Prediction of stock price direction using the LASSO-LSTM model combines technical indicators and financial sentiment analysis.

ABSTRACT: Correctly predicting the stock price movement direction is of immense importance in the financial market. In recent years, with the expansion of dimension and volume in data, the nonstationary and nonlinear characters in finance data make it difficult to predict stock movement accurately. In this article, we propose a methodology that combines technical analysis and sentiment analysis to construct predictor variables and then apply the improved LASSO-LASSO to forecast stock direction. First, the financial textual content and stock historical transaction data are crawled from websites. Then transfer learning Finbert is used to recognize the emotion of textual data and the TTR package is taken to calculate the technical indicators based on historical price data. To eliminate the multi-collin

SUBMITTER: Yang J 

PROVIDER: S-EPMC9680880 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature