ICIC Express Letters

International Journal of Innovative Management, Information & Production ISME Internationalⓒ2011 ISSN 2185-5439 Volume 2, Number 2, December 201 1 PP. 8-19 BLUE CHIP BLUES? KUNHUANG HUARNG1,TIFFANY HUIKUANG YU2 AND CINGJING CHEN3 1, 3Department of International Trade Feng Chia University 100 Wenhwa Rd., Seatwen, Taichung 40724 Taiwan TOC [email protected] 2Department of Public Finance Feng Chia University [email protected] ABSTRACT. An issue of Business Week reported that the blue chip stocks in the U.S. were becoming blue. Hence, this study intends to examine whether the blue chips in the NASDAQ and Dow Jones are really blue as well as to explore whether those stocks in the TAIEX are also becoming blue. Two methods are used to group the stocks in the TAIEX, NASDAQ and Dow Jones during the period from 1996 to 2005: a K-means method is applied to group the stocks, and the stocks are separated evenly into three groups of large, medium, and small capitalizations. The empirical analyses show that the average rates of return for blue chips (large capitalization stocks) are not significantly different from than those for the small capitalization stocks. The empirical results demonstrate that the blue chips in the TAIEX, NASDAQ and Dow Jones were all blue. Keywords: Dow Jones; k-means; NASDAQ; return rates; TAIEX 1. Introduction. Blue chips have long been valued, and mutual funds have tended to include blue chips in order to generate a profit. However, in a recent issue of Business Week, the editors expressed doubt over the value of blue chips. They stated that the $22 billion Fidelity Blue Chip Growth fund had asked its shareholders to approve a change from Standard & Poor’s 500-stock index (S&P 500) to the Russell 1000 Growth Index (Farzad, 2006). While the S&P 500 has long been regarded as a traditional blue-chip barometer, the Russell 1000 Growth Index encompasses a greater variety of stocks including many smaller companies. The S&P 500 has returned just 4.3% annually in the past five years, which is far less than its long-term average of 10%. Furthermore, the S&P 100 stock index, the bluest of the blue chips, has returned just 2.03% annually in the past five years (Farzad, 2006). Moreover, the legendary value investor Warren Buffett, who made a fortune with big investments in blue chips such as Coca-Cola Co. and Gillette, recently disclosed that he had made big bets on four major stock indexes. Three of which were outside the U.S. Based on these facts, it seems that blue chips are becoming blue in the U.S. This study intends to examine if the blue chips are really blue in Taiwan and the U.S. The target indices are the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) BLUE CHIP BLUES? 9 in Taiwan, and the National Association of Securities Dealers’ Automated Quotation (NASDAQ) and the Dow Jones Industrial Average (Dow Jones) in the U.S. Empirical analysis is conducted to analyze the returns for these indices over the past 10 years. Section 2 reviews different definitions of blue chips. Section 3 introduces the grouping methods used in this study. Section 4 provides the empirical analyses to test the returns for these indices, and Section 5 concludes this paper. 2. Literature Review. “Blue chip” originated from gambling, where it was used for the highest value gambling chip (Pennant-Rea and Emmot, 1990). There are different definitions of “blue chip” in the literature. In the Online Trader’s Dictionary: The Most Up-to-Date and Authoritative Compendium of Financial Terms (Shook, 2001), blue chips are referred to as “nationally known common stocks with a lengthy history of profit, growth, and quality management.” The Dictionary of Finance and Investment Terms (Downes and Goodman, 2003) has a similar definition for a blue chip, namely, “a common stock of a nationally known company that has a long record of profit growth and/or dividend payment and a reputation for quality management, products, and services.” In the Ultimate Business Dictionary (Perseus, 2003), a “blue chip” is defined as “the highest quality and lowest-risk ordinary equity share,” or a “high-quality established stable company.” Stafford (1987) states that “blue chips are shares in very sound, well-established and usually large companies.” Weiss and Lowe (1988) emphasized that “blue chip companies have sophisticated research centers, elaborate advertising programs, and a long history of profitable progress. They are also usually the first stocks to rise in the bull market and the last stocks to fall when the market declines.” Belsky (1993) gives a more quantitative interpretation by listing the following requisite characteristics of “blue chip” companies: (1) estimated revenues of at