بررسی داده کاوی از همکاری جنبش ها روی بازارهای سهام تایوان و چین برای پرتفوی سرمایه گذاری آینده
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|22287||2013||13 صفحه PDF||سفارش دهید||9890 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 40, Issue 5, April 2013, Pages 1542–1554
On June 29, 2010, Taiwan signed an Economic Cooperation Framework Agreement (ECFA) with China as a major step to open markets between Taiwan and China. Thus, the ECFA will contribute by creating a closer relationship between China and Taiwan through economic and market interactions. Co-movements of the world’s national financial market indexes are a popular research topic in the finance literature. Some studies examine the co-movements and the benefits of international financial market portfolio diversification/integration and economic performance. Thus, this study investigates the co-movement in the Taiwan and China (Hong Kong) stock markets under the ECFA using a data mining approach, including association rules and clustering analysis. Thirty categories of stock indexes are implemented as decision variables to observe the behavior of stock index associations during the periods of ECFA implementation. Patterns, rules, and clusters of data mining results are discussed for future stock market investment portfolio.
With the increasing significance of international flows of goods, services and capital, co-movements of economic variables in different countries is becoming increasingly evident. The extent to which globalization causes domestic economies to move along with economies in the rest of the world or in their particular region, is a major concern for policy-makers. Thus, it is believed by many that regional trade integration and regional free trade agreements (RFTAs) are beneficial to a nation’s economy (Edwards and Ginn, 2011 and Kearney and Muckley, 2007). In the case of Taiwan and China, due to the nature of the political relationship across the Taiwan Straits, the R.O.C. Taiwan has been excluded from the rising trend of ASEAN economic integration, and thus is facing the risk of marginalization. However, since R.O.C. Taiwan President, Ma Ying-jeou took office in May 2008, cross-strait relationships have experienced their most rapid improvement in decades. On April 26, 2009, at the third round of cross-strait talks in Nanjing, Taipei and Beijing inked three new agreements. Such initiatives are crucial to normalize cross-strait economic ties. More importantly, Taiwan recognizes the need to establish more institutionalized cooperation platforms with its neighbors. On June 29, 2010, Taiwan signed an Economic Cooperation Framework Agreement (ECFA) with China as a major step in this direction. For Taiwan, the ECFA may promote greater market opportunities and the possibility of signing Free Trade Agreements (FATs) with other ASEAN countries. For China, the ECFA may contribute in eliminating economic imbalances caused by the rapid economic progress since China’s reform and open-up policy in 1978. Thus, the ECFA will contribute by creating a closer relationship between China and Taiwan through economic and market interactions, thus establishing mutual understanding and trust, the basis of peace across the Straits. July 29, 2011, the Ministry of Economic Affairs (MOEA) expressed satisfaction in the results of the early harvest program in the Economic Cooperation Framework Agreement (ECFA) with mainland China. Taiwan’s gross export value to mainland China amounted to US$61.56 billion over the first six months this year, with tariff exemptions and reductions of US$53.71 million. On the other hand, Taiwan’s import value from mainland China, during the same period, grossed US$21.92 billion, saving the Chinese about US$9.43 million on tariffs. It is also noteworthy that the American Chamber of Commerce (AmCham) in Taipei announced that nearly half of Taiwan’s 2010 GDP growth came from its trade with China. Taiwan has become reliant on economic and trade relationships with China, which drove 47% of Taiwan’s economic growth in 2010. Therefore, there is a co-movement relationship between the Straits not only on trade exchange, but also on industry and financial markets under the ECFA. On the other hand, co-movements of the world’s national financial market indexes are a popular research topic in the finance literature (Meric et al., 2008, Chow et al., 2011, Graham et al., 2012 and Liao et al., 2011). Some studies examine the co-movements and the benefits of international financial market portfolio diversification/integration and economic performance (Liljeblom, Löflund, & Krokfors, 1997; Meric et al., 2001; Chue, 2002, Aslanidis et al., 2010, Beine and Candelon, 2010 and Madaleno and Pinho, 2012). In addition, the Asian financial crisis has stimulated a great deal of interest in how economic shocks are transmitted across different countries (Arestis et al., 2005, Brown et al., 2008, Chang, 2002 and Jang and Sul, 2002). The international stock market is one of the most popular forms of investment due to the expectations of high profit. However, higher expected profit also implies higher risk. Thus, numerous studies have proposed different analysis methods to assist investors in analysis and decision-making. In addition, many individual investors, stockbrokers, and financial analysts attempt to predict stock market price activities and their potential development. This mass behavior runs counter to the counsel of the many academic studies, which contend that the prediction of international stock market development is ineffective. This point of view is codified in what is referred to as the efficient markets hypothesis (Fama, 1991 and Haugen, 1997). In particular, Forbes and Rigobon (1999) and Rigobon (1999), in their studies of international stock market co-movements, discovered no significant changes in the international transmission mechanism of shocks during the financial crises, but found it puzzling why the degree of co-movement is so high at all times. Thus, the international financial capital market efficiency becomes an interesting issue for research on international financial market co-movements. In addition, there are three degrees of international financial capital market efficiency. The first degree is the strong form of the efficient market hypothesis, which states that all information that is knowable is immediately factored into the market’s price for security. If this is true, then all price predictors are definitely wasting their time, even if they have access to private information. The second degree is the semi-strong form of the efficient market hypothesis, in which all public information is considered to possess private information, which can be used for profit. The third degree is the weak form, which holds only that any information gained from examining a security’s past trading history is reflected in price. Indeed, past trading