RETRACTED: Investor behaviour monitoring based on deep learning (Retracted Article)

Through theoretical research and empirical analysis, we explore the application path of deep learning in the stock market investment theory. Based on the summarised theoretical results, we establish a relatively complete theoretical framework for stock investment strategies through tools such as deep learning, reinforcement learning, and intensity deepening learning, and uses the theoretical framework as a starting point to construct a targeted investment strategy model. And through empirical analysis, the effectiveness of the investment strategy model is back-tested. Finally, we analyse the profit effects of various stock investment strategies to understand the specific characteristics of different investment strategies. The empirical analysis results show that the introduction of deep learning in the construction of investment strategy models is conducive to improving the ability of investors to capture KD indicator trading signals. Therefore, in the process of determining investment strategies, an investment model with deep learning as the core can be constructed. Not only that, through empirical analysis of a large number of stocks, it can be found that the investment strategy model based on deep learning is a typical high-probability profit strategy, which is conducive to reducing investment risk by analysing investment methods, thereby improving profitability at a low-risk level.