Accrual anomaly
PortfolioAdvanced
The accrual anomaly, associated with Sloan, ranks companies on the gap between accounting earnings and cash earnings. High accruals mean earnings are high relative to cash flow. The portfolio shorts or avoids high-accrual firms and buys low-accrual firms. It is a quality-of-earnings sort, not a price pattern, and it depends on financial statements being public.
Not investment advice. Nothing here is a recommendation to buy or sell anything. Backtests and historical studies are not guarantees, and a rule that worked in one sample can fail in the next. Trading can lose money. Links are for education, not endorsements. Do your own research.
- Rules
- After each annual or quarterly filing becomes public, compute accruals as the non-cash component of earnings, scaled by assets, using a stated formula. Rank the universe. Overweight low accruals and underweight high accruals. Hold until the next filing. Exclude names with unusable statements. Do not use a later restatement.
- Where AI fits
- AI can compute the accrual ratio from a stated set of line items and stamp the filing timestamp on the trade. It can reconcile cases where cash-flow and balance-sheet versions of accruals disagree, and it should pick one version in advance. A language model guessing 'earnings quality' from a headline is not the anomaly.
- Markets
- Individual stocks with full financial statements, usually excluding financials if the formula does not fit them.
- Timeframes
- Quarterly or annual, matching the filing.
- Risks
- Accruals are noisy, especially quarterly. Industry differences in working capital can look like a signal. The pattern's payoff in later samples is a research question, not a fact you can spend. Shorting high-accrual growth stocks is a violent short. Data vendors backfill restatements.