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Case Retrieval with Clustering for a Case-Based Reasoning and Inverse Problem Methodology: An Investigation of Financial Bubbles

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 88))

Abstract

This paper proposes an approach for predicting abnormal asset performance in traded securities, often referred to as ‘financial bubbles’. It uses an ensemble technique based on Case-based Reasoning (CBR) and Inverse Problems (IP), which we term IPCBR. More specifically we propose a Machine Learning formative strategy to determine the causes of stock behaviour, rather than to predict future time series points in fuzzy environments. In so doing, our paper contributes to more robust strategies in investigating financial bubbles. The framework uses a geometric pattern description of historical time series and applies clustering techniques to derive a model that generalizes those patterns onto observations. The model constitutes the forward approach to the IPCBR framework; our results demonstrate that, given the target problem, our CBR model provides a computationally inexpensive description of abnormal asset performance.

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Notes

  1. 1.

    Although they both provide different information that can be used for analysis, the closing price is the raw price and only indicates end of sales price whereas the Adjusted price mirrors stock value after adjustments for any corporate actions like all applicable splits and dividend distributions, which better reflects the assets’ perceived value by investors.

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Author information

Authors and Affiliations

  1. University of Brighton, Brighton, UK

    Francis Ekpenyong & Stelios Kapetanakis

  2. University of Greenwich, London, UK

    Georgios Samakovitis

  3. Middlesex University, London, UK

    Miltos Petridis

Authors
  1. Francis Ekpenyong
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  2. Georgios Samakovitis
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  3. Stelios Kapetanakis
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  4. Miltos Petridis
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Corresponding author

Correspondence to Francis Ekpenyong .

Editor information

Editors and Affiliations

  1. College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UK

    Hongying Meng

  2. School of Electronical Information and Artificial Engineering, Shaanxi University of Science and Technology, Xi’an, China

    Tao Lei

  3. College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UK

    Maozhen Li

  4. College of Electrical and Information, Hunan University, Changsha, China

    Kenli Li

  5. Division of Intelligent Future Technologies, Mälardalen University, Västerås, Västmanlands Län, Sweden

    Ning Xiong

  6. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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Ekpenyong, F., Samakovitis, G., Kapetanakis, S., Petridis, M. (2021). Case Retrieval with Clustering for a Case-Based Reasoning and Inverse Problem Methodology: An Investigation of Financial Bubbles. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_164

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