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Behavioral finance, financial, decision making, Systematic Literature Review (SLR).
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Katenova M, Frolova Y and Schmunkamp P. Behavioral Finance in the sphere of investment: Systematic Review of Literature between 2020 and 2025 [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:949 (https://doi.org/10.12688/f1000research.166104.1)
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Systematic Review
Behavioral Finance in the sphere of investment: Systematic Review of Literature between 2020 and 2025
[version 1; peer review: awaiting peer review]
PUBLISHED 19 Sep 2025
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Abstract
Corresponding author:
Maya Katenova
Competing interests:
No competing interests were disclosed.
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The author(s) declared that no grants were involved in supporting this work.
Copyright:
© 2025 Katenova M et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Katenova M, Frolova Y and Schmunkamp P. Behavioral Finance in the sphere of investment: Systematic Review of Literature between 2020 and 2025 [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:949 (https://doi.org/10.12688/f1000research.166104.1)
First published: 19 Sep 2025, 14:949 (https://doi.org/10.12688/f1000research.166104.1)
Latest published: 19 Sep 2025, 14:949 (https://doi.org/10.12688/f1000research.166104.1)
Background
What is behavioral finance? Behavioral finance is an interdisciplinary field that integrates insights from psychology and cognitive science to explain how emotional and cognitive biases influence financial decision making. Unlike traditional finance theories, like the Efficient Market Hypothesis (EMH) and Modern Portfolio Theory (MPT), which assume that investors are rational decision makers, behavioral finance recognizes that emotions, biases, and heuristics often lead to irrational investment behaviors. When it comes to behavioral finance, it should be noted that Tversky and Kahneman (1974) demonstrated that the decision-making process of individuals in situations of uncertainty is affected by heuristics and cognitive biases. As a result, individuals are not rational, as indicated by traditional financial theories. Kahneman (2011) presented a research device based on the progressive approximation of a problem, where each step is provisional and biases are demonstrated as systematic errors. Investors behave irrationally due to their emotions, biases, and heuristics. This behavior leads to losses and creates problems for investment analysts, portfolio managers, and risk managers.
The expected utility theory was strongly criticized by Kahneman and Tversky (1979), who developed a new prospect theory that describes how individuals choose between alternatives that involve risk. The probabilities of the results are uncertain, and heuristics and biases are considered. It is discussed that individuals are risk averse in situations of gains but risk prone in situations of guaranteed losses. Interestingly, investors are risk-averse when they wish to secure their gains but risk-seeking when they wish to recover their losses. This leads to decisions that deviate from rational traditional models. This contradicts traditional finance theories that are based on the idea that investors always make rational financial decisions. In addition, losses generate a greater emotional impact on people than gains. This theory shows that investors asymmetrically perceive gains and losses. Barberis and Thaler (2003) mentioned that behavioral finance began in the 1980s, when research developed models of financial markets that did not consider rational agents. Shefrin and Statman (1985) presented a tendency for investors to sell “winning” shares or assets much faster than “losing” stocks or assets, which tend to decline in value. Therefore, investors will retain “losing” assets for longer period of time due to their emotions. Although more than 40 years have passed since, Kahneman and Tversky’s (1979) prospect theory has been actively discussed in academia. Current articles dealing with prospect theory and new research ideas include Jiao (2017) and Kohsaka et al. (2017).
Behavioral finance is an interdisciplinary field that draws on insights from psychology and cognitive science to better understand how emotional and cognitive biases affect financial decision-making. In contrast to traditional financial theories—such as the Efficient Market Hypothesis (EMH) and Modern Portfolio Theory (MPT), which assume that investors are fully rational actors—behavioral finance acknowledges that human behavior is often influenced by heuristics, emotions, and systematic biases, leading to deviations from rationality. Seminal work by Tversky and Kahneman (1974) revealed that individuals rely on cognitive shortcuts, or heuristics, when making decisions under uncertainty. These heuristics frequently lead to predictable and systematic biases, suggesting that actual investor behavior often diverges from the assumptions of rational choice models. Kahneman (2011) further developed these ideas by demonstrating how problem-solving is guided by a sequence of provisional steps prone to bias, resulting in consistent decision-making errors in financial contexts.
