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The authors’ goal is to provide an overview and historical context for the various alternatives to the efficient market hypothesis (EMH) that have emerged over time. The authors found eight current alternatives that have emerged to address the EMH's flaws. Each of the proposed alternatives improves some of the assumptions made by the EMH, such as investor homogeneity, the immediate incorporation of information into asset values and the inadequacy of rationality to explain asset prices.

To come up with the list of studies relevant to this review article, the authors used three databases, namely Scopus, Web of Science and Google Scholar. The first two were mostly used to get peer-reviewed articles while Google Scholar was used to extract articles that are still work in progress. The following words were used as the search queries; “efficient market hypothesis” and “alternatives to the efficient market hypothesis”.

The alternatives to the EMH presented in this article demonstrate that market efficiency is a dynamic concept that can be best understood with a multidisciplinary approach. To better comprehend how financial markets work, it is crucial to draw on concepts, theories and ideas from a variety of disciplines, including physics, economics, anthropology, sociology and others.

The authors comprehensively summarise the current state of the behavioural finance literature on alternatives to the EMH.

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 paper aims to examine the extent of price clustering in a selection of Islamic stocks listed in Indonesia, Malaysia and Pakistan and also investigates the determinants of the phenomenon at the firm level.

The author test the uniformity of price distribution in the selected securities. Then, the determinants of price clustering were investigated through multivariate analysis based on a binary logistic regression model. Following the arguments of Narayan et al. (2011), who emphasize the importance of considering firm heterogeneity when studying the phenomenon, the author conducts the empirical study at the firm level.

The evidence indicates that Islamic stocks show a mild level of price clustering. Only half of the stocks under analysis rejected the uniformity test in the distribution of prices. In these cases, investors exhibited a preference for prices ending at zero and five. The evidence does not confirm the cultural clustering theories. Price clustering is found to be positively associated with price level and relative bid-ask spread. Overall, the negotiation hypothesis, which predicts that investors prefer round prices to minimize the costs associated with negotiations, best explains most of our results.

The existence of price clustering is difficult to reconcile with the prediction of the efficient market hypothesis that prices should follow a random walk. Moreover, the evidence indicates that Muslim investors share a preference for round prices in some settings, under the assumption that Islamic stocks are mostly traded by Muslim investors.

To the author’s best knowledge, this is the first study to address the subject of price clustering in Islamic stocks.

This paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.

The authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.

The results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.

The results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.

This study aimed to examine the efficient market hypothesis (EMH) for the stock markets of 12 member countries of the Organization of Islamic Cooperation (OIC), such as Egypt, Indonesia, Jordan, Kuwait, Malaysia, Morocco, Pakistan, Saudi Arabia, Tunisia, Turkey and the United Arab Emirates (UAE), during the global financial crisis (GFC) and the COVID-19 (CV-19) epidemic. The objective was to classify the effects on individual indices.

The study employed the multifractal detrended fluctuation analysis (MF-DFA) on daily returns. After calculation and analysis, the data were then divided into two significant events: the GFC and the CV-19 pandemic. Additionally, the market deficiency measure (MDM) was utilized to assess and rank market efficiency.

The findings indicate that the average returns series exhibited persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. The study employed MF-DFA to analyze the sequence of normal returns. The results suggest that the average returns series displayed persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. Furthermore, all markets demonstrated efficiency during the two crisis periods, with Turkey and Tunisia exhibiting the highest and deepest levels of efficiency, respectively. The multifractal properties were influenced by long-range correlations and fat-tailed distributions, with the latter being the primary contributor. Moreover, the impact of the fat-tailed distribution on multifractality was found to be more pronounced for indices with lower market efficiency. In conclusion, this study categorizes indices with low market efficiency during both crisis periods, which subsequently affect the distribution of assets among shareholders in the stock markets of OIC member countries.

Multifractal patterns, especially the long memory property observed in stock markets, can assist investors in formulating profitable investment strategies. Additionally, this study will contribute to a better understanding of market trends during similar events should they occur in the future.

This research marks the initial effort to assess the impact of the GFC and the CV19 pandemic on the efficiency of stock markets in OIC countries. This undertaking is of paramount importance due to the potential destabilizing and harmful effects of these events on global financial markets and societal well-being. Furthermore, to the best of the authors’ knowledge, this study represents the first investigation utilizing the MFDFA method to analyze the primary stock markets of OIC countries, encompassing both the GFC and CV19 crises.

This article aims to systematically review the literature published in recognized journals focused on cognitive heuristic-driven biases and their effect on investment management activities and market efficiency. It also includes some of the research work on the origins and foundations of behavioral finance, and how this has grown substantially to become an established and particular subject of study in its own right. The study also aims to provide future direction to the researchers working in this field.

For doing research synthesis, a systematic literature review (SLR) approach was applied considering research studies published within the time period, i.e. 1970–2021. This study attempted to accomplish a critical review of 176 studies out of 256 studies identified, which were published in reputable journals to synthesize the existing literature in the behavioral finance domain-related explicitly to cognitive heuristic-driven biases and their effect on investment management activities and market efficiency as well as on the origins and foundations of behavioral finance.

