The increasing adoption of Artificial Intelligence (AI) in financial markets has significantly transformed traditional investment decision-making processes. While conventional investment decisions rely heavily on human judgment, behavioural finance literature has consistently highlighted the presence of psychological and emotional biases influencing investor behaviour. This study empirically examines the impact of AI adoption on investor behaviour and investment decision-making. Primary data were collected from 260 respondents, including individual investors, fund managers, and financial analysts, using a structured questionnaire. Descriptive statistics, correlation analysis, and chi-square tests were employed to analyse the data. The findings reveal that AI-based tools help reduce behavioural biases and improve rational decision-making; however, investors largely perceive AI as a supportive tool rather than a complete replacement for human expertise. The study concludes that hybrid decision-making models integrating both human intelligence and AI are the most effective approach in modern financial markets.
Table of Contents
1. Introduction
2. Review of Literature
3. Objectives of the Study
4. Research Hypotheses
5. Data and Methodology
6. Empirical Results and Discussion
6.1 Demographic Profile of Respondents
6.2 Purpose of Using AI-Based Tools
6.3 Correlation Analysis
6.4 Preferred Decision-Making Model
7. Findings of the Study
8. Conclusion
9. Managerial and Theoretical Implications
9.1 Managerial Implications
9.2 Theoretical Implications
10. Limitations of the Study and Scope for Future Research
11. Author’s Contribution
12. Conflict of Interest
13. Funding Acknowledgement
14. Acknowledgement
15. References
Research Objectives and Core Themes
This study investigates the impact of Artificial Intelligence (AI) on investor behavior and decision-making processes within the Indian financial market, specifically focusing on whether AI tools can mitigate psychological and emotional biases to improve rational outcomes.
- The influence of AI adoption on modern investment decision-making.
- The role of AI in reducing common behavioral biases like overconfidence and herd behavior.
- Investor trust and confidence in AI-driven versus human-led financial strategies.
- Preference for hybrid decision-making models over fully automated or fully manual approaches.
- Analysis of empirical data from investors, fund managers, and financial analysts regarding AI utility.
Excerpt from the Book
Managerial Implications
The findings of this study offer several important implications for investment managers, financial institutions, and FinTech firms. First, the growing acceptance of AI-based tools among investors suggests that asset management companies should integrate AI-driven analytics into their advisory and portfolio management services. Rather than replacing human expertise, AI should be positioned as a decision-support system that enhances analytical accuracy and reduces emotional biases.
Second, investment managers should focus on building investor trust in AI systems by improving transparency, explainability, and communication regarding how AI-generated recommendations are derived. Training programs and investor education initiatives can help bridge the confidence gap, especially among experienced investors who remain cautious about full automation.
Finally, FinTech firms can leverage the preference for hybrid decision-making models by designing platforms that combine human advisory services with AI-based insights. Such an approach can improve customer satisfaction, decision quality, and long-term investor engagement.
Summary of Chapters
Introduction: Provides the context of investment decision-making and introduces the emergence of AI as a tool to counteract behavioral biases in financial markets.
Review of Literature: Surveys existing research on the intersection of behavioral finance and technology, highlighting the benefits and concerns regarding AI implementation.
Objectives of the Study: Outlines the five primary goals of the research, ranging from examining investor behavior to identifying preferred decision-making models.
Research Hypotheses: Presents the three testable assumptions regarding AI adoption, investor confidence, and the reduction of behavioral biases.
Data and Methodology: Describes the descriptive and empirical research design, including the sampling of 260 respondents and the use of statistical analysis.
Empirical Results and Discussion: Presents the data findings, including demographic profiles, AI usage purposes, correlations, and preferences for decision-making models.
Findings of the Study: Summarizes the key insights derived from the empirical data regarding AI adoption and trust.
Conclusion: Synthesizes the overall research findings and confirms the effectiveness of hybrid models over purely human or purely AI-based approaches.
Managerial and Theoretical Implications: Offers practical strategies for financial institutions and discusses the contribution to behavioral finance theory.
Limitations of the Study and Scope for Future Research: Acknowledges constraints in sample size and methodology, providing suggestions for future academic exploration.
Keywords
Artificial Intelligence, Investor behaviour, Behavioural Biases, Investment Decisions, FinTech, Financial Markets, Hybrid Decision-Making, Data Analysis, Risk Assessment, Portfolio Optimization, Investor Trust, Algorithmic Trading.
Frequently Asked Questions
What is the primary focus of this research?
The research examines how Artificial Intelligence influences investor behavior and decision-making, specifically whether it helps mitigate psychological biases commonly found in financial markets.
What are the central themes of the study?
The study centers on AI adoption, behavioral finance, the reduction of cognitive biases, investor trust, and the preference for hybrid human-AI decision models.
What is the main objective of the paper?
The primary goal is to assess whether AI acts as a tool to reduce human bias and to understand the evolving relationship between human expertise and machine intelligence in investment.
Which methodology was employed to conduct this study?
The researcher used a descriptive and empirical design, collecting primary data from 260 respondents through a structured questionnaire and applying statistical tests like correlation and chi-square analysis.
What topics are covered in the main body of the work?
The body covers a literature review, research hypotheses, empirical results regarding respondent demographics and tool usage, and a detailed discussion on hybrid decision-making models.
Which keywords characterize this study?
Key terms include Artificial Intelligence, Investor behaviour, Behavioural Biases, FinTech, and Hybrid Decision-Making.
Why do most investors prefer hybrid decision-making models?
Respondents perceive AI as a powerful supportive tool for data analysis, but they still value human judgment, accountability, and experience, leading them to prefer models that combine both.
How does this study contribute to behavioral finance theory?
It empirically demonstrates that technological interventions like AI can partially correct behavioral inefficiencies and biases that have been traditionally highlighted in behavioral finance literature.
- Quote paper
- S. J. Sanjana (Author), 2025, From Human Bias to Machine Intelligence. An Empirical Study on Investor Behaviour and AI-Based Investment Decision Making, Munich, GRIN Verlag, https://www.grin.com/document/1684113
