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The error of conducting historical research on a dataset containing only assets that survived to the present.
The error of conducting historical research on a dataset containing only assets that survived to the present.
Survivorship bias occurs when backtests exclude bankrupt or delisted companies, causing strategy returns to appear artificially high.
Requires point-in-time historical data containing delisted symbols.
Particularly severe in equity long-only and stock selection research.
Financial machine learning is the application of supervised, unsupervised, and reinforcement learning methods to financial prediction, classification, and decision problems. The defining methodological constraint is that financial data are serially correlated, not independently and identically distributed, which means that the standard machine learning toolkit must be substantially adapted. The dominant practitioner reference is López de Prado (2018).
Research methodology is the set of practices that distinguish a rigorous quant research process from a hopeful one. The central topics are backtest audit, multiple testing correction, purged and combinatorial cross-validation, walk-forward optimisation, the triple-barrier labelling method, and the deflated Sharpe ratio. The dominant practitioner reference is López de Prado (2018).