Algorithm for predicting financial investor behavior based on biomechanical data and planning recognition
Abstract
In complex and volatile financial markets, investor behavior is an important factor driving market volatility. Traditional studies have mostly relied on methods such as financial market data, questionnaires and psychological scales, but these methods have limitations such as data lag, subjectivity and difficulty in quantification. In recent years, with the development of biomechanics and neuroscience, researchers have begun to explore the use of biomechanical data to predict the behavioral trends of financial investors, which provides a new perspective and methodology for financial market research. This study aims to predict financing behavior in the stock market by constructing an investor sentiment index and combining it with a planning recognition model. Based on the close relationship between biomechanics and emotions, the biomechanical representations of different emotions of investors and their effects on behavior are dissected. Meanwhile, the partial least squares method is used to construct the investor sentiment index, and a planning identification model consisting of Markov chain, planning identification graph and expected utility function is introduced to predict the stock market trend and financing behavior, which is more accurate than the Markov model based on objective data only. In order to enrich the theoretical system of financial market prediction through this study, it provides more accurate market prediction and investment advice for financial institutions and investors. By introducing biomechanical data and planning recognition model, it provides new ideas and methods for understanding the intrinsic connection between investor sentiment and market volatility, and promotes the development of financing business in the domestic market as well as maintains the health and stability of the domestic stock market.
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