Determination of the world stock indices' co-movements by association rule mining
Keywords:
Data mining, Association rules, Stock market index, Global financial marketsAbstract
Purpose
This study aims to provide preliminary information to the investor by determining which indices co-movement, with the data mining method.
Design/methodology/approach
In this context, data sets containing daily opening and closing prices between 2001 and 2019 have been created for 11 stock market indexes in the world. The association rule algorithm, one of the data mining techniques, is used in the analysis of the data.
Findings
It is observed that the US stock market indices take part in the highest confidence levels between association rules. The XU100 stock index co-movement with both the European stock market indices and the US stock indices. In addition, the Hang Seng Index (HSI) (Hong Kong) takes part in the association rules of all stock market indices.
Originality/value
The important issue for data sets is that the opening/closing values of the same day or the previous day are taken into account according to the open or closed status of other stock market indices by taking the opening time of the stock exchange index to be created. Therefore, data sets are arranged for each stock market index, separately. As a result of this data set arranging process, it is possible to find out co-movements of the stock market indexes. It is proof that the world stock indices have co-movement, and this continues as a cycle.
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References
Agrawal, R., Imilienski, T. and Swami, A. (1993), “Mining association rules between sets of items in large databases”, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207-216.
Arafah, A.A. and Mukhlashi, I. (2015), “The application of fuzzy association rule on co-movement analyze of Indonesian stock price”, Procedia Computer Science, Vol. 59, pp. 235-243.
Argiddi, R. and Apte, S.S. (2012), “Future trend prediction of Indian it stock market using association rule mining of transaction data”, International Journal of Computer Applications, Vol. 39 No. 10, pp. 30-34.
Baumöhl, E., Kocenda, E., Lyocsa, S. and Vyrost, T. (2018), “Networks of volatility spillovers among stock markets”, Physica A, Vol. 490, pp. 1555-1574.
Caporale, G.M., Gil-Alan, L.A. and Orlando, J.C. (2016), “Linkages between the US and European stock markets: a fractional cointegration approach”, International Journal of Finance and Economics, Vol. 21, pp. 143-153.
Chan, K.C., Gup, B.E. and Pan, M.-S. (1997), “International stock market efficiency and integration: a study of eighteen nations”, Journal of Business Finance and Accounting, Vol. 24 No. 6, pp. 803-813.
Dimpfl, T. (2014), “A note on cointegration of international stock market indices”, International Review of Financial Analysis, Vol. 33, pp. 10-16.
Fu, R. and Pagani, M. (2012), “On the cointegration of international stock indices”, Journal of Economics and Finance, Vol. 36, pp. 463-480.
Garg, K. and Kumar, D. (2013), “Comparing the performance of frequent pattern mining algorithms”, International Journal of Computer Applications, Vol. 69 No. 25, pp. 29-32.
Ghosh, A., Saidi, R. and Johnson, K.H. (1999), “Who moves the Asia-Pasific Stock Markets-US or Japan? Empirical evidence based on the theory of cointegration”, The Financial Review, Vol. 34, pp. 159-170.
Han, J. and Kamber, M. (2006), Data Mining Concepts and Techniques, 2nd ed., Morgan Kaufman Publishers, Academic Press, San Francisco, CA.
Han, J., Pei, J. and Yin, Y. (2000), “Mining frequent patterns without candidate generation”, ACM Sigmod Record, Vol. 29 No. 2, pp. 1-12.
Hoon Na, S. and Sohn, So Y. (2011), “Forecasting changes in Korea composite stock price index (KOSPI) using association rules”, Expert Systems with Applications, Vol. 38, pp. 9046-9049.
Hung, N.T. (2019), “Return and volatility spillover across equity markets between China and Southeast Asian countries”, Journal of Economics, Finance and Administrative Science, Vol. 24 No. 47, pp. 66-81.
Johnson, R. and Soenen, L. (2009), “European economic integration and stock market co-movement with Germany”, The Multinational Business Review, Vol. 17 No. 3, pp. 205-227.
Jung, R.C. and Maderitsch, R. (2014), “Structural breaks in volatility spillovers between international financial markets: contagion or mere interdependence?”, Journal of Banking and Finance, Vol. 47, pp. 331-342.
Kasibhatla, K.M., Stewart, D., Sen, S. and Malindretos, J. (2006), “Are daily stock price indices in the major European equity markets cointegrated? tests and evidence”, The US Economist, Vol. 50 No. 2, pp. 47-57.
