Financial time series forecasting with machine learning techniques: A survey

Bjoern Krollner; Bruce Vanstone; Gavin Finnie

Financial time series forecasting with machine learning techniques: A survey

Bjoern Krollner, Bruce Vanstone, Gavin Finnie

Bond Business School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

124 Citations (Scopus)

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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 language	English
Title of host publication	Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN 2010)
Subtitle of host publication	Computational Intelligence and Machine Learning
Pages	25-30
Number of pages	6
Publication status	Published - 2010
Event	European Symposium on Artificial Neural Networks: Computational Intelligence and Machine Learning - Bruges, Belgium Duration: 28 Apr 2010 → 30 Apr 2010 Conference number: 18th

Conference

Conference	European Symposium on Artificial Neural Networks
Abbreviated title	ESANN 2010
Country/Territory	Belgium
City	Bruges
Period	28/04/10 → 30/04/10

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title = "Financial time series forecasting with machine learning techniques: A survey",

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.",

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Financial time series forecasting with machine learning techniques: A survey. / Krollner, Bjoern; Vanstone, Bruce; Finnie, Gavin.
Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN 2010): Computational Intelligence and Machine Learning. 2010. p. 25-30.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

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AU - Vanstone, Bruce

AU - Finnie, Gavin

N1 - Conference code: 18th

PY - 2010

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N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:84882966021

SN - 2930307102

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BT - Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN 2010)

Y2 - 28 April 2010 through 30 April 2010

ER -

Financial time series forecasting with machine learning techniques: A survey

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