Financial time series forecasting with machine learning techniques: A survey

Bjoern Krollner, Bruce Vanstone, Gavin Finnie

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Abstract

Stock index forecasting is vital for making informed investment decisions. This paper surveys recent literature in the domain of machine learning techniques and artificial intelligence used to forecast stock market movements. The publications are categorised according to the machine learning technique used, the forecasting timeframe, the input variables used, and the evaluation techniques employed. It is found that there is a consensus between researchers stressing the importance of stock index forecasting. Artificial Neural Networks (ANNs) are identified to be the dominant machine learning technique in this area. We conclude with possible future research directions.

Original languageEnglish
Title of host publicationProceedings of the 18th European Symposium on Artificial Neural Networks (ESANN 2010)
Subtitle of host publicationComputational Intelligence and Machine Learning
Pages25-30
Number of pages6
Publication statusPublished - 2010
Event European Symposium on Artificial Neural Networks: Computational Intelligence and Machine Learning - Bruges, Belgium
Duration: 28 Apr 201030 Apr 2010
Conference number: 18th

Conference

Conference European Symposium on Artificial Neural Networks
Abbreviated titleESANN 2010
CountryBelgium
CityBruges
Period28/04/1030/04/10

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  • Cite this

    Krollner, B., Vanstone, B., & Finnie, G. (2010). Financial time series forecasting with machine learning techniques: A survey. In Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN 2010): Computational Intelligence and Machine Learning (pp. 25-30)