##plugins.themes.bootstrap3.article.main##

  •   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.

Keywords: Artificial Intelligence, Asset Bubble, Case-based Reasoning, Inverse Problems, Machine learning

References

Witten, I.H., Frank, E., Hall, M.a.: Data Mining: Practical Machine Learning Tools and Techniques (Google eBook). (2011).

Huang, S.H.: Supervised feature selection: A tutorial. Artificial Intelligence Re- search 4(2) (2015).

Shokouhi, S.V., Skalle, P., Aamodt, A.: An overview of case-based reasoning applications in drilling engineering. Artificial Intelligence Review 41(3) (2014) 317–329.

Ou, P., Wang, H.: Prediction of stock market index movement by ten data mining techniques. Modern Applied Science 3(12) (2009) 28–42

Milosevic, N.: Equity forecast: Predicting long term stock price movement using machine learning. Journal of Economics Library 3(2) (2016) 8.

Xu, Y., Cohen, S.B.: Stock Movement Prediction from Tweets and Historical Prices. Acl (2018) 1–10.

Barberis, N., Greenwood, R., Jin, L., Shleifer, A.: Extrapolation and Bubbles (2017).

Sornette, D., Cauwels, P.: Financial bubbles: mechanisms and diagnostics. (January) (2014) 1–24.

Jenny Freeman, T.Y.: Early warning on stock market bubbles via methods of optimization, clustering and inverse problems. Annals of Operations Research 260(1-2) (2018) 293–320.

Jiang, Z.Q., Zhou, W.X., Sornette, D., Woodard, R., Bastiaensen, K., Cauwels, P.: Bubble diagnosis and prediction of the 2005-2007 and 2008-2009 Chinese stock market bubbles. Journal of Economic Behavior and Organization 74(3) (2010) 149–162.

Katja Taipalus: Detecting asset price bubbles with time-series methods Detecting asset price bubbles with time-series methods. (2012).

Dvhg, D.V.H., Eulg, H., Iru, V., Ixqgdphqwdo, R.Q., Whfkqlfdo, D.Q.G., Vlv, D., Ghflvlrq, I.R.U.: A Case-Based Reasoning-Decision Tree Hybrid System for Stock Selection. 10(6) (2016) 1181–1187.

Zhou, W.X.: Should Monetary Policy Target Asset Bubbles? –. (2007)

Ince, H.: Short term stock selection with case-based reasoning technique. Applied Soft Computing Journal 22 (2014) 205–212.

Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications. IOS Press 7(1) (1994) 39–59.

Lei, Y., Peng, Y., Ruan, X.: Applying case-based reasoning to cold forging process planning. Journal of Materials Processing Technology 112(1) (2001) 12–16.

Merelli, E., Luck, M.: Technical Forum Group on Agents in Bioinformatics. Knowledge Engineering Review 20(2) (2004) 117–125.

Marketos, G., Pediaditakis, K., Theodoridis, Y., Theodoulidis, B.: Intelligent Stock Market Assistant using Temporal Data Mining. Citeseer (May 2014) (1999) 1–11.

Pecar, B.: Case-based Algorithm for Pattern Recognition and Extrapolation ( APRE Method ). SGES/SGAI International Conference on Knowledge Based Systems and Applied Artificial Intelligence (2002).

Berthold, M.R., Hoppner, F.: On Clustering Time Series Using Euclidean Distance and Pearson Correlation. (2016).

Momeni, M., Mohseni, M., Soofi, M.: Clustering Stock Market Companies via K-Means Algorithm. Kuwait Chapter of Arabian Journal of Business and Management Review 4(5) (2015) 1–10.

Datta, T., Ghosh, I.: Using Clustering Method to Understand Indian Stock Market Volatility. Communications on Applied Electronics 2(6) (2015) 35–44.

L´opez, B.: Case-Based Reasoning: A Concise Introduction. Synthesis Lectures on Artificial Intelligence and Machine Learning 7(1) (2013) 1–103.

H., S., Elmogy, M.: Case Based Reasoning: Case Representation Methodologies. International Journal of Advanced Computer Science and Applications 6(11) (2015) 192–208.

Argoul, P.: Overview of Inverse Problems, Parameter Identification in Civil Engineering. (2012) 1–13.

Bal, G.: Introduction to Inverse Problems. (2012).

