Identifying Behavioral Biases Affecting Fund Performance in Investment Fund Managers

Introduction: Behavioral biases of investment fund managers can have significant negative impacts on fund performance, and to reduce these impacts, fund managers can use advanced analytical tools, structured decision-making processes, and training related to financial psychology. Also, investors can choose more efficient and rationally managed funds by being aware of these aspects. These assessments not only help investors in selecting appropriate funds, but also help managers in improving performance and reducing the negative effects of biased behaviors. Identifying behavioral biases of investment fund managers can also help investors in selecting appropriate funds and reducing the risks arising from incorrect decisions. This issue requires more extensive research and the use of new analytical methods, and in this regard, the present study was conducted with the aim of identifying behavioral biases affecting fund performance in investment fund managers. Methods: The present study is of fundamental and exploratory purpose and the data collection stage is of the library-field type with a qualitative method with a content analysis approach. The participants in this study were 20 experts from investment funds who were selected with a purposeful method based on the conditions of expertise. The resources used in the library section included specialized books in the field of finance and behavioral finance, scientific-research articles published in reputable domestic and foreign journals, academic theses and dissertations, official reports and electronic resources and reputable scientific databases. In the field section, the behavioral biases of managers were identified and localized using the Delphi method and the participation of a panel of experts. The content analysis method and the implementation of the Delphi approach were also used to analyze the data. Also, in order to ensure the reliability and stability of coding, multiple coders were used and the level of agreement between them was calculated. Results and discussion: After the initial identification of 80 behavioral trends in the form of 12 main criteria, the collected data were presented to the selected experts of the 80 behavioral trends identified, 54 trends were able to achieve the initial consensus criterion (mean ≥ 3.5). In the second round, the results and average opinions of the first round were given feedback to the experts. They were then asked to evaluate the trends again. At this stage, 68 behavioral trends were able to achieve the consensus criterion and only 12 trends remained outside the consensus range. In the third round, the focus was on final consolidation of consensus and elimination of ambiguous cases. The results showed that at this stage, 72 behavioral biases were agreed upon by experts in the form of 12 final criteria, which include cognitive biases, emotional biases, personality biases, market biases, risk biases, experiential biases, communication biases, time biases, strategic biases, organizational biases, ethical biases and reporting and informational biases. Conclusions: While confirming the prominent role of behavioral factors in the performance of investment funds, the present study highlights the importance of a combined view of managers' decisions; meaning that combining financial engineering tools with behavioral analysis can reduce the gap between actual decisions and optimal decisions. Thus, this study can provide fund managers, investors, regulatory institutions, and financial policymakers with valuable guidance to improve the decision-making process and enhance the performance of investment funds.