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Quantitative Signals & Scoring

Signal Turnover Efficiency

The ratio of net signal-driven trading profit to the total transaction costs incurred in executing positions based on that signal, measuring how much alpha a signal generates per unit of implementation friction.

Signal Turnover Efficiency is a critical metric in quantitative insider-trading detection and quant scoring platforms, as high-conviction signals often demand frequent rebalancing and position adjustments. The metric explicitly penalizes signals that require excessive portfolio turnover relative to their profit contribution, which is particularly important in regulated environments where trading patterns themselves may trigger surveillance scrutiny. By factoring in market impact, commissions, bid-ask spreads, and opportunity costs, this measure isolates the true economic value of a signal net of all execution frictions.

In insider-trading surveillance contexts, signals exhibiting low turnover efficiency often correlate with information leakage or suspicious trading patterns, since inefficient signals may be driven by non-public information that creates artificial urgency rather than fundamental value. Conversely, high turnover efficiency signals tend to be more robust, longer-lived, and less vulnerable to regulatory challenge, making them preferred inputs for compliance-sensitive scoring systems. This metric also helps distinguish between genuine alpha and merely front-running or market-timing behavior that dissipates quickly under transaction costs.

Formula

\textSTE
Signal Turnover Efficiency ratio
\textPt^\textsignal
Profit generated by signal-driven position at time t
\textPt^\textbenchmark
Benchmark profit (passive holding or null position) at time t
\textTCt
Total transaction costs at time t, including commissions and spreads
\textMIt
Market impact cost at time t
Sources: Arnott, R.D., Beck, S.L., Kalesnik, V., & West, J. (2016). How Can 'Alpha' Be So Persistent? Research Affiliates Publications. · Novy-Marx, R., & Velikov, M. (2016). A taxonomy of anomalies and their trading costs. Financial Analysts Journal.