Universal portfolio algorithm
Portfolio selection algorithm From Wikipedia, the free encyclopedia
The universal portfolio algorithm is a portfolio selection algorithm from the field of machine learning and information theory. The algorithm learns adaptively from historical data and maximizes the log-optimal growth rate in the long run. It was introduced by the late Stanford University information theorist Thomas M. Cover.[1]
The algorithm rebalances the portfolio at the beginning of each trading period. At the beginning of the first trading period it starts with a naive diversification. In the following trading periods the portfolio composition depends on the historical total return of all possible constant-rebalanced portfolios.[2]
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