overreaction. GAECR is used to determine contrarian profit. The data range of pre-COVID-19 is from 1-Jan-2015 to 31-Dec-2019 while the period of COVID-19 is from 1-Jan-2020 to 31-Dec-2020.

In pre-COVID-19, firm-specific information (volatility, ROA, ROE and EPS) and macroeconomic variables are found to be correlated to stock return in US and Chinese portfolios except Chinese winner portfolio. Nonetheless, the impact of firm-specific information has vanished and macroeconomic variables are significant to stock return in COVID-19. It shows that investors rely on the economic indicators to trade in turbulent period due to emergence of COVID-19 as a disruption in market. Furthermore, US and Chinese portfolios are overreacted during COVID-19. Chinese loser portfolio has higher tendency of overreaction than US loser portfolio while US winner portfolio has higher tendency of overreaction than Chinese winner portfolio.

The results of this study assists academician, practitioners and investors on understanding and create awareness to the existence of market overreaction and the determinants that can cause the phenomenon.

The purpose of this paper is to examine price overreactions in the case of the following cryptocurrencies: bitcoin, litecoin, ripple and dash.

A number of parametric (t-test, ANOVA, regression analysis with dummy variables) and non-parametric (Mann–Whitney U-test) tests confirm the presence of price patterns after overreactions: the next day price changes in both directions are bigger than after “normal” days. A trading robot approach is then used to establish whether these statistical anomalies can be exploited to generate profits.

The results suggest that a strategy based on counter-movements after overreactions is not profitable, whilst one based on inertia appears to be profitable but produces outcomes not statistically different from the random ones. Therefore, the overreactions detected in the cryptocurrency market do not give rise to exploitable profit opportunities (possibly because of transaction costs) and cannot be seen as evidence against the efficient market hypothesis (EMH).

The overreactions detected in the cryptocurrency market do not give rise to exploitable profit opportunities (possibly because of transaction costs) and cannot be seen as evidence against the EMH.

This purpose of this study is to empirically investigate the investors overreaction and underreaction behaviours across the sectoral stock indices in the Indonesian stock market.

Nine weekly sectoral stock indices, comprising agriculture; mining; basic industry and chemicals; miscellaneous industry; consumer goods industry; property and real estate; infrastructure, utilities and transportation; finance; and trade, service and investment for the period 2009-2012 were analysed using the paired dependent sample t-test. To provide more insightful empirical evidence, the presence of market anomaly of investor’s overreaction and underreaction was examined on five observations with different vulnerable times.

The study documented that the overreaction anomaly was present among the winner portfolios in the entire sectoral indices. With the exception of the sectoral index of basic industry and chemicals on the loser portfolio, the study documented the presence of underreaction anomaly among all other sectoral indices in Indonesia. These findings implied that the investors might be able to gain significant profits investing their monies in the sectoral stock market in Indonesia by implementing the contrarian strategy.

Originality in this paper lies in the discussion of overreaction of investors in Indonesia where the stock market has great potential and has different characteristics and different problems from other regions.

This study aims to investigate which stock characteristics and corporate governance variables affect stock price overreaction and volatility during the COVID-19 pandemic period.

A set of stock characteristics and corporate governance variables which may affect price overreaction and volatility were identified following a review of the literature. A dummy variable was created for the cross-sectional analysis to take into account the unique sector effect in the consumer staples sector. Out of sample analysis was conducted to confirm the robustness of the main results.

The empirical results consistently show that size, dividend and trading volume determine the stock price reactions when the market is in turmoil during the pandemic period. Board size and average board tenure exhibit moderate effects on reducing the stock price reactions, but the effects become insignificant while controlling for the firm characteristics in the regressions. The results remain robust when tested out of the sample. More interestingly, a consumer staples sector effect is identified and tested. The test results show that the consumer staples sector effect mitigates the stock price reactions.

