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a r t i c l e i n f o a b s t r a c t Keywords: Growth optimal portfolio Threshold rebalancing Proportional transaction cost Discrete-time stock market We investigate how and when to diversify capital over assets, i.e., the portfolio selection problem, from a signal processing perspective. To this end, we first construct portfolios that achieve the optimal… (More)

We analyze an online learning algorithm that adaptively combines outputs of two constituent algorithms (or the experts) running in parallel to estimate an unknown desired signal. This online learning algorithm is shown to achieve and in some cases outperform the mean-square error (MSE) performance of the best constituent algorithm in the steady state.… (More)

- Hui Yang, O. Erhun Kundakcioglu, Jing Li, Teresa Wu, Joseph Ross Mitchell, Amy K. Hara +20 others
- IEEE Intelligent Systems
- 2014

We study optimal investment in a financial market having a finite number of assets from a signal processing perspective. We investigate how an investor should distribute capital over these assets and when he should reallocate the distribution of the funds over these assets to maximize the cumulative wealth over any investment period. In particular, we… (More)

We investigate portfolio selection problem from a signal processing perspective and study how an investor should distribute wealth over two assets in order to maximize the cumulative wealth. We construct portfolios that provide the optimal growth in i.i.d. discrete time two-asset markets under proportional transaction costs. As the market model, we consider… (More)

—We introduce a new analysis of an adaptive mixture method that combines outputs of two constituent filters running in parallel to model an unknown desired signal. This adaptive mixture is shown to achieve the mean square error (MSE) performance of the best constituent filter, and in some cases outperforms both, in the steady-state. However, the MSE… (More)

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