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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 study proposes a novel measure for management’s horizon (short-termism or myopia vs long-termism or hyperopia) derived from easily obtainable firm-level accounting and stock market performance data. The authors use the measure to explore the impact of managements’ horizon on firms’ investment efficiency.

The authors rely on two commonly used but uncorrelated measures of management performance: accounting performance (return on capital employed, ROCE) and stock market performance (average abnormal return, AAR). The authors combine these measures to develop a multidimensional framework for performance, which classifies firms into four groups: efficient (high accounting and high market performance), poor (low accounting and low market performance), myopic (high accounting and low market performance) and hyperopic (low accounting and high market performance). The authors validate this framework and deploy it to explore the relationship between horizon and firms’ investment efficiency.

In validation tests, the authors show that management myopia (hyperopia) explains firms’ decision to cut (grow) research and development investments. Further, as expected, myopic (hyperopic) firms are associated with significantly more (less) accrual and real earnings management. The empirical tests on the link between horizon and investment efficiency suggest that myopic managers cut new investments while their hyperopic counterparts grow the same. Ultimately, the authors find that myopia (hyperopia) exacerbates(mitigates) the over-investment of free cash flow problem.

The authors introduce a framework for assessing management’s horizon using easily obtainable measures of performance. The framework explains inconsistencies in prior empirical research using different measures of performance (accounting versus market). The authors demonstrate its utility by showing that the measure explains decisions around research and development investment, earnings management and firm investments.

This study aims to use a qualitative approach to explore and clarify the mechanism by which heuristic-driven biases influence the decisions and performance of individual investors actively trading on the Pakistan Stock Exchange (PSX). It also aims to identify how to overcome the negative effect of heuristic-driven biases, so that finance practitioners can avoid the expensive errors which they cause.

This study adopts an interpretative approach. Qualitative data was collected in semistructured interviews, in which the target population was asked open-ended questions. The sample consists of five brokers and/or investment strategists/advisors who maintain investors’ accounts or provide investment advice to investors on the PSX, who were selected on a convenient basis. The researchers analyzed the interview data thematically.

The results confirm that investors often use heuristics, causing several heuristic-driven biases when trading on the stock market, specifically, reliance on recognition-based heuristics, namely, alphabetical ordering of firm names, name memorability and name fluency, as well as cognitive heuristics, such as herding behavior, disposition effect, anchoring and adjustment, repetitiveness, overconfidence and availability biases. These lead investors to make suboptimal decisions relating to their investment management activities. Due to these heuristic-driven biases, investors trade excessively in the stock market, and their investment performance is adversely affected.

This study provides a practical framework to explore and clarify the mechanism by which heuristic-driven biases influence investment management activities. To the best of authors’ knowledge, the current study is the first to focus on links between heuristic-driven biases, investment decisions and performance using a qualitative approach. Furthermore, with the help of a qualitative approach, the investigators also highlight some factors causing an increased use of heuristic variables by investors and discuss practical approaches to overcoming the negative effects of heuristics factors, so that finance practitioners can avoid repeating the expensive errors which they cause, which also differentiates this study from others.

This study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.

This study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.

The findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.

This study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.

This study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.

This study aims to investigate the impact of earnings predictability and truthfulness on nonprofessional investors’ investment willingness.

Earnings predictability is captured by quarterly earnings autocorrelation, and earnings truthfulness is indicated by real earnings management (REM). The average of investment attractiveness and willingness measures investment willingness. The authors use experiments to isolate the impact of quarterly earnings autocorrelation and REM on investors’ investment behaviors.

From the 2 × 2 design, the authors observe that investors weight more on earnings predictability than earnings truthfulness.

The generalization of the findings may be constrained for the following reasons. First, the authors use only one proxy, REM, to measure earnings truthfulness. In addition, the authors provide the participants, Amazon Mechanical Turk, with earnings predictability. Results may no longer hold if each participant has different understanding and analysis of earnings predictability.

In periods of unprecedented and severe financial uncertainty (i.e. the COVID-19 pandemic), investors rely more on earnings predictability than on earnings truthfulness. The study assists managers to strategically emphasize the predictability of earnings to attract investors, especially when firms face financial challenges or uncertainty.

This study contributes to understanding investor behavior and the critical role of earnings predictability and truthfulness in shaping investment decisions.

This paper contributes to the literature of earnings properties in financial reporting, particularly by shedding light on the nuanced interplay between earnings predictability and earnings truthfulness. The research also demonstrates that elevated earnings autocorrelation indirectly stimulates investment willingness by enhancing the investors’ perception of earnings persistence of targeted firms.

The purpose of this study is to investigate (1) how managers respond to stock price crashes, (2) why they respond and (3) how their responses affect shareholders.

