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
- Abstract
- Keywords
- Introduction
- Review of Literature
- Objectives of the Study
- Research Hypotheses
- Data and Methodology
- Empirical Results and Discussion
- Demographic Profile of Respondents
- Purpose of Using AI-Based Tools
- Correlation Analysis
- Preferred Decision-Making Model
- Findings of the Study
- Conclusion
- Managerial and Theoretical Implications
- Managerial Implications
- Theoretical Implications
- Limitations of the Study and Scope for Future Research
- Author's Contribution
- Conflict of Interest
- Funding Acknowledgement
- Acknowledgement
- References
Objective & Thematic Focus
This study aims to empirically analyze investor behavior toward AI-based investment decision-making and to assess whether Artificial Intelligence (AI) effectively reduces human bias, specifically within the Indian investment context. It seeks to provide valuable insights into the evolving relationship between human judgment and machine intelligence in financial markets.
- Examining the influence of Artificial Intelligence on investment decision-making processes.
- Analyzing how psychological and emotional biases affect investor behavior.
- Assessing the effectiveness of AI-based tools in mitigating behavioral biases among investors.
- Evaluating investor trust in AI-driven decisions and their perception of AI as a supportive tool.
- Identifying the preferred investment decision-making models, particularly hybrid approaches integrating human and AI intelligence.
- Providing empirical evidence from the Indian investment landscape regarding AI adoption.
Excerpt from the Book
From Human Bias to Machine Intelligence: An Empirical Study on Investor Behaviour and AI-Based Investment Decision Making
Investment decision-making is a fundamental aspect of financial markets and wealth creation. Traditionally, investment decisions have been guided by human expertise, intuition, and experience. However, behavioural finance theory challenges the assumption of investor rationality, emphasizing that psychological biases such as overconfidence, herd behaviour, anchoring, and loss aversion significantly influence financial decisions. These biases often lead to irrational trading behaviour and sub-optimal investment outcomes.
With rapid advancements in technology, Artificial Intelligence (AI) has emerged as a powerful tool in the financial sector. AI-based systems are increasingly used for portfolio optimization, algorithmic trading, robo-advisory services, and risk management. These systems process large volumes of data and generate insights with greater speed and accuracy than traditional human analysis. Despite these advantages, investor trust in AI-driven decisions remains a critical concern. This study aims to empirically analyse investor behaviour toward AI-based investment decision-making and assess whether AI reduces human bias in the Indian investment context.
Previous studies have extensively examined the intersection of behavioural finance and technological innovation. Bartram et al. (2020) found that AI-driven investment strategies outperform traditional models during volatile market conditions. Bahoo et al. (2024) identified AI as a general-purpose technology capable of enhancing forecasting accuracy and reducing information asymmetry in financial markets.
Behavioural finance research emphasizes that emotional and cognitive biases often impair rational decision-making. Frendberg and Leitner (2023) argued that algorithmic systems reduce behavioural inconsistencies by enforcing rule-based execution. Singh et al. (2025) observed that novice investors exhibit higher trust in AI-based recommendations compared to experienced investors, who prefer combining AI insights with human judgment. However, concerns related to transparency, explainability, and ethical accountability continue to limit full reliance on AI systems. This study contributes to existing literature by providing empirical evidence from Indian investors.
Chapter Summaries
Abstract: Summarizes the study's scope, methodology, key findings regarding AI's role in reducing biases, and the preference for hybrid decision-making models.
Introduction: Introduces the traditional and behavioral aspects of investment decision-making and sets the stage for examining the role of AI in mitigating human biases.
Review of Literature: Discusses existing research on behavioural finance, technological innovation in finance, and AI's impact on investment strategies, highlighting gaps addressed by this study.
Objectives of the Study: Outlines five specific goals, including examining investor behaviour, AI tool adoption, AI's role in bias reduction, investor trust in AI, and preferred decision-making models.
