Deep neural network model enhanced with data preparation for the directional predictability of multi-stock returns

Abstract

This study presents novel deep neural networks (DNNs) that integrate a thorough data preparation process, including dollar bar sampling, trend scanning labeling, and piecewise aggregate approximation (PAA), to extract features for predicting the directional returns of leading technology stocks on the US stock exchange, such as Apple, Google, Microsoft, and Intel. Our DNN approach seeks to enhance the quality of input data for the models, thereby improving their predictive accuracy. The empirical analysis, conducted with high-frequency data from 2012 to 2022, reveals that DNN models—particularly the one employing dollar bars and trend scanning labeling (DB-TSC)—exhibit vital accuracy and generalizability in forecasting stock return directions. Our research emphasizes the importance of data preparation and fine-tuning model parameters for reliable predictions. This study presents valuable insights into applying deep neural networks (DNNs) for financial forecasting. It is an effective tool for investors and financial analysts aiming to navigate the complexities of the stock market.