Why ML Models Fail at Stock Price Prediction and What Works Instead

Machine learning models are widely hyped for stock market prediction, but experts warn that markets are too noisy and adaptive to forecast reliably with standard approaches. A practical analysis shows that LSTM models trained to predict exact future prices tend to overfit historical data and perform poorly on unseen market conditions. The key insight is that ML works better when reframed around predicting the direction of price movement or volatility rather than precise price targets. Gradient-boosted classifiers using engineered features like returns, volume, and technical indicators offer a more robust and probabilistic edge. The practical value of ML in finance lies in informing risk management and position sizing, not in generating guaranteed profit signals.