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AI predictors of stock prices are prone to underfitting as well as overfitting. This can impact their accuracy, as well as generalisability. Here are 10 suggestions to assess and mitigate these risks in an AI stock trading predictor: 1. Examine the model’s performance using both out-of-sample and in-sample data What’s the reason? Poor performance in both of these areas could be indicative of underfitting. How: Check if the model is performing consistently over both in-sample (training) and outside-of-sample (testing or validation) data. Out-of-sample performance that is significantly lower than expected indicates the possibility of an overfitting. 2. Check for Cross-Validation …

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