Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
An ensemble of LSTM neural networks for high‐frequency stock market classification
Ist Teil von
Journal of forecasting, 2019-09, Vol.38 (6), p.600-619
Ort / Verlag
Chichester: Wiley Periodicals Inc
Erscheinungsjahr
2019
Quelle
EBSCOhost Business Source Ultimate
Beschreibungen/Notizen
We propose an ensemble of long–short‐term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible nonstationarities in an innovative way. The performance of the models is measured by area under the curve of the receiver operating characteristic. We evaluate the predictive power of our model on several US large‐cap stocks and benchmark it against lasso and ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles.