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Statistically Principled Application of Computational Intelligence Techniques for Finance

In: Financial Decision Making Using Computational Intelligence

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  • Jerome V. Healy

    (University of East London)

Abstract

Computational techniques forregression have been widely applied to asset pricing, return forecasting, volatility forecasting, credit risk assessment, and value at risk estimation, among other tasks. Determining probabilistic bounds on results is essential in these contexts. This chapter provides an exposition of methods for estimating confidence and prediction intervals on outputs, forcomputational intelligence tools used for data modelling. The exposition focuses on neural nets as exemplars. However, the techniques and theory outlined apply to any equivalent computational intelligence technique used for regression. A recently developed robust method of computingprediction intervals, appropriate to any such regression technique of sufficient generality, is described.

Suggested Citation

  • Jerome V. Healy, 2012. "Statistically Principled Application of Computational Intelligence Techniques for Finance," 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 1-33, Springer.
    • Handle: RePEc:spr:spochp:978-1-4614-3773-4_1
    DOI: 10.1007/978-1-4614-3773-4_1
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