Corpus ID: 2742460

Online Learning of Commission Avoidant Portfolio Ensembles

@article{Uziel2016OnlineLO,
  title={Online Learning of Commission Avoidant Portfolio Ensembles},
  author={Guy Uziel and Ran El-Yaniv},
  journal={ArXiv},
  year={2016},
  volume={abs/1605.00788}
}
We present a novel online ensemble learning strategy for portfolio selection. The new strategy controls and exploits any set of commission-oblivious portfolio selection algorithms. The strategy handles transaction costs using a novel commission avoidance mechanism. We prove a logarithmic regret bound for our strategy with respect to optimal mixtures of the base algorithms. Numerical examples validate the viability of our method and show significant improvement over the state-of-the-art. 
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References

SHOWING 1-10 OF 42 REFERENCES
Online Lazy Updates for Portfolio Selection with Transaction Costs
TLDR
An efficient primal-dual based online algorithm that performs lazy updates to the parameter vector and shows that its performance is competitive with reasonable strategies which have the benefit of hindsight. Expand
Online Portfolio Selection with Group Sparsity
TLDR
An online portfolio selection algorithm that can take advantage of sector information through the use of a group sparsity inducing regularizer while making lazy updates to the portfolio while establishing the robustness and scalability of OLU-GS. Expand
CORN: Correlation-driven nonparametric learning approach for portfolio selection
TLDR
This article proposes a novel learning-to-trade algorithm termed CORrelation-driven Nonparametric learning strategy (CORN) for actively trading stocks that effectively exploits statistical relations between stock market windows via a nonparametricLearning approach. Expand
Semi-Universal Portfolios with Transaction Costs
TLDR
A novel on-line PS strategy named semi-universal portfolio (SUP) strategy, which attempts to avoid rebalancing when the transaction cost outweighs the benefit of trading, and is efficient and scalable in practice. Expand
Universal switching portfolios under transaction costs
  • S. Kozat, A. Singer
  • Computer Science
  • 2008 IEEE International Conference on Acoustics, Speech and Signal Processing
  • 2008
TLDR
A sequential algorithm for portfolio selection that asymptotically achieves the wealth of the best piecewise constant rebalanced portfolio tuned to the underlying individual sequence of price relative vectors where the authors pay a fixed percent commission for each transaction. Expand
PAMR: Passive aggressive mean reversion strategy for portfolio selection
TLDR
By analyzing PAMR’s update scheme, it is found that it nicely trades off between portfolio return and volatility risk and reflects the mean reversion trading principle. Expand
Nonparametric nearest neighbor based empirical portfolio selection strategies
TLDR
This paper introduces some nearest neighbor based portfolio selectors that are log-optimal for the very general class of stationary and ergodic random processes and shows very good finite-horizon performance when applied to different markets with different dimensionality or scales. Expand
On-Line Portfolio Selection Using Multiplicative Updates
We present an on-line investment algorithm that achieves almost the same wealth as the best constant-rebalanced portfolio determined in hindsight from the actual market outcomes. The algorithmExpand
Can We Learn to Beat the Best Stock
TLDR
The empirical results on historical markets provide strong evidence that this type of technical trading can "beat the market" and moreover, can beat the best stock in the market. Expand
Risk-Sensitive Online Learning
TLDR
This paper initiates the investigation of explicit risk considerations in the standard models of worst-case online learning by giving a modified best expert algorithm that achieves no regret for a “localized” version of the mean-variance criterion. Expand
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