{
  "id": "2004.14048",
  "version": "v1",
  "published": "2020-04-29T09:48:25.000Z",
  "updated": "2020-04-29T09:48:25.000Z",
  "title": "On Feedback Control in Kelly Betting: An Approximation Approach",
  "authors": [
    "Chung-Han Hsieh"
  ],
  "comment": "To appear in the proceedings of the 2020 IEEE Conference on Control Technology and Applications (CCTA)",
  "categories": [
    "math.OC",
    "q-fin.CP",
    "q-fin.MF"
  ],
  "abstract": "In this paper, we consider a simple discrete-time optimal betting problem using the celebrated Kelly criterion, which calls for maximization of the expected logarithmic growth of wealth. While the classical Kelly betting problem can be solved via standard concave programming technique, an alternative but attractive approach is to invoke a Taylor-based approximation, which recast the problem into quadratic programming and obtain the closed-form approximate solution. The focal point of this paper is to fill some voids in the existing results by providing some interesting properties when such an approximate solution is used. Specifically, the best achievable betting performance, positivity of expected cumulative gain or loss and it associated variance, expected growth property, variance of logarithmic growth, and results related to the so-called survivability (no bankruptcy) are provided.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-04-29T09:48:25.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "approximation approach",
      "feedback control",
      "kelly betting",
      "simple discrete-time optimal betting problem",
      "logarithmic growth"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}