Weekly Research Insights
Properties of Drawdowns | News-Driven Commodity Trading | Extracting Alpha from Crowding
It’s time for a new Thursday post, discussing three interesting papers. Thank you for reading and don’t forget to hit the like button.
In This Post:
Properties of Drawdowns
News-Driven Commodity Trading
Extracting Alpha from Crowding
Properties of Drawdowns
The historical maximum drawdown of a trading strategy or fund manager is a key consideration for risk and performance measurement. However, unlike most other risk metrics, drawdowns depend on the sequence of returns, making them more challenging to analyze. While many investors and traders set drawdown limits to determine when to exit a manager or strategy, it is less clear how to assess the probability of hitting a drawdown threshold and how this should influence manager selection and risk management.
The paper “Drawdowns” by van Hemert, Ganz, Harvey, Rattray, Sanchez Martin, and Yawitch addresses this gap by examining the factors that drive drawdowns, testing various drawdown-based decision rules, and evaluating their effectiveness in identifying underperforming fund managers or strategies.
Key Findings
Dataset & Methodology
The study uses simulated return data and bootstrapped historical US equity returns (since 1926) to analyze the probability of hitting different drawdown levels.
It introduces "drawdown Greeks," which measure the sensitivity of drawdowns to factors such as evaluation horizon, Sharpe ratio, and return autocorrelation.
The authors compare total return-based rules (which focus on cumulative returns and Sharpe ratios) with drawdown-based rules for deciding when to replace managers or turn off a strategy
They explore constant vs. time-adjusted drawdown thresholds.
Main Results
Probability of Drawdowns
Simulations, assuming normal and IID returns, show that for a strategy with a Sharpe ratio of 0.5, there is a 43% chance of experiencing a 2 standard deviation drawdown (e.g., a 20% drawdown if annualized volatility is 10%) over a 10-year period.
Lower Sharpe ratios, longer evaluation periods, and positively autocorrelated returns increase the likelihood of drawdowns.
Financial crises and large market shocks show that extreme drawdowns occur more frequently than normal distribution models suggest.
Manager / Strategy Replacement
If a manager's skill or a strategy’s edge remains constant, a return-based evaluation focusing on total returns and Sharpe ratios while ignoring drawdowns is superior for deciding whether to fire a manager or stop a strategy.
If there is a real risk that a manager or a strategy has lost its edge then strict drawdown-based limits are more effective.
When deciding whether to turn off a strategy, consider the relative costs of turning off a good strategy (Type 1 error) vs. keeping an unprofitable strategy (Type 2 error).
Fixed drawdown thresholds often lead to the premature termination of a strategy. Instead, the study recommends time-adjusted drawdown thresholds that increase over time, recognizing that the probability of hitting a drawdown naturally rises with investment duration.
Investor Takeaways
The paper suggests that setting strict drawdown limits for strategies still believed to have an edge can lead to suboptimal decisions if applied too rigidly. Instead, using drawdown thresholds that increase over time better reflects the fact that the probability of experiencing larger drawdowns naturally rises with time, helping to avoid prematurely terminating good strategies.
While strict drawdown limits are useful for strategies that have truly lost their edge, distinguishing between drawdowns caused by short-term noise or unfavorable market regimes versus a persistent decline in alpha remains challenging. Persistent alpha decay could for example be identified through a sustained drop in performance, structural shifts in the market environment, the disappearance of the strategy’s original economic rationale, or declining returns as the strategy becomes widely known and arbitraged away.
This discussion is based on the following research paper. For full details, please refer to the original source:
van Hemert, Otto, Mark Ganz, Campbell R. Harvey, Sandy Rattray, Eva Sanchez Martin, and Darrel Yawitch, 2020, Drawdowns, Journal of Portfolio Management 46, 34-50. (SSRN Working Paper 3583864)
News-Driven Commodity Trading
News and media sentiment have long been known to influence stock prices, with research showing that positive news leads to short-term price increases and negative news leads to declines, often followed by reversals. However, less research has explored whether news sentiment similarly affects commodity futures.