Research Hypotheses: Presents three hypotheses relating AI adoption to investment decision-making, investor confidence to trust in AI, and AI's ability to reduce behavioural biases.
Data and Methodology: Details the descriptive and empirical research design, primary data collection from 260 respondents, and the statistical analysis methods used.
Empirical Results and Discussion: Presents quantitative findings on respondent demographics, the purpose of AI tool usage, correlation between AI confidence and trust, and preferred investment decision-making models.
Findings of the Study: Lists key observations, such as increasing AI adoption, AI's role in reducing biases, AI as a supportive tool, and the preference for hybrid decision-making models.
Conclusion: Synthesizes the study's findings, affirming AI's positive influence on decision-making quality, the importance of investor confidence, and the relevance of hybrid frameworks.
Managerial and Theoretical Implications: Explores practical advice for investment managers and financial firms regarding AI integration, trust-building, and leveraging hybrid models, while also discussing the study's theoretical contributions to behavioural finance.
Limitations of the Study and Scope for Future Research: Discusses constraints related to sample size, reliance on self-reported perceptions, and focus on overall AI adoption, offering directions for future longitudinal and comparative studies.
Author's Contribution: States that the author conceptualized, designed, collected, analyzed, interpreted, and wrote all sections of the paper.
Conflict of Interest: Declares that there is no conflict of interest associated with the research.
Funding Acknowledgement: Confirms that the research received no specific grant from funding agencies.
Acknowledgement: Expresses sincere gratitude to faculty, mentors, and survey respondents.
References: Provides a list of academic sources cited within the paper.
Keywords
Artificial Intelligence, Investor behaviour, Behavioural Biases, Investment Decisions, FinTech, Financial Markets, Decision-Making, Hybrid Models, Trust in AI, Portfolio Optimization, Risk Management, Algorithmic Trading, Robo-advisory Services, Indian Investment Context, Psychological Biases
Frequently Asked Questions
What is this work fundamentally about?
This work fundamentally explores the transition from human bias to machine intelligence in investment decision-making, empirically studying investor behavior in the context of AI adoption.
What are the central thematic fields?
The central thematic fields include Artificial Intelligence in finance, investor behavior, behavioral biases, investment decision-making processes, and financial technology (FinTech).
What is the primary objective or research question?
The primary objective is to empirically analyze investor behavior toward AI-based investment decision-making and to assess whether AI effectively reduces human bias, specifically within the Indian investment context.
Which scientific method is used?
The study employs a descriptive and empirical research design, collecting primary data through a structured questionnaire and analyzing it using descriptive statistics, correlation analysis, and chi-square tests.
What is covered in the main part?
The main part covers an empirical analysis of respondent demographics, the purposes for using AI tools, the correlation between confidence in AI and trust in AI-based decisions, and investors' preferred decision-making models.
Which keywords characterize the work?
Key terms characterizing this work include Artificial Intelligence, Investor behaviour, Behavioural Biases, Investment Decisions, FinTech, Hybrid Models, and Trust in AI.
How do investors typically perceive AI in investment decision-making?
Investors largely perceive AI as a supportive tool that enhances analytical capability and reduces behavioral biases, rather than a complete replacement for human expertise.
What is the preferred investment decision-making model identified by the study?
The majority of respondents prefer a hybrid decision-making model, integrating human judgment with AI-driven insights, highlighting the importance of combining both intelligences.
What are the key implications for investment managers from this study?
Investment managers should integrate AI analytics as decision-support, focus on building investor trust through transparency and education, and leverage hybrid platforms combining human advisory services with AI insights.
What are the limitations of this study?
Limitations include a sample size of 260 respondents from a specific context, reliance on self-reported perceptions which may introduce bias, and a focus on overall AI adoption rather than specific algorithm performance.
- Arbeit zitieren
- S. J. Sanjana (Autor:in), 2025, From Human Bias to Machine Intelligence. An Empirical Study on Investor Behaviour and AI-Based Investment Decision Making, München, GRIN Verlag, https://www.grin.com/document/1684113
