A multi-feature stock price prediction model based on multi-feature calculation, LASSO feature selection, and Ca-LSTM network

Abstract

This paper addresses the crucial realm of stock price prediction, highly coveted by individual investors and institutions for its substantial economic implications. The inherent non-stationary and intricate nature of stock market fluctuations, coupled with real-time transactions, poses a formidable challenge for accurate and swift prediction. Unlike prevailing research that predominantly focuses on forecasting methods, our novel approach places a paramount emphasis on processing original data, introducing 57 technical indicators to better represent economic aspects for stock price prediction. Signifying the importance of each feature, we employ the LASSO algorithm to derive an optimal feature combination. Additionally, our methodology utilizes the Ca-LSTM (cascade long short-term memory) technique, enhancing information extraction from individual features. Experimental results, gauged by mean error, underscore the superiority of the Ca-LSTM model over other time series prediction models and conventional long short-term memory approaches. Notably, our model's integration with the accumulation-based VMD-LSTM model demonstrates enhanced forecasting accuracy. This proposed method holds considerable potential to refine stock price prediction, thereby delivering heightened value to investors in the dynamic financial landscape.

Keywords:

• Stock
• long short-term memory
• feature selection
• predict
• sliding window

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets used in this study are not publicly available but can be obtained from the corresponding author upon reasonable request.