least $300 million; (2) solid balance sheets with debt no more than 40% of total capital; (3) likely profit growth of at least 15%; and (4) projected share-price gains of 20% or more. Willis (1993) asserts that “blue chip companies are companies that dominate niches in their markets and that are capable of solid long-term earnings growth potential.” Chen (2004) noted that “blue chips are the shares of high price, large market capitalization, and good reputation.” However, recent reports have given rise to less favorable observations regarding blue chips. Farzad (2006) says that blue chips were performing worse than the stocks of smaller companies. For instance, diversification by investors was creating more competition for blue chips, and mutual funds were increasing the value of their holdings by dumping the larger companies. Yoella, Meyer and Budescu (2003) report that the basic process that underlies choice behavior under internal uncertainty and especially the effect of framing is similar to the process of choice under external uncertainty and can be described quite accurately by prospect theory. 10 KUNHUANG HUARNG, TIFFANY HUIKUANG YU AND CINGJING CHEN 3. Research Methodology. 3.1. Setup. There are various definitions of blue chips. To facilitate the quantitative analysis, this study considers blue chips based on the market capitalization of the stocks. We separate stocks into groups based on their market capitalization. Then, we can calculate their returns and determine whether different groups yield various results: More specifically, if the group with the largest market capitalization creates better returns than the other groups. We use two techniques to divide the stocks: k-means and even grouping. We sort all stocks in descending order based on their market capitalization. Then, we divide all stocks into three groups based on k-means and even grouping, respectively. The group with the largest market capitalization is named Group 1, the group with medium market capitalization is referred to as Group 2, and that with the smallest market capitalization is termed Group 3. 3.2. Return Rates. We use the annual data for market capitalization and the return rates on all stocks in our analysis. The daily return rate is calculated as follows: i i i Rd = Ln((Pd *(1+α + β )+ Di ) (Pd −1 +α *C))*100(%) (1) i i i where Rd is the daily return for company i on day d, Pd and Pd −1 are the closing prices for company i on days d and d-1, respectively, α is the current ex-right subscriptions rate, β is the current ex-right non-reward dividend payout rate, C is the current ex-right cash subscription price, and Di is the current cash dividend for company i. The annual return rate and related return rates are calculated as follows: i i Ry = ∑ Rd (2) N j i ∑ Ry j i=1 ARy = (3) N j M j ∑ ARy = TAR j = y 1 (4) M i j where Ry is the annual return rate for company i in year y, ARy is the average annual return rate of Group j ( j = 1,2,3) in year y, N j is the total number of companies in Group j, TAR j is the total average annual return rate of Group j ( j =1,2,3) , and M is the total number of years. 3.3. K-Means. Clustering techniques are important for knowledge acquisition, and the k-means clustering algorithm is one of the most commonly used algorithms in clustering analysis (Ralambondrainy, 1995). Clustering is the process of grouping data into clusters so that objects in the same cluster have a high degree of similarity in comparison to each other, but are very dissimilar to objects in other clusters (Tian, et al., 2005). Hence, we apply BLUE CHIP BLUES? 11 clustering to the groups of data to simplify the data efficiently so as to derive useful information. The k-means method is very popular because of its capability to cluster huge amounts of numerical data both quickly and efficiently (Ralambondrainy, 1995). The k-means algorithm is an algorithm used to cluster objects based on attributes into k partitions. It is a variant of the expectation-maximization algorithm in which the goal is to determine the k means of data generated from Gaussian distributions. It assumes that the object attributes form a vector space. The objective is to minimize total intra-cluster variance, which can be expressed by the following function: k 2 = − µ V ∑∑ x j i (5) i=1 j∈Si where there are k clusters Si , i=1, 2, …, k and µi is the centroid or mean point of all the points x j ∈ Si . 3.4. Testing Hypotheses. The major objective of this study is to compare the total average return rates of different capitalizations. We use one-way analysis of variance (ANOVA) to test the significance of differences in the total average return rates of all groups.

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