Empirical Bayes stock market portfolios
Advances in Applied Mathematics
Elements of information theory
Elements of information theory
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Universal portfolios with and without transaction costs
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Universal portfolios with side information
IEEE Transactions on Information Theory
Tracking a Small Set of Experts by Mixing Past Posteriors
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Tracking the best linear predictor
The Journal of Machine Learning Research
Tracking a small set of experts by mixing past posteriors
The Journal of Machine Learning Research
Discrete denoising with shifts
IEEE Transactions on Information Theory
Factor graphs for universal portfolios
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Regret minimization algorithms for pricing lookback options
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Pricing exotic derivatives using regret minimization
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
| Hi-index | 0.06 |
A constant rebalanced portfolio is an asset allocation algorithm which keeps the same distribution of wealth among a set of assets along a period of time. Recently, there has been work on on-line portfolio selection algorithms which are competitive with the best constant rebalanced portfolio determined in hindsight [6, 11, 81. By their nature, these algorithms employ the assumption that high returns can be achieved using a fixed asset allocation strategy. However, stock markets are far from being stationary and in many cases the wealth achieved by a constant rebalanced portfolio is much smaller than the wealth achieved by an ad-hoc investment strategy that adapts to changes in the market. In this paper we present an efficient Bayesian portfolio selection algorithm that is able to track a changing market. We also describe a simple extension of the algorithm for the case of a general transaction cost, including the transactions cost models recently investigated in [4]. We provide a simple analysis of the competitiveness of the algorithm and check its performance on real stock data from the New York Stock Exchange accumulated during a 22-year period.