Natural Computing in Finance – A Review

Brabazon, Anthony; Dang, Jing; Dempsey, Ian; O’Neill, Michael; Edelman, David

doi:10.1007/978-3-540-92910-9_51

Cited by 5 publications

(2 citation statements)

References 103 publications

(98 reference statements)

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“…Most theoretical financial asset pricing models make strong assumptions which are often not satisfied in real-world asset markets. They are therefore good candidates for the application of model induction tools, such as Grammatical Evolution, which are used to recover the underlying data generating processes [3].…”

Section: Model Inductionmentioning

confidence: 99%

A Preliminary Investigation of Overfitting in Evolutionary Driven Model Induction: Implications for Financial Modelling

Tuite

Agapitos

O’Neill

et al. 2011

Applications of Evolutionary Computation

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Abstract. This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, which has been shown to lead to a significant degradation of out-of-sample performance. If we assume some sort of performance metric maximisation, the most widely used early training stopping criterion is the moment within the learning process that an unbiased estimate of the performance of the model begins to decrease after a strictly monotonic increase through the earlier learning iterations. We are conducting an initial investigation on the effects of early stopping in the performance of Genetic Programming in symbolic regression and financial modelling. Empirical results suggest that early stopping using the above criterion increases the extrapolation abilities of symbolic regression models, but is by no means the optimal training-stopping criterion in the case of a real-world financial dataset.

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Section: Model Inductionmentioning

confidence: 99%

A Preliminary Investigation of Overfitting in Evolutionary Driven Model Induction: Implications for Financial Modelling

Tuite

Agapitos

O’Neill

et al. 2011

Applications of Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…This will also motivate the discussion in Section 3, where we will discuss shortcomings of much of this work. We do not seek to provide a comprehensive review of this voluminous literature and readers interested in this are referred to [16,21,71] for more details on individual studies and a description of how each area of application has developed over time. Figure 1 illustrates some of the main application areas.…”

Section: Taxonomy Of Existing Literaturementioning

confidence: 99%