Good Rank, No Money: The IC-to-P&L Gap
Key takeaways
- A positive information coefficient (good ranking) does not guarantee positive, tradeable P&L.
- Our highest-IC composite (+0.037, the best in the study) made only +0.24 long/short Sharpe — the spread was thin.
- A naive mean-variance optimizer destroys value: it maximizes estimation error (the Markowitz “error-maximizer”).
- Equal-weight (1/N) is a stubborn benchmark most optimizers fail to beat without a long covariance window and turnover control.
- Disciplined construction is a signal multiplier (it lifted one factor from ~0 to ~0.28 Sharpe), not a signal generator.
It is tempting to stop at the information coefficient — the rank correlation between a signal’s forecast and subsequent returns. A positive IC feels like success. But ranking stocks correctly and making money from them are different problems, and the gap between them is where a great many strategies quietly die.
Why doesn’t good ranking pay?
A signal can sort names in roughly the right order while the dollar spread between the top and bottom is too thin to clear costs, or while the payoff is concentrated in a few names you cannot hold at size. Our leak-free rebuild of a famous momentum-plus-earnings-plus-quality composite had the highest cross-sectional rank-IC in the entire study, +0.037 — and a long/short Sharpe of only +0.24. It ordered names beautifully and barely made money. Re-weighting the legs to emphasize the earnings component (which carries the dollar spread) roughly doubled the Sharpe to +0.52, without improving the IC. Same ranking, very different P&L.
| IC scale we observed (full S&P 500, monthly) | Interpretation |
|---|---|
| IC ≈ +0.03 to +0.09 | Strong ranking (but check the P&L spread) |
| IC ≈ +0.01 to +0.02 | Marginal real factor |
| IC ≈ 0, t ≈ 0 | No signal |
Why can an optimizer make it worse?
A mean-variance optimizer maximizes apparent return per unit risk — and it does so by leaning hardest on the inputs with the most estimation error, a pathology known as error-maximization. Fed noisy expected returns and a short covariance window, it produces confident, fragile, over-concentrated portfolios that underperform plain equal-weight out of sample. In our tests a 252-day covariance optimizer on gross profitability landed near a Sharpe of 0.11; extending to a 504-day window with a turnover penalty lifted it to ~0.28. The optimizer is only as safe as its covariance estimate and its turnover discipline.
The honest framing: construction is a multiplier, not a generator. Better weighting lifted a weak factor’s information ratio by ~0.25 with no new signal — real, and worth doing — but it amplifies whatever sign the signal has, good or bad. It cannot turn a weak edge into a strong one. That is exactly why we size overlays simply and conservatively rather than chasing the optimizer that wins the backtest.
Why we size simply
An optimizer that maximizes a backtest is usually maximizing its own errors. We prefer robust, simple sizing that survives out-of-sample to clever weighting that wins only in hindsight.
About this series: every figure comes from a leak-free research harness on US equities — point-in-time index membership, fundamentals keyed to filing date, expanding-window walk-forward, and transaction costs charged. Statistics are gross and in-sample unless noted, and describe published anomalies, not a Yayati product. Standing caveats: roughly a third of true historical index members are unpriced by the naive data source (survivorship); a 2 bps cost assumption is optimistic; fundamentals are post-2009 XBRL.
The discipline behind our strategies
This article is for educational and informational purposes only and is not investment, tax, or legal advice. It describes findings from an internal research program about publicly documented market anomalies and research methodology; it is not a description of any Yayati product or its results. Research statistics are gross, in-sample illustrations subject to survivorship, data-coverage, transaction-cost, and modeling limitations described in the text, and do not represent actual trading or any client account. Past performance and backtested results are not indicative of future results. Yayati Asset Management is a Registered Investment Adviser. © Yayati Asset Management. VOLT™ and PLASMA™ are trademarks of Yayati.
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