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Towards the Ensemble: IPCBR Model in Investigating Financial Bubbles

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  • Francis Ekpenyong
  • Georgios Samakovitis
  • Stelios Kapetanakis
  • Miltos Petridis

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.

Suggested Citation

  • Francis Ekpenyong & Georgios Samakovitis & Stelios Kapetanakis & Miltos Petridis, 2020. "Towards the Ensemble: IPCBR Model in Investigating Financial Bubbles," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 4(4), July.
    • Handle: RePEc:epw:ejece0:v:4:y:2020:i:4:id:19193
    DOI: 10.24018/ejece.2020.4.4.193
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    References listed on IDEAS

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    1. Didier SORNETTE & Peter CAUWELS, 2014. "Financial Bubbles: Mechanisms and Diagnostics," Swiss Finance Institute Research Paper Series 14-28, Swiss Finance Institute.
    2. Nikola MILOSEVIC, 2016. "Equity Forecast: Predicting Long Term Stock Price Movement using Machine Learning," Journal of Economics Library, EconSciences Journals, vol. 3(2), pages 288-294, June.
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    4. Nikola MILOSEVIC, 2016. "Equity Forecast: Predicting Long Term Stock Price Movement using Machine Learning," Journal of Economics Library, KSP Journals, vol. 3(2), pages 288-294, June.
    5. Didier Sornette & Peter Cauwels, 2014. "Financial bubbles: mechanisms and diagnostics," Papers 1404.2140, arXiv.org.
    6. Phichhang Ou & Hengshan Wang, 2009. "Prediction of Stock Market Index Movement by Ten Data Mining Techniques," Modern Applied Science, Canadian Center of Science and Education, vol. 3(12), pages 1-28, December.
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