Machine Learning Analysis of Key Features in Household Financial Decision-Making
Year: 2024
Type: article
Abstract: This paper explores the potential and challenges of mobile Internet in household investment decisions. The rapid development of mobile Internet has brought opportunities and challenges to household asset allocation, especially in promoting greater participation in venture asset investment. This paper focuses on the application potential of machine learning in analyzing household investment behavior patterns and trends. It reveals the potential household income rules, consumption patterns and asset allocation preferences through extensive data analysis. However, machine learning faces many challenges, such as data privacy protection, algorithmic interpretation, and data acquisition costs. Finally, the paper calls for further research and exploration to deepen understanding of how technological innovation can drive intelligent and optimized household financial decisions.
DOI: 10.54097/gapmwq55
Authors: Shenghan Zhao, Tianxiang Zhang, Ningxin Li
Institutions: Cornell University, New York University, Columbia University
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4401847985