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The Capacity of Trading Strategies

Last Updated on 10 February, 2024 by Rejaul Karim

In the research paper “The Capacity of Trading Strategies,” authors Maxime Bonelli, Augustin Landier, Guillaume Simon, and David Thesmar explore the relationship between portfolio size and the financial performance of trading strategies, taking into account non-linear transaction costs.

Using a dynamic trading model, the study derives formulas for the performance-to-scale frontier achieved by competitive traders with a signal that predicts stock returns. The decline in the realized Sharpe ratio when scale increases is less pronounced for strategies that trade in more liquid stocks, have slower-fading signals, and exhibit strong frictionless performance.

Examining four well-known strategies, the research uncovers that the capacity of strategies expanded in the 2000s compared to the 1990s, due to increased liquidity. It is also observed that strategies based on low volatility and past accounting profitability are highly scalable, even in the mid-cap range. However, underestimating the number of competitors trading similar signals can have a considerable negative impact on performance.

Abstract Of Paper

Due to non-linear transaction costs, the financial performance of a trading strategy decreases with portfolio size. Using a dynamic trading model a la Garleanu and Pedersen (2013), we derive closed-form formulas for the performance-to-scale frontier reached by competitive traders endowed with a signal predicting stock returns. The decay with scale of the realized Sharpe ratio is slower for strategies that (1) trade more liquid stocks (2) are based on signals that do not fade away quickly and (3) have strong frictionless performance. We apply the framework to four well-known strategies. The capacity of strategies has increased in the 2000s compared to the 1990s due to increased liquidity. Because low volatility and past accounting profitability are persistent characteristics, strategies based on them are highly scalable, including in the mid-cap range. When traders underestimate the number of competitors trading a similar signal, their performance is strongly negatively impacted.

Original paper – Download PDF

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Author

Maxime Bonelli
London Business School – Department of Finance

Augustin Landier
HEC

Guillaume Simon
Capital Fund Management

David Thesmar
Massachusetts Institute of Technology (MIT) – Sloan School of Management; National Bureau of Economic Research (NBER); Centre for Economic Policy Research (CEPR)

Conclusion

In conclusion, the research paper “The Capacity of Trading Strategies” by Maxime Bonelli, Augustin Landier, Guillaume Simon, and David Thesmar demonstrates that non-linear transaction costs lead to a decrease in the financial performance of trading strategies with increasing portfolio size.

The authors employ a dynamic trading model to derive formulas for the performance-to-scale frontier of competitive traders. It is found that the decay in the realized Sharpe ratio is slower for strategies trading more liquid stocks, based on durable signals, and displaying strong frictionless performance. The analysis of four popular strategies indicates an increase in the capacity of strategies during the 2000s, attributable to enhanced liquidity.

Furthermore, strategies based on low volatility and past accounting profitability are highly scalable, even within mid-cap ranges. The research highlights the importance of understanding the competitive landscape, as underestimating the number of competitors trading similar signals can significantly impact performance negatively.

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FAQ

Q1: What is the primary focus of the research paper “The Capacity of Trading Strategies,” and what does it investigate regarding the relationship between portfolio size and financial performance?

The research paper explores the impact of non-linear transaction costs on the financial performance of trading strategies in relation to the size of the portfolio. Using a dynamic trading model, the authors derive formulas for the performance-to-scale frontier, considering factors such as stock liquidity, signal persistence, and frictionless performance.

Q2: What key insights does the paper provide about the scalability of trading strategies, and how does it analyze the capacity of strategies in different market conditions?

The paper reveals that the decay in the realized Sharpe ratio is slower for strategies trading more liquid stocks, based on signals that do not fade away quickly, and exhibit strong frictionless performance. It analyzes four well-known strategies and finds that the capacity of these strategies increased in the 2000s compared to the 1990s due to increased liquidity. Strategies based on low volatility and past accounting profitability are identified as highly scalable, even within mid-cap ranges.

Q3: What is the significance of understanding the competitive landscape in the context of trading strategies, and how does underestimating the number of competitors impact performance?

The research underscores the importance of understanding the competitive landscape, as underestimating the number of competitors trading similar signals can have a considerable negative impact on performance. The study emphasizes the need for traders to be aware of the broader market conditions and competitor activities when implementing trading strategies to achieve optimal financial performance.

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