Cross-Autocorrelation in Asian Stock Markets. First Draft: March1998. Abstract

Transcription

1 Cross-Autocorrelation in Asian Stock Markets Eric C. Chang a, Grant R. McQueen b, and J. Michael Pinegar b First Draft: March1998 Abstract Five Asian stock markets (Hong Kong, Japan, South Korea, Taiwan, and Thailand) and the U.S. stock market are evaluated for evidence of cross-autocorrelation. We find evidence of Lo and MacKinlay s (1990a) cross-autocorrelation in each of the five Asian markets. Specifically, within each country, monthly returns on a portfolio of small stocks are correlated with the lagged returns on a portfolio of large stocks. We also find weak evidence of across-ocean and inter-asian cross-autocorrelation; in several countries, monthly returns on portfolios of small Asian stocks are correlated with the lagged return on a portfolio of large U.S. stocks and large stocks in other Asian countries. We also confirm McQueen, Pinegar, and Thorley s (1996) finding of directional asymmetry in the U.S., with small stocks responding to lagged good, but not bad, news. However, the directional asymmetry is not universal and is significant only in the U.S. and Taiwan. Similarly, after correcting for small stock autocorrelation, large stocks retain their predictive power in some, but not all, of the markets. We also find that the degree of cross-autocorrelation has not weakened since the market correction of Our findings help shed light on existing explanations of the cross-autocorrelation puzzle and give direction for further research. a Georgia Institute of Technology, Atlanta, GA , USA. b Marriott School of Management, Brigham Young University, Provo, UT 84602, USA. We wish to thank Kalok Chan, Allaudeen Hameed, and Steven Thorley for helpful comments. The Harold F. Silver Fund at BYU provided financial support for data bases and research assistants. Grant McQueen received financial support from the Goldman Sachs fellowship. Corresponding author: Grant McQueen, (801) , grant_mcqueen@byu.edu.

2 Cross-Autocorrelation in Asian Stock Markets Abstract Five Asian stock markets (Hong Kong, Japan, South Korea, Taiwan, and Thailand) and the U.S. stock market are evaluated for evidence of cross-autocorrelation. We find evidence of Lo and MacKinlay s (1990a) cross-autocorrelation in each of the five Asian markets. Specifically, within each country, monthly returns on a portfolio of small stocks are correlated with the lagged returns on a portfolio of large stocks. We also find weak evidence of across-ocean and inter-asian cross-autocorrelation; in several countries, monthly returns on portfolios of small Asian stocks are correlated with the lagged return on a portfolio of large U.S. stocks and large stocks in other Asian countries. We also confirm McQueen, Pinegar, and Thorley s (1996) finding of directional asymmetry in the U.S., with small stocks responding to lagged good, but not bad, news. However, the directional asymmetry is not universal and is significant only in the U.S. and Taiwan. Similarly, after correcting for small stock autocorrelation, large stocks retain their predictive power in some, but not all, of the markets. We also find that the degree of cross-autocorrelation has not weakened since the market correction of Our findings help shed light on existing explanations of the cross-autocorrelation puzzle and give direction for further research.

3 1. Introduction While searching for the source of abnormal returns in contrarian investment strategies, Lo and MacKinlay (1990a) find that the weekly return on a portfolio of small U.S. stocks is positively correlated with the lagged weekly return on a portfolio of large U.S. stocks. Explanations for this cross-autocorrelation are categorized into three camps by Boudoukh, Richardson, and Whitelaw (1994, hereafter BRW). "Loyalist" explanations attempt to uphold market efficiency, attributing the predictability to data or market imperfections such as nonsynchronous trading. "Revisionists" simply label the predictable component in small stock returns as time-varying risk premiums. "Heretics " look to market fads, bubbles, overreaction, and behavioral models to explain the cross-autocorrelation. None of the camps is completely successful in explaining the cross-autocorrelation puzzle. BRW extend Lo and MacKinlay s (1990b) nonsynchronous trading explanation (Loyalist) and show how it could explain a significant amount of the cross-autocorrelation. However, this explanation alone is inadequate because Mech s (1993) "synchronized " portfolios still exhibit significant cross-autocorrelation. Mech s (1993) transaction cost explanation (Loyalist) is not supported by his own time-series tests or by McQueen, Pinegar, and Thorley s (1996, hereafter MPT) cross-sectional tests. Mech (1993) and MPT also question Revisionist explanations by pointing out that one would have to accept frequent and significantly negative small stock risk premiums if one were a Revisionist. MPT show further that significant crossautocorrelation exists after correcting for the predictability associated with time-varying risk premiums. Finally, MPT document directional asymmetrybsmall stocks lag large stocks after good, but not bad newsbwhich is not predicted by any of the existing explanations. Thus, a 2