This review reveals that investors often use cognitive heuristics to reduce the risk of losses in uncertain situations, but that leads to errors in judgment; as a result, investors make irrational decisions, which may cause the market to overreact or underreact – in both situations, the market becomes inefficient. Overall, the literature demonstrates that there is currently no consensus on the usefulness of cognitive heuristics in the context of investment management activities and market efficiency. Therefore, a lack of consensus about this topic suggests that further studies may bring relevant contributions to the literature. Based on the gaps analysis, three major categories of gaps, namely theoretical and methodological gaps, and contextual gaps, are found, where research is needed.

The skillful understanding and knowledge of the cognitive heuristic-driven biases will help the investors, financial institutions and policymakers to overcome the adverse effect of these behavioral biases in the stock market. This article provides a detailed explanation of cognitive heuristic-driven biases and their influence on investment management activities and market efficiency, which could be very useful for finance practitioners, such as an investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making their financial management strategies.

Currently, no recent study exists, which reviews and evaluates the empirical research on cognitive heuristic-driven biases displayed by investors. The current study is original in discussing the role of cognitive heuristic-driven biases in investment management activities and market efficiency as well as the history and foundations of behavioral finance by means of research synthesis. This paper is useful to researchers, academicians, policymakers and those working in the area of behavioral finance in understanding the role that cognitive heuristic plays in investment management activities and market efficiency.

This chapter delves into the impact of digital initiatives on firms and sheds light on how they can be explained through market reactions and the resource/capabilities mechanism. By providing a novel conceptual framework that reflects the potential impact of digital initiatives on the sensing, seizing and transforming capabilities of dynamic capabilities, this chapter reveals the tremendous potential of digital initiatives to help firms become more adaptive to their environment and create sustainable competitive advantages that elicit positive market responses. This conceptual framework represents an original contribution to the literature. It enhances the understanding of the resource-based view and efficient market hypothesis, providing a fresh perspective on the influence of digital initiatives on firm performance and the dynamic capabilities mechanism that has hitherto been overlooked. As a result, this chapter enables researchers to develop testable hypotheses that examine the causal relationships between digital initiatives, dynamic capabilities and market performance using robust quantitative research methods. Furthermore, this chapter offers valuable insights for managers seeking to develop a more focused approach to digital transformation and enhance their competitive advantage. By exploring the impact of digital initiatives on sensing, seizing and transforming capabilities, managers can gain a deeper understanding of how they can leverage digital initiatives to improve their organisational performance and respond more effectively to the demands of an ever-changing landscape.

This study undertakes a comprehensive investigation into the far-reaching repercussions of Covid-19 stimulus packages and containment policies on stock returns, meticulously examining a diverse array of 14 distinct markets.

This study employed the Panel SVAR model to analyze the relationships between various policies and stock market performance during the Covid-19 outbreak. The sample comprises 5432 daily observations spanning from December 2020 to January 2022 for the 14 selected markets, with missing data excluded.

The findings reveal three consistent impacts across all 14 markets. Firstly, stock returns immediately reversed and decreased within a day when Governments tightened containment policies. Secondly, economic stimulus packages led to a fall in stock returns. Thirdly, an increasing death rate caused the stock return to decrease in the following two days. These findings are supported by the uniform impulse responses in all three shocks, including common, composite and idiosyncratic shocks. Furthermore, all inverse root tests satisfy the stability conditions, indicating the stability and reliability of Panel SVAR estimations.

One vital implication is that all government decisions and measures taken against the shock of Covid-19 must consider economic impacts to avoid unnecessary financial losses and support the effective functioning of stock markets during similar shocks. Secondly, investors should view the decline in stock returns due to Covid-19 effects as temporary, resulting from anxiety about the outbreak. The study highlights the importance of monitoring the impact of policies on financial markets and the broader economy during crises. Overall, these insights can prove helpful for investment decisions and policymaking during future crises.

This study constitutes a noteworthy addition to the literature on behavioural finance and the efficient market hypothesis, offering a meticulous analysis of the multifaceted repercussions of Covid-19 on market interactions. In particular, it unveils the magnitude, duration and intricate patterns of market volatilities linked to significant shock events, encompassing a comprehensive dataset spanning 14 distinct markets.

This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.

The empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.

The results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.

First, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”

This study aims to examine in depth the impact of merger activities on banks in Saudi Arabia.

Event study, financial ratio and efficient frontier analyses with a mixture of parametric and non-parametric tests are used for the sample period 2016Q1–2022Q4.

Event study analysis shows that merging banks (bidders) have higher positive cumulative abnormal returns than merged banks (targets), indicating that investors believe that bidding banks will benefit the most from the merger strategy. It was also found that the efficiency measures of the combined banks of Saudi British Bank and Alawwal Bank deteriorated, while they improved for the combined banks of National Commercial Bank and Saudi American Bank in the post-merger period, confirming investors' views.

Although the study focuses on the Saudi banking sector, its findings could be generalized to other banks in the region, as the Saudi banking sector is one of the largest in the Middle East region and is expected to grow further in the future.

The mere act of merging two banks does not guarantee the realization of cost synergies or efficiency gains. This research shows that mergers are not automatically cost-effective and that their success depends on good integration and restructuring strategies.

To the best of the author's knowledge, this is the first study to provide a comprehensive analysis of the short- and long-term impacts of merger activities in the Saudi banking sector.