# Signal and Alpha Decay

> Signal decay is how fast a signal's predictive power fades; alpha decay is how edges shrink over years. Learn both, the evidence and how traders adapt.

Source: https://learn.tradelabsai.com/research/signal-and-alpha-decay/  
Track: Research and Backtesting · Level: Advanced · Updated: 2026-10-03  
Publisher: TradeLabs AI (https://tradelabsai.com). Education, not financial advice.  
Cite as: TradeLabs Learn, "Signal and Alpha Decay", https://learn.tradelabsai.com/research/signal-and-alpha-decay/

"Decay" means two related things in trading research. Signal decay describes how quickly a signal's predictive power fades after it is observed: a news signal might matter for minutes, a value signal for months. Alpha decay describes how an edge shrinks over the years as more traders discover and exploit it. Understanding both helps traders choose holding periods, control costs and accept that every strategy needs ongoing research to stay profitable.

## Signal decay: the time dimension

After a signal fires, prices adjust as the market absorbs the information. Measuring returns at different horizons after the signal shows its decay profile.

| Signal type | Typical decay horizon |
|---|---|
| Order flow and microstructure | Seconds to minutes. See [High-Frequency Trading](https://learn.tradelabsai.com/algo-trading/high-frequency-trading/) |
| News and earnings surprises | Hours to weeks. See [Earnings Reactions and Post-Earnings Drift](https://learn.tradelabsai.com/fundamentals/post-earnings-drift/) |
| Short term reversal | Days to a month. See [Short and Long-Term Reversal](https://learn.tradelabsai.com/research/short-and-long-term-reversal/) |
| Momentum | Months. See [Momentum Factor](https://learn.tradelabsai.com/research/momentum-factor/) |
| Value and quality | Many months to years. See [Value Factor](https://learn.tradelabsai.com/research/value-factor/) |

**Example: Measuring a decay curve**
A researcher measures the information coefficient of an analyst revision signal at different horizons: 1 day 0.020, 5 days 0.035, 21 days 0.045, 63 days 0.050, 126 days 0.050. Cumulative predictive power grows quickly in the first month, then levels off. Most of the signal's value is captured within one to three months, suggesting monthly rebalancing balances capture against costs. See [Signal Turnover, Breadth and Neutralization](https://learn.tradelabsai.com/research/signal-turnover/).

## Alpha decay: the long term dimension

Edges shrink as capital chases them. Evidence includes:

- **Publication effects:** McLean and Pontiff (2016) studied 97 anomalies from academic papers and found their returns were about 26% lower out of sample before publication and about 58% lower after publication, consistent with both overfitting and investors trading on the published findings.
- **Crowding:** popular quant factors have shown periods of weaker returns as assets in factor products grew. See [Factor Timing, Crowding and Crashes](https://learn.tradelabsai.com/research/factor-crowding/).
- **Technology:** faster execution and better data made many short term inefficiencies disappear.

## Why alpha decays

| Cause | Mechanism |
|---|---|
| Competition | More traders exploiting the same edge push prices to fair value faster |
| Publication and education | Ideas spread through papers, books and forums |
| Market structure changes | New rules, venues or participants change behaviour |
| Data availability | Data that was once rare becomes common. See [Alternative Data Explained](https://learn.tradelabsai.com/alternative-data/alternative-data-explained/) |
| Regime changes | Conditions that created the edge disappear. See [Structural Breaks and Regime Changes](https://learn.tradelabsai.com/math/regime-changes/) |
| Original overfitting | Some "edges" never existed |

## Recognising decay in your strategy

- **Rolling performance trending down** while volatility is unchanged.
- **Shorter signal decay curves:** the market reacts faster than before.
- **Rising costs and crowding indicators.**
- **Lower information coefficients** over time.

Distinguishing decay from a normal drawdown requires comparing performance with expected ranges over a meaningful period. See [Monitoring Positions, P&L and Risk](https://learn.tradelabsai.com/algo-trading/live-monitoring/) and [The Strategy Lifecycle](https://learn.tradelabsai.com/research/the-strategy-lifecycle/).

## Adapting to decay

1. **Keep researching:** develop new signals continually.
2. **Diversify signals** so no single decaying edge dominates. See [Combining Signals](https://learn.tradelabsai.com/research/combining-signals/).
3. **Improve execution** to capture more of a fast decaying signal.
4. **Seek less crowded niches:** smaller markets, new data, longer horizons.
5. **Size by capacity:** stay small enough not to erode your own edge. See [Alpha Capacity and Crowding](https://learn.tradelabsai.com/research/alpha-capacity-and-crowding/).

## Decay and costs

Fast decaying signals must be traded quickly and often, which raises costs. If a signal loses most of its value within a day, slow execution can capture little of it. Measuring how much of the theoretical return survives after realistic delays and costs is part of judging any signal. See [Costs and Slippage in Backtests](https://learn.tradelabsai.com/research/costs-and-slippage-in-backtests/).

## Frequently asked questions

### What is signal decay?

How quickly a trading signal's predictive power fades after it is observed, which determines the best holding period.

### What is alpha decay?

The long term shrinking of a trading edge as more traders discover and exploit it, or as market conditions change.

### Why do published trading strategies stop working?

Because some were overfit to begin with and others become crowded as investors trade on them, reducing returns.

Next, learn to blend several signals in [Combining Signals](https://learn.tradelabsai.com/research/combining-signals/).

## Continue learning

- Next lesson: [Combining Signals](https://learn.tradelabsai.com/research/combining-signals/)
- Previous lesson: [Signal Discovery](https://learn.tradelabsai.com/research/signal-discovery/)
- Related: [Signal Discovery](https://learn.tradelabsai.com/research/signal-discovery/): Signal discovery is the search for variables that predict returns. Learn where ideas come from, how to test signals with information coefficients and decay curves.
- Related: [The Strategy Lifecycle](https://learn.tradelabsai.com/research/the-strategy-lifecycle/): Trading strategies are born, mature and decay. Learn the stages of a strategy's life, how to scale up, how to monitor decay and when to retire a strategy.
- Related: [Alpha Capacity and Crowding](https://learn.tradelabsai.com/research/alpha-capacity-and-crowding/): Capacity is how much capital a strategy can trade before returns shrink; crowding is when too many traders chase the same edge. Learn to estimate and manage both.
- Related: [Factor Timing, Crowding and Crashes](https://learn.tradelabsai.com/research/factor-crowding/): Factor crowding happens when too much capital chases the same factor. Learn how crowding affects returns and crash risk, how to measure it and how to cope.
- Related: [Signal Turnover, Breadth and Neutralization](https://learn.tradelabsai.com/research/signal-turnover/): Turnover measures how much a portfolio trades. Learn how to calculate it, how it links signal decay to costs, and techniques to cut turnover without losing alpha.