=Paper=
{{Paper
|id=Vol-1663/invited-abstract-2
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1663/bmaw2016_invited-abstract-2.pdf
|volume=Vol-1663
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1663/bmaw2016_invited-abstract-2.pdf</pdf>
<pre>
                                 Stochastic Portfolio Theory:
                                A Machine Learning Approach



              Yves-Laurent Kom Samo                                         Alexander Vervuurt
           Machine Learning Research Group                                 Mathematical Institute
      Oxford-Man Institute of Quantitative Finance                Oxford-Man Institute of Quantitative Finance
                 University of Oxford                                       University of Oxford
             YLKS@ROBOTS.OX.AC.UK                                     VERVUURT@MATHS.OX.AC.UK



                                                     Abstract

    In this paper we propose a novel application of Gaussian processes (GPs) to financial asset allocation. Our
    approach is deeply rooted in Stochas tic Portfolio Theory (SPT), a stochastic analysis framework introduced by
    Robert Fernholz that aims at flexibly analysing the performance of certain investment strategies in stock
    markets relative to benchmark indices. In particular, SPT has exhibited some investment strategies based on
    company sizes that, under realistic assumptions, outperform benchmark indices with probability 1 over certain
    time horizons. Galvanised by this result, we consider the inverse problem that consists of learning (from
    historical data) an optimal investment strategy based on any given set of trading characteristics, and using a
    user-specified optimality criterion that may go beyond outperforming a benchmark index. Although this in-
    verse problem is of the utmost interest to investment management practitioners, it can hardly be tackled using
    the SPT framework. We show that our machine learning approach learns investment strategies that conside rably
    outperform existing SPT strategies in the US stock market.


This poster from the UAI 2016 conference was given as an invited presentation at the Bayesian Modeling Applications
Workshop.




                                                BMAW 2016 - Page 57 of 59

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