history is public information, implying that the weak form is a specialization of the semi-strong form, which itself is a specialization of the strong form of the efficient market hypothesis. Thus, integration of co-movement and portfolio analysis in financial market, in terms of investment and risk management, has become a critical research issue (Alexakis et al., 2005, Bohl et al., 2009, Boyer and Zheng, 2009, Liao et al., 2011 and Oh and Parwada, 2007). Due to the different degrees of international financial capital market efficiency, academic researchers investigate the efficient market hypothesis by exploring unknown and valuable knowledge from historical data, using techniques such as data mining. Enke and Thawornwong (2005) introduced an information gaining technique used in machine learning for data mining to evaluate the predictive relationships of numerous financial and economic variables. Neural network models for the estimation and classification of levels are then examined for their ability to provide an effective forecast of future values. Boginski, Butenko, and Pardalos (2006) proposed a network representation of stock market data referred to as a market graph. This graph is constructed by calculating cross correlations between pairs of stocks based on opening price data over a certain period of time. Chun and Park (2005) proposed a learning technique to extract new case vectors using Dynamic Adaptive Ensemble CBR (DAE CBR). The main idea of DAE CBR originates from finding combinations of parameters and updating and applying an optimal CBR model to an application or domain area. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index. In addition, Rapach and Wohar (2006) analyzed in-sample and out-of-sample tests of stock return predictability to better understand the nature of the empirical evidence in return predictability. Their study found that certain financial variables display significant in-sample and out-of sample predictive ability with respect to stock returns. Overall, most studies consider stock market analysis as a time series problem, and there have been few studies using stock market efficiency to explore the possible cause-and-effect relationships among different stock categories or the influence of outside factors (Liao, Ho, & Lin, 2008). Thus, this study investigates the co-movement in the Taiwan and China (Hong Kong) stock markets under the ECFA using a data mining approach, including association rules and cluster analysis. Specifically, this study investigates the following research issues: (1) the study of the relationships among Taiwan, China and Hong Kong stock market indexes by association rules to find a similar trend in transaction data, and also to identify any co-movement of market performance; (2) the use association rules to understand the co-movement between stock market indexes and their categorical stock indexes in Taiwan, China and Hong Kong stock market; (3) according to the findings, this study puts forward recommendations for investment portfolios and management as a follow-up reference. The rest of this study is organized as follows. In Section 2, we present the background of the Taiwan and China (Hong Kong) stock markets. Section 3 describes the methodology, including the research framework, data sources, and database design. Section 4 presents the data mining approach, association rules, Cluster analysis (K-means), and data mining tool – SPSS Clementine - and discusses research findings. Section 5 illustrates the data mining results. Finally, Section 6 presents a brief conclusion and discussion.
نتیجه گیری انگلیسی
Data mining results show that the Taiwan stock market has strong co-movement on Electronics, Financial and Insurance, and Semi-conductor stock indexes with the TAIEX index. On the other hand, Hong Kong stock market has clear co-movement on Real Estate, Tele-communications, and Financial Services stock indexes with the HIS index. Manufacturing, Machinery, and Electronics stock indexes have co-movement with the SZSE for the Shenzhen stock market. In addition, Industrial Products, Energy, and the financial stock indexes have co-movement with the SSE for the Shanghai stock market. Each stock market reflects its industrial and business features on market development. For example, the financial services industry is the main feature of the Hong Kong stock market. Shenzhen has a strong foundation in manufacturing industries. Shanghai is the center for financial operations and has a strong market niche for developing the financial industry. Taiwan,for its part, has great potential for the market in high-tech products. In addition, during the Early Harvest Program, there has been co-movement on Transportation, and Electronics category stock between Taiwan and Hong Kong. Once Hong Kong Real Estate and Information Technology stock indexes begin to fall, Taiwan Electronics, Semi-conductor, and Shipping stock indexes also begin to fall. At the same time, Taiwan Shipping and Electronics stock indexes also tend to drop, which influences the Shanghai and Shenzhen Wholesale, and Manufacturing stock indexes with the same falling co-movement. In the ECFA period, Hong Kong, Shanghai, and Shenzhen Medical and Electronics stock indexes are influenced by the Crowding-Out Effect on Taiwan similar category stock indexes. In addition, the Hong Kong Industrial and Information stock indexes could affect the market performance of the Taiwan Financial stock index. This indicates that Hong Kong might tend to lead co-movement of Financial, and Service category stock indexes to Taiwan. Another interesting finding was that before ECFA implementation Taiwan Papermaking, and Glass stock indexes performance were influenced by the Shanghai and the Shenzhen stock markets. However, after ECFA implementation, new co-movements on the market performance began to affect Electronics and Biotechnology stock indexes.This study investigates co-movements on the Taiwan and the China stock markets under ECFA using a data mining approach. Thirty categories of stock indexes are implemented as decision variables to observe the behavior of stock index associations during the periods of the Early Harvest Program and the ECFA implementation from the standpoint of Taiwan. This paper considers that a stock market has strong associations with both inside and outside factors. Some stock index categories of stock rise or fall together at the same time or are mutually influenced by domestic or foreign economic, social, and political situations. For individual or institutional investors, finding indications of trends in stock market association is an important ability. Thus, this case study of implementing data mining approaches and integrating them into international or regional stock market research on the Taiwan and China (Hong Kong) stock markets is an example for future cross-national stock research and implementation.