Comprehensive Stock Market Insight: Bayesian Networks for Multi-output Forecasting

Ali Ben Mrad (), Brahim Hnich (), Amine Lahiani () and Salma Mefteh-Wali ()
Additional contact information
Ali Ben Mrad: Qassim University
Brahim Hnich: ENIS, University of Sfax
Amine Lahiani: LEO-Laboratoire d’Economie d’Orléans
Salma Mefteh-Wali: ESSCA School of Management

Computational Economics, 2025, vol. 66, issue 5, No 26, 4329-4349

Abstract: Abstract This paper presents a solution to the challenge of predicting multiple potential outcome variables for stock market evolution using Bayesian Networks. We develop three models based on Bayesian networks and analyze their performance using seven criteria: Model Building, Model Complexity, Flexibility/Generality, Interpretability, Ease of Deployment, Extensibility, and ease of Development. The experiments employ daily data concerning stock market indices from three regions (G7, BRICS, Gulf Cooperation Countries), two commodities prices (WTI, Gold), VIX index, and cryptocurrency prices (Bitcoin, Ethereum, Dash, Monero, Ripple). The proposed holistic Bayesian network model outperforms the competing Bayesian networks models, and the results show that it allows predicting either one output variable given a set of inputs or a set of output variables given a set of input variables, making it important for financial analysts, investors and traders.

Keywords: Decision support systems; Stock market prediction; Multi output prediction; Machine learning; Bayesian networks; Financial analysis; Portfolio management (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-025-10852-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:66:y:2025:i:5:d:10.1007_s10614-025-10852-0

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-025-10852-0

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().