Theoretical Discussion on Individual Investor Behavior from a Quantitative Finance Perspective: Possibilities for Machine Learning Applications ()
ABSTRACT
Understanding the behaviors of individual investors
is a complex and crucial task in the modern financial landscape. As the
financial markets continue to grow in complexity and digitalization, the
behavior patterns and decision- making processes of
individual investors are increasingly drawing the attention of scholars and market regulators. This study embarks
from a quantitative finance perspective, theorizing on the potential
application of machine learning in analyzing and predicting individual investor
behavior. Despite the myriad influences on investor behavior—such as individual
differences, market conditions, information factors, and psychological biases—we
still identify common patterns in their behavior. Furthermore, we propose that
machine learning technology holds significant potential for predicting the
behavior of individual investors. This study presents new perspectives and
methods for understanding and predicting the behavior of individual investors.
Share and Cite:
Zhou, X. (2023) Theoretical Discussion on Individual Investor Behavior from a Quantitative Finance Perspective: Possibilities for Machine Learning Applications.
Open Journal of Business and Management,
11, 2802-2810. doi:
10.4236/ojbm.2023.116153.
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