Kahneman and Tversky’s (1979) Prospect Theory represents a fundamental challenge to the traditional Expected Utility Theory. It explains how individuals evaluate risk and make choices between uncertain outcomes. Prospect Theory shows that people tend to be risk-averse when dealing with potential gains, but risk-seeking when attempting to avoid losses. This behavior is asymmetrical; losses have a greater psychological impact than equivalent gains—a phenomenon known as loss aversion. Consequently, investors may make suboptimal decisions, such as holding onto losing investments for too long while selling winning assets too quickly. This behavioral pattern was empirically examined by Shefrin and Statman (1985), who documented what is now known as the disposition effect—the tendency of investors to prematurely sell assets that have gained in value, while retaining those that are losing. These decisions are often driven by emotional reactions rather than rational evaluation, complicating the work of portfolio managers, analysts, and risk professionals. Barberis and Thaler (2003) highlighted the emergence of behavioral finance in the 1980s, a period during which models of financial markets began to incorporate the reality of non-rational investor behavior. Despite the passage of several decades, Prospect Theory remains highly influential and continues to inspire research in behavioral economics and finance. More recent contributions, such as those by Jiao (2017) and Kohsaka et al. (2017), expand on these foundations by applying behavioral insights to contemporary market phenomena. In sum, behavioral finance challenges the core assumption of investor rationality in traditional financial theory, providing a more nuanced and empirically grounded understanding of how real-world financial decisions are made.
Irrational investment decisions are influenced by heuristics, which represent a systematic distortion in the information process but can lead to rapid decision-making processes. Heuristics can influence information perception, processing, and decision-making.
Four major biases can be identified that influence investors’ decisions:
• Overconfidence Bias – investors overestimate their knowledge and predictive abilities, leading to excessive trading and increased risk taking (Shunmugasundaram & Sinha, 2024; Talwar et al., 2021).
• Herding Behavior – Investors follow the actions of others rather than conduct independent analysis, often exacerbating market volatility (Talwar et al., 2021).
• Anchoring Bias: Investors rely too heavily on the first piece of information they receive when making financial decisions, even if it is irrelevant (Mahmood et al., 2024; Jain et al., 2021).
• Loss Aversion: The tendency to avoid losses rather than acquire equivalent gains, which often results in suboptimal investment choices (Jain et al., 2021; Talwar et al., 2021).
Beyond individual biases, behavioral finance has implications for corporate finance, investment strategies, risk management, and policymaking. For example, understanding investor psychology can help financial institutions design better investment products, assist policymakers in crafting effective financial regulations, and enable investors to make informed decisions.
As financial markets evolve owing to globalization, digital finance, and Artificial Intelligence-driven trading, behavioral finance continues to play a critical role in understanding investment behavior. This study builds on the recent literature (2020–2025) to analyze emerging trends and their implications for modern financial markets. This study shapes the view on behavioral finance in the sphere of investment by employing the most recent high-quality papers and adds to the literature by finding new biases such as automation biases. The practical and theoretical implications of studies conducted between 2020 and 2025 are discussed in this study. The current study is important because it discovers new implications, innovations, and biases in the existing literature.
Research gap and contribution
Behavioral finance has been a rapidly evolving field, particularly in the past two decades, as scholars and practitioners have attempted to understand the role of cognitive biases, emotional influences, and psychological factors in the investment decision-making process. Existing studies have extensively examined behavioral biases, such as overconfidence, herding, anchoring, and loss aversion, demonstrating their impact on financial decision-making across different investor demographics and economic contexts. However, a significant gap remains in the understanding of how recent global events, technological advancements, and changing market structures between 2020 and 2025 have influenced investor behavior. There is hardly any research on the period 2020-2025, or no active research was carried out in this period.
Despite the growing body of research, most prior literature reviews have primarily focused on historical perspectives (pre-2020) or examined behavioral finance through a limited regional or sector-specific lens. Few studies have systematically analyzed the latest trends in behavioral finance research, particularly in response to unprecedented financial disruptions, such as the COVID-19 pandemic, the rise of artificial intelligence (AI) in investment strategies, and the increasing role of fintech innovations. A systematic review of literature (SLR) focuses on the results of papers in a particular sphere. In addition, SLR is centered on the critical analysis of existing studies in a particular sphere. Furthermore, while numerous empirical studies exist on individual biases, there is a lack of a comprehensive synthesis exploring the interplay between multiple biases and their cumulative effect on investment decisions.
