#CurrencyPairPrediction Risks of Overfitting in Financial Models Overfitting occurs when a financial model learns noise or random fluctuations in the training data instead of genuine market patterns. This leads to several risks: Poor Generalization: The model performs well on historical data but fails on new, unseen data. False Confidence: Overfitted models may show misleadingly high backtest results, giving a false sense of accuracy and profitability. Increased Losses: In live trading, these models can generate unreliable signals, leading to poor decisions and financial losses. Sensitivity to Market Changes: Overfitted models are often rigid and fail to adapt to evolving market conditions or rare events. To mitigate overfitting, techniques like cross-validation, regularization, simpler models, and out-of-sample testing are essential.