Multiperiod portfolio selection and Bayesian dynamic models
Techniques inspired by Bayesian statistics provide a solution to the classic investment problem of optimally planning a sequence of trades in the presence of transaction costs, according to Petter Kolm and Gordon Ritter
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Planning a sequence of trades extending into the future is a very common problem in finance. All trading is costly, and the need for intertemporal optimisation is more acute when trading costs are considered. The total cost due to market impact is known to be superlinear as a function of the trade size (Almgren et al (2005) measured an exponent of about 0:6 for impact itself, hence 1:6 for total cost), implying a large order may be more efficiently executed
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