| KellyRatio {JFE} | R Documentation |
calculate Kelly criterion ratio (leverage or bet size) for a strategy
Description
Kelly criterion ratio (leverage or bet size) for a strategy.
Usage
KellyRatio(R, Rf = 0)
Arguments
R |
a vector of returns to perform a mean over |
Rf |
risk free rate, in same period as your returns |
Details
The Kelly Criterion was identified by Bell Labs scientist John Kelly, and applied to blackjack and stock strategy sizing by Ed Thorpe.
The Kelly ratio can be simply stated as: “bet size is the ratio of edge over odds.” Mathematically, you are maximizing log-utility. As such, the Kelly criterion is equal to the expected excess return of the strategy divided by the expected variance of the excess return, or
leverage=\frac{(\overline{R}_{s}-R_{f})}{StdDev(R)^{2}}
As a performance metric, the Kelly Ratio is calculated retrospectively on a particular investment as a measure of the edge that investment has over the risk free rate. It may be use as a stack ranking method to compare investments in a manner similar to the various ratios related to the Sharpe ratio.
Author(s)
Ho Tsung-wu <tsungwu@ntnu.edu.tw>, College of Management, National Taiwan Normal University.
References
Thorp, Edward O. (1997; revised 1998). The Kelly Criterion in
Blackjack, Sports Betting, and the Stock Market.
See also package PerformanceAnalytics.
Examples
data(assetReturns)
R=assetReturns[, -29]
KellyRatio(R, Rf=0)