A Novel Approach for Stock Market Prediction Using Feature Enhanced Learning Model (FELM)
Year: 2025
Type: article
Abstract: - Stock market prediction is an important task that is required by investors, traders and financial institutions in order to make informed decisions. Traditional models have the following problems like volatility, historical data inputs are dependent on features, model cannot adjust to real-time market changes. A novel feature-enhanced learning model, a Feature Enhanced Learning Model (FELM), is proposed which integrates advanced feature engineering with a hybrid learning method based on Extreme Gradient Boosting (XGBoost). Using technical indicators and sentiment analysis as well as macroeconomic data as inputs, the model gains greater accuracy which is around 99%-100% when compared with previous traditional models like ARIMA and LSTM.
Author: B. Anupama , Dr . R . Suganya
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6968645162
FWCI:
Cites:
Cited by:
Related to: