Abstract
In order to model and explain the dynamics of the market, different types and sources of information should be taken into account. We propose to use a Bayesian network as a quantitative financial tool for market signals detection. We combine and incorporate in the model, accounting, market, and sentiment data. The network is used to describe the relationships among the examined variables in an immediate way. Furthermore, it permits to identify in a mouse-click time scenario that could lead to operative signals. An application to the analysis of S&P 500 index is presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chow, C.K., Liu, C.N.: Approximating Discrete Probability distributions with dependence trees. IEEE Trans. Inf. Theor. 14, 462–467 (1968)
Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Springer, New York (1999)
Greppi, A.: Bayesian Networks Models for Equity Market, Ph.D. Thesis, University of Pavia (2016)
Jensen, F.V.: Bayesian Networks. UCL press, London (1996)
Lakonishok, J., Shleifer, A., Vishny, R.W.: Contrarian Investment, Extrapolation, and Risk. J. Finance 49(5), 1541–1578 (1994)
Lauritzen, S.L.: The EM Algorithm for Graphical Association Models with Missing Data. Comp. Stat. & Data Anal. 19, 191–201 (1995)
Nielsen, A.E.: Goal - Global Strategy Paper No. 1, Goldman Sachs Global Economics - Commodities and Strategy Research. http://www.goldmansachs.com/our-thinking/archive/, (2011)
Patel, P.N., Yao, S., Carlson, R., Banerji, A., Handelman, J.: Quantitative Research - A Disciplined Approach, Credit Suisse Equity Research (2011)
Steck, H.: Constraint-Based Structural Learning in Bayesian Networks Using Finite Data, Ph.D. thesis, Institut für Informatik der Technischen Universität München (2001)
Acknowledgements
We are grateful to the referee for valuable comments and suggestions.The first author is grateful to the Doctoral Research in Economics and Management of Technology (DREAMT). The work of the second author was partially supported by MIUR, Italy, PRIN MISURA 2010RHAHPL.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Greppi, A., De Giuli, M.E., Tarantola, C., Montagna, D.M. (2018). Bayesian Networks for Financial Market Signals Detection. In: Mola, F., Conversano, C., Vichi, M. (eds) Classification, (Big) Data Analysis and Statistical Learning. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-55708-3_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-55708-3_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55707-6
Online ISBN: 978-3-319-55708-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)