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Towards the ensemble: IPCBR model in investigating financial bubbles

Towards the ensemble: IPCBR model in investigating financial bubbles

Petridis, Miltos, Kapetanakis, Stelios, Samakovitis, Georgios ORCID: 0000-0002-0076-8082 and Ekpenyong, Francis (2020) Towards the ensemble: IPCBR model in investigating financial bubbles. European Journal of Electrical Engineering and Computer Science, 4 (4). pp. 1-7. ISSN 2506-9853 (Online) (doi:

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Asset value predictability remains a major research concern in financial market especially when considering the effect of unprecedented market fluctuations on the behaviour of market participants.

This paper presents preliminary results toward the building a reliable forward problem on ensemble approach IPCBR model, that leverages the capabilities of Case based Reasoning(CBR) and Inverse Problem Techniques (IPTs) to describe and model abnormal stock market fluctuations (often associated with asset bubbles) using datasets from historical stock market prices. The framework uses a rich set of past observations and geometric pattern description and then applies a CBR to formulate the forward problem, Inverse Problem formulation is then applied to identify a set of parameters that can statistically be associated with the occurrence of the observed patterns.

This research work presents a formative strategy aimed to determine the causes of behaviour, rather than predict future time series points which brings a novel perspective to the problem of asset bubbles predictability, and a deviation from the existing research trend. The results depict the stock dynamics and statistical fluctuating evidence associated with the envisaged bubble problem.

Item Type: Article
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 International License. The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party. Submission of the manuscript represents that the manuscript has not been published previously and is not considered for publication elsewhere.
Uncontrolled Keywords: Artificial Intelligence, Asset Bubble, Case-based Reasoning, Inverse Problems, Machine learning.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified: 23 May 2022 11:06

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