Document Type : Research Paper

Authors

• Aylin Rahmani Esbagrani ¹
• Farzaneh Heydarpoor ²
• Zahra Poorzamani ²

¹ PhD student of the field Accounting, Department of Accounting, CT.C., Islamic Azad University, Tehran, Iran

² Professor in Accounting, Department of Accounting, CT.C., Islamic Azad University, Tehran, Iran

Abstract

Reducing undesirable behavioral biases of managers and investors is one of the basic challenges in financial markets that can have significant effects on the quality of decisions and the efficiency of capital markets. Behavioral biases such as over-optimism, fear of loss, and emotional influences can lead to irrational decisions and market anomalies that ultimately negatively affect economic performance and financial markets. Although behavioral biases are not always detrimental for instance, optimism, which constitutes a component of overconfidence bias, can have positive outcomes for firms with optimistic managers, such as improving financial performance and enhancing earnings predictability. the literature predominantly emphasizes their adverse effects. In this research, the aim is to identify the factors affecting the behavioral biases of managers and investors. using a qualitative grounded theory approach under an interpretive paradigm The statistical population in this research includes 15 accounting and financial experts who were selected in a purposeful (judgmental) and (snowball) manner and were interviewed with a semi-structured interview tool until saturation was reached. The output of this stage included 10 secondary themes, which led to 4 organizing themes of "motivational factors", "personality factors", "factors related to self-confidence and competence" and "cognitive and attitudinal factors" for the theme of behavioral biases. A comparative analysis of the data indicated that while some factors affecting the reduction of behavioral biases are shared between managers and investors, their origins and intensities differ. For managers, strategic decision pressures, accountability responsibilities, and managerial overconfidence were identified as major sources of bias, whereas for investors, loss aversion, herding behavior, and framing effects were more pronounced. Accordingly, the study suggests that educational and policy interventions aimed at reducing behavioral biases should be designed separately for these two groups to enhance their effectiveness.

Introduction

Behavioral biases represent fundamental challenges in financial markets, significantly affecting decision-making quality and capital market efficiency. These biases, including overconfidence, loss aversion, and emotional influences, lead to irrational decisions and market anomalies that negatively impact economic performance. While behavioral finance has grown substantially as a field combining psychology and social sciences, a significant gap remains in understanding the specific mechanisms through which these biases can be reduced, particularly in the Iranian context. Most existing research has focused on identifying biases rather than developing practical frameworks for their mitigation. Furthermore, while managers and investors both experience behavioral biases, the contextual differences in how these biases manifest remain underexplored. Managers operate within organizational governance frameworks with stakeholder accountability, while investors are primarily influenced by market volatility and public information. This research employs a qualitative grounded theory approach to identify and explain factors influencing the reduction of behavioral biases among both groups in Iran’s capital market.

Theoretical Framework

Behavioral finance challenges traditional assumptions of rational actors with complete information, emphasizing that financial decisions are influenced by emotions, biases, and cognitive errors. Since the 1980s, particularly following Kahneman and Tversky’s Prospect Theory, the field has demonstrated fundamental shifts in understanding investor behavior. Behavioral biases fall into two categories: cognitive biases (confirmation bias, anchoring) are more amenable to correction through education and structured feedback, while emotional biases require regulation of decision-making conditions and emotional responses. While the literature has focused on individual investor biases, managerial biases such as CEO overconfidence and loss aversion directly impact corporate decisions. Research shows that although biases in managers and investors occur in different contexts, common psychological foundations influence both groups. Managers primarily suffer from biases under organizational pressures and stakeholder expectations, while investors are more influenced by market volatility and herding behavior.

Research Questions

The main research question is: What factors lead to the reduction of behavioral biases in managers and investors in Iran’s capital market?
Sub-questions include:

What motivational factors influence the reduction of behavioral biases in managers and investors?
What cognitive and attitudinal factors play a role in reducing behavioral biases?
What personality traits can help reduce behavioral biases?
How do self-confidence and individual competencies affect the reduction of behavioral biases?
What differences exist in the origin, mechanism, and intensity of behavioral biases between managers and investors?
Methodology

This exploratory qualitative research employed an interpretive paradigm and grounded theory methodology to understand the subjective experiences of managers and investors. Semi-structured interviews lasting 45-60 minutes were conducted with 15 experts (7 managers and 8 active investors) selected through purposive and snowball sampling until theoretical saturation was reached. Data were analyzed using Braun and Clarke’s (2006) thematic analysis with MAXQDA 2020. Initially, 98 primary codes were extracted and reduced to 54 final codes, organized into 10 sub-themes, and ultimately grouped into 4 organizing themes. Independent coding by a domain expert achieved 87% agreement, confirming data reliability. Data from both groups were analyzed separately, then compared to identify comprehensive patterns.

