The Impact of Market Regimes on Stop Loss Performance

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Stop loss is a risk management technique. It has been advocated as a way to control portfolio risk, but how effective is it? In this issue, I will discuss certain aspects of stop loss.

In this issue:

• Latest Posts

• When Are Stop Losses Effective?

  • Findings

• Fixed and Trailing Stop Losses in the Commodity Ma …

  • Findings

• Closing Thoughts

• Educational Video

  • Trading Risk Management Guide by Tom Basso

• Volatility Weekly Recap

• Around the Quantosphere

Latest Posts

• The Limits of Out-of-Sample Testing (12 min)

• Sentiment as Signal: Forecasting with Alternative Data and Generative AI (12 min)

• Behavioral Biases and Retail Options Trading (10 min)

• The Rise of 0DTE Options: Cause for Concern or Business as Usual? (11 min)

• How Machine Learning Enhances Market Volatility Forecasting Accuracy (12 min)

When Are Stop Losses Effective?

A stop loss serves as a risk management tool, helping investors limit potential losses by automatically triggering the sale of a security when its price reaches a predetermined level. This level is set below the purchase price for long positions and above the purchase price for short positions.

Reference [1] investigates the effectiveness of stop losses by formulating a market model based on fractional Brownian motion to simulate asset price evolution, rather than using the conventional Geometric Brownian motion.

Findings

• In long positions, stop loss levels are placed below purchase prices, while in short positions, they are positioned above to protect invested capital.

• Stop-loss strategies improve buy-and-hold returns when asset prices display long-range dependence, capturing fractal characteristics of financial market behavior over time.

• The Hurst parameter, expected return, and volatility significantly influence stop-loss effectiveness, making their measurement crucial for optimizing strategy performance.

• Simulation results confirm that optimizing stop-loss thresholds for these variables can significantly enhance investment returns and reduce downside risks.

• Polynomial regression models were developed to estimate the optimal relationship between stop-loss thresholds and influencing variables for better trading outcomes.

• In mean-reverting market conditions, stop losses tend to reduce risk-adjusted returns, highlighting the importance of adapting strategies to market regimes.

In short, the paper formulated a market model based on fractional Brownian motion. Using this model, we can formally study the effectiveness of stop losses. It showed that stop losses enhance the risk-adjusted returns of the buy-and-hold investment strategy when the asset price is trending.

We note, however, that when the underlying asset is in the mean-reverting regime, stop losses decrease the risk-adjusted returns.

Reference

[1] Yun Xiang and Shijie Deng, Optimal stop-loss rules in markets with long-range dependence, Quantitative Finance, Feb 2024

Fixed and Trailing Stop Losses in the Commodity Market

Building on previous discussion of the theoretical foundations of stop-loss strategies, Reference [2] examines their real-world application in the commodity market. It evaluates the performance of fixed and trailing stop losses, uncovering key factors that influence their effectiveness and impact on returns.

Findings

• The study analyzed fixed and trailing stop-loss strategies in commodity factor trading, focusing on their effectiveness in improving returns and reducing risk exposure.

• Results showed unmanaged factors performed poorly after accounting for transaction costs, while applying simple stop-loss rules significantly improved factor performance at the asset level.

• Fixed-stop strategies achieved an average Sharpe ratio of 0.92, whereas trailing-stop strategies delivered a higher average Sharpe ratio of 1.28.

• Both fixed and trailing stop-loss approaches maintained maximum drawdowns below 20 percent, with generally positive return skewness except for the skewness factor.

• The effectiveness of stop-loss strategies was not regime-dependent, but influenced by the quality of trading signals, commodity return volatility, and serial correlations.

• Transaction costs also played a significant role in determining stop-loss strategy performance, highlighting the importance of cost-efficient execution in commodity markets.

• Dynamically adjusting stop-loss thresholds based on realized volatility further enhanced factor performance compared to static fixed thresholds, especially in volatile trading environments.

• Stop-loss strategies were most effective when applied to factors built with high-conviction weighting schemes, maximizing their potential to capture commodity premia.

• Positive return autocorrelation and higher commodity return volatility were key conditions under which stop-loss strategies delivered the most meaningful performance improvements.

In short, in the commodity market, stop losses are effective when the autocorrelation of returns is positive, which is consistent with the findings of Reference [1]. Additionally, the volatility of returns influences how effective stop losses are.

A notable result of this study is that using trailing-stop with dynamic thresholds could enhance factor performance compared to using fixed thresholds.

Reference

[2] John Hua FAN, Tingxi ZHANG, Commodity Premia and Risk Management, 2023

Closing Thoughts

In summary, the first paper formulates a market model based on fractional Brownian motion to formally study the effectiveness of stop losses. It finds that stop losses improve the risk-adjusted returns of a buy-and-hold strategy when the asset price exhibits trending behavior, but reduce returns in mean-reverting regimes. The second paper focuses on the commodity market and shows that stop losses are effective when return autocorrelation is positive, aligning with the first study’s findings. It also highlights that return volatility affects stop loss effectiveness, and notably, that trailing stops with dynamic thresholds can enhance factor performance compared to fixed thresholds.

Educational Video

Trading Risk Management Guide by Tom Basso

In this video, Market Wizard Tom Basso shares his approach to becoming an “all-weather” trader, able to perform in bull, bear, and sideways markets. He emphasizes mental discipline, systematic timing, extreme diversification, and precise position sizing. Basso explains combining multiple uncorrelated strategies, rebalancing regularly, and keeping systems simple to reduce stress, control risk, and achieve consistent, long-term trading success.

Volatility Weekly Recap

The figure below shows the term structures for the VIX futures (in colour) and the spot VIX (in grey).

The S&P 500 began the week on solid footing, ending a 4-day losing streak. Friday’s strong earnings reports gave the market enough bullish momentum to close the week on a positive note. Overall, the S&P 500 gained 2.43% and the Nasdaq rose 3.87%. Oil prices fell this week, while gold was a winner amidst volatile headline news. Bitcoin traded within its prior week’s range.

On the volatility front, the spot VIX declined again, with both spot and VIX futures in contango. The roll yield became elevated once more. VXX and VIXM posted negative returns of -9.51% and -1.94%, respectively, due to contango and a decline in volatility level.

Around the Quantosphere

• Quant hedge funds clawed back some July losses after a brutal summer (Business Insider)

• Traders at Goldman Sachs, JPMorgan & Morgan Stanley are working harder for their bonuses (eFinancial Careers)

• Wall Street and AI Startups Are Fighting Over Entry-Level Quants (advisorperspectives)

• The most unusual job in quant trading is at a Champions League-winning football club (eFinancial Careers)

• Opalesque Roundup: Long biased hedge funds post strongest performance: hedge fund news (Opalesque)

• Hedge fund WorldQuant stages global competition to find next generation of quants (Fnlondon)

• How hedge funds performed in turbulent July (Reuters)

• Technology salaries in 2025: good times for product managers, bad times for data engineers (efinancialcareers)

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