ICIC Express Letters

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. The hypotheses are listed as follows: 1 2 3 H0 :TAR = TAR = TAR (6) H1 : if H0 is not true (7) 1 2 3 where H0 is the null hypothesis; H1 is the alternative hypothesis; and TAR ,TAR ,TAR are the total average return rates of Groups 1, 2, and 3, respectively. We employ the F-statistics to test the hypotheses and set the level of significance at

α=0.05. The null hypothesis H0 is rejected at level α, if F > Fα (k −1, N − k) , where k is the number of groups and N is the total number of observations in the sample. This means that the total average return rates for all groups are significantly different from each other. On the other hand, the null hypothesis cannot be rejected if F < Fα (k −1, N − k) . This means that the total average return rates of all groups are not significantly different.

4. Empirical Analysis. We use the annual data from year 1996 to 2005 for all stocks in the TAIEX, NASDAQ, and Dow Jones in the analysis. The data are collected from the TEJ and CRSP databases. We separate the stocks into three groups based on k-means and even j j grouping, respectively, and calculate ARy and TAR for each group.

4.1. Grouping by K-Means. First, we separate the TAIEX stocks into three groups based j j on k-means and calculate ARy and TAR . The results are reported in Table 1 and are depicted in Figure 1. TAR1 is equal to 24.8458, TAR2 is equal to 21.1924, and TAR3 is equal to -1.7536. We apply the F-statistics for joint hypothesis and obtain 12 KUNHUANG HUARNG, TIFFANY HUIKUANG YU AND CINGJING CHEN

j F = 1.0672 < F0.05 (2,27) = 3.3541, which fails to reject H0. This indicates that these TAR are not significantly different from each other. The results are listed in Table 2.

j TABLE 1. ARy for TAIEX by k-means Average return rate (%) 1 2 3 y (Year) ARy ARy ARy 1996 41.7080 28.4806 27.8372 1997 108.9700 32.6356 16.4144 1998 -8.4300 0.4065 -24.4712 1999 106.2300 116.1900 -11.4942 2000 -50.8000 -50.7550 -58.8372 2001 44.5000 22.0767 13.2493 2002 -62.4500 -11.5153 3.6740 2003 47.6100 36.4739 23.3768 2004 -8.5100 16.7421 -6.3853 2005 29.6300 21.1894 -0.8998 TAR j 24.8458 21.1924 -1.7536

140 120 100 80 60 Group 1 40 return rate Group 2 20 Group 3 0 -20 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 -40 -60 -80 year

FIGURE 1. Average return rate for TAIEX by k-means

TABLE 2. ANOVA (k-means for TAIEX) Source of Variation df Sum of Sq. Mean Sq. F-Value Between groups 2 4157.9877 2078.9939 Within groups 27 52599.103 1948.1174 1.0672 Total 29 56757.1580 BLUE CHIP BLUES? 13

Similarly, we separate the NASDAQ stocks into three groups based on k-means. The j j 2 ARy and TAR are listed in Table 3, and depicted in Figure 2. TAR is equal to 0.7471, TAR1 is equal to 0.3882, and TAR3 is equal to 0.1859. However, j F = 0.9309 < F0.05 (2,27) = 3.3541, which fails to reject H0. This indicates that these TAR are not significantly different from each other, either. The results are listed in Table 4.

j TABLE 3. ARy for NASDAQ (k-means) Average return rate (%) 1 2 3 y (Year) ARy ARy ARy 1996 1.3130 0.8832 0.1061 1997 0.5643 0.0745 0.1437 1998 1.1460 1.5111 -0.0328 1999 0.6836 4.5705 0.6529 2000 -0.2859 -0.2866 -0.1840 2001 0.5274 -0.2388 0.2068 2002 -0.2196 -0.2315 -0.1530 2003 0.0681 0.9579 0.9585 2004 0.0892 0.2379 0.1603 2005 -0.0046 -0.0069 0.0007 TAR j 0.3882 0.7471 0.1859

5.00

4.00

3.00 Group1 return rate 2.00 Group2 Group3 1.00

0.00 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 -1.00 year

FIGURE 2. Average return rate for NASDAQ (k-means)

14 KUNHUANG HUARNG, TIFFANY HUIKUANG YU AND CINGJING CHEN

TABLE 4. ANOVA (k-means for NASDAQ) Source of Variation df Sum of Sq. Mean Sq. F-Value Between groups 2 1.6157 0.8078 Within groups 27 23.4387 0.8681 0.9309 Tot al 29 25.0544

According to both tests, the TAR j for both the TAIEX and NASDAQ are not significantly different. In other words, the total average annual return of blue chips is not significantly better than those of the other two groups. This implies that the blue chips blue in the TAIEX and NASDAQ.

