Financial time series forecasting with machine learning techniques: A survey
Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad i Gynhadledd
StandardStandard
Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN 2010). 2010. t. 25-30.
Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad i Gynhadledd
HarvardHarvard
APA
CBE
MLA
VancouverVancouver
Author
RIS
TY - GEN
T1 - Financial time series forecasting with machine learning techniques: A survey
AU - Krollner, Bjoern
AU - Vanstone, Bruce
AU - Finnie, Gavin
N1 - European Symposium on Artificial Neural Networks : Computational Intelligence and Machine Learning, ESANN 2010 ; Conference date: 28-04-2010 Through 30-04-2010
PY - 2010
Y1 - 2010
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.
M3 - Conference contribution
SN - 2930307102
SP - 25
EP - 30
BT - Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN 2010)
ER -