Determination of the world stock indices' co-movements by association rule mining.
| Autor | Kartal, Burcu |
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Introduction
Financial deepening and financial integration process that the world stock markets experienced increases the co-movement of world stock indexes. Investors need to foresee which direction other stock exchanges will move after the fall or rise of any stock market. This information will help investors take quick and efficient decisions. Ghosh et al. (1999) found that Hong Kong, India and Korea have a long-term relationship with the US market, while Indonesia, the Philippines and Singapore have long-term relationships with Japan. Johnson and Soenon (2009) found a significant association between the German and European Union stock markets. Kasibhatla et al. (2006) detected a long-run relationship between London (FTSE100), Frankfurt (DAX30) and Paris (CAC40) indices. Madaleno and Pinho (2012) investigated the long-term relationship between FTSE 100, Dow Jones 30, Nikkei 225 and Bovespa, and concluded that the relationship is strong but not homogeneous. Vuran (2010) concluded that the Borsa Istanbul (XU100) Index is related to the FTSE 100, DAX, BOVESPA, MERVAL and IPC indices in the long term.
Chan et al. (1997) concluded that international diversification is important because stock markets do not co-movements in the long run. Caporale et al. (2016) could not determine the cointegration relationship between the US and European stock indices. Fu and Pagani (2012) state that there is a cointegration between international stock indices, but the evidence is weak. Dimpfl (2014) concluded that it would not be appropriate to investigate the relations and integration between financial markets through cointegration analysis, and cointegration is a coincidence. Can co-movement between stock markets, which cannot be determined by cointegration analysis, be analysed by another method? The association rule can answer this question. The association rule is a pattern of frequently used items appearing together. It reflects behaviours inwhich the same behaviour often occurs. With globalization, the potential of stock markets to exhibit common behaviours is increasing. In particular, return and volatility spillovers are frequently studied topics in the finance literature. These studies are also includedin the literature partof this study.It ispossibletoshow common behaviours with algorithms within the frameworkofassociation rule.In this study, the apriori algorithm, which is frequently used in the literature, is used. Also, Eclat and FP- Growth algorithms, which are other association rule algorithms, were used for the robustness of the results.
This study aims to consider the basic research question: Are the co-movement of stock indices present by the association rule? In this study, we investigate the co-movement of daily price changes of other stock market index values in the world for each of the index values of 11 world exchanges with the association rule algorithm. Association rule algorithm, one of the data mining techniques, is used to reveal this information. This method, also known as market-basket analysis, can help investors create investment plans using preliminary information. It is aimed to determine how the change in the opening/closing values of each stock market index is affected by the fluctuations in foreign markets and which international indices co-movement. Briefly, this study aims to provide preliminary information to the investor by determining which indices co-movement, with the data mining method. Details about the association rule give in the method section.
To be able to fill the gap in the literature and to guide the potential investors about alternative investment opportunities, this study aims to analyse world stock indices' co-movements by association rule mining selected samples in economies. To identify the gap in the literature, a literature review is presented in the second section. Then, the data set is presented in the third section. The methods and findings are indicated in the fourth and fifth sections, respectively.