This study aims to address these gaps by conducting a systematic literature review (SLR) covering the period 2020 to 2025. The focus of this research is on this period, as some studies that used an SLR covered the period up to 2019 and 2020, such as Savitha (2021). By synthesizing the findings from the latest research, this study contributes to the existing body of knowledge in three key ways.
1. Identifying new trends: It studies highlights how emerging behavioral biases, such as digital overconfidence and algorithmic bias, shape investment decisions in the modern financial landscape.
2. Bridging theoretical and practical insights: It study examines the implications of behavioral finance research on investment strategies, financial literacy programs, and regulatory frameworks.
3. Predicting future research directions: By assessing the trajectory of behavioral finance research, this study offers insights into potential areas for future investigation, including the role of big data, behavioral nudging, and AI-driven financial decision-making.
Through this comprehensive review, the study not only consolidates existing findings, but also provides a forward-looking perspective on behavioral finance, ensuring that both researchers and practitioners can navigate the evolving financial landscape more effectively. This study provides a thorough review of the literature for the period 2020–2025 by focusing on behavioral finance in the sphere of investments.
Main aim of the study
The primary objective of this study is to provide an updated and comprehensive review of the recent literature (2020–2025) on behavioral finance, particularly in the context of investment decision-making. As behavioral finance continues to challenge traditional finance theories by incorporating psychological and cognitive biases, this study seeks to bridge the gap between theoretical models and real-world investor behavior.
Specifically, this study aims to:
1. Identify Key Psychological Factors influencing investment decisions – By analyzing recent literature, this study explores the most prevalent behavioral biases affecting individual and institutional investors, including overconfidence, herding, anchoring, and emerging biases driven by digital and algorithmic finance.
2. Evaluate the Practical Implications of Behavioral Finance – This study assesses how behavioral insights are applied in financial markets, investment strategies, and regulatory policies. It also explores how financial literacy and investor education can mitigate the adverse effects of such biases.
3. Highlight Research Gaps and Future Directions – By systematically reviewing the latest studies, this study identifies underexplored areas in behavioral finance, such as the influence of fintech, AI-driven decision-making, and the role of behavioral nudging in investment strategies. This also outlines the potential directions for future research.
Furthermore, this study not only enhances the understanding of behavioral finance, but also provides actionable insights for investors, policymakers, and financial professionals navigating modern financial markets.
Research methodology
This study employs a Systematic Literature Review (SLR) approach to analyze recent research on behavioral finance and its impact on investment decision making. The SLR method ensures a structured and replicable review process, allowing for a comprehensive synthesis of existing studies.
Data collection and selection criteria
The research process involved a structured search across reputable academic databases including Scopus, Emerald Insight, Elsevier, Sage, Wiley, Google Scholar, and ScienceDirect. To ensure the inclusion of high-quality relevant studies, the following inclusion criteria were applied:
• Studies published between 2020 and 2025
• Peer-reviewed journal articles, book chapters, and conference proceedings
• Studies written in English
• Research focusing on the relationship between behavioral finance and investment decisions
• Studies providing empirical, theoretical, or conceptual insights into behavioral finance
To refine the search, key terms were used, such as “behavioral finance,” “investment psychology,” “cognitive biases in finance,” and “decision-making in investment.”
Data analysis and categorization
The selected studies were analyzed and categorized based on key research themes, including:
• Psychological Biases – Overconfidence, herding, anchoring, loss aversion, etc.
• Investor Behavior Trends – Market reactions to crises, fintech and AI-driven investment decisions, and emerging global patterns in behavioral finance.
• The methodologies used in Behavioral Finance Research include surveys, experiments, case studies, and econometric models.
Scope and limitations
This study is global in scope, and does not focus on specific regions or countries. However, they are limited to publicly accessible studies and do not include proprietary or restricted financial reports. Additionally, while the Systematic Literature Review (SLR) method ensures comprehensive coverage, it does not conduct primary data collection and relies solely on secondary sources. The period includes the period from 2020 to 2025.
Results and discussion
The systematic literature review identified 30 relevant studies published between 2020 and 2025 that analyzed behavioral finance and its influence on investment decision-making. These studies were examined on the basis of research themes, methodologies, and key findings. The results indicate that investor psychology continues to play a dominant role in financial decision making, with biases shaping investment behaviors across different economic and technological contexts.