The paper “Newswire Tone-Overlay Commodity Portfolios” by Fernandez-Perez, Fuertes, Miffre, and Zhao introduces a "tone-overlay" strategy that adjusts traditional commodity trading signals, such as momentum or basis, by incorporating commodity-specific news sentiment. The authors argue that investors overreact to news, creating short-term mispricing that a systematic trading strategy can exploit.
Key Findings
Dataset & Methodology
The study uses news sentiment data from RavenPack, spanning the period 2000 to 2020.
Each news article is assigned a sentiment score (0-100), where values above 50 indicate optimism and below 50 indicate pessimism.
Sentiment scores are aggregated weekly for 26 commodities, and the tone-overlay strategy adjusts traditional trading signals based on the prevailing news sentiment.
The strategy rebalances weekly and constructs cross-sectional long-short portfolios based on the adjusted signals.
Main Results
The tone-overlay strategy significantly outperforms traditional strategies, with an annualized alpha of 7.78% and a Sharpe ratio gain of 0.69.
The strongest results occur when news sentiment is extreme, suggesting that investors overreact to news, creating short-term mispricing.
Returns reverse within a week, indicating that this is a short-term trading signal rather than a persistent risk premium.
After accounting for a transaction cost of about 9 basis points, the net Sharpe ratio gain is 0.40 compared to traditional strategies, which is economically meaningful. The overall breakeven transaction cost is estimated at 0.88%.
The biggest effects are found in illiquid and speculative commodities, where mispricing typically is more prevalent, reinforcing the behavioral explanation.
Investor Takeaways
The paper suggests that short-term mispricing in commodity futures can be systematically exploited using a news sentiment overlay on traditional signals. The strategy works best in high-sentiment, high-speculation markets and relies on frequent rebalancing, making low transaction costs critical for success. Short-term mispricing anomalies tend to decay faster than other anomalies, making execution efficiency essential for maintaining profitability.
This discussion is based on the following research paper. For full details, please refer to the original source:
Fernandez-Perez, Adrian, Ana-Maria Fuertes, Joelle Miffre, and Nan Zhao, 2024, Newswire tone-overlay commodity portfolios, SSRN Working Paper 4484107.
Extracting Alpha from Crowding
It is well known that many investors focus on capturing various investment anomalies. However, when too many institutional investors pile into the same trades and strategies, it can create a situation known as crowding, where liquidity dries up and exiting positions becomes difficult. Prior research has studied the role of institutional investors, but the specific impact of crowding on anomaly returns remains underexplored.
The paper “Crowded Space and Anomalies” by Chincarini, Lazo-Paz, and Moneta examines how crowded trades affect the profitability and risk of anomaly-based strategies.
Key Findings
Dataset & Methodology
Uses U.S. common stocks from the NYSE, AMEX, and Nasdaq between 1980 and 2021, excluding stocks below $5, financials, and utilities.
Measures crowding using Days-ADV, updated quarterly, which estimates how many days it would take institutions to exit their positions based on the past three months of trading volume.
Examines 11 well-known stock market anomalies, such as momentum, value, and profitability, sorting stocks into quintiles based on Days-ADV and tracking their returns.
Main Results
Crowded stocks deliver superior returns: The most crowded stocks outperform significantly, while the least crowded stocks tend to underperform. The return spread between the top and bottom quintiles, sorting on Days-ADV, is economically and statistically significant.
Anomaly returns are concentrated in crowded stocks: When anomaly portfolios exclude crowded stocks, their excess returns disappear, suggesting that anomaly profits stem largely from institutional crowding.
Crash risk increases for crowded stocks: During market crises, crowded stocks suffer steeper declines, suggesting that institutional crowding amplifies downside risks.
Investor Takeaways
The paper suggests that heavily crowded stocks tend to outperform less crowded stocks but carry a higher risk of crashes. Moreover, the success of anomaly strategies depends on crowding dynamics, as anomalies must attract institutional interest to generate excess returns, merely identifying an anomaly isn’t enough. While the paper relies on quarterly 13-F filings to measure crowdedness, more frequent data on institutional positioning could improve performance.
This discussion is based on the following research paper. For full details, please refer to the original source:
Chincarini, Ludwig, Renato Lazo-Paz, and Fabio Moneta, 2025, Crowded spaces and anomalies, SSRN Working Paper 4618248.
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