Research On Stock Price Prediction Model And Quantitative Trading Based On INFORMER Mechanism

With the rapid development of artificial intelligence technology,various financial information within the financial market has become increasingly complex.It is of great significance to study how to reasonably and effectively process financial information and build models for analysis and prediction.In this paper,we propose a stock price prediction model based on the INFORMER mechanism and construct a set of quantitative trading strategies based on this model.The main contribution of this research is the introduction of the INFORMER mechanism into the field of stock price prediction and its validation in combination with financial quantitative trading research.The effectiveness of the model is demonstrated in empirical analysis.Firstly,in terms of model construction,we propose a stock price prediction model based on the INFORMER mechanism,which introduces Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),and the INFORMER mechanism.INFORMER uses an adaptive length time-series decoder to construct a deep learning prediction model.The model adopts multi-level and multi-granularity attention mechanisms,as well as optimization techniques such as residual connections and layer normalization,which reduce the root mean square error(RMS)by more than 5% compared to traditional deep learning models.Secondly,in terms of quantitative trading,based on the constructed prediction model,we propose a dynamic stop-loss trading strategy.The strategy adjusts the stop-loss point dynamically according to the prediction results and market volatility,and provides investors with the holding operation under this strategy,achieving a certain degree of risk control and profit optimization.The main focus of this paper is to propose a stock price prediction model based on the INFORMER mechanism and apply it to quantitative trading.Compared with other models,this prediction model achieved higher prediction accuracy and stability in experiments.Furthermore,we established a "dynamic capital allocation strategy" that enables investors to allocate stock positions in a certain proportion according to the predicted rise and fall,thereby achieving risk avoidance and maximizing profits in the financial market.