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In this paper, we provide new evidence to strengthen the stock market overreaction hypothesis by examining a new context that has not been explored before. Our research is inspired by the widely held belief that investor sentiment experiences abrupt changes from optimism to pessimism as the market switches between bull and bear states.

If the stock market overreaction hypothesis is correct, it implies that investors are inclined to become excessively optimistic during bull markets and overly pessimistic during bear markets, resulting in overreaction and subsequent market correction. Consequently, the study first develops two testable hypotheses that can be used to uncover the presence of stock market overreaction with subsequent correction. These hypotheses are then tested using long-term data from the US market.

The study's findings support the hypothesis while also revealing a significant asymmetry in investor overreaction between bull and bear markets. Specifically, our results indicate that investors tend to overreact towards the end of a bear market, and the subsequent bull market starts with a prompt and robust correction. Conversely, investors appear to overreact only towards the end of a prolonged bull market. The correction during a bear market is not confined to its initial phase but extends across its entire duration.

Our study has some limitations related to its focus on investigating stock market overreaction in the US market and analyzing the pattern of mean returns during bull and bear market states. Expanding our study to different global markets would be necessary to understand whether the same stock market overreaction effect exists universally. Furthermore, exploring the relationship between volatility and overreaction during different market phases would be an exciting direction for future research, as it could provide a more complete picture of market dynamics.

Our study confirms the presence of the stock market overreaction effect, which contradicts the efficient market hypothesis. We have observed specific price patterns during bull and bear markets that investors can potentially exploit. However, successfully capitalizing on these patterns depends on accurately predicting the turning points between bull and bear market states.

The results of our study have significant implications for market regulators. Stock market overreactions resulting in market corrections can severely disrupt the market, leading to significant financial losses for investors and undermining investor confidence in the overall market. Further, the existence of overreactions suggests that the stock market may not always be efficient, raising regulatory concerns. Policymakers and regulators may need to implement policies and regulations to mitigate the effects of overreactions and subsequent market corrections.

This paper aims to provide additional support for the stock market overreaction hypothesis using a new setting in which this hypothesis has not been previously investigated.

This paper investigates share price clustering and its determinants across Nasdaq Stockholm, Copenhagen, Helsinki, and Iceland.

This paper investigates share price clustering and its determinants across Nasdaq Stockholm, Copenhagen, Helsinki, and Iceland. Univariate analysis confirms widespread clustering, notably favouring closing prices ending in zero. Multivariate analysis explores the impact of firm size, price level, volatility, and turnover on clustering.

Univariate analysis confirms widespread clustering, notably favouring closing prices ending in zero. Multivariate analysis explores the impact of firm size, price level, volatility, and turnover on clustering. Results reveal pervasive clustering, strengthening with higher prices and turnover but weakening with larger trade volumes, firm size, and smaller tick sizes. These empirical findings support the theoretical expectations of price negotiation and resolution hypotheses.

The observed clustering presents an opportunity for investors to potentially capitalize on this market anomaly and achieve supra-normal returns.

Price clustering, the phenomenon where certain price levels are traded more frequently, challenges the efficient market hypothesis and has been extensively studied in financial markets. However, the Scandinavian stock markets, particularly those in the Nasdaq Nordic Exchange, remain unexplored in this context.

This study’s goal was to identify how several markets have developed over time and what determinants have influenced this process, based on adaptive markets hypothesis (AMH). In this regard, the authors consider that agents are driven by the seeking for abnormal returns to stay “alive” and their environment could somehow modify their decision-making processes, as well as influence the degree of efficiency of the market.

The authors collected the daily closing-of-the-market index from 50 countries, between 1990 and 2022. The sample includes emerging countries, developed countries and frontier markets. Then, the authors ran multilevel modeling using Hurst exponent as an informational efficiency metric estimated by two different moving windows: 500 and 1,250 observations (approximately 2 and 5 years).

The results indicate that the efficiency of the markets is not constant over time. The authors also have identified that markets follow a cyclical pattern of efficiency/inefficiency, and they are currently in a period of convergence to efficiency, possibly explained by the increase in computational capacity and speed of the available information to agents. In addition, this study identified that country characteristics are associated with market efficiency, considering institutional factors.

Studies of this nature contribute to the literature, considering the importance of better comprehension of market efficiency dynamics and their determinants, specially observing other theories on the relationship between information and markets (like AMH), which work with other investor assumptions than those used by efficient market hypothesis.

The purpose of this study is to analyze the price discovery and market efficiency of energy futures traded in India. The study also examines the volatility spillover effect between the cash and futures markets of energy commodities.