Overview of recent literature
A summary of the key studies analyzed in this study is presented in Table 1. It includes details on the author, journal, research topic, and publisher, and provides an overview of the latest contributions to the field of behavioral finance. Table 2 is devoted to all types of investment biases, their examples, and authors as well. Methodologies were also discussed in Table 2.
Table 1. The most popular journal in the sphere of behavioral finance.
Table 2. Summary of key behavioral finance studies (2020-2025).
| Author(s) & Year | Journal/Publisher | Research focus | Key biases studied | Methodology |
|---|---|---|---|---|
| Rasool & Ullah (2020) | Journal of Economics, Finance and Administrative Science | Financial literacy and biases in Pakistan Stock Exchange | Financial literacy, Overconfidence | Survey (300 investors) |
| Raheja & Dhiman (2020) | Rajagiri Management Journal | Emotional intelligence and behavioral biases | Overconfidence, Loss Aversion | Survey (500 finance specialists) |
| Fujii et al. (2021) | Journal of Capital Markets Studies | Equity crowdfunding & financial literacy in Japan | Risk Aversion, Overconfidence | Empirical study (96 companies) |
| Ahmad & Shah (2022) | SAGE Open | Overconfidence heuristic & risk perception | Overconfidence, Risk Perception | Survey (450 investors) |
| Majewski & Majewska (2022) | Procedia Computer Science | Behavioral portfolio strategies | Herding, Anchoring Bias | Experimental Study |
The abovementioned studies explore a range of behavioral biases affecting investor decision-making, with a particular emphasis on how psychological factors such as overconfidence, herding, and emotional responses influence behavioral finance. All research articles were carefully studied, and key biases were discussed using their respective methodologies.
Dominant behavioral biases in investment decisions
Behavioral finance research between 2020 and 2025 highlights several recurring psychological biases that significantly affect investors’ decision-making. Some of the most frequently studied biases include the following.
• Overconfidence Bias: Investors overestimate their knowledge and predictive abilities, leading to excessive trading and increased risk taking (Shunmugasundaram & Sinha, 2024; Talwar et al., 2021).
• Herding Behavior: Investors mimic the actions of others rather than conduct independent analyses, which can exacerbate market bubbles or crashes (Talwar et al., 2021).
• Anchoring Bias: Investors rely too heavily on initial information when making decisions, often ignoring new and more relevant data (Mahmood et al., 2024; Jain et al., 2021).
• Loss Aversion: Investors are more sensitive to losses than to equivalent gains, leading to irrational decision-making such as holding onto losing stocks for too long (Jain et al., 2021; Talwar et al., 2021).
What is new to our study? We discovered a new type of bias, automation bias. Only the four biases mentioned above were analyzed in advance. However, because of new innovations in the market, we see new types of bias, such as automation bias. This is linked to the development of Artificial Intelligence (AI), which makes it easier to make choices or decisions relevant to investment today. Automation bias is associated with an over-reliance on AI and automated trading tools. Nowadays, investors do not analyze and make decisions because they rely fully on Artificial Intelligence and automated trading tools. Shukla and Shukla (2023) studied the presence and impact of automation bias and status-quo bias on the capital market investment decisions of Indian retail investors. According to the authors, automation bias is the excessive dependency of investors on automated or computer-generated information for stock selection decisions. The study took place in India, and 496 retail investors of the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) participated in this study. Questionnaires were distributed to the respondents. The results of this study confirm the presence of an automation bias in the investment decisions of investors in India. Moreover, there is a strong and statistically significant impact of automation bias on Indian investors’ investment decisions. In addition, the study concludes that investors must take certain steps to avoid behavioral biases in investment decisions and prevent mistakes and losses. According to Mosier et al. (1996), automated procedural and decision aids may paradoxically increase errors.
A detailed categorization of these biases, including their presence across different studies, is presented in Table 3. The definitions and their impacts on investment are presented here.
