Machine learning is often misunderstood as a magic bullet. In reality, the quality of features and training data determines whether a model can add forecasting edge in the stock market.
Common Models for Equity Prediction
- Random Forests: Good for discrete classification tasks like predicting next-day direction based on technical and fundamental factors.
- LSTM / RNN: Useful for sequence data and intraday price movement patterns.
- Transformer models: Best for multi-modal inputs that combine price, news, and alternative data.
Why Data Matters More Than Models
A smaller, well-curated dataset of price action, corporate events, and sentiment often outperforms a larger, noisy dataset. Clean labels, aligned timestamps, and avoided lookahead bias are critical.
Feature Engineering That Works
- Normalized momentum relative to sector peers
- Volume spikes adjusted for average daily turnover
- News sentiment scores with decay and source weighting
- Macro triggers for commodities, currency, and rate expectations
Practical Use Case
For MicroStocks, ML models rank candidate stocks based on expected 5-day return probability, not absolute price prediction. This probability ranking is combined with liquidity and governance filters before any signal becomes actionable.