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]
References edit
- ^ Cover, Thomas M. (1991). "Universal Portfolios". Mathematical Finance. 1 (1): 1–29. doi:10.1111/j.1467-9965.1991.tb00002.x. S2CID 219967240.
- ^ Dochow, Robert (2016). Online Algorithms for the Portfolio Selection Problem. Springer Gabler. ISBN 9783658135270.