Iulian, M.M.: Direct Problems and Inverse Problems in Biometric Systems. III(5) (2013) 1–14.

Ulrych, T.J., Sacchi, M.D., Woodbury, A.: A Bayes tour of inversion: A tutorial. Geophysics 66(1) (2001) 55–69.

Yao, Z., Eddy, W.F.: A statistical approach to the inverse problem in magnetoencephalography. The Annals of Applied Statistics 8(2) (2014) 1119–1144.

Jeewana, C.: Domain Decomposition Based Algorithms for Some Invers E Problems. (August) (2000).

Elshafiey, I.M.: Neural network approach for solving inverse problems. (1991).

Yu, G., Sapiro, G., Mallat, S.: Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 21(5) (2012) 2481–2499.

Al-Jamal, M.F.: Numerical solutions of elliptic inverse problems via the equation error method. (2012).

Knight, B., Petridis, M., Woon, F.L.: Case Selection and Interpolation in CBR Retrieval. 10(1) (2010) 31–38

Sever, A.: A Machine Learning Algorithm Based on Inverse Problems for Soft- ware Requirements Selection. Journal of Advances in Mathematics and Computer Science 23(2) (2017) 1–16.

Michael V, M.V.K.: The Cost Minimizing Inverse Classification Problem : A Genetic Algorithm Approach. Science Direct Volume 29,(3) (2000) 283–300.

Acevedo, N.I.A., Roberty, N.C.: An explicit formulation for the inverse trans- port problem using only external detectors – Part I : Computational Modelling. Transport 29 (2010) 343–358.

Sever, A.: An inverse problem approach to pattern recognition in industry. Applied Computing and Informatics 11(1) (2015) 1–12.

Search, H., Journals, C., Contact, A., Iopscience, M., Address, I.P.: Inverse problems in in Machine Learning : machine learning : an application Interpretation application to activity interpretation. Theory and Practice 012085 (2008).

Floyd, M.W., Esfandiari, B.: An active approach to automatic case generation. McGinty L., Wilson D.C. (eds) Case-Based Reasoning Research and Development. ICCBR 2009, LNCS 5650 (2009) 150–164.

Manzoor, J., Asif, S., Masud, M., Khan, M.J.: Automatic Case Generation for Case-Based Reasoning Systems Using Genetic Algorithms. 2012 Third Global Congress on Intelligent Systems (2012) 311–314.

Xing, G., Ding, J., Chai, T., Afshar, P., Wang, H.: Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process. Engineering Applications of Artificial Intelligence 25(2) (2012) 418–429.

Woon, F.L.: Using CBR to Improve the Usability of Numerical Models By. (2005).

Lin, Jessica, Sheri Williamson, Kirk Borne, D.D.: Chapter 1 Pattern Recognition in Time Series. (2011).

Van Rossum, G., Drake Jr, F.L.: Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam (1995)

McKinney, W.: Data structures for statistical computing in python. In van der Walt, S., Millman, J., eds.: Proceedings of the 9th Python in Science Conference. (2010) 51 – 56.

Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Nic- ulae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., Varoquaux, G.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning. (2013) 108–122.

He, H., Chen, J., Jin, H., Chen, S.: Stock trend analysis and trading strategy. Proceedings of the 9th Joint Conference on Information Sciences, JCIS 2006 2006(January) (2006).

Chen, S.H., Wang, P.P., Kuo, T.W.: Computational intelligence in economics and finance: Volume II. Computational Intelligence in Economics and Finance: Volume II (January) (2007) 1–227.

Ding, C., He, X.: K-means clustering via principal component analysis. Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 (2004) 225–232.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12 (2011) 2825–2830.

Baarsch, J., Celebi, M.E.: Investigation of internal validity measures for K-means clustering. Lecture Notes in Engineering and Computer Science 2195 (2012) 471– 476.

Downloads

Download data is not yet available.

##plugins.themes.bootstrap3.article.details##

How to Cite
[1]
Ekpenyong, F., Samakovitis, G., Kapetanakis, S. and Petridis, M. 2020. Towards the Ensemble: IPCBR Model in Investigating Financial Bubbles. European Journal of Electrical Engineering and Computer Science. 4, 4 (Jul. 2020). DOI:https://doi.org/10.24018/ejece.2020.4.4.193.