Table 3. Categorization of behavioral biases in investment decisions (2020-2025).
| Bias type | Definition & Impact on investment decisions | Key studies referenced |
|---|---|---|
| Overconfidence Bias | Investors overestimate their knowledge, leading to excessive risk-taking | Shunmugasundaram & Sinha (2024), Talwar et al. (2021) Retnoning Ambarwati, Ika Yoga (2025) |
| Herding Behavior | Investors mimic others instead of conducting independent analysis | Talwar et al. (2021), Ah Mand et al. (2021) |
| Anchoring Bias | Investors rely too heavily on initial information, even if irrelevant | Mahmood et al. (2024), Jain et al. (2021), Zhang (2025) |
| Loss Aversion | Fear of losses leads to suboptimal investment decisions | Jain et al. (2021), Talwar et al. (2021) |
| Automation Bias | Over-reliance on AI/automated trading tools | Emerging trend (2023-2025), Mosier et al. (1996), Shukla and Shukla (2023) |
Emerging trends in behavioral finance (2020–2025)
Beyond traditional biases, several new trends have emerged in behavioral finance research:
• Impact of the COVID-19 Pandemic: Increased uncertainty has led to heightened risk aversion, panic selling, and short-term speculation. COVID-19 has affected investors worldwide.
• Influence of Fintech and Artificial Intelligence: The rise of algorithmic trading has introduced new biases, such as automation bias (over-reliance on AI recommendations) and digital overconfidence (over-trust in online trading platforms).
• Retail Investor Behavior: The growth of commission-free trading and social media-driven investing (e.g., meme stocks) has intensified herding behavior and overconfidence, particularly among younger investors.
Research methodologies in behavioral finance
The reviewed studies used various research methodologies, which can be categorized into four main approaches.
• Survey-Based Research: The common method, with sample sizes ranging from 200 to 900 respondents, primarily focuses on retail investors.
• Experimental Studies: Used controlled environments to analyze how biases affect investment choices.
• Econometric Models: Statistical tools were employed to quantify the impact of biases on market trends.
• Qualitative Case Studies: Focused on institutional investor behavior and corporate finance decisions.
A summary of the methodologies used along with their results is presented in Table 4.
Table 4. Research methodologies in behavioral finance (2020-2025).
| Research approach | Description & Application | Example studies |
|---|---|---|
| Survey-Based Research | Structured questionnaires analyzing investor psychology | Rasool & Ullah (2020), Ahmad & Shah (2022) |
| Experimental Studies | Controlled environments testing bias impact on decisions | Majewski & Majewska (2022) |
| Econometric Models | Statistical analysis of behavioral factors in markets | Fujii et al. (2021) |
| Qualitative Case Studies | In-depth study of institutional investor behavior | Raheja & Dhiman (2020) |
Table 5 provides an overview of the sample sizes and populations analyzed in these studies, demonstrating the broad geographical scope of recent behavioral finance research. Table 5 focuses on the most recent research articles, and the most popular data collection method was questionnaires. The table shows numerous authors who presented their studies in different countries and continents. Table 6 demonstrates categories of journals and their influence.
Table 5. Review of Literature: Studies, samples, and populations.
| Author(s) & Year | Sample size | Population studied | Data collection method |
|---|---|---|---|
| Rasool & Ullah (2020) | 300 | Retail investors and sub-accounts | Questionnaire |
| Raheja & Dhiman (2020) | 500 | Ludhiana Stock Exchange finance specialists | Questionnaire |
| Aren & Nayman Hamamci (2020) | 446 | Turkey's investor population | Questionnaire |
| Haritha & Uchil (2020a) | 875 | Indian stock exchange individual investors | Questionnaire |
| Haritha & Uchil (2020b) | 366 | 7,669 firms analyzed | Questionnaire |
| Fujii et al. (2021) | 96 | Japanese companies engaging in ECF campaigns | Secondary Data |
| Ah Mand et al. (2021) | - | Conventional stock investors in Malaysia | Secondary Data |
| Ritika & Kishor (2022) | 274 | Individual investors | Questionnaire |
| Ahmad & Shah (2022) | 450 | Stock traders in Pakistan | Questionnaire |
| Andriamahery and Qamruzzaman (2022) | 795 | 950 women-owned SMEs | Questionnaire |
| Hanaffy & Widyastuti (2022) | 350 | Top 10 best securities firms | Questionnaire |
| Van Nguyen et al. (2022) | 601 | 669 investors | Questionnaire |
| Majewski & Majewska (2022) | - | Warsaw Stock Exchange firms | Experimental Study |
| Ahmed et al. (2022) | 450 | Pakistan stock market investors | Questionnaire |
| Weixiang et al. (2022) | 450 | Stock market investors | Questionnaire |
| Adil et al. (2022) | 253 | Individual investors in Delhi-NCR region | Questionnaire |
| Schuhen et al. (2022) | 212 | Respondents from real-life financial scenarios | Digital Questionnaire |
Table 6. Behavioral finance papers: Journal categorization.
