Abstract
Financial data is increasingly made available in high quantities and in high quality for companies that trade in the stock market. However, such data is generally made available comprising many distinct financial indicators and most of these indicators are highly correlated and non-stationary. Computational tools for visualizing the huge diversity of available financial information, especially when it comes to financial indicators, are needed for micro and macro-economic financial analysis and forecasting. In this work we will present an automatic tool that can be a valuable assistant on this process: the Ubiquitous Self-Organizing Map (UbiSOM). The UbiSOM can be used for performing advance exploratory data analysis in company fundamental data and help to uncover new and emergent correlations in companies with similar company financial fundamentals that would remain undetected otherwise. Our results show that the generated SOM are stable enough to function as conceptual maps, that can accurately describe and adapt to the highly volatile financial data stream, even in the presence of financial shocks. Moreover, the SOM is presented as a valuable tool capable of describing different technological companies during the period of 2003–2018, based solely on four key fundamental indicators.
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
Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23(4), 589–609 (1968)
Altman, E.I.: Predicting financial distress of companies: revisiting the Z-score and ZETA® models. In: Handbook of Research Methods and Applications in Empirical Finance, Edward Elgar Publishing (2013)
Chen, N., Ribeiro, B., Vieira, A., Chen, A.: Clustering and visualization of bankruptcy trajectory using self-organizing map. Expert Syst. Appl. 40(1), 385–393 (2013)
Deboeck, G., Kohonen, T. (eds.): Visual Explorations in Finance with Self-Organizing Maps. Springer, London (1998) https://doi.org/10.1007/978-1-4471-3913-3
Gorricha, J.M.L.: Exploratory data analysis using self-organising maps defined in up to three dimensions. Ph.D. thesis, Universidade Nova de Lisboa (2015)
Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences. 3 edn. Springer, Berlin (2001) https://doi.org/10.1007/978-3-642-56927-2
Kovalerchuk, B.: Visual Knowledge Discovery and Machine Learning. ISRL, vol. 144. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73040-0
Marques, N.C., Silva, B., Santos, H.: An interactive interface for multi-dimensional data stream analysis. In: 20th International Conference Information Visualisation (IV), pp. 223–229. IEEE, Lisbon, Portugal (July 2016). https://doi.org/10.1109/IV.2016.72
Qian, J., et al.: Introducing self-organized maps (SOM) as a visualization tool for materials research and education. Results Mater. 4, 100020 (2019)
Silva, B., Marques, N.C.: The ubiquitous self-organizing map for non-stationary data streams. J. Big Data 2(1), 1–22 (2015). https://doi.org/10.1186/s40537-015-0033-0
Silva, B.M.N.d.: Exploratory Cluster Analysis from Ubiquitous Data Streams using Self-Organizing Maps. Ph.D. thesis, Universidade Nova de Lisboa (2016). https://run.unl.pt/handle/10362/19974
Stock, J.H., Watson, M.W.: Twenty years of time series econometrics in ten pictures. J. Econ. Perspect. 31(2), 59–86 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Carrega, M., Santos, H., Marques, N. (2021). Data Streams for Unsupervised Analysis of Company Data. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_48
Download citation
DOI: https://doi.org/10.1007/978-3-030-86230-5_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86229-9
Online ISBN: 978-3-030-86230-5
eBook Packages: Computer ScienceComputer Science (R0)