We present a methodological framework integrating topological data analysis (TDA) with deep learning for financial forecasting. TDA extracts geometric features from market data through persistent homology, capturing multi-scale patterns overlooked by traditional methods. We validate the approach on EUR/USD high-frequency data (2020–2023), combining topological features with CNN-LSTM networks. The TDA-enhanced model achieves 67.3% directional accuracy versus 54.2% baseline (24% improvement), Sharpe ratio of 2.14 versus 0.87 (146% increase), and maximum drawdown of -5.9% versus -12.3% (52% reduction). Topological features exhibit strong correlation with volatility (ρ = 0.73) and provide early signals for market events. Despite promising results, computational costs, parameter sensitivity, and limited validation scope require further research before practical deployment.
Rachid et al. (Thu,) studied this question.