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Literature review
Studies on the co-movement and integration of stock markets focus on returns and volatility spillovers. Emerging markets, both in the long run and in the short run, are more affected by their own past shocks than developed markets (Li and Giles, 2015;Vo and Tran, 2020; Jung and Maderitsch, 2014). Wang and Wang (2010) statedthat volatility spilloversare greater than price spillovers between China, Japan andUS markets. This effect decreases with the openness ofthe markets and increases with the geographical distance. Yang et al. (2020) identified risk spillovers between Hong Kong and Shanghai. The study of Zhou et al. (2012) revealed that the volatility spillover between China, Japan and India markets is greater than the volatility spillover between China, the USA and the UK. These findings support the findings of Wang and Wang (2010) regarding geographical distance. Zhoung and Liu (2021) identified volatility spillovers between China and Singapore, Thailand, Indonesia, Malaysia and the Philippines. According to Hung (2019), China seriously affects Vietnam, Thailand, Singapore and Malaysia in terms of volatility spillover. There is mutual causality in the mean between
Colombia, Indonesia, Vietnam, Egypt, Turkey and South Africa (Korkmazetal., 2012).Majdoub and Mansour (2014) found that the conditional correlation estimates between the USA, Turkey, Indonesia, Pakistan, Qatar and Malaysia are insufficient. Volatility spillover from European markets tothe US marketis greater than that from theUS to the European market (Savva et al., 2009). Correlations between the BRICS countries and the markets of developed countries such as the USA, Europe and Japan that change over time (Mensietal., 2017; McIver and Kang, 2020) found bidirectional volatility and return spillover between BRICS and US stock markets. There is a one-way return spillover from the US markets to the Latin markets (Yousaf et al., 2020). Farther of the closing hours between the stock exchanges affects the size of the volatility spillover (Baumohletal., 2018). When co-movementoffinancial dataisevaluated with the use of the association rule algorithm, it is observed that the fluctuation movements of the stocks, indexes and exchange rates in various countries' financial markets are examined. Liao et al. (2008) investigated the problems that arise in stock market investments in Taiwan using the two-stage data mining method, and they applied the association rule and then used the k-means clustering method with 19 international indices. As a result of the analysis, Nikkei 225, Hang Seng and KOSPI stock indexes' co-movement with various stocks traded on the Taiwan Stock Exchange in the 60-75% confidence level. Hoon Na and Sohn (2011) used the association analysis, and they stated that the Korean Stock Exchange index moved in the same direction with the US and European stock exchange indices. Argiddi and Apte (2012) used the association rule for the exchange of stocks in the India Information Technology index. In addition, it is tried to find solutions with two different methods in the study. They created the data set from the opening prices of the relevant stocks for the last three years. The rules of association that emerged as a result of the analysis are interpreted. Liao and Chou (2013) examined the changes in Taiwan and China stock indexes in the stock market using the association rule method and cluster analysis between June 2008 and March 2011, strong comovement between the electronics, finance, and insurance and semiconductors index and Taiwan Stock Index (TAIEX) in Taiwan Stock Exchange.
Arafah and Mukhlash (2015) studied the exchange of shares of 10 companies in Indonesia studiedusingthefuzzyassociationrulesmethod.Thedatasetiscreatedbyobtainingtheclosing prices ofthe relevant company stocks between January 2010 and December 2014. Asa result of the analysis, they established rules at a minimum support level of 0.1, 0.07 and 0.06 and found that the increase in certain stocks caused increases and decreases over other stocks. Masum (2019) has examined the co-movement of the stocks of 36 companies listed on the Tehran stock market index.Intotal, 249,061 records are testedbyassociation analysis. Many association rules and recommendations have been developed. The association rules with over 81% confidence value, over 1% support value and over two lift values are detected in three and four items.
When the above return and volatility spillover literature review is examined, it is found that stock markets affect each other in terms of return and volatility, and causal relationships are established. However, these studies do not reveal any findings concerning the up and down of other markets. In the studies conducted on the concept of association rule algorithm, it has been determined that the association rule is frequently used in the analysis of index comovements. It is seen that the studies are mostly done on the indices in the Far East countries, and the focus is on the changes in the domestic markets. Globally, there is no study involving a total analysis of the movement of countries' stock indexes with other country stock indexes. For this reason, a research gap has been identified in this area.
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Method
3.1 Data
In this study, data from 4,924 trading days between January 2001 and November 2019, which included 11 world stock market indices, are used. The resulting data set is Brazil (BVSP), USA (S&P 500, DJI, IXIC), France (FCHI), Germany (GDAXI), UK (FTSE), China (SSEC), Hong Kong (HSI), Japan (N225) and Turkey (XU100) consists of opening and closing prices of the stock market. According to each stock market index, a total of 11 different data sets are created. 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...
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