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A hybrid supervised semi-supervised graph-based model to predict one-day ahead movement of global stock markets and commodity prices

Negahdari Kia, A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.eswa.2018.03.037
  3. Abstract:
  4. Market prediction has been an important machine learning research topic in recent decades. A neglected issue in prediction is having a model that can simultaneously pay attention to the interaction of global markets along historical data of the target markets being predicted. As a solution, we present a hybrid supervised semi-supervised model called HyS3 for direction of movement prediction. The graph-based semi-supervised part of HyS3 models the markets global interactions through a network designed with a novel continuous Kruskal-based graph construction algorithm called ConKruG. The supervised part of the model injects results extracted from each market's historical data to the network whenever the hybrid model allows with an innovative conditional mechanism. The significance of higher prediction accuracy of HyS3 is comparing to other models is proved statistically against other models including supervised models and network-based semi-supervised predictions. © 2018 Elsevier Ltd
  5. Keywords:
  6. Financial markets prediction ; Hybrid machine learning models ; Semi-supervised learning ; Artificial intelligence ; Electronic trading ; Financial markets ; Forecasting ; Graphic methods ; International trade ; Learning algorithms ; Supervised learning ; Direction of movements ; Global stock markets ; Graph algorithms ; Hybrid machine learning ; Machine learning research ; Prediction accuracy ; Semi- supervised learning ; Semi-supervised graphs ; Motion estimation
  7. Source: Expert Systems with Applications ; Volume 105 , 2018 , Pages 159-173 ; 09574174 (ISSN)
  8. URL: https://www.sciencedirect.com/science/article/pii/S0957417418301829