This paper examines how LSTM networks capture temporal dependencies in financial data. It discusses data quality, overfitting, regime shifts and explainability, and places forecasting models within a broader framework of risk controls and continuous evaluation.
LSTM-Based Stock Market Forecasting
How LSTM models handle financial time series, and where data quality, overfitting and changing market regimes limit their use.
Overview
Inside this document
- Temporal patterns and forecasting
- Model strengths and implementation limits
- Risk controls and continuous evaluation