The results have practical implications for investors who aim to manage desired levels of risk in their portfolios during the pandemic. The results also provide meaningful insights to stock market speculators regarding pandemic-related speculation opportunities.

This study makes a meaningful connection between the irrational stock market anomalies and the COVID-19 pandemic.

The purpose of the study is to examine overreaction effect in the Chinese stock market after the global financial crisis (GFC) of 2007 for all the stocks listed in Shanghai Stock Exchange (SSE) Composite 50 index.

To capture overreaction effect in the stock listed at SSE 50 Index, a time series analysis of average cumulative abnormal return within a unified framework is applied for the period of January 2009 to December 2015. From these loser and winner portfolios, contrarian strategy is applied to build arbitrage portfolio, which is the difference of mean reversions between loser and winner portfolios. The portfolio construction is based on a 12-month formation period and 6-month testing period for intermediate-term analysis and. for short-term analysis, 6 month formation and 3 month testing periods. The authors also applied regression analysis to test a return reversal effect for the sampled period.

Results show that contrarian strategy yields positive excess returns for the arbitrage portfolio for most of the testing periods. The intermediate baseline case shows the arbitrage portfolio producing an average excess return of 14.1%, while even the short-term one produces 4%, which is statistically significant at the 5% level. The study finds asymmetrical overreactions in the SSE especially for loser portfolios. The biggest winner and loser portfolios follow the mean reversal effect. Moreover, before-after test for the biggest winner and loser portfolios shows that the losers recovered and beat the market immediately.

The study could benefit government, policy makers and regulators by studying how presence of more individual investors than institutional investors of China stock market leads to more irrational decisions giving rise to volatility. The regulators could build favourable policies for institutional investors to give them incentive to invest more than individual investors through which market volatility could be controlled.

This research contributes to market behaviour research, showing how working under hypotheses of overreaction; gains can be made with contrarian investment strategy through arbitrage portfolios. The authors provide specific additional support for the short and medium-term overreaction in the SSE for the period 2009–2015 using regression analysis.

This research contributes to market behaviour research, showing how working under hypotheses of overreaction; gains can be made with contrarian investment strategy through arbitrage portfolios. We provide specific additional support for the short and medium-term overreaction in the SSE for the period 2009–2015 using regression analysis.

The purpose of this paper is to assess the performance of a contrarian investment strategy focusing on frequently traded large-cap US stocks. Previous criticisms that losers’ gains are not due to overreaction but due to their tendency to be thinly traded and smaller-sized firms than winners are addressed.

Portfolios based on past performance are constructed and it is examined whether contrarian returns exist. The Capital Asset Pricing Model (CAPM), Fama and French three-factor model and the Carhart’s (1997) momentum portfolio are used to test whether excess returns are feasible in a contrarian strategy.

The results show an asymmetric performance following portfolio formation. Although both, winners and losers portfolios, have gains during holding periods, losers outperform winners at all times, and with a differential of up to 29.2 per cent 36 months after portfolio formation. Furthermore, the loser and the winner portfolios’ alphas are significant, suggesting that the CAPM and the multifactor models are unable to explain return differentials between winners and losers. Our evidence supports two main conclusions. First, stock market overreaction still holds for a sample of large firms. Second, this is robust to the Fama and French’s (1993, 1996) three-factor model and Carhart’s (1997) momentum portfolio. Findings emphasize the relevance of a contrarian strategy when rebalancing investment portfolios.

Portfolio managers can improve stock returns by selling past winners and buying previous loser large-cap US stocks.

This paper is the first, to the authors’ knowledge, to examine frequently traded large-cap US stocks to avoid infrequent trading and size concerns.

The purpose of this study is to analyze market inefficiencies in the market for cryptocurrencies by providing a comprehensive analysis of short-term (over)reactions that follow significant price changes of such currencies.

