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Multi-Armed Bandit Algorithm For Best Sharpe Ratio Detection

Author

Listed:
  • Sabrina Khurshid

    (Indian Institute of Technology Delhi)

  • Gourab Ghatak

    (Indian Institute of Technology Delhi)

  • Mohammed Shahid Abdulla

    (Indian Institute of Management Kozhikode)

Abstract

The computational agent or program in a bandit problem is required to perform a maximization of the long-run reward obtained from the environment. However, this maximization may be constrained in several ways depending on the problem. For example, in ultra-reliable wireless communications, apart from maximizing the data rate (reward) - the network must ensure that the signal strength is not highly intermittent (risk). Similarly, in finance, an investment strategy depends on a low level of volatility (risk) just as much as it does on the average returns (reward). The risk-adjusted performance of a portfolio is characterized using a quantity known as the Sharpe Ratio. This is a ratio where the numerator is the average return - adjusted against any risk-free means of returns. Correspondingly, the denominator is the standard deviation of these returns over the same time period. In this paper we propose a bandit algorithm that discovers, with least experimentation, a particular financial time series that has the highest Sharpe Ratio among the many time series available.

Suggested Citation

  • Sabrina Khurshid & Gourab Ghatak & Mohammed Shahid Abdulla, 2023. "Multi-Armed Bandit Algorithm For Best Sharpe Ratio Detection," Working papers 557, Indian Institute of Management Kozhikode.
    • Handle: RePEc:iik:wpaper:557
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