The relationship between model complexity and forecasting performance for computer intelligence optimization in finance

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• 作者：Adam Ghandar^a ; ^bAuthor Vitae ; Zbigniew Michalewicz^b ; ^c ; ^dAuthor Vitae ; Ralf Zurbruegg^e ; ^{Ralf.Zurbrugg@adelaide.edu.au" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Financial forecasting ; Computer intelligence optimization ; Evolutionary algoritms
• 刊名：International Journal of Forecasting
• 出版年：2016
• 出版时间：July-September 2016
• 年：2016
• 卷：32
• 期：3
• 页码：598-613
• 全文大小：1441 K

文摘

The objective of this paper is to show that the ability of nature-inspired optimization routines to construct complex models does not necessarily imply any improvement in performance. In fact, the reverse may be the case. We demonstrate that under the dynamic conditions found in most financial markets, complex prediction models that seem, ex-ante, to be at least as good as more simple models, can underperform in out-of-sample tests. The correct application of these optimization methods requires a knowledge of how and when these techniques will yield beneficial outcomes. We highlight the need for future research to focus on appropriate protocols and a systematic approach to model selection when computer intelligence optimization methods are being utilized, particularly within the realm of financial forecasting.