Adaptive Bayesian Optimisation for Online Portfolio Selection

We present a Bayesian approach for online portfolio selection, a fundamental problem in computational finance. We pose the problem as the global optimisation of an expensive, time-varying, black-box function. As the optimum is itself dynamic, we use a model that allows us to capture time-dependent patterns of the function and to provide sequential decision processes that enable us to select optimal portfolios to invest in an online manner.

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