Author(s)

Jiahui Fu

Corresponding Author

Jiahui Fu

Abstract

In the field of financial economics, data science, as a discipline that integrates statistics, computer science, and economic principles, provides powerful tools and insights for solving complex problems in financial markets. This paper will explore the application and challenges of data science in financial economics, with a focus on analyzing data-driven model construction, prediction methods, and risk management strategies. It will also discuss how to address the challenges of data quality, privacy protection, and model interpretability. The application of data science in financial economics is mainly reflected in market forecasting, credit rating, asset pricing, and risk management. Machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and Deep Learning Networks can be used to deeply mine historical data, predict future market trends, and provide a decision-making basis for investors. In addition, through big data analysis, potential investment opportunities can be identified, investment portfolios can be optimized, market risks can be evaluated and controlled, and the competitiveness of financial institutions can be enhanced. Data science has shown great potential in the financial field, but it also faces a series of challenges. Firstly, there is an issue with data quality. Financial market data often contains noise, missing values, and non-linear relationships, which require the establishment of robust data cleaning and preprocessing processes. Secondly, there are considerations for data privacy and security. When using sensitive personal or corporate information, balancing data utilization and privacy protection has become a major challenge. Furthermore, as the complexity of the model increases, its interpretability gradually decreases, making it difficult for regulatory agencies and decision-makers to understand and validate the output of the model, increasing the risk of compliance and transparency.

Keywords

Data Science; Financial Economics; Market Forecasting; Risk Management