TABLE 5. Companies in Group 1 for the TAIEX and NASDAQ based on k-means Year TAIEX NASDAQ TSMC Chang Hwa Bank 1996 China Steel Corporation Intel Nan Ya Plastics Corporation Formosa Plastics Corporation 1997 TSMC Microsoft 1998 TSMC Microsoft 1999 TSMC Microsoft 2000 TSMC Cisco 2001 TSMC Microsoft 2002 TSMC Microsoft 2003 TSMC Microsoft 2004 TSMC Microsoft 2005 TSMC Microsoft

However, there is one interesting result to note based on the k-means grouping. There is usually only one stock in Group 1 for both the TAIEX and NASDAQ for many years. For example, the only stock in the TAIEX from 1997 to 2005 is the Taiwan Semiconductor Manufacturing Company (TSMC). The only stock in NASDAQ is the Intel Corporation in 1996, Microsoft Corporation from 1997 to 1999, Cisco Systems Incorporated in 2000, and Microsoft Corporation from 2001 to 2005. These companies are listed in Table 5. Grouping by k-means shows that a few companies have a relatively large market capitalization compared with the others.

BLUE CHIP BLUES? 15

4.2. Even Grouping. To avoid having only a few stocks (or even one stock) in Group 1, we further divide the stocks evenly. First, we sort all the TAIEX stocks based on the market capitalization in each year. Then we divide these stocks into three groups evenly. Group 1 consists of those with the largest market capitalization, followed by Group 2, and Group 3 j comprises those with the smallest market capitalization. We calculate ARy in each year. 1 The results are listed in Table 6, and are depicted in Figure 3. We find that ARy is larger 2 3 1 2 than ARy and ARy in each year except 2002. TAR is equal to 10.907, TAR is equal to 3 -0.732, and TAR is equal to -14.466. From the F-statistics, F = 2.2210 < F0.05 (2,27) = 3.3541, j which fails to reject H0 . This means that these TAR are not significantly different from each other. The results are listed in Table 7. Similarly, we sort all the stocks in NASDAQ according to their market capitalization and j divide these stocks into three groups evenly. We calculate ARy in each year. The results are reported in Table 8, and depicted in Figure 4.

j TABLE 6. ARy for TAIEX (Even grouping) Average return rate (%) 1 2 3 y (Year) ARy ARy ARy 1996 36.478 26.351 21.190 1997 24.168 16.415 10.311 1998 -12.869 -28.702 -28.841 1999 24.180 -15.068 -42.301 2000 -45.107 -59.871 -71.411 2001 25.796 21.227 -7.011 2002 1.976 5.203 2.328 2003 29.604 27.867 13.763 2004 10.142 -5.130 -22.757 2005 14.706 4.386 -19.929 TAR j 10.907 -0.732 -14.466

140 120 100 80 60 Group 1 40 return rate Group 2 20 Group 3 0 -20 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 -40 -60 -80 year

FIGURE 3. Average return rate for TAIEX (Even grouping) 16 KUNHUANG HUARNG, TIFFANY HUIKUANG YU AND CINGJING CHEN

TABLE 7. ANOVA (Even grouping for TAIEX) Source of Variation df Sum of Sq. Mean Sq. F-Value Between 2 3226.3172 1613.1586 Within 27 19610.8721 726.3286 2.2210 Tot al 29 22837.1893 0.1838

j TABLE 8. ARy for NASDAQ (Even grouping) Average return rate (%) 1 2 3 y (Year) ARy ARy ARy 1996 0.3564 0.1244 -0.1614 1997 0.4174 0.1142 -0.1004 1998 0.2844 -0.0846 -0.2936 1999 1.4519 0.4202 0.1062 2000 0.0657 -0.1898 -0.4280 2001 0.3470 0.3008 -0.0276 2002 -0.0755 -0.1308 -0.2531 2003 1.0136 1.0444 0.8167 2004 0.2741 0.0884 0.1189 2005 -0.0068 0.0009 0.0081 TAR j 0.4128 0.1688 -0.0214

2.00

1.50

1.00 Group1 return rate 0.50 Group2 Group3 0.00 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 -0.50

-1.00 year

FIGURE 4. Average return rate for NASDAQ (Even grouping)

TAR1 is equal to 0.4128, TAR2 is equal to 0.1688, and TAR3 is equal to -0.0214.

