Universal Portfolios With and Without Transaction Costs

A constant rebalanced portfolio is an investment strategy which keeps the same distribution of wealth among a set of stocks from period to period. Recently there has been work on on-line investment strategies that are competitive with the best constant rebalanced portfolio determined in hindsight (Cover, 1991, 1996; Helmbold et al., 1996; Cover & Ordentlich, 1996a, 1996b; Ordentlich & Cover, 1996). For the universal algorithm of Cover (Cover, 1991),we provide a simple analysis which naturallyextends to the case of a fixed percentage transaction cost (commission ), answering a question raised in (Cover, 1991; Helmbold et al., 1996; Cover & Ordentlich, 1996a, 1996b; Ordentlich & Cover, 1996; Cover, 1996). In addition, we present a simple randomized implementation that is significantly faster in practice. We conclude by explaining how these algorithms can be applied to other problems, such as combining the predictions of statistical language models, where the resulting guarantees are more striking.

[1]  John L. Kelly,et al.  A new interpretation of information rate , 1956, IRE Trans. Inf. Theory.

[2]  Alfredo De Santis,et al.  Learning probabilistic prediction functions , 1988, [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science.

[3]  A. R. Norman,et al.  Portfolio Selection with Transaction Costs , 1990, Math. Oper. Res..

[4]  Vladimir Vovk,et al.  Aggregating strategies , 1990, COLT '90.

[5]  N. Littlestone Mistake bounds and logarithmic linear-threshold learning algorithms , 1990 .

[6]  David Haussler,et al.  How to use expert advice , 1993, STOC.

[7]  Dean P. Foster,et al.  A Randomization Rule for Selecting Forecasts , 1993, Oper. Res..

[8]  David Haussler,et al.  Tight worst-case loss bounds for predicting with expert advice , 1994, EuroCOLT.

[9]  Vladimir Vovk,et al.  A game of prediction with expert advice , 1995, COLT '95.

[10]  Erik Ordentlich,et al.  Universal portfolios with side information , 1996, IEEE Trans. Inf. Theory.

[11]  Thomas M. Cover,et al.  Universal data compression and portfolio selection , 1996, Proceedings of 37th Conference on Foundations of Computer Science.

[12]  T. Cover Universal Portfolios , 1996 .

[13]  Yoram Singer,et al.  On‐Line Portfolio Selection Using Multiplicative Updates , 1998, ICML.

[14]  Manfred K. Warmuth,et al.  Exponentiated Gradient Versus Gradient Descent for Linear Predictors , 1997, Inf. Comput..

[15]  Erik Ordentlich,et al.  The Cost of Achieving the Best Portfolio in Hindsight , 1998, Math. Oper. Res..

[16]  Dean P. Foster,et al.  Regret in the On-Line Decision Problem , 1999 .

[17]  Yoram Singer,et al.  A Comparison of New and Old Algorithms for a Mixture Estimation Problem , 1995, COLT '95.