Timing the Momentum Factor Using Its Own Volatility

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Timing the Momentum Factor Using Its Own Volatility: A Historical Perspective

The momentum factor is one of the most well-known and persistent anomalies in finance. Traditionally, it exploits the tendency of stocks that have performed well in the past to continue outperforming in the short to medium term. But while the momentum premium is compelling, its returns can be highly volatile — with notorious drawdowns during market reversals.

What if we could improve the performance of momentum by timing our exposure based on its own risk?

In this post, we explore a simple yet powerful idea: applying volatility-based timing to the momentum factor itself. Inspired by the low volatility anomaly — which shows that lower risk often coincides with better risk-adjusted returns — we ask:

Can we enhance the performance of the momentum factor by going long only when its recent volatility is below a certain threshold?

To answer this, we use the daily momentum factor data from the Kenneth R. French Data Library, one of the most respected academic sources for asset pricing factors. The database includes daily momentum returns from 1926 to the present, giving us nearly a century of data to test this idea.

We’ll:

Estimate historical volatility using a rolling lookback window
Define simple thresholds to classify “low volatility” regimes
Compare the cumulative and risk-adjusted returns of the volatility-filtered strategy vs the standard momentum factor

Let’s see if less risk really means more reward — even for a high-octane factor like momentum.

Measuring the Momentum Factor’s Volatility

To time the momentum factor, we first need a way to measure how volatile it is at any given time. For this, we use a simple approach: we calculate the rolling historical volatility of the daily momentum returns.

Specifically, we look at the past 252 trading days (roughly one calendar year) and calculate the standard deviation of those daily returns — a common way to estimate how “bumpy” a ride the factor has been. Then, we annualize that number to make it easier to compare over time.

Here’s what we get: a time series that tells us how much the momentum factor has fluctuated, day by day, over the past century.

We plot this rolling volatility over time, and unsurprisingly, it spikes during major market events like the Great Depression, the dot-com crash, and the 2008 financial crisis.

Momentum Returns Across Volatility Regimes

Before jumping into a timing strategy, we wanted to understand a basic question:

Does the momentum factor perform differently depending on how volatile it is?

To test this, we grouped the historical daily momentum returns into five buckets based on their trailing 1-year (252-day) volatility. These groups, or quintiles, range from the calmest periods (Q1) to the most turbulent ones (Q5).

Here’s what we found:

Volatility Quintile Avg. Daily Momentum Return

Q1 (Lowest Vol)	+0.0589%
Q2	+0.0307%
Q3	+0.0282%
Q4	+0.0293%
Q5 (Highest Vol)	–0.0238%

Tuning the Volatility Threshold for Optimal Momentum Exposure

After observing that the momentum factor tends to perform better during periods of lower volatility, we asked:

What’s the best volatility threshold to follow momentum and avoid excessive noise?

To explore this, we created a simple strategy:

Go long the momentum factor when its trailing 1-year volatility is below a given target.
Stay out of the market when volatility exceeds the target.

We varied the volatility target from 0 to 50 and tracked the strategy’s average daily return and Sharpe ratio at each level.

📈 Results: Performance Across Volatility Targets

Here are the key findings:

Return peaks around a volatility threshold of 16–17%, reaching a daily average of ~0.032%.
Sharpe ratio peaks earlier, around 7–8%, with a Sharpe above 1.2, indicating strong risk-adjusted returns.
After a certain point (above 30%), returns and Sharpe both begin to decline, suggesting excess volatility degrades momentum effectiveness.

🔍 Visualization

🧠 Takeaway

This exercise highlights how volatility itself can be used as a timing tool for factor exposure. Not only does this strategy improve average returns, but it also improves risk-adjusted performance compared to blindly holding the factor.

The next step would be to test the robustness of this finding across:

Different lookback windows for volatility,
Other factors (value, size, etc.),
Out-of-sample validation or cross-validation windows.

Conclusion

Volatility-based timing offers a promising approach to enhancing momentum investing by systematically reducing exposure during turbulent periods. Our historical analysis shows that applying a simple volatility filter can lead to better average returns and superior risk-adjusted performance, helping to mitigate the large drawdowns that often accompany momentum strategies.

While these findings are encouraging, investors should treat them as a starting point rather than a final solution. Further research and testing, especially in live or out-of-sample settings, are essential before adopting such timing strategies in practice.

By combining classic factor insights with risk management techniques like volatility timing, we can strive toward more resilient investment approaches that better navigate the complexities of financial markets.

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