Interpretation and implications
The results reaffirm that behavioral biases significantly influence investment decisions, often leading to suboptimal financial outcomes. However, it is recommended that certain interventions help mitigate these biases.
• Financial Literacy Programs: Studies show that increasing financial knowledge reduces susceptibility to common biases (Ahmad & Shah, 2022).
• Behavioral Nudges: Small interventions, such as default investment options and risk-awareness prompts, can help investors make rational choices (Madrian, 2014).
• Regulatory Measures: Given the growing role of fintech, policies should ensure that digital investment platforms do not exploit cognitive biases through aggressive marketing strategies (Brenncke, 2018).
During the period studied, the Journal of Behavioral Finance published the largest number of papers in the sphere of behavioral finance and investments. The second most influential journal is the Journal of Behavioral and Experimental Finance and the Journal of Economic Behavior & Organization. It was discovered that other journals published a limited number of papers.
Conclusions and further recommendations
Conclusion
The findings of this study confirm that behavioral biases play a critical role in shaping investment decisions, often leading to suboptimal financial choices. Psychological factors, such as overconfidence, herding behavior, anchoring, and loss aversion continue to influence investor behavior, sometimes overriding rational decision-making models. Recent research (2020–2025) has also highlighted the growing impact of technological advancements, including algorithmic trading and digital finance platforms, on shaping modern investment behaviors.
While traditional finance assumes that investors act rationally, behavioral finance demonstrates that emotions, cognitive biases, and heuristics often drive financial decision making. This study reinforces the need for a holistic approach that combines behavioral insights with financial literacy, regulatory measures, and structured decision-making frameworks to improve investor outcomes.
Implications for investors, regulators, and financial institutions
1. For Individual Investors:
• Increasing awareness of behavioral biases can help investors make more rational financial decisions.
• Diversification strategies and structured decision-making processes can mitigate impulsive investment behaviors.
• Financial literacy plays a significant role in the protection and safety mechanism for investors.
2. For Regulators and Policymakers:
• Regulatory frameworks should incorporate behavioral insights to protect retail investors from biased decision making (e.g., regulating gamified trading apps and social media-driven investments).
• “Nudging” policies, such as default investment options or mandatory risk disclosures, can encourage better financial choices.
3. For Financial Institutions and Fintech Platforms:
Future research directions
Despite recent advancements in behavioral finance, several potential topics can be mentioned:
• The role of Artificial Intelligence and big data in investment decisions – Future studies should explore how AI influences investor psychology and decision-making biases.
• Cross-Cultural Comparisons – Most behavioral finance studies focus on specific markets; comparative studies across different economic and cultural settings can provide deeper insights.
• The Long-Term Impact of Digital Finance – With the rise of decentralized finance (DeFi) and algorithmic trading, understanding how these innovations affect investor behavior over time remains an important area of research.
Final remarks
Behavioral finance has transformed the way investment decisions are understood, challenging rational market theories and emphasizing the human element in behavioral finance. As technology and financial markets evolve, integrating behavioral insights into investment strategies, policymaking, and financial education will be crucial in ensuring a more stable and informed financial environment. This study serves as an avenue for further research.
The behavioral finance sphere is growing and developing, and there are many research opportunities in this area. In the era of digitalization and Artificial Intelligence (AI), the issue of behavioral finance continues to grow. Artificial Intelligence may influence investors’ decision-making processes. Artificial Intelligence may help investors make rational decisions in the future. New forms of investment also appear in the market today, such as cryptocurrency. Further research should focus on cryptocurrency and further innovations. Behavioral finance may incorporate new methods, forms, and ways because of the digitalization and transformation of society today. Further research may significantly improve current studies and present new innovative approaches. In addition to behavioral finance, financial literacy should be studied further. This topic is global and indiscriminately affects investors worldwide. Further research may employ longer periods and better approaches. Further studies should focus their attention only on particular variables and methodological approaches. In addition, further studies should focus on particular countries and continents. It would be interesting to see a new study covering a longer period. This study provides a promising basis for future research.
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Katenova M, Frolova Y and Schmunkamp P. Behavioral Finance in the sphere of investment: Systematic Review of Literature between 2020 and 2025 [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:949 (https://doi.org/10.12688/f1000research.166104.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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