Simplicity Over Complexity

Qaisar Hasan critiques the pitfalls of complex multi-factor KPI models in Alternative Data forecasting. Drawing from his hedge fund experience, Hasan argues that simpler, intuition-driven models outperform compute-heavy approaches by avoiding issues like overfitting and spurious correlations, offering transparency and reliability for investment decisions.

• Overfitting Risks: Multi-factor models often mistake noise for signal, producing high R-squared back-tests that fail in real-world trading due to over-tuning to historical data.
• Spurious Correlations: Adding numerous data inputs increases the chance of random correlations, like pirates vs. global temperatures, which lack causal links and derail predictions.
• Limited Observations: Quarterly KPI reports restrict data points, and long historical data can mislead as company dynamics evolve, undermining model accuracy.
• Data Complexity: Alternative Data’s many variations, like segmented spending data, create countless variables, complicating models and amplifying errors.
• Look-Forward Bias: Using future data in back-tests inflates model performance, giving false confidence that collapses when applied to real-time decisions.

Download the whitepaper to discover how simpler, intuitive models can enhance your Alternative Data strategy and drive better investment outcomes.

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