Nội dung

Journal of Applied Finance & Banking, vol. 8, no. 6, 2018, 131-155 ISSN: 1792-6580 (print version), 1792-6599 (online) Scienpress Ltd, 2018 Industry Herding, Spillover Index and Investment Strategy Tung-Yueh Pai1 and Yen-Hsien Lee2 Abstract This study investigates the spillover effects of the herding behavior of institutional investors in industries using the new spillover index. We further examine the lead-lag relationship between the herding spillover index and stock market. Finally, this paper furthers our understanding of the momentum strategy in industries. The empirical evidence indicates that industry herding in terms of semi-conductor manufacturing has had a significant impact on other types of industry herding. Second, since the industry herding spillover index and the selling industry herding spillover index have led to stock index returns, we conjecture that the industry herding spillover effect is a predicate to stock returns. Finally, the results support the claim that an institutional investor is an industry momentum trader. Moreover, we find that a long position in relation to higher or lower herding winners and a short position in relation to low herding losers yields good subsequent returns. JEL classification numbers: G02; G23 Keywords: Industry herding, Spillover Index, Momentum 1 Introduction Recent studies report evidence on institutional industry herding. This study examines whether institutional industry herding plays an important role, and has 1 Department of Institute of Management, Minghsin University of Science and Technology, Taiwan 2 (Corresponding Author) Department of Finance, Chung Yuan Christian University, Taiwan Article Info: Received: June 4, 2018. Revised : June 22, 2018 Published online : November 1, 2018 132 Tung-Yueh Pai and Yen-Hsien Lee three primary objectives. First, this study uses institutional investor data to calculate the institutional industry herding spillover effect and to construct an institutional industry herding spillover index employing the new spillover approach proposed by Diebold and Yilmaz (2012). In particular, this paper defines “the institutional industry herding spillover effect” as the degree of cross-industry spillover captured by the share of cross-industries error variance in the variance decomposition relative to the total error variance of the markets examined. Second, this study examines the effect of institutional industry herding spillover index on the stock index return. Moreover, this study tests for asymmetry in the relationship between the buy and sell institutional industry herding spillover index, which contends that sell institutional industry herding spillover could send a stronger signal than buy institutional industry herding spillover on stock index returns. Finally, we examine the impact of industry herding on return momentum. Unlike most studies that use a CSSD or CSAD variable for herding, we consider the variable for herding put forward by Lakonishok, Shleifer and Vishny (1992, hereafter LSV). The CSSD or CSAD method uses market prices to estimate herding, but not precisely measure the herding behavior like the LSV method. Thus far the LSV method remains important when measuring herding, and for this reason this study uses the second method to analyze herding effects. We examine whether institutional industry herding is a successful signal for subsequent returns. For the first issue, many studies employ the spillover index, which divides spillovers into those coming from (or to) a particular asset and, thus, identifies the main recipients and transmitters of shocks proposed by Diebold and Yilmaz (2009, 2012 and 2016) on the stock, exchange rate, real estate and commodities markets. However, they do not consider the spillover index of institutional industry herding. The spillover index of institutional industry herding is able to further our understanding of the contributions made by the spillovers of volatility shock across industries of institutional herding to the total forecast error variance. The spillover effect on herding behavior across industries is seldom investigated in the literature. Thus, this study first estimates the spillover index of industry herding proposed by Diebold and Yilmaz (2012) and then analyzes the inflow, outflow and net spillover effect across industry herding behaviors. For the second issue, practitioners and investors are able to invest or hedge if they know the rotation across industries. Junhua (2008) reported sector rotation strategies that guide investment across the different industries during different rates of inflation. However, the identification of peaks and valleys using inflation information obtained from official government data can be only be confirmed after a wait of at least one year. However, investors cannot wait until after these turning points are announced to invest. Therefore, this study investigates whether the industry spillover of institutional herding predicts stock market returns. This paper uses the change on spillover index of institutional industry herding to measure whether the herding behavior of institutional investors is active or inactive in rotations across industries. When the herding behavior of institutional investors is active across industries, it will positively affect stock market Industry Herding, Spillover Index and Investment Strategy 133 movements. Jiang, Yao and Yu (2007) pointed out that industry rotation plays an important role in the investment strategies of funds, and found funds adjust asset allocations according to high (low) beta industries when expecting market upswings (downturns). Hong, Torous, and Valkanov (2007) pointed out that a significant number of industry returns are able to predict the stock market based on the US stock markets from 1946 to 2002, and argue that this finding is robust for the eight largest non-US stock markets from 1973 to 2002. Past studies focus on how returns of industry portfolios impact on stock market returns; however, it is unclear how returns of industry portfolios impact industry herding diffusion. There is even less work undertaken with the express purpose of investigating the predictability of aggregate stock returns based on the spillover index of institutional industry herding. Moreover, the change of the institutional industry herding spillover index is often measured without distinguishing whether the imbalance is on the buy or on the sell side. Thus, this paper extends the spillover of institutional industry herding measure to define the measures for buying and selling institutional industry herding spillover index (SBIH and SSIH) and investigates whether SBIH and SSIH predict stock market returns in order to thereby understand the buying and selling decisions of herding move stock prices. Finally, we investigate whether return momentum is impacted on by institutional industry herding. Momentum refers to a strategy of buying stocks or other securities that have had high past returns and selling those that have had poor returns over the past n months; momentum strategies