The Success Rate Illusion: How Misguided Optimization Undermines Systematic Hedging Strategies

Baldea, Ioan (2025): The Success Rate Illusion: How Misguided Optimization Undermines Systematic Hedging Strategies.

Abstract

This pedagogical study presents a comprehensive framework for systematic optimization of hedging trading strategies across diverse market regimes. We demonstrate that traditional parameter selection approaches often yield suboptimal results due to constrained search spaces, while systematic exploration reveals non-intuitive optimal configurations. Using a modified geometric Brownian motion process with regime-specific parameters, we generate synthetic market data across six distinct regimes and test a simultaneous long-short hedging strategy with ATR-based position sizing. Our multi-seed validation approach ensures statistical robustness, revealing that optimal parameters (stop-loss multiplier: 1.37, take-profit multiplier: 1.50) achieve 97.2\% hedging success rate, significantly outperforming intuitively selected parameters. This research emphasizes the importance of broad parameter exploration, proper statistical validation, and the fundamental tradeoff between success frequency and profit magnitude in systematic trading.

\textbf{At the same time and even more importantly pragmatically, our analysis reveals a more fundamental methodological insight:} successful optimization requires alignment between objective functions and practical goals. While we achieved ``attractive'' success rates, this study demonstrates how even rigorous optimization can yield practically suboptimal results when objectives mismatch real-world priorities. Because what matters is not frequency of success alone, but the fundamental relationship between profit magnitude and loss magnitude across the strategy's entire return distribution.

\textbf{Disclaimer:} This research represents academic simulation work for educational purposes only. All trading involves substantial risk of loss, and past performance does not guarantee future results.

• Item Type: MPRA Paper
• Language: English
• Keywords: Objective function selection Optimization criteria Performance metric alignment Metric selection bias Optimization artifacts Parameter optimization pitfalls
• Subjects: C - Mathematical and Quantitative Methods > C0 - General; C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics

References

Hull, J. C. (2020). Options, futures and other derivatives (11th ed.). Pearson Education.

Tsay, R. S. (2010). Analysis of financial time series (3rd ed.). John Wiley & Sons.

Hamilton, J. D. (1994). Time series analysis. Princeton University Press.

Guidolin, M., and Pedio, M. (2016). Markov switching models in empirical finance. In Advances in Principal Component Analysis (pp. 1-86). Springer.

Uhlenbeck, G. E., and Ornstein, L. S. (1930). On the theory of the Brownian motion. Physical Review,36(5), 823.

Vasicek, O. (1977). An equilibrium characterization of the term structure. Journal of Financial Economics, 5(2), 177-188.

Dixit, A. K., and Pindyck, R. S. (1994). Investment under uncertainty. Princeton University Press.

Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.

Chan, E. P. (2021). Quantitative trading: How to build your own algorithmic trading business (2nd ed.). John Wiley & Sons.

Bailey, D. H., and López de Prado, M. (2014). The deflated Sharpe ratio: Correcting for selection bias, backtest overfitting, and non-normality. The Journal of Portfolio Management, 40(5), 94-107.

Harvey, C. R., and Liu, Y. (2017). Backtesting. The Journal of Portfolio Management, 40(4), 13-28.