Application Scenarios of Perception Reinforcement Learning in Portfolio Business Analysis in the Fintech Era

Abstract

 In the context of the emerging trend of the development of fintech, this paper investigates the use of perceptual reinforcement learning in portfolio business analysis as a practical application value. It focuses on the shortcomings of conventional portfolio analysis systems, which are structural inflexibility and inability to respond to the market nonlinear characteristics. Combining literature review with industry practice, the present paper is an overview of the current accomplishments regarding financial engineering and intellectual finance, as well as the technical principles and market adaptation laws of the perceptual reinforcement learning. The paper categorizes five large application scenarios. It confirms that this technology based on market perception, interactive decision-making and dynamic iteration can address the shortcomings of conventional methods in market cycle switching, cross-asset allocation, and personalized wealth management which covers critical businesses such as asset allocation, robo-advisory, quantitative trading, and cross-border risk control. It will also consider practical limitations such as computing cost, defect in data governance, lack of interpretable algorithms and regulation by the industry. The results of the research can be used as references in the process of digitization of securities research and asset management industries, and can serve as practical recommendations on how to apply this technology properly.