@quantfinancetogo: ➡️ Part 28 of Quantitative Finance From Scratch: the low volatility anomaly. Here, risk means volatility, how much a stock's price swings. The CAPM (part 15) says expected return rises with beta, and wild stocks tend to have higher betas. Malcolm Baker, Brendan Bradley and Jeffrey Wurgler (Financial Analysts Journal, 2011) sorted all listed US stocks every month into five groups by the volatility of their monthly returns over the previous two to five years. From January 1968 to December 2008, $1 in the calmest fifth grew to $59.55, while $1 in the wildest fifth shrank to $0.58 (nominal, weighted by company size, rebalanced monthly, before trading costs; the authors note that trading costs were even higher for the wild stocks). The wildest fifth had the highest beta in the study, 1.71. Reason one is volatility drag (part 9): growth is roughly the average return minus half the variance. In our test run (assumed numbers: five groups with the same average return of 11 % a year and the volatilities of the study's groups, 13.1 % to 32 %, 41 years of monthly returns, 10,000 simulated histories each), a typical dollar ends at $63.29 in the calmest group and at $11.46 in the wildest. Luck still matters in a single history: with equal averages, the wildest group finished ahead of the calmest in about 22 % of the histories. In the study, the first four groups had almost the same average return above cash (about 4 to 5 % a year), so the drag explains most of their gap. The wildest group averaged 1.7 % a year less than cash, which the drag cannot explain. Reason two, as researchers suggest: many investors buy wild stocks like lottery tickets, hoping for a big win, so they pay too much and later earn less. The authors also point to fund managers who are judged against an index and rarely borrow. Low volatility funds use this idea, but the study covers one period of history, wild stocks soared in the technology bubble around 2000, and nobody knows whether the effect will hold in the future. Education only, not financial advice. #quantfinance #lowvolatility #factorinvesting #python #finance

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Monday 19 October 2026 13:21:48 GMT