Exploration of Optimization Paths Based on Data Modeling in Financial Investment Decision-Making

Chuhan Wang

doi:10.71222/a10qet36

Authors

Chuhan Wang Carey Business School, Johns Hopkins University, Washington, D.C., 20001, USA Author

DOI:

https://doi.org/10.71222/a10qet36

Keywords:

financial investment decision, data modeling, data preprocessing, market volatility, hardware resource optimization

Abstract

With the continuous development of the financial industry, the data analysis in the investment decision process has become more and more complex. The application of data modeling technology in financial investment decisions has become a key tool to improve the accuracy and efficiency of decision making. This paper delves into conventional financial investment model regression analysis and machine learning, analyzing challenges such as insufficient data quality, market volatility, hardware resource constraints, and model overfitting. In order to improve the stability and generalization ability of the model, some optimization paths are proposed, such as improving the quality of data preprocessing, introducing robust models and optimizing distributed computing.

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