An ensemble of LSTM neural networks for high-frequency stock market classification

Svetlana Borovkova*, Ioannis Tsiamas

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)600-619
Number of pages20
JournalJournal of Forecasting
Issue number6
Publication statusPublished - 2019


  • deep learning
  • ensemble models
  • high-frequency trading
  • LSTM neural networks


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