Stock Price Volatility Prediction System Integrating LSTM and Sentiment Analysis

This open-source project combines Long Short-Term Memory (LSTM) networks and sentiment analysis technology to build a stock price and volatility prediction system. By fusing structured price data (including technical indicators) and unstructured market sentiment data (news, social media, etc.), it improves prediction accuracy and provides data support for quantitative trading decisions and risk assessment. The project demonstrates a modern data-driven financial prediction method, breaking through the limitations of traditional single data sources.

Stock market prediction is a long-standing challenge in the financial field, with the Random Walk Theory and Efficient Market Hypothesis debating the predictability of the market. With the breakthroughs of machine learning, especially deep learning, in time series processing, data-driven methods are transforming quantitative finance practices. This project aims to combine traditional technical analysis (price history) with emerging sentiment analysis (market sentiment), using LSTM to capture complex time dependencies and explore more effective prediction paths.

The core innovation of the project lies in dual-source data fusion:

• Structured data: Open/close/high/low prices, trading volume, turnover, and technical indicators such as moving averages, RSI, and MACD;
• Unstructured data: News titles and content, social media discussions (Twitter/Reddit), analyst reports, and earnings call transcripts.

As the core of time series modeling, LSTM has the following advantages:

1. Memory capability: Captures multi-scale time patterns (intraday, weekly, monthly) through gating mechanisms;
2. Nonlinear modeling: Handles complex nonlinear dynamics like bull/bear market transitions;
3. Sequence learning: Flexibly processes variable-length inputs and outputs single-point or sequence predictions.

Data preprocessing:

• Price data normalization: Z-score, Min-Max scaling, log returns (to improve stationarity);
• Sequence construction: Sliding window method (input past 60 days of data to predict future 1-5 days of price/volatility).

Sentiment analysis module:

• Text preprocessing: Tokenization, stemming, stopword filtering, financial-specific dictionaries (e.g., Loughran-McDonald);
• Sentiment extraction: Dictionary method, SVM/Naive Bayes classifiers, FinBERT pre-trained model;
• Feature engineering: Daily sentiment scores, sentiment volatility, momentum, polarity distribution.

LSTM model architecture:

• Input layer: Concatenated price and sentiment features;
• 2-3 stacked LSTM layers (50-200 units) + Dropout (0.2-0.5) to prevent overfitting;
• Fully connected layer mapped to prediction targets;
• Multi-task learning: Shared LSTM encoder, dual output branches for price and volatility prediction, joint loss function to balance tasks.

Volatility modeling:

• Handles heteroscedasticity (volatility clustering);
• Output layer uses ReLU/softplus to ensure non-negativity;
• Log transformation to improve distribution normality.

Evaluation metrics:

• Price prediction: RMSE, MAE, MAPE, direction accuracy;
• Volatility prediction: MSE, QLIKE loss, correlation with realized volatility.

Backtesting framework:

• Walk-forward Validation: Simulates real trading, uses rolling training-test windows to avoid data leakage;
• Transaction cost considerations: Includes slippage, commissions, market impact, and other actual costs.

Quantitative trading strategies:

• Trend following: Go long if predicted to rise, short/reduce position if predicted to fall;
• Volatility trading: Buy options if volatility is predicted to rise, sell options if predicted to fall.

Risk management:

• Reduce position during high volatility periods;
• Calculate VaR (Value at Risk) and ES (Expected Shortfall);
• Dynamically adjust hedge ratios.

Portfolio optimization: Input price trends and volatility predictions into the mean-variance optimization framework to build risk-adjusted optimal portfolios.

Limitations:

• Model risk: Overfitting (low signal-to-noise ratio in financial data), market structure changes (e.g., financial crises) making historical patterns invalid, black-box problem (lack of interpretability);
• Data quality: Survivor bias (only includes surviving companies), look-ahead bias (using future information), sentiment data noise (interference from irrelevant information).

Future directions:

• Attention mechanisms (Transformer complementing LSTM);
• Graph neural networks (modeling stock correlations);
• Reinforcement learning (directly optimizing trading strategies);
• Explainable AI (improving model transparency to meet regulatory requirements).

This project demonstrates the application potential of machine learning in financial prediction. Through dual-source data fusion and LSTM modeling, it provides more comprehensive market insights. However, it is important to recognize that the financial market is full of uncertainties, and no model can consistently outperform the market. The value of the project lies in data-driven decision support, not a "holy grail of prediction". Quantitative practitioners should focus on understanding the model's capabilities and limitations rather than pursuing perfect accuracy.