4 search for a new, more viable, explanation is on. In this paper we extend the understanding of cross-autocorrelation in two ways. Our first extension is to test for cross-autocorrelation in Asia. We find the puzzle is not limited to the U.S. market. All five Asian stock markets we examine (Hong Kong, Japan, South Korea, Taiwan, and Thailand) exhibit cross-autocorrelation, although the degree of the small stock lag is strongest in the U.S. Our finding of cross-autocorrelation in Asian markets rules out explanations that focus on particular market micro-structures or institutional characteristics that are unique to a specific country. Furthermore, our evidence is based on monthly returns indicating a longer lagged relationship than previously believed and, consequently, a relationship probably caused by more than simple market micro-structure frictions. Our second extension is to use both Asian and U.S. stock markets to shed light on existing explanations and to give direction for future explanations for the slow response of some small stocks. Specifically, we test for across-ocean lead/lag relationships, for international directional asymmetry, for cross-autocorrelation after controlling for small stock autocorrelation, and for the stability of the puzzle over time. These additional tests often yield results that are problematic for existing Loyalist, Revisionist, and Heretic explanations. Our paper is organized as follows: Section 2 describes the data, reports some summary statistics, then reports institutional characteristics (potentially related to market inefficiencies) of the five Asian markets. Section 3 performs tests for cross-autocorrelation including tests for across-ocean and cross-country cross-autocorrelation, for international directional asymmetry, and for stability over time. Our conclusions are presented in Section 4. 3

5 2. Data The monthly return data for the Asian markets comes from the University of Rhode Island s Pacific-Basin Capital Market Research Center (PACAP) data base. Returns for the U.S. come from the University of Chicago s Center for Research in Security Prices (CRSP) data base. All returns are denominated in local currencies and include dividends. Data for all countries extends through December, Data begins in January, 1976 for all countries except Hong Kong (which begins in January, 1981) and South Korea (which begins in January, 1978). For each country, stocks are sorted into five portfolios each year based on the market value of the securities at the end of the prior year. In the U.S., we create quintile portfolios using all New York Stock Exchange (NYSE ) stocks. Table 1 reports summary statistics on firm size and the number of firms in the sizeranked quintile portfolios of the smallest (quintile 1) and largest (quintile 5) firms in the five Asian countries and the U.S. The mean firm size in Thailand s large stock portfolio is only $314.3 million, which is the smallest of all the quintile 5 portfolios. The limited average stock size, together with the significant autocorrelation in return, indicate that Thailand s "large stock portfolio" may be mislabeled. The smallest quintile in the U.S. has an average size of $38.9 million and allows for comparisons with Japan ($50.5 million) and Taiwan ($31.0 million). However, the smallest 20 percent of U.S. firms are much larger than the typical small firm in Hong Kong, South Korea, and Thailand with first quintile portfolios between $5.7 and $9.4 million. Consequently, we also create a portfolio consisting of the smallest 5 percent of the NYSE firms (average size $13.7 million) for comparison purposes. Mathematically, the median number of firms in the quintile 1 and 5 portfolios should be the same; however, because of 4

6 occasional incomplete data for some small firms, usually early in the sample period, the median number of firms in quintile 1 is often less than in quintile 5. Table 2 reports summary statistics for monthly returns of the smallest and largest sizeranked portfolios. For each country, the small stock portfolio is characterized by greater average returns, higher volatility, and greater first-order autocorrelation than their larger counterparts. Excluding Thailand, none of the large stock portfolios exhibit autocorrelation. With the exception of Taiwan, all of the small stock portfolios exhibit significant first degree autocorrelation. MPT note that if some small stocks respond slowly to macroeconomic news, the small stock portfolio will be both autocorrelated and cross-autocorrelated with the large stock portfolio. Since many of the Loyalist explanations for cross-autocorrelation are based on volume, transaction costs, the number of analysts, etc., we report in Table 3 some information about the Asian stock markets activity and capital market restrictions. The data come from Harrison (1994) and our discussion follows Chan, McQueen, and Thorley (1998). Japan is by far the largest market in terms of market capitalization and number of stocks listed. In 1993, Japan had over twice as many stocks and was almost 10 times bigger in terms of capitalization than the next closest Asian country listed in Table 3. The daily turnover gives an indication of the stock market activity. Taiwan, with a capitalization of only 3 percent of Japan s, has daily turnover of almost 40 percent of Japan s, indicating that stocks in Taiwan have the opportunity to respond more quickly to news and are less susceptible to nonsynchronous trading problems. Table 3 also reports the typical charges on a transaction of US $100,000. These charges include commissions and stamp duty. Investors in Hong Kong, Taiwan, and Thailand enjoy relatively low trading 5

