How Machine Learning Predicts Market Movements

Today I’ll walk you through how neural networks are used to predict market moves.

Why ML can help (but be careful)

At a high level, ML models learn patterns from historical prices, volumes, news, and alternative signals. The trick is turning raw streams into features that a model can learn from — that’s feature engineering, and it strongly affects outcomes; practical guides and recent studies show how indicator sets and engineered features change model performance.

Feature selection and preprocessing

Before you feed anything into a network you should clean data, remove look‑ahead leakage, and choose features (OHLCV, technical indicators, sentiment, fundamentals). Surveys of feature selection techniques explain methods like PCA, mutual information, and wrapper methods for reducing noise and overfitting. Good preprocessing often beats a fancier model.

Core neural architectures (short tour)

• LSTM / GRU: recurrent units that remember information across time steps; useful for medium‑term dependencies.
• CNN: captures local temporal motifs (think short patterns or candlestick shapes).
• Transformers: use self‑attention to model long‑range relationships and cross‑series interactions; they scale well and have been adapted for financial time series in recent work. Each architecture has tradeoffs: LSTMs are intuitive for sequences, CNNs are fast at local pattern extraction, and Transformers excel when long context matters.

Hybrid and graph approaches

Combining models — for example, LSTM layers with Transformer attention or adding Graph Neural Networks to model inter‑asset relationships — often improves robustness and captures different signal types; hybrid LSTM–Transformer models have shown promising results in recent studies. Hybridization is a practical way to hedge model weaknesses.

Training objectives and evaluation

Standard losses (MSE) measure price error, but trading performance needs different metrics: directional accuracy, Sharpe ratio, drawdown, and transaction‑aware backtests. Always use time‑aware cross‑validation and simulate transaction costs to avoid optimistic results. Optimize for the metric that matches your goal (e.g., directional accuracy for a signal, Sharpe for portfolio returns).

Quick definitions

LSTM

A recurrent neural unit that keeps a memory cell and gates to control information flow across time steps.

Transformer / Self‑attention

A model that weighs all time steps relative to each other so it can learn long‑range dependencies without recurrence.

Sharpe ratio

A risk‑adjusted return metric: average return divided by return volatility.

Directional accuracy

The percentage of times the model correctly predicts up vs down.

Practical tips

• Start simple: baseline models and clean features.
• Avoid leakage: never use future info in training.
• Backtest realistically: include costs, slippage, and realistic execution.
• Monitor and retrain: markets change; models degrade.

For deeper reading, check practical feature engineering guides and surveys on feature selection, Transformer adaptations for finance, and hybrid LSTM–Transformer studies:

Assessing the Impact of Technical Indicators on Machine Learning Models for Stock Price Prediction

Survey of feature selection and extraction techniques for stock market prediction