Uncertainty Awareness for Predicting Noisy Stock Price Movements

Yun Hsuan Lien, Yu Syuan Lin, Yu Shuen Wang*

*Corresponding author for this work

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

1 Scopus citations


Predicting stock price movements is challenging because financial markets are noisy – signals and patterns in different periods are dissimilar and often conflict with each other. Consequently, irrespective of whether the price rises or falls, none of the previous methods achieve high prediction accuracy in this binary classification task. In this study, we consider aleatoric uncertainty and model uncertainty when training neural networks to forecast stock price movements. Specifically, aleatoric uncertainty is known as statistical uncertainty. It indicates that similar historical price trajectories may not lead to similar future price movements. On the other hand, model uncertainty is caused by the model’s mathematical structures and parameter values, which can be used to estimate whether the models are familiar with the testing sample. Considering that most of the existing uncertainty estimation methods focus on model uncertainty, we transform the aleatoric uncertainty in financial markets to model uncertainty by removing samples with similar historical price trajectories and different future movements. The Bayesian neural network is then adopted to estimate the model uncertainty during inference. Experiment results demonstrated that the networks achieved high accuracy when they were certain about their predictions.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings
EditorsMassih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783031264214
StatePublished - 2023
Event22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022 - Grenoble, France
Duration: 19 Sep 202223 Sep 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13718 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022


  • Aleatoric uncertainty
  • Model uncertainty
  • Stock price movement prediction
  • Uncertainty quantification


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