Asset Allocation Method Based on Sentiment Signals and Causal Information using Multi-asset Classes

  • Rei Taguchi School of Engineering, The University of Tokyo
  • Hiroki Sakaji The University of Tokyo, School of Engineering
  • Kiyoshi Izumi The University of Tokyo, School of Engineering
  • Yuri Murayama The University of Tokyo, School of Engineering
Keywords: Financial news, MLM scoring, causal inference, change-point detection, risk-parity portfolio


In this study, we demonstrate the usefulness of financial text for asset allocation with multi-asset classes, including stocks and bonds, by creating polarity indexes for several types of financial news through natural language processing. We performed clustering using a change-point detection algorithm on the created polarity indexes. We also constructed a multi-asset portfolio using an optimization algorithm and rebalanced it based on the detected change points. The results show that the proposed asset allocation method performed better than the comparison method, suggesting that polarity indexes can be useful in constructing asset allocation methods with multi-asset classes.


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