Towards the Ensemble: IPCBR Model in Investigating Financial Bubbles

Francis Ekpenyong, Georgios Samakovitis, Stelios Kapetanakis and Miltos Petridis
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Francis Ekpenyong: University of Brighton, UK
Georgios Samakovitis: University of Greenwich, UK
Stelios Kapetanakis: University of Brighton, UK
Miltos Petridis: Middlesex University, UK

European Journal of Electrical Engineering and Computer Science, 2020, vol. 4, issue 4

Abstract: 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.

Keywords: Artificial Intelligence; Asset Bubble; Case-based Reasoning; Inverse Problems; Machine learning (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejece0:v:4:y:2020:i:4:id:19193

DOI: 10.24018/ejece.2020.4.4.193

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