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Bayesian Networks for Portfolio Analysis and Optimization

In: Financial Decision Making Using Computational Intelligence

Author

Listed:
  • Simone Villa

    (University of Milano – Bicocca
    Saint George Capital Management)

  • Fabio Stella

    (University of Milano – Bicocca)

Abstract

Portfolio analysis studies the impact of economic and financial scenarios on the performance of an investment portfolio, while portfolio optimization concerns asset allocation to achieve a trade-off between risk and return. In this chapter we exploit the interplay between modern portfolio theory and Bayesian networks to describe a new framework for portfolio analysis and optimization. Bayesian networks provide an effective way to interface models to data, allow efficient evidential reasoning, while their graphical language offers an intuitive interface by which the analyst can elicit his/her knowledge. The proposed framework leverages on evidential reasoning to understand the behavior of an investment portfolio in different economic and financial scenarios. It allows to formulate and solve a portfolio optimization problem, while coherently taking into account the investor’s market views. The Bayesian network framework for portfolio analysis and optimization is instantiated on the DJ Euro Stoxx 50 Index. Examples of portfolio analysis and optimization, exploiting evidential reasoning on Bayesian networks, are presented and discussed.

Suggested Citation

  • Simone Villa & Fabio Stella, 2012. "Bayesian Networks for Portfolio Analysis and Optimization," Springer Optimization and Its Applications, in: Michael Doumpos & Constantin Zopounidis & Panos M. Pardalos (ed.), Financial Decision Making Using Computational Intelligence, edition 127, chapter 0, pages 209-232, Springer.
    • Handle: RePEc:spr:spochp:978-1-4614-3773-4_8
    DOI: 10.1007/978-1-4614-3773-4_8
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