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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 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.

Introduction: According to the effective market hypothesis, investors act rationally when making an investment decision. The hypothesis assumes that investors invest in a way that maximizes their returns, taking into account the new information received. If the information released on the market is interpreted in the same way by all investors, no investor would be able to earn above the market. This hypothesis is valid in case of efficient markets. In the event that investors show irrational behavior to the information released on the market, the markets move away from efficiency. Overreaction behavior is one of the non-rational behaviors of investors. Overreaction behavior involves investors overreacting by misinterpreting the new information released to the market. According to De Bondt and Thaler’s (1985), overreaction hypothesis in the event that investors overreact to the news coming to the market, after a period the false evaluation, the price of the security is corrected with the reversal movement, without the need of any positive or negative information. Aim: The purpose of this study is to examine investors’ overreaction behavior in mergers and acquisitions. For this purpose, overreaction behavior was analyzed for companies whose stocks are traded on the Borsa Istanbul, which were involved in mergers or acquisitions. Method: In the study, companies that made mergers and acquisitions for the period 2007–2017 were determined, and abnormal returns and cumulative abnormal returns were calculated by using monthly closing price data of these companies. Moreover, whether investors overreact to the merger and acquisition decision is examined separately for one-, three- and five-year periods. Findings: As a result of the research, it has been observed that there is a reverse return for one-, three-, and five-year periods. However, it has been determined that the overreaction hypothesis is valid for only one year.

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 paper explores abnormal price changes in the FOREX by using both daily and intraday data on the EURUSD, USDJPY, USDCAD, AUDUSD and EURJPY exchange rates over the period 01.01.2008–31.12.2018.

It applies a dynamic trigger approach to detect abnormal price changes and then various statistical methods, including cumulative abnormal returns analysis, to test the following hypotheses: the intraday behaviour of hourly returns on overreaction days is different from that on normal days (H1), there are detectable patterns in intraday price dynamics on days with abnormal price changes (H2) and on the following days (H3).

The results suggest that there are statistically significant differences between intraday dynamics on days with abnormal price changes and normal days respectively; also, prices tend to change in the direction of the abnormal change during that day, but move in the opposite direction on the following day. Finally, there exist trading strategies that generate abnormal profits by exploiting the detected anomalies, which can be seen as evidence of market inefficiency.

New evidence on abnormal price changes and related trading strategies in the FOREX.

This paper aims to analyze the impact of price-limit hits by hit type and when such hits start and stop using intraday trades and quotes at a one-second frequency for firms included in the BIST-50 index during the 13-months starting with March 2008. Like the recent COVID-19 period, this period includes the heightened stress in global financial markets in September 2008.

Using intra-day trades and quotes at a one-second frequency, the authors examine the market effects of price limits for firms included in the BIST-50 index during the global financial crisis. The authors compare the values of various metrics for 60 min centered on price-limit hit periods. The authors conduct robustness tests using auto regressive integrated moving average (ARIMA) models with trade-by-trade and with 3-min returns.

The findings are supportive of the following hypotheses: magnet price effects, greater informational asymmetric effects of market quality and each version of price discovery. Results are robust using samples differentiated by cross-listed status, same-day quotes instead of transaction prices and equidistant and trade-by-trade returns.

The authors use intraday data to reduce measurement error that is particularly pronounced when daily data are used to assess price limits that start and/or stop during a trading session. The authors contribute to the micro-structure literature by using ARIMA models with trade-by-trade and 3-min returns to alleviate some bias due to the autocorrelations in returns around price-limit hits in the presence of a magnet effect. The authors include some recent regulation changes in various countries to illustrate the importance of circuit breakers using price limits during COVID-19.

Builds on the work of Damodaran (1993) and Brisley and Theobald (19967) on measuring the speed with which stock markets convert information into price changes by using a simpler model of the price adjustment coefficient and applying it to 1988‐1966 data from the Hong Kong, US and Japanese markets and the Morgan Stanley Capital International indexes. Explains the methodology and presents the detailed results, which show that the Hong Kong adjustment is similar to the US and Japan for systematic and for all information; although the range of adjustment speeds depends on the sector and composition of the indexes. Makes many comparisons between the three markets and suggests that this method of describing market efficiency could provide a more consistent and objective ranking of worlds capital markets.

This paper aims to explore the election cycle and financial markets puzzle in a unique emerging market like Malaysia.

By employing an event-study methodology and wavelet analyses, the author tests for uncertain information hypothesis by examining the reactions of the Kuala Lumpur Composite Index (KLCI) and ringgit surrounding Malaysian general elections, spanning from GE5 (1978) to GE14 (2018). This paper also explores the relationship between KLCI and ringgit.

While the author does not find support for the uncertain information hypothesis, the author uncovers that KLCI tends to overreact following elections, regardless of the winning coalition. The author also records no relationship between KLCI and ringgit in the short run, but the author observes that ringgit leads KLCI in the long run.

The study’s findings bear implications for investors' disposition in the Malaysian equity market. Investors should square off their positions before the general elections to avoid equity market overreactions and potential losses.

Before Malaysia GE14 (2018) general election, Barisan Nasional carried the reputation as one of the longest-serving ruling coalitions in the world since Malaya independence in 1957. However, the ruling coalition was voted out in GE14 (2018), and the Malaysian equity has since dropped.

While the classic futures pricing tool works well for capital markets that are less affected by sentiment, it needs further modification in China's case as retail investors constitute a large portion of the Chinese stock market participants. Their expectations of the rate of return are prone to emotional swings. This paper, therefore, explores the role of investor sentiment in explaining futures basis changes via the channel of implied discount rates.

Using Chinese equity market data from 2010 to 2019, the authors augment the cost-of-carry model for pricing stock index futures by incorporating the investor sentiment factor. This design allows us to estimate the basis in a better way that reflects the relationship between the underlying index price and its futures price.

The authors find strong evidence that the measure of Chinese investor sentiment drives the abnormal fluctuations in the basis of China's stock index futures. Moreover, this driving force turns out to be much less prominent for large-cap stocks, liquid contracting frequencies, regulatory loosening periods and mature markets, further verifying the sentiment argument for basis mispricing.

This study contributes to the literature by relying on investor sentiment measures to explain the persistent discount anomaly of index futures basis in China. This finding is of great importance for Chinese investors with the intention to implement arbitrage, hedging and speculation strategies.