Findings

Analysis revealed four main organizing themes: motivational factors, personality factors, self-confidence and competence factors, and cognitive and attitudinal factors.
Motivational Factors: Goal-oriented motivations enable better rational decision-making. Among managers, motivation to maintain professional credibility deterred hasty decisions. Among investors, motivation for short-term returns could both lead to bias and, with proper education, result in financial discipline.
Cognitive and Attitudinal Factors: Evidence-based attitudes help prevent cognitive biases. Managers’ cognitive biases typically arises from incomplete internal data and excessive emphasis on performance indicators. Investors showed weakness in understanding risk-return ratios and susceptibility to news and social networks. Attitudes toward markets and acceptance of uncertainty were key components in reducing biases.
Personality Factors: Traits like adaptability, neuroticism, and extraversion significantly influence risk-taking. Managers with higher extraversion and adaptability showed better cognitive resistance to decision pressures and less confirmation bias. Investors with high neuroticism showed a greater tendency toward herding behavior during market volatility, consistent with previous research findings.
Self-Confidence and Competence: Financial literacy and accurate data analysis help prevent incorrect decisions. Among managers, excessive self-confidence stems from experience and leads to an “illusion of control.” Among investors, it results from incorrect market analysis, misinterpretation, or past successes. Behavioral training and continuous feedback can moderate this bias in both groups.
Comparative analysis revealed that while the four main themes are common, their manifestation mechanisms differ. Manager-specific themes included strategic decision pressure, illusion of control, and organizational accountability. Investor themes of loss aversion, herding behavior, and framing effects were more prominent. Common themes included financial literacy, emotional intelligence, and structured feedback, which were significantly associated with reduced behavioral biases. Results indicate that factors affecting bias reduction have a two-level structure: cognitive-emotional (individual) and structural-organizational (environmental).

Discussion and Conclusion

Findings indicate that motivational, cognitive, attitudinal, and personality factors play important roles in bias emergence and reduction. Short-term motivations can lead to biases like overconfidence or loss aversion, necessitating a focus on long-term goals and aligning rewards with actual results. Accurate economic analysis and business intelligence tools reduce confirmation bias effects. Personality traits such as conscientiousness, adaptability, and emotional control prove effective in complex decision-making. Emotional intelligence’s role in managing emotions during market fluctuations is critical for controlling biases.
Financial literacy and managed self-confidence are crucial. Individuals with high financial literacy can better analyze market information and avoid emotional decisions. Strengthening financial literacy through simulation-based training is among the most effective tools for correcting behavioral biases. Structured feedback and organizational learning through transparent decision-making environments help reduce errors in both groups. Excessive managerial self-confidence and market-oriented investor emotions are the two main sources of behavioral biases requiring specific educational interventions.
Comparative analysis between managers and investors revealed that although the overall structure of influential factors is similar, their origin, mechanism, and impact intensity differ. For managers, strategic decision pressure, accountability requirements, and professional credibility maintenance are primary sources of cognitive and emotional biases. Among investors, market emotions, loss aversion, and herding behavior play more prominent roles in creating behavioral deviations. This fundamental difference highlights the necessity of developing separate educational programs and corrective policies for each group.

Practical Implications

For Managers: Develop financial analysis and emotion management training programs; create an organizational culture based on long-term goal setting; implement balanced reward systems that avoid short-term decision encouragement; utilize group consultation to reduce individual biases.
For Investors: Increase financial literacy through specialized training; use data-based analytical tools; strengthen emotional intelligence to control emotional reactions to market fluctuations; set specific investment goals with clear time horizons.
For Regulatory Bodies: Design public education programs in behavioral finance; create warning tools for identifying herding behaviors; strengthen market information transparency; develop AI-based decision support systems.
Creating an organizational culture based on long-term goal setting, decision transparency, and reliance on data-driven analyses is a key step in reducing behavioral biases and increasing capital market efficiency.

Limitations and Future Research

This research included 15 experts, which was sufficient for theoretical saturation but requires caution in generalizing the results. The qualitative approach facilitates a deeper understanding of the phenomenon but does not allow quantitative testing of relationships. Future research could employ quantitative approaches to test the presented conceptual model and measure each identified factor’s impact on reducing behavioral biases. Examining variables such as organizational culture, financial experience, and learning orientation in relation to managerial biases could provide new research avenues.
 
 
 

Keywords

• Behavioral Biases
• Decisions Of Investors And Managers
• Theme Analysis

Main Subjects

• Accounting and various aspects of finance