7 Appendix A Supplemental Tables

8 Table 7A Asymmetric Regressions of Small Stock Portfolio Monthly Returns on the Lagged Small Stock Portfolio Returns and on the Contemporaneous and Lagged Own-Country Large Stock Portfolio Returns R 1,t '"% ((R 1, t&1 % $ up 0 (* 1 (R 5,t % $ 0 ((1&* 1 )(R 5, t % $up 1 (* 2 (R 5,t&1 % $ 1 ((1&* 2 )(R 5, t&1 %, t Asymmetric Contemporaneous and Lagged Coefficients (t-statistics) Hypothesis Test P 2 Statistics [p-values] Market " ( $ 0 up $ 0 $ 1 up $ 1 $ 0 up = $ 0 $ 1 up = $ 1 $ 0 up +$ 1 up =$ 0 + $ 1 Hong Kong (N = 165) 2.33 (1.83)* (3.36)** (3.34)** (6.20)** (0.89) (1.90)* 0.08 [.771] [.408] 0.08 [.777] Japan (N = 225) 1.16 (2.17)** (2.82)** (3.38)** (7.00)** (0.83) (0.57) 1.08 [.298] 0.04 [.833] 0.95 [.327] South Korea (N = 201) 2.56 (3.18)** (2.53)** (5.60)** (5.43)** (-1.10) (1.54) 0.22 [.637] 3.40 [.064]* 4.77 [.028]** Taiwan (N = 225) 1.06 (1.25) (2.25)** (5.84)** (10.04)** (1.83)* (-1.61) [.000]** 4.86 [.027]** 1.36 [.242] Thailand (N = 225) 1.23 (2.03)** (2.69)** (5.04)** (4.86)** (-0.56) (-0.38) 0.58 [0.445] 0.03 [.864] 0.79 [.373] US (Small 1/20) (N = 225) (-1.18) (1.37) (3.47)** (5.97)** (2.20)** (-0.59) 1.57 [.211] 5.00 [.025]** 1.24 [.265] US (Quintile 5) (N = 225) (-0.88) (1.89)* (6.85)** (8.35)** (2.32)** (-1.78)* 3.87 [.049]** [.001]** 0.99 [.319] Notes: R 1, t and R 5, t are the monthly small- and large-cap quintile portfolio returns in the respective markets in Asia and the U.S. The US (Small 1/20) portfolio consists of the smallest 5 percent of stocks listed on the NYSE. Return data for the Asian markets comes from PACAP and data for the U.S. comes from CRSP. ( measures the response of small stock portfolio returns to lagged small stock portfolio returns. * 1 and * 2 are dummy variables that indicate, respectively, that R 5, t and R 5, t-1 are positive. $ 0 up and $ 0 measure the small stock response to contemporaneous upward and downward movements in large stock returns. $ 1 up and $ 1 measure the small stock response to lagged upward and downward movements in large stock returns. t-statistics, in parentheses, are corrected for heteroskedasticity using Newey and West (1997). p-values are in brackets. A double (single) asterisk indicates significance at the.05 (.10) level.

