Contrasting the efficiency of stock price prediction models using various types of LSTM models aided with sentiment analysis

ArXiv ID: 2307.07868 “View on arXiv”

Authors: Unknown

Abstract

Our research aims to find the best model that uses companies projections and sector performances and how the given company fares accordingly to correctly predict equity share prices for both short and long term goals.

Keywords: Equity prediction, Sector performance, Fundamental analysis, Projection modeling, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced LSTM architectures (Bidirectional, Seq2Seq, Two-Path) and sentiment analysis, indicating high mathematical complexity. It demonstrates strong empirical rigor through backtesting on real historical data (12 months) for multiple stocks, comparing multiple model architectures and including quantitative error metrics (RMSE, MAE).

  flowchart TD
    A["Research Goal: Find optimal LSTM model for equity price prediction using sector performance & sentiment"] --> B{"Data Collection & Processing"}
    B --> C["Stock Price Data<br>Historical Prices"]
    B --> D["Sector & Company Fundamentals<br>Projections & Performance"]
    B --> E["Sentiment Data<br>News & Social Media"]
    
    C & D & E --> F{"Computational Models"}
    F --> G["LSTM Variants<br>GRU, BiLSTM, Stacked LSTM"]
    F --> H["Feature Engineering<br>Technical Indicators"]
    
    G & H --> I["Model Training & Evaluation"]
    I --> J["Key Findings<br>Optimal Model Selected<br>Sentiment significantly improves accuracy<br>Short vs Long-term prediction variance"]