Prediction model of stock return on investment based on hybrid DNN and TabNet model.

ABSTRACT: With the development of the social economy, research on stock market prediction is in full swing. However, the fluctuations in stock price and returns are influenced by many factors, including political policies, market environment, investor psychology, and so on. The traditional analysis method, based on subjective experience, requires significant time and effort, and its prediction accuracy is often poor. Now, the application of machine learning algorithms to predict stock returns has become a hot topic among scholars. This article comprehensively analyzes the advantages and disadvantages of support vector machine (SVM), tree-based algorithms, and neural network algorithms in processing tabular data and time series data. It proposes a hybrid model based on the deep neural network (DNN) a

SUBMITTER: Zhang T 

PROVIDER: S-EPMC11639136 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature