Set out the steps by which a quantitative active equity strategy is developed
The idea
A quantitative strategy starts from a hypothesis about what drives returns, for example that cheap, high-quality stocks outperform. Data are gathered and cleaned, using point-in-time records to avoid look-ahead bias and including delisted firms to avoid survivorship bias. The signal is then back-tested: stocks are ranked each period, the information coefficient between scores and next-period returns is measured, and returns of top and bottom quintiles are compared. Robust signals are combined into a multifactor model, and the portfolio is built with an optimiser that uses a risk model and transaction cost estimates. Pitfalls include data-mining and overfitting, back-tests that understate turnover and costs, signal decay as others crowd in, and liquidity squeezes when many quant funds unwind similar positions.
In the app: 2 practice questions on this outcome, a narrated walkthrough with diagrams, spaced review that brings it back before you forget it.
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