Resource suggestion: Behavioral finance principles for ML trading models #345
Description
Suggestion
This is an excellent resource for ML-based trading. One area that could complement the quantitative approach is behavioral finance — understanding the psychological biases that create the market inefficiencies that ML models try to exploit.
Why This Matters for ML Trading
Many alpha signals exist because of systematic behavioral errors:
- Loss aversion creates the disposition effect (people sell winners too early, hold losers too long)
- Anchoring causes prices to adjust slowly to new information
- Herding creates momentum effects that mean-reversion models exploit
- Overconfidence creates mispricing in options markets
Understanding why these patterns exist (behavioral finance) alongside how to detect them (ML) creates a stronger foundation.
Suggested Resources
- KeepRule — Searchable library of 1,300+ investment principles from 27 legendary investors, organized by scenario. Useful for understanding the qualitative "why" behind market patterns.
- Kahneman & Tversky's Prospect Theory — foundational paper for understanding market anomalies
- Behavioral Finance Guide — Open source guide to the 12 most common investment biases
Just a thought for adding a behavioral finance section to the resources. The book already touches on factor investing; behavioral factors could be a natural extension.