9 Table 8A Subperiod Asymmetric Regressions of Small Stock Portfolio Monthly Returns on Contemporaneous and Lagged Own-Country Large Stock Portfolio Returns R 1,t '"%$ up 0 (* 1 (R 5,t %$ 0 ((1&* 1 )(R 5,t %$up 1 (* 2 (R 5,t&1 %$ 1 ((1&* 2 )(R 5,t&1 %, t First Period Asymmetric Contemporaneous and Lagged Coefficients Second Period Asymmetric Contemporaneous and Lagged Coefficients {P 2 - statistics: Before = After} Hypothesis Tests P 2 - statistics [p-values] Market " $ 0 up $ 0 $ 1 up $ 1 First Period Model = Second Period Model Hong Kong Jan 81: Dec ** 1.68 {2.77}* 0.649** 0.621** {0.01} 0.527** 1.167** {8.08}** ** {0.36 } 0.610** {9.26}** 5.89 [.015]** Japan Jan 76: Dec ** 0.97 {1.53} ** {14.35}** 0.773** 0.898** {0.20} {0.05} ** {2.83}* 1.32 [.250] South Korea Jan 78: Dec * 4.38** {1.46} 0.838** 0.584** {0.82} 0.856** 0.859** {0.00} {1.12} ** {4.26}** 2.63 [.105] Taiwan Jan 76: Dec {0.65} 0.593** 0.706** {0.26} 1.042** 1.518** {3.56}* ** {0.48} {3.07}* 0.07 [.787] Thailand Jan 76: Dec ** {2.32} 1.297** 0.684** {4.01}** 0.353* 1.087** {9.12}** {0.47} 0.210** {1.92} 0.07 [.786]

10 Table 8 A [Continued] US Small 1/20 Jan 76: Dec {2.63} 0.765** {0.00} 1.482** {0.47} 0.529** 2.083* {2.00} {0.92} 0.82 [.362] US Quintile 5 Jan 76: Dec {2.49} 0.777** 0.802** {0.00} 1.383** 1.056** {1.29} 0.368** 0.868** {1.23} * 0.422** {6.72}** 0.72 [.394] Notes: R 1, t and R 5, t are the monthly small- and large-cap quintile portfolio returns in the respective markets in Asia and the U.S. The US (Small 1/20) portfolio consists of the smallest 5 percent of studies listed on the NYSE. Return data for the Asian markets comes from PACAP and data for the US comes from CRSP. * 1 and * 2 are dummy variables that indicate, respectively, that R 5, t and R 5, t-1 are positive. $ 0 up and $ 0 measure the small stock response to contemporaneous upward and downward movements in large stock returns. $ 1 up and $ 1 measure the small stock response to lagged upward and downward movements in large stock returns. A double (single) asterisk indicates significance at the.05 (.10) level.

11 Table 5A Regressions of Small Stock Portfolio Monthly Returns on Contemporaneous and Lagged Own-Country and Large U.S. Stock Portfolio Returns R 1,t '"% $ 0 R 5,t % $ 1 R 5,t&1 % $ US 0 R US,t % $US 1 R US,t&1 %, t Contemporaneous and Lagged Coefficients (t-statistics) Panel A " $ 0 $ 1 $ US 0 $ US 1 Hong Kong (N=167) 4.01 (4.06)** (1.12) (2.71)** Japan (N=227) 1.46 (2.71)** (2.67)** (2.08)** South Korea (N=203) 2.64 (4.17)* (1.30) (1.79)** Taiwan (N=227) 1.69 (1.85)* (3.073)** (1.79)* Thailand (N=227) 3.38 (3.62)** (0.844) (1.64) Panel B Hong Kong ( N = 167) 3.03 (4.00)** (6.61)** (4.13)** (-1.19) (1.28) Japan (N = 227) 1.05 (2.76)** (7.59)** (3.19)** (-0.16) (-0.65) South Korea (N = 203) 1.59 (3.02)** (7.71)** (2.03)** (-0.24) (0.93) Taiwan (N = 227) 0.27 (0.45) (8.68)** (1.71)* (1.40) (0.46) Thailand (N = 227) 2.20 (4.25)** (7.93)** (2.37)** (-0.96) (0.50)

12 Notes: R 1, t and R 5, t are the monthly small- and large-cap quintile portfolio returns in the respective markets in Asia. R US, t is the return on the large-cap quintile portfolio returns in the U.S. Return data for the Asian markets comes from PACAP and data for the U.S. comes from CRSP. $ 0 and $ 1 measure the small stock response to contemporaneous and lagged movements in own-country large stock returns. $ US 0 and $ US 1 measure the small stock response to contemporaneous and lagged movements in U.S. large stock returns. t-statistics, in parentheses, are corrected for heteroskedasticity using Newey and West (1997). A double (single) asterisk indicates significance at the.05 (.10) level.