• DOI:10.2139/ssrn.3580920
• Corpus ID: 215828412

Awareness of Crash Risk Improves Kelly Strategies in Simulated Financial Time Series

@article{Gerlach2020AwarenessOC,
  title={Awareness of Crash Risk Improves Kelly Strategies in Simulated Financial Time Series},
  author={J. C. Gerlach and Jerome Kreuser and Didier Sornette},
  journal={Decision-Making \& Management Science eJournal},
  year={2020}
}

• J. Gerlach, J. Kreuser, D. Sornette
• Published 23 March 2020
• Economics
• Decision-Making & Management Science eJournal

We simulate a simplified version of the price process including bubbles and crashes proposed in Kreuser and Sornette (2018). The price process is defined as a geometric random walk combined with jumps modelled by separate, discrete distributions associated with positive (and negative) bubbles. The key ingredient of the model is to assume that the sizes of the jumps are proportional to the bubble size. Thus, the jumps tend to efficiently bring back excess bubble prices close to a normal or fundamental value (efficient crashes). This is different from existing processes studied that assume jumps that are independent of the mispricing. The present model is simplified compared to Kreuser and Sornette (2018) in that we ignore the possibility of a change of the probability of a crash as the price accelerates above the normal price. We study the behaviour of investment strategies that maximize the expected log of wealth (Kelly criterion) for the risky asset and a risk-free asset. We show that the method behaves similarly to Kelly on Geometric Brownian Motion in that it outperforms other methods in the long-term and it beats classical Kelly. As a primary source of outperformance, we determine knowledge about the presence of crashes, but interestingly find that knowledge of only the size, and not the time of occurrence, already provides a significant and robust edge. We then perform an error analysis to show that the method is robust with respect to variations in the parameters. The method is most sensitive to errors in the expected return.