Distributional Robust Kelly Investing
发布者：程梦琴发布时间：2019-08-26浏览次数：242
| Speaker: | 孙卿云 |
| Host: | 付彦伟（复旦大学大数据学院） |
| Time: | 9:30 am, Sep. 2, 2019 |
| Location: | Room N205, Zibin Building, Fudan University |
| Abstract: | In classic Kelly gambling, bets are chosen to maximize the expected log growth of wealth, under a known probability distribution. In this note we consider the distributional robust version of the Kelly gambling problem, in which the probability distribution is not known, but lies in a given set of possible distributions. The bet is chosen to maximize the worst-case (smallest) expected log growth among the distributions in the given set. This distributional robust Kelly gambling problem is convex, but in general need not be tractable. We show that it can be tractably solved in a number of useful cases when there is a finite number of outcomes. |
| Bio: | 孙卿云， 2014-2019斯坦福数学系博士， 研究统计， 优化， 机器学习方向， 导师是David Donoho, Stephen Boyd. 2014年本科毕业于北京大学数学学院 |