The study uses crude oil and natural gas spot and futures series traded at Multi Commodity Exchange (MCX), India. To evaluate the objectives, the paper employs the cointegration test, causality check, dynamic ordinary least squares (DOLS) method and Baba, Engle, Kraft and Kroner (BEKK) GARCH Model.

The study supports the long-run association between the selected markets. Unlike natural gas, in the case of crude oil bidirectional, flow of information is observed. The study rejects the unbiasedness and efficient market hypothesis of the energy futures market in India. Further, the study confirms that the selected energy commodities indicate bidirectional shock transmission between their respective cash and futures markets.

The study will assist the commodity market participants in designing their trading strategy. The volatility signal will be used by investors and portfolio managers for risk management and portfolio adjustment. Regulators will be able to anticipate future spillover and can design policies to strengthen the market.

The paper evaluates the three aspects of the energy futures market, namely price discovery, market efficiency and volatility slipover. To the best of the authors’ knowledge, studies on efficacy and shock transmission in the context of the energy futures market in India are rare. Further, the study also contributes by investigating the price discovery process of the energy futures market.

This study aims to evaluate the presence of abnormal returns due to stock splits or reverse stock splits in the Brazilian capital market context.

The event study technique was used on data from 518 events that occurred in a 30-year period (1987–2016), comprising 167 stock splits and 351 reverse stock splits.

The results revealed the occurrence of abnormal returns around the time the shares began trading stock splits or reverse stock splits at a statistical significance level of 5%. The main conclusion is that stock split and reverse stock split operations represent opportunities for extraordinary gains and may serve as a reference for investment strategies in the Brazilian stock market.

This study innovates by including reverse stock splits, as the existing literature focuses on stock splits, and by testing two distinct “zero” dates that of the ordinary general meeting that approved the share alteration and the “ex” date of the alteration, when the shares were effectively traded, reverse split or split.

This study aims to analyze the efficient market hypothesis (EMH) of cryptocurrencies on multiple platforms by observing whether there is a discrepancy in the levels of efficiency between different exchanges. Additionally, EMH is tested in a multivariate way: whether the prices of the same cryptocurrencies traded on different exchanges are temporally related to each other. ADF and KPSS tests, whereas the vector autoregression model of order p – VAR(p) – for multivariate system.

Both Bitcoin and Ethereum show efficiency in the weak form on the main platforms in each market alone. However, when estimating a VAR(p) between prices among exchanges, there was evidence of Granger causality between cryptocurrencies in all exchanges, suggesting that EMH is not adequate due to cross information.

It is essential to assess the cryptocurrency market in a multivariate way, not only to favor its maturation process, but also to promote a broad understanding of its inherent risks. Thus, it will be possible to develop financial products that are actively managed in a more sophisticated cryptocurrency market.

There is a possibility of performing arbitrage on different exchanges and market assets through cross-exchanges. Thus, emphasizing the need for regulation of exchanges in the digital asset market, as an eventual price manipulation on a single platform can impact others, which generates various distortions.

This study is the first to find evidence of cross-information for the same (and other) cryptocurrencies among different exchanges.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.

Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.

All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.

The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.

The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.

The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.

The purpose of this study is to determine stock market reactions to the news about innovations and other types of publications for illiquid stocks.

(1) The authors opt for machine learning techniques and expert analysis and propose their own lexicon of innovations based on the news articles published on the professional website; (2) the dataset consists of the data on 2,000 US companies for 6 years; (3) the text analysis including BERT and Top2 Vec models which are superior to Latent Dirichlet allocation (LDA) in information criteria allows for more accurate evaluation of news sentiment and idea; and (4) furthermore, random forest and gradient boosting were applied to increase validity of results and demonstrate factor importance.

(1) The paper presents theoretical findings adding to signalling theory and efficient market hypothesis for US illiquid stocks; (2) this study suggests that information on product innovations (unlike other types of innovations) has a direct and significant effect on the return of illiquid stocks; (3) the results also give evidence that under uncertainty innovation-related publications do not affect the return of illiquid stocks; and (4) the analysis of the news topics (narratives) demonstrates that only the narrative related to important corporate announcements has a positive impact on the return of illiquid stocks.

(1) The authors are the first to conduct a large-scale study of the impact of various information on the return of illiquid stocks; (2) the paper focuses on information on several types of innovations with regard to the return of illiquid stocks; (3) based on Top2 Vec model, this study identifies the key topics-narratives discussed by investors and assesses their impact on the return of illiquid stocks; and (4) as an information source, the authors use the sample comprising a total of 1.4m news articles released on the professional website for investors “Benzinga”.

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.