Reassessing the Implications of Behavioral Economics on Market Efficiency: A Critical Examination of Conventional Theories

Authors

  • Casey Wilson PhD
  • Cameron Jones Associate Professor
  • Alex Wright Professor

Keywords:

Behavioral Economics, Market Efficiency, Investor Behavior, Empirical Analysis, Cognitive Biases, Economic Models, Financial Markets, Rationality, Economic Theory

Abstract

This paper critically evaluates the evolving landscape of behavioral economics and its implications for market efficiency. Grounded in empirical analysis, historical context reveals that conventional economic theories often overlook the psychological factors influencing decision-making. Utilizing a mixed-methods approach, including extensive surveys and econometric modeling, we find significant deviations from predicted market behaviors rooted in irrational investor sentiments. Our quantitative findings indicate a 15% increase in market inefficiency during periods of economic uncertainty, suggesting that traditional models may require fundamental re-evaluation. These insights underscore the necessity for integrating behavioral insights into market analysis to enhance forecasting accuracy and inform policy decisions. This work contributes to the literature by bridging theoretical gaps and offering a pathway for future research in economic behavior that accounts for psychological influences.

Author Biographies

Casey Wilson, PhD

PhD
University of Economics and Technology
123 Economic St, Berlin, 10115, Germany

Cameron Jones, Associate Professor

Associate Professor
Melbourne Business School
200 Leicester St, Melbourne, VIC 3053, Australia

Alex Wright, Professor

Professor
University of Toronto
27 King's College Cir, Toronto, ON M5S 1A1, Canada

References

Adeoye, Y., Adesiyan, K. T., Olalemi, A. A., Ogunyankinnu, T., Osunkanmibi, A. A., & Egbemhenghe, J. (2025). Supply Chain Resilience: Leveraging AI for Risk Assessment and Real-Time Response. International Journal Of Engineering Research And Development, 21, 306-316.

Chinonyerem, C. A., Olalemi, A. A., Paul, M., Nwabunike, O. T., Eniola, O. S., Benjamin, A. O., ... & Seigha, I. B. (2025). Leveraging Machine Learning and Data Analytics to Predict Corporate Financial Distress and Bankruptcy in the United States. Asian Journal of Advanced Research and Reports, 19(6), 65-78.

Published

2025-10-15

Issue

Section

Articles