Predictive Uncertainty in Neural Network-Based Financial Market Forecasting

  • Iwao Maeda The University of Tokyo
  • Hiroyasu Matsushima The University of Tokyo
  • Hiroki Sakaji The University of Tokyo
  • Kiyoshi Izumi The University of Tokyo
  • David deGraw Daiwa Securities Co. Ltd.
  • Atsuo Kato Daiwa Institute of Research Ltd.
  • Michiharu Kitano Daiwa Institute of Research Ltd.
Keywords: Financial data mining, Financial market forecasting, Uncertainty consideration, Neural networks


In financial market forecasting, various methods based on statistical analysis and neural networks have been proposed. Accurate forecasting of future market states can be helpful in decision-making related to investment behavior; however, existing forecasting methods have considerable deficiencies due to the nature of financial markets and their complexity, influenceability, and nonstationarity. Forecasting of complex systems, such as financial markets, should be performed considering predictive uncertainty, and decision-making needs to be adjusted accordingly. In the present study, we introduce the concept of uncertainty to neural network-based financial market forecasting. A sparse variational dropout Bayesian neural network (SVDBNNs) is used for stochastic prediction, and on this basis, the corresponding decision-making process is proposed. The proposed method is validated by conducting investment simulation on the historical orderbook data from the Tokyo Stock Exchange and is confirmed to enable more efficient and safe investments compared with the considered alternative approaches.


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