Optimization of Quantitative Investment Strategies in the Financial Big Data Environment

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.

Big Data's Future Potential as an Innovation in Competitive Advantage and Decision-Making Techniques

“…The findings also underscore the importance of investment in the technological infrastructure and human resources required to support the application of Big Data innovations in decision-making strategies, which aligns with previous research (Ahmed et al, 2022;Ashsifa & Ali, 2019;Basile et al, 2024;Cui et al, 2022;Gravili et al, 2023;Keshavarz et al, 2021;Koshcheeva & Lyapina, 2022;Perifanis & Kitsios, 2023;Wang, 2023). In an increasingly connected and digital business environment, organisations need to ensure that they have robust technology systems and employees skilled in data analysis and interpretation.…”