This study identifies and analyzes overreactions and mispricing in markets for cryptocurrencies by applying a broad set of thresholds that depend on market-specific dynamics and volatilities. This study also analyzes the returns on days following abnormal returns and identifies significant differences from normal returns using the t-test and the Mann–Whitney U-test. The researchers further complement the literature by using end-of-the-day returns in addition to high-low returns. Additionally, this study considers a broad sample of 50 cryptocurrencies for an expanded time span (2015–2020) that includes the big currencies as well as smaller currencies.

Findings detect the existence of overreactions and, thus, market inefficiencies in crypto markets. The findings for different methodological approaches are similar, which underpins the robustness of the findings. By considering a broad sample that includes small and big currencies, we can show the existence of a market size effect. By considering a broad set of thresholds, the authors further found evidence for a magnitude effect, which means that higher initial abnormal returns are related to higher inefficiencies.

This paper has practical implications. Market inefficiencies were detected, which can be used in practical trading to obtain excess returns. In fact, methodological approach of this study and its results can be used to derive a strategy for trading in cryptocurrencies that can be easily implemented. Based on the study’s findings, the authors can expect positive access returns by applying this trading strategy.

The authors complement the literature on market inefficiencies and mispricing in crypto markets by analyzing price patterns after initial abnormal returns. Researchers contribute by applying different methodological approaches in addition to the approaches used so far, by considering a set of different thresholds and by applying a much broader data set that enables the study to analyze additional aspects.

This article aims to examine the link between uncertainty and analysts' reaction to earnings announcements for a sample of European firms during the period 1997-2007. In the same way as Daniel et al., the authors posit that overconfidence leads to an overreaction to private information followed by an underreaction when the information becomes public.

In this study, the authors test analysts' overconfidence through the overreaction preceding a public announcement followed by an underreaction after the announcement. If overconfidence occurs, over- and underreactions should be, respectively, observed before and after the public announcement. If uncertainty boosts overconfidence, the authors predict that these two combined misreactions should be stronger when uncertainty is higher. Uncertainty is defined according to technology intensity, and separate two types of firms: high-tech or low-tech. The authors use a sample of European firms during the period 1997-2007.

The results support the overconfidence hypothesis. The authors jointly observe the two phenomena of under- and overreaction. Overreaction occurs when the information has not yet been made public and disappears just after public release. The results also show that both effects are more important for the high-tech subsample. For robustness, the authors sort the sample using analyst forecast dispersion as a proxy for uncertainty and obtain similar results. The authors also document that the high-tech stocks crash in 2000-2001 moderated the overconfidence of analysts, which then strongly declined during the post-crash period.

This study offers interesting insights in two ways. First, in the area of financial markets, it provides a test of a major over- and underreaction model and implements it to analysts' reactions through their revisions (versus investors' reactions through stock returns). Second, in a broader way, it deals with the link between uncertainty and biases. The results are consistent with the experimental evidence and extend it to a cross-sectional analysis that reinforces it as pointed out by Kumar.

The purpose of this paper is to present a new strategy of portfolio selection.

After making a comparative survey of different strategies of portfolio selection adopted by portfolio managers in Tunisia, the paper proposes a new strategy, which it calls weighted overreaction strategy. This strategy consists in over‐weighting the stocks having bad performances in the past.

The new proposed strategy turned out to be more performing than size, PER, and overreaction strategies in the Tunisian stock market via a mean equality test. Those who adopt it should create a loser portfolio and should sell it at a later period (12 months) and generate average annual returns of 241.75 percent.

This result deserves generalization to other stock markets. As the Tunisian stock market is marked by its looseness and low capitalization, applying this strategy over similar or more developed market would open the way for research aiming to define other strategies and to select the best one for each market. Indeed, it should investigate investors' behavior, which is certainly not the same in each stock market and outline the specific strategy for each market.

The weighted overreaction strategy generated a considerable gain compared to other portfolios.

The new proposed strategy turned out to be more performing than the other ones.