Using recommendations for trade returns prediction with machine learning

Ling Cen, Dymitr Ruta, Andrzej Ruta

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Automatically predicting stock market behavior using machine learning and/or data mining technologies is quite a challenging and complex task due to its dynamic nature and intrinsic volatility across global financial markets. Forecasting stock behavior solely based on historical prices may not perform well due to continuous, dynamic and in general unpredictable influence of various factors, e.g. economic status, political stability, voiced leaders’ opinions, emergency events, etc., which are often not reflected in historic data. It is, therefore, useful to look at other data sources for predicting direction of market movement. The objective of ISMIS 2017 Data Mining Competition was to verify whether experts’ recommendations can be used as a reliable basis for making informed decisions regarding investments in stock markets. The task was to predict a class of a return from an investment in different assets over the next three months, using only opinions given by financial experts. To address it, the trading prediction is formulated as a 3-class classification problem solved within supervised machine learning domain. Specifically, a hybrid classification system has been developed by combining traditional probabilistic Bayesian learning and Extreme Learning Machine (ELM) based on Feed-forward Neural Networks (NN). Assuming feature space narrowed down to just the latest experts recommendations probabilistic and ELM classifiers are trained and their outputs fed to train another baseline ELM classifier. The outputs from baseline classifiers are combined by voting at the decision level to generate final decision class. The presented hybrid model achieved the prediction score of 0.4172 yielding 8th place out of 159 teams competing in the ISMIS’ 2017 competition.

Original languageBritish English
Title of host publicationFoundations of Intelligent Systems - 23rd International Symposium, ISMIS 2017, Proceedings
EditorsMarzena Kryszkiewicz, Henryk Rybinski, Zbigniew W. Ras, Dominik Slezak, Andrzej Skowron, Annalisa Appice, Andrzej Skowron
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319604374
StatePublished - 2017
Event23rd International Symposium on Methodologies for Intelligent Systems, ISMIS 2017 - Warsaw, Poland
Duration: 26 Jun 201729 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10352 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Symposium on Methodologies for Intelligent Systems, ISMIS 2017


  • Decision level fusion
  • Extreme Learning Machines (ELM)
  • Hybrid classification
  • Maximum a posteriori estimation
  • Trading prediction


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