Korkmaz, T., Çevik, E. and Atukeren, E. (2012), “Return and volatility spillovers among CIVETS stock markets”, Emerging Markets Review, Vol. 13, pp. 230-252.
Li, Y. and Giles, D.E. (2015), “Modelling volatility spillover effects between developed stock markets and Asian emerging stock markets”, International Journal of Finance and Economics, Vol. 20, pp. 155-177.
Liao, S. and Chou, S. (2013), “Data mining investigation of co-movements on Taiwan and China stock markets for future investment portfolio”, Expert Systems with Applications, Vol. 40, pp. 1542-1554.
Liao, S., Ho, H. and Lin, H. (2008), “Mining stock category association and cluster on Taiwan stock market”, Expert Systems with Applications, Vol. 35, pp. 19-29.
Madaleno, M. and Pinho, C. (2012), “International stock market indices co-movements: a new look”, International Journal of Finance and Economics, Vol. 17, pp. 89-102.
Majdoub, J. and Mansour, W. (2014), “Islamic equity market integration and volatility spillover between emerging and US stock markets”, North American Journal of Economics and Finance, Vol. 29, pp. 452-470.
Masum, Z.H. (2019), “Mining stock category association on Tehran stock market”, Soft Computing, Vol. 23, pp. 1165-1177.
McIver, R.P. and Kang, S.H. (2020), “Financial crises and the dynamics of the spillovers between the U.S. and BRICS stock markets”, Research in International Business and Finance, Vol. 54, pp. 1-17.
Mensi, W., Hammoudeh, S. and Kang, S.H. (2017), “Risk spillovers and portfolio management between developed and BRICS stock markets”, North American Journal of Economics and Finance, Vol. 41, pp. 133-155.
Savva, C.S., Osborn, D.R. and Gill, L. (2009), “Spillovers and correlations between US and major European stock markets: the role of the Euro”, Applied Financial Economics, Vol. 19 No. 19, pp. 1595-1604.
Vo, X.V. and Tran, T.T.A. (2020), “Modelling volatility spillovers from the US equity market to ASIAN stock markets”, Pacific-Basin Finance Journal, Vol. 59, pp. 1-8.
Vuran, B. (2010), “The determination of long-run relationship between ISE 100 and international equity indices using cointegration analysis”, Istanbul University Journal of the School of Business Administration, Vol. 39 No. 1, pp. 154-168.
Wang, P. and Wang, P. (2010), “Price and volatility spillovers between the Greater China markets and the developed markets of US and Japan”, Global Finance Journal, Vol. 21, pp. 304-317.
Wang, Y.F., Chuang, Y.L., Hsu, M.H. and Keh, H.C. (2004), “A personalized recommender system for the cosmetic business”, Expert Systems with Applications, Vol. 26 No. 3, pp. 427-434.
Yang, K., Wei, Y., Li, S. and He, J. (2020), “Asymmetric risk spillovers between Shanghai and Hong Kong stock markets under China's capital account liberalization”, North American Journal of Economics and Finance, Vol. 51, pp. 1-12.
Yousaf, I., Ali, S. and Wong, W.K. (2020), “Return and volatility transmission between World-Leading and Latin American stock markets: portfolio implications”, Journal of Risk Financial Management, Vol. 13, p. 148.
Yu, X. and Wang, H. (2014), “Improvement of Eclat algorithm based on support in frequent itemset mining”, Journal of Computers, Vol. 9 No. 9, pp. 2116-2123, doi: 10.4304/jcp.9.9.2116-2123.
Zaki, M.J. (2000), “Scalable algorithms for association mining”, IEEE Transactions on Knowledge and Data Engineering, Vol. 3, pp. 372-390, doi: 10.1109/69.846291.
Zhou, X., Zhang, W. and Zhang, J. (2012), “Volatility spillovers between the Chinese and world equity markets”, Pacific-Basin Finance Journal, Vol. 20, pp. 247-270.
Zhoung, Y. and Liu, J. (2021), “Correlations and volatility spillovers between China and Southeast Asian stock markets”, The Quarterly Review of Economics and Finance, Vol. 81, pp. 57-69.
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Copyright (c) 2022 Burcu Kartal, Mehmet Fatih Sert, Melih Kutlu
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