{"id":254614,"date":"2026-08-28T11:12:59","date_gmt":"2026-08-28T15:12:59","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=254614"},"modified":"2026-08-28T11:13:14","modified_gmt":"2026-08-28T15:13:14","slug":"skewness-as-a-hidden-driver-of-anomaly-returns","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/skewness-as-a-hidden-driver-of-anomaly-returns\/","title":{"rendered":"Skewness as a Hidden Driver of Anomaly Returns"},"content":{"rendered":"\n

The article “Skewness as a Hidden Driver of Anomaly Returns” was originally posted on Alpha Architect<\/a> blog.<\/em><\/p>\n\n\n\n

Behavioral finance research has established that investors dislike negative skewness because it exposes them to rare but severe losses, while they embrace positive skewness because it offers the chance of occasional outsized gains \u2014 the lottery-like appeal that persists even when expected payoffs are modest. In behavioral models, this preference for positively skewed assets bids up their prices, depressing future expected returns. Conversely, negatively skewed assets must offer higher expected returns to attract capital.<\/p>\n\n\n\n

Rui Gong, John Lynch, and Richard Ogden, authors of the June 2026 study, \u201cSkewness Managed Portfolios<\/a>,\u201d make a simple but powerful point: many well-known stock-market anomalies owe a surprising amount of their performance to a small set of stocks with extreme positive returns. The authors ask two questions. First, do these outlier stocks materially shape the returns of popular long-short strategies? Second, can investors improve those strategies by explicitly managing for skewness rather than ignoring it?<\/p>\n\n\n\n

Their answer is yes on both counts. The paper shows that many anomaly portfolios are not just exposed to familiar factors like value, profitability, or investment; they are also implicitly exposed to return asymmetry \u2014 especially the kind created by rare but very large upside moves in individual stocks.<\/p>\n\n\n\n

What They Examined<\/strong><\/p>\n\n\n\n

The authors studied 18 well-known anomalies, including value, size, momentum, profitability, investment, accruals, issuance, and distress-related signals. They first asked whether extreme right-tail returns matter by winsorizing<\/a> stock returns \u2014 capping extreme values to limit outlier influence \u2014 and measuring how much the anomaly portfolios changed as a result. They applied three caps after portfolio formation, using historical thresholds for the 99th, 95th, and 90th percentile. Capping even the largest upside observations frequently produced a substantial reduction in returns and Sharpe ratios \u2014 a clear signal that a small number of outliers were doing most of the heavy lifting.<\/p>\n\n\n\n

They then built what they call a skewness-managed strategy. In plain English, they forecast which stocks are likely to have highly skewed returns in the next month, then \u201ctilt\u201d the long leg of each anomaly toward stocks with high expected skewness and the short leg toward stocks with low expected<\/em> skewness.<\/p>\n\n\n\n

The forecast is based on firm characteristics and recent return behavior, not hindsight. Specifically, the skewness forecast is driven primarily by low profitability, poor recent returns, and small market capitalization \u2014 the same characteristics that tend to identify stocks with high idiosyncratic upside potential.<\/p>\n\n\n\n

How the Tilt Worked<\/strong><\/p>\n\n\n\n

They first formed each anomaly as usual using the characteristic sort, then split both the long and short legs into terciles based on predicted skewness\u2014taking the long leg from the high-expected-skewness tercile and the short leg from the low-expected-skewness tercile. In other words, they did not just buy the anomaly winner and sell the loser; they biased the long side toward stocks with more upside-skewed return distributions and biased the short side away from them. The skewness forecast was built from monthly cross-sectional regressions of realized skewness on lagged firm characteristics such as volatility, momentum, prior return, size, industry, and exchange indicators. Stocks with the highest forecast skewness were favored on the long side, while stocks with the lowest forecast skewness were favored on the short side.<\/p>\n\n\n\n

Intuition<\/strong><\/p>\n\n\n\n

This tilt was meant to capture the upside from stocks with a greater chance of extreme positive returns in the long book, while avoiding shorting stocks that could unexpectedly jump higher. The authors say this is especially useful because many anomalies have skewness concentrated in only one leg, so the value of the tilt depends on where those positively skewed stocks sit.<\/p>\n\n\n\n

Practical Example<\/strong><\/p>\n\n\n\n

For a value-style anomaly, they would still go long value stocks and short growth stocks, but within those legs they would choose the high-skewness names for the long side and the low-skewness names for the short side. That is the core implementation of the \u201cskewness-managed\u201d portfolio.<\/p>\n\n\n\n

Their data sample covered the period July 1963 to December 2024.<\/p>\n\n\n\n

What They Found<\/strong><\/p>\n\n\n\n