Optimization of Quantitative Investment Strategies in the Financial Big Data Environment

Jinhong Wang

doi:10.54097/fbem.v12i2.14595

Authors

Jinhong Wang

DOI:

https://doi.org/10.54097/fbem.v12i2.14595

Keywords:

Financial big data, Quantitative investment strategies, Strategy optimization, Data-driven.

Abstract

With the rapid progress of technology, the financial field is rapidly shifting towards a data-driven environment, with "financial big data" becoming its core component. This transformation has brought about profound changes in the financial market, especially with a significant impact on algorithmic quantitative investment strategies. This article delves into the optimization of quantitative investment strategies in the financial big data environment, analyzes how the characteristics of big data affect quantitative investment, and how to utilize these characteristics to optimize investment strategies. The article first outlines the definition, characteristics, and sources of financial big data, with a focus on describing its core characteristics such as capacity, speed, diversity, and authenticity. This article highlights the application of machine learning and deep learning in financial analysis, as well as the new perspective provided by unstructured data for quantitative strategies. Finally, the conclusion section of the article summarizes the opportunities and challenges brought by the integration of financial big data and quantitative strategies, calling on financial institutions and investors to have a deeper understanding and utilization of these two major trends.

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