liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Gated Bayesian Networks for Algorithmic Trading
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-8678-1164
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
2016 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 69, 58-80 p.Article in journal (Refereed) Published
Abstract [en]

Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to model dynamical systems consisting of several distinct phases. In this paper, we present an algorithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how using the learnt GBNs can substantially lower risks towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strategy buy-and-hold.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 69, 58-80 p.
Keyword [en]
Probabilistic graphical models, Bayesian networks, algorithmic
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-124066DOI: 10.1016/j.ijar.2015.11.002ISI: 000368957000004OAI: oai:DiVA.org:liu-124066DiVA: diva2:895685
Note

Funding agencies: Center for Industrial Information Technology, Linkoping University (CENIIT) [09.01]; Swedish Research Council [2010-4808]

Available from: 2016-01-19 Created: 2016-01-19 Last updated: 2016-03-10

Open Access in DiVA

fulltext(1055 kB)46 downloads
File information
File name FULLTEXT01.pdfFile size 1055 kBChecksum SHA-512
07200aa08d7507064ed3cff66532c8ff1942814255d2b0ff9c62cb342d7c7d80a7cd0f504448d27d0e77274dc9e5d17968e568c35781eee0bb8b66252868d00e
Type fulltextMimetype application/pdf

Other links

Publisher's full textlänk till fulltext

Search in DiVA

By author/editor
Bendtsen, MarcusPeña, Jose M.
By organisation
Database and information techniquesFaculty of Science & Engineering
In the same journal
International Journal of Approximate Reasoning
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 46 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 425 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf