Factor Crowding And Decay . Every Anomaly Published Is A Factor In Decay

Monday morning. Friend forwards a smart beta brochure. Cover number "Backtested 18 pct CAGR since 2008." Small print at the bottom . the fund launched in 2018.

Pulled the live track record. 9 pct over the seven years money could actually buy it.

Half the headline gave up the ghost the day the academic paper got published and the ETF hit the shelf. The brochure prints the in sample. The fund pays the out of sample. The market quietly charges you the gap for reading a famous paper.

McLean and Pontiff 2016 Journal of Finance read 97 published anomalies in two windows, before and after the paper landed. Median surviving alpha was 42 pct of the in sample number. 58 pct of the brochure return walked.

Five named factors. Banz 1981 size lost roughly 71 pct of its premium after publication. Fama French 1992 value lost 67 pct, partly because the 2010s gave value a decade long sleep. Jegadeesh Titman 1993 momentum was sturdier at 45 pct decay. Novy Marx 2013 profitability gave back about half. The one that aged best is low volatility, published earliest, with the smallest crowd of imitators . roughly 20 pct decay.

The pipe runs anomaly to publication to product to commodity. The anomaly works because few people are looking. Publication gives the pattern a name and a citation that makes it screenable in every shop with a Bloomberg. ETFs and smart beta funds open the gates to retail, pension, and institutional rupees. AUM funds the arbitrage. The premium compresses to whatever friction keeps the next dollar out.

Three rules I keep coming back to. Read the publication date alongside the backtest dates. Price the crowding . the same factor in Rs 50000 cr of AUM is not the same factor in Rs 500 cr. If you cannot say why the factor should persist, expect it to decay further.

Every factor that worked was first an anomaly. Every anomaly published is a factor in decay. Read the publication date the way you would read the expiry on a carton of milk