However, from the F-statistics, F = 3.0127 < F0.05 (2,27) = 3.3541, which fails to reject H0 . This means that these TAR j are not significantly different from each other. The results are listed in Table 9. BLUE CHIP BLUES? 17

TABLE 9. ANOVA (Even grouping for NASDAQ) Source of Variation df Sum of Sq. Mean Sq. F-Value Between 2 0.9477 0.4738 Within 27 4.2467 0.1573 3.0127 Tot al 29 5.1944

j j In addition, we calculate ARy and TAR for the Dow Jones, and use those data to replace Group 1 of the NASDAQ. The results are listed in Table 10, and depicted in Figure 5. TAR1 is equal to 0.1116, TAR2 is equal to 0.1688, and TAR3 is equal to -0.0214. From the F-statistics, F = 1.0334 < F0.05 (2,27) = 3.3541, which fails to reject H0 . This means that these TAR j are not significantly different from each other. The results are listed in Table 11. Tabl es 7, 9 and 11 all show that their TAR j are not significantly different from each other. Hence, we know that the blue chips blue in all the TAIEX, NASDAQ, and Dow Jones based on even grouping.

j TABLE 10. ARy for Dow Jones Average return rate (%) 1 2 3 y (Year) ARy ARy ARy 1996 0.2514 0.1244 -0.1614 1997 0.2720 0.1142 -0.1004 1998 0.1823 -0.0846 -0.2936 1999 0.2764 0.4202 0.1062 2000 -0.0522 -0.1898 -0.4280 2001 -0.0468 0.3008 -0.0276 2002 -0.1655 -0.1308 -0.2531 2003 0.3196 1.0444 0.8167 2004 0.0611 0.0884 0.1189 2005 0.0174 0.0009 0.0081 TAR j 0.1116 0.1688 -0.0214

TABLE 11. ANOVA (Dow Jones) Source of Variation df Sum of Sq. Mean Sq. F-Value Between 2 0.1905 0.0952 Within 27 2.4885 0.0922 1.0334 Tot al 29 2.6790

18 KUNHUANG HUARNG, TIFFANY HUIKUANG YU AND CINGJING CHEN

1.20 1.00 0.80 0.60 Group 1 0.40 return rate Group 2 0.20 Group 3 0.00 -0.20 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 -0.40 -0.60

year

FIGURE 5. Average return rate for Dow Jones

5. Conclusion. People used to think that blue chips were more profitable, and mutual funds were also likely to include these stocks in their portfolios. However, there have been reports that blue chips perform worse in the U.S.; and hence, some funds have started to remove those stocks from their portfolios. So blue chip blues? In this paper, we have used annual data to analyze if blue chips are better than the stocks of companies with smaller capitalization in the TAIEX, NASDAQ and Dow Jones. We have also used k-means and even grouping to cluster the stocks, and analyze the stocks in the TAIEX, NASDAQ and Dow Jones for 10 years. When clustering based on k-means, the TAR j in both the TAIEX and NASDAQ are not significantly different each other. This means that the total average annual return on blue chips is not significantly better than those for the other two groups. This implies that the blue chips in the TAIEX and NASDAQ were blue. To cluster stocks evenly, the TAR j in both the TAIEX and NASDAQ are not significantly different from each other either. When we replace the Dow Jones in Group 1 of the NASDAQ, the result also indicates that the values for TAR j are not significantly different. Hence, we conclude that the blue chips were blue in these indexes. The results of the empirical analysis show that the TAR j in the case of the blue chips are not significantly larger than the other groups for these indexes. From the empirical results, we conclude that the blue chips were blue in each of the TAIEX, NASDAQ and Dow Jones.

REFERENCES

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[3] G. Weiss and J. Lowe (1998), Dividends Don’t Lie: Finding Value in Blue Chip Stocks, Chicago, Longman Financial Services Publishing. [4] H. Ralambondrainy (1995), A Conceptual Version of the K-means Algorithm, Pattern Recognition Letters, vol. 16, pp.1147-1157. [5] J. Downes and J.E. Goodman (2003), Dictionary of Finance and Investment Terms, Hauppauge, N.Y.. [6] J. Tian, L. Zhu, S. Zhang, and L. Liu (2005), Improvement and Parallelism of the k-Means Clustering Algorithm, Tsinghua Science and Technology, vol. 10, no. 3, pp.277-281. [7] J. T. Stafford (1987), The Share-Owner’s Guide: How to Invest Profitably and Safely in Shares, Cambridge, Woodhead-Faulkner. [8] Perseus Publishing (2003), The Ultimate Business Dictionary, Cambridge, MA. [9] R. Farzad (2006), Blue Chip Blues, Business Week, no.3980, pp.66-75. [10] R. J. Shook (2001), Online Trader’s Dictionary: The Most Up-to-Date and Authoritative Compendium of Financial Terms, Franklin Lakes, NJ. [11] R. Pennant-Rea and B. Emmot (1987), Pocket Economist, Oxford, Basil Blackwell. [12] W. W. Chen (2004), On the Adoption of Blue Chip Stocks in Asset-Liability Management, Graduate School of Finance, I-Shou University.