Coupling macro-sector-micro financial indicators for learning stock representations with less uncertainty

Guifeng Wang, Longbing Cao, Hongke Zhao, Qi Liu, Enhong Chen*

*Corresponding author for this work

Research output: Contribution to journalConference paperpeer-review

13 Citations (Scopus)

Abstract

While the stock movement prediction has been intensively studied, existing work suffers from weak generalization because of the uncertainty in both data and modeling. On one hand, training a stock representation on stochastic stock data in an end-to-end manner may lead to excessive modeling, which involves the model uncertainty. On the other, the analysis of correlating stock data with its relevant factors involves the data uncertainty. To simultaneously address such uncertainty both from data and modeling perspectives, a fundamental yet challenging task is to learn a better stock representation with less uncertainty by considering hierarchical couplings from the macro-level to the sector-and micro-level. Accordingly, we propose a copula-based contrastive predictive coding (Co-CPC) method. Co-CPC first models the dependence between a certain stock sector and relevant macroeconomic variables that are sequential and heterogeneous, e.g., macro-variables are associated with different time intervals, scales, and distributions. Then, by involving a macro-sector context, stock representations are learned in a self-supervised way that can further be used for downstream tasks like stock movement prediction. Extensive experiments on two typical stock datasets verify the effectiveness of our Co-CPC method.

Original languageEnglish
Pages (from-to)4418-4426
Number of pages9
JournalProceedings of the AAAI Conference on Artificial Intelligence
Volume35
Issue number5
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
Duration: 2 Feb 20219 Feb 2021

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