Application of an ANN and LSTM-based Ensemble Model for Stock Market Prediction

2024/10/26 by Shaobo Guo, Liu, Fang, Qianwen Xing +12 · 3 citations
Decision Sciences · #Computational Engineering #FOS: Computer and information sciences #Finance #Stock Market Forecasting Methods #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2410.20253

openalex publication_date 2024/10/26 · openalex created_date 2024/11/15 · openalex updated_date 2026/07/28

Abstract

Stock trading has always been a key economic indicator in modern society and a primary source of profit for financial giants such as investment banks, quantitative trading firms, and hedge funds. Discovering the underlying patterns within the seemingly volatile yet intrinsically structured economic activities has become a central focus of research for many companies. Our study leverages widely-used modern financial forecasting algorithms, including LSTM, ANN, CNN, and BiLSTM. We begin by comparing the predictive performance of these well-known algorithms on our stock market data, utilizing metrics such as R2, MAE, MSE, RMSE for detailed evaluation. Based on the performance of these models, we then aim to combine their strengths while mitigating their weaknesses, striving to construct a powerful hybrid model that overcomes the performance limitations of individual models.Through rigorous experimentation and exploration, we ultimately developed an LSTM+ANN model that breaks through prior performance bottlenecks, achieving promising and exciting results.

Cited by

• Integrating Attention-Enhanced LSTM and Particle Swarm Optimization for Dynamic Pricing and Replenishment Strategies in Fresh Food Supermarkets
• A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond
• Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks

Related

• Predicting Stock Market Time-Series Data using CNN-LSTM Neural Network Model
• An Advanced Ensemble Deep Learning Framework for Stock Price Prediction Using VAE, Transformer, and LSTM Model
• Gradient Boosting Decision Tree with LSTM for Investment Prediction
• Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies
• FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis