Quants should take care with synthetic data – Lehalle - Risk.net
Quants should take care with synthetic data – Lehalle - Risk.net
Synthetic data fails to reduce estimation errors in financial modeling, challenging its utility for training machine learning strategies. This finding warns that relying on generated data may introduce significant biases rather than improving predictive accuracy. For open data, this highlights the critical need for authentic, high-quality datasets to ensure valid algorithmic outcomes and trustworthy results.
Source: risk.netPublished on 2025-03-15
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