This study employs a panel regression with various firm-level controls and firm- and year-fixed effects. The sample is comprised of 101,532 firm-year observations with 11,727 unique firms from 1950 to 2019. Using mutual fund flow redemption pressure as an exogenous variable to stock price crashes, the paper provides further evidence of the causality of documented findings.

Management becomes more focused on improving transparency, raising investment efficiency, reducing agency conflicts and regaining the trust of shareholders by investing in social capital and employee welfare. These actions increase firm value. This study also suggests that management undertakes these actions out of concern for their tenure of employment.

The catalysts of stock price crashes are well documented, but much less is known about what happens following stock price crashes. This study provides more insights into the understanding of corporate crisis management practices following adverse events.

We explore how sustainable management education (SME) can help prepare future leaders to manage crises effectively. Precisely, the intricacies of articulating moral and economic imperatives for businesses in a manner that engages students in sustainable behavior are a serious challenge for SME. We study how to integrate reminders of moral and economic imperatives in a socially responsible investment (SRI) stock-picking simulation created for SME.

Adopting an experimental design, we analyzed how the reminders affected the average environment social governance (ESG) integration in the portfolios of 127 graduate students in finance over a twelve-week period.

Our results show how essential it is to balance the two imperatives. The highest level of sustainable investment is attained when utilizing both reminders.

Our findings have practical implications for implementing and organizing SME in business schools to educate responsible leaders who are able to effectively manage crises. Learning responsible management is most effective when students are exposed to the inherent tension between moral and economic imperatives. Hence, our findings corroborate the win-win conception of SME.

No management decision study has experimentally measured the effects of SME practices on students' actual behavior. Our research fills this gap by complementing previous studies on the effectiveness of teaching practices, first by drawing on behavioral sciences and measuring changes in students' actual sustainability behavior and second by introducing moral and economic imperatives into an innovative teaching resource (TR) dedicated to SME.

This paper aims to provide bibliometric data from previously published research in Islamic fund management. Several categories, such as the most influential authors, the keywords and themes of the previously published research, were analyzed. Also, this paper provides a rigorous background for Islamic mutual funds research by synthesizing and reviewing the content of the previously published research to identify the gaps and provide future research direction.

Data were gathered from Scopus databases from the year 2007 until 2022. A total of 115 articles were found published over the period of 15 years. Three different software, namely, R-Studio, VOSviewer and Excel are used to analyze the data that depict, among others, the most impactful authors in the field, the top journals covering Islamic fund management research and the most cited document. Content analysis of the previously published research was also provided.

Publication in Islamic funds started gaining attention in the year 2012 and after. Collaborative works on Islamic funds are not strong yet among the contributory nations, although the USA and Malaysia contributed the highest number of publications. This study also found that there was a lack of research collaboration among authors in this research field, and most of the articles published were concentrated on the performance measurements of Islamic funds.

Researchers might find the results of this paper useful, as they provide a comprehensive analysis of the previously published research in the field.

While these findings gave an overview of the intellectual structure of the research field, they also have the potential to inspire scholars working on Islamic funds research to collaborate on new research projects. Managers can also use this research to further enhance the emergence of Islamic fund management.

This is a comprehensive paper that examines previously published research in the field of Islamic fund management. The findings of this research benefit practitioners and researchers wishing to embark on research in this niche area.

Applying universal ownership theory and drawing on a multiplecase study design, this study aims to analyze what drives institutional investors to engage with government entities and what challenges they find in the process.

The authors relied on document analysis and conducted 12 semi-structured interviews with representatives from asset owners, asset managers, investor associations and academia.

The authors identify a trend where investors conduct policy engagement to fulfill their fiduciary duty, improve investment risk management and create an enabling environment for sustainable investments. As for engagement challenges, investors report the longer-term horizon, a perceived limited influence toward governments, the need for capacity building for investors and governments, as well as the difficulty in accessing government representatives.

This research contributes to filling a gap in the literature on this new form of investor activism, as a growing number of investors engage with sovereign entities on environmental, social and governance issues.

This study aims to investigate the impact of supply chain concentration (SCC) on corporate research and development (R&D) investment and determine the moderating roles of industry concentration and financing constraints on the relationship between SCC and R&D investment.

The study collected data from Chinese listed companies, used the fixed effects model to test the research hypotheses and further used the two-stage Heckman test and propensity score matching (PSM) to address potential endogeneity issues.

The result reveals a negative impact of SCC on corporate R&D investment. In addition, industry concentration mitigates the negative impact of SCC on corporate R&D investment, but financing constraints strengthen the negative impact.

This study introduces the concept of SCC and empirically tests its effect on R&D investment, further explaining the lack of corporate innovation. This study inspires companies to strengthen SC management and weigh the level of SCC with environmental factors.