then secure positive returns for the following n months. Jegadeesh and Titman (1993) found that adopting momentum strategies ensures a profit for the following n months using US stock data from 1965 to 1989. Nofsinger and Sias (1999) found that institutional investors with positive-momentum trade more than individual investors. Moreover, Moskowitz and Grinblatt (1999) found evidence of industry momentum and find that momentum profits industry portfolios rather than individual stock portfolios. Before Celiker, et al. (2015) and Demirer, Lien, and Zhang (2015), the impact of industry herding on momentum returns were rarely noticed. Demirer, Lien, and Zhang (2015) found further asymmetry in the relationship between herding and momentum and yield positive returns depending on different industry herding effects using the CSAD and CSSD methods to measure herding in the Chinese stock market for the period January 1996 through December 2013. However, because Demirer, Lien and Zhang (2015) used the CSAD and CSSD methods, which do not accurately or precisely measure herding because they only use market price data; this paper uses LSV to measure herding by institutional investor behavior. Moreover, Jegadeesh and Titman (1993 and 2001) considered the price momentum of individual stocks in order to obtain superior returns by holding a zero-cost portfolio. Our paper further uses the zero-cost portfolio to examine whether the relationship between industry herding and momentum return is able to assemble an investment portfolio. This paper fills a gap in the literature on the spillover effects of herding behavior of institutional investors in industries by the spillover index. Second, this study 134 Tung-Yueh Pai and Yen-Hsien Lee examines the lead-lag relationship between the herding spillover index and stock markets. Finally, this paper further studies the momentum strategy in industries. Thus, our empirical study significantly contributes to this field of research and thereby fills a gap in the literature. The empirical evidence indicates that industry herding in the semi-conductor manufacturing industry has a significant impact on other industry herding. Second, since the industry herding spillover index and selling industry herding spillover index have lead to stock index returns, this study conjectures that industry herding spillover indices have predicate stock markets. Finally, the results clearly support the fact that institutional investors are industry momentum traders. Moreover, we see that taking a long position in high or low herding winners and a short position in low herding losers yields good subsequent returns, implying that the profitability of zero-cost industry momentum strategies depends on the level of industry herding. These findings are consistent with those of Demier Lien and Zhang (2015). The remainder of this paper is organized as follows: Section 2 presents literature review, Section 3 briefly presents our methodology and data; Section 4 presents the results of the empirical analysis; Section 4 provides summary conclusions. This is the text of the introduction. This document can be used as a template for doc file. You may open this document then type over sections of the document or cut and paste to other document and then use adequate styles. The style will adjust your fonts and line spacing. Please set the template for A4 paper (14 x 21.6 cm). For emphasizing please use italics and do not use underline or bold. Please do not change the font sizes or line spacing to squeeze more text into a limited number of pages. 2 Literature Review 2.1 Spillover index Spillovers measure the identification of the interaction between assets. Diebold and Yilmaz (2012) considered the new spillover index by applying the Cholesky factor identification to examine whether forecast-error variance decompositions are variant, depending on the ordering of the variables and refined measures of directional spillovers and net spillovers. There are abundant studies that use the new spillover index proposed by Diebold and Yilmaz (2012). Studying the spillover effect in stock markets can be found in Diebold and Yilmaz (2009), Wang and Wang (2010), Zhou, Zhang and Zhang (2012), Tsai (2014) and Diebold and Yilmaz (2016); using the exchange rate to analyze the spillover effect (Bubák, Kocenda and Zikeš, 2011; Antonakakis, 2012); using the real estate market (Liow and Newell, 2012) and using stocks, bonds, currencies and commodities markets (Diebold and Yilmaz, 2012). Past literature, however, has seldom investigated the spillover effect on herding behavior across industries. Industry Herding, Spillover Index and Investment Strategy 135 2.2 Herding measure review Herding behavior refers to a group of investors from the same background making the same decision or behaving in the same way (Nofsinger and Sias, 1999). Herding measures have two different operational definitions in the literature. The first definition is investors’ herding towards market returns using returns data to measure CSSD by Christie and Huang (1995) and CSAD by Chang, Cheng and Khorana (2002); that is, the market returns approach. The second definition considers institutional investors’ herding towards particular stocks using the imbalance in the number of institutional investors from Lakonishok, Shleifer and Vishny (1992), Wermers, (1999) and Sias, (2004). Lakonishok, Shleifer and Vishny (1992) used the net trading of fund managers to determine buyer or seller to calculate herding, and also find herd behavior in small cap stocks. Wermers (1999), who extends LSV's measure to define buy and sell herding measures, find more funds in the United States exhibit herd behavior in relation to smaller stock trading. The first method uses market prices to estimate herding, but does not as directly or precisely measure herding behavior as the second method; the LSV method. 2.3 Industry herding Industry herding is defined as a group of investors trading in the same direction into the same industry over a period of time (Choi and Sias, 2009). Industry herding can also parallel the two abovementioned descriptions of herding. The first definition refers to investors’ industry herding towards market returns (Yan, Yan and Sun, 2012; Lee, Chen and Hsieh, 2013; Demirer, Lien and Zhang, 2015). Yan, Yan and Sun (2012) found that industry herding can predict future price movement and that the momentum effect is magnified when there is a low level of industry herding, using the CSSD and CSAD methods in the US stock market from January 1980 to December 2008. Lee, Chen and Hsieh (2013) found the existence of industry herding in both bull and bear markets and in China’s A-share markets from the 17th of May 2001 to the 16th of May 2011. Demirer, Lien and Zhang (2015) identified the impact of industry herding on the industry momentum effect in the Chinese stock market from January 1996 through December 2013. The second definition considers institutional investors’ herding towards particular industries (e.g. Voronkova and Bohl, 2005; Choi and Sias, 2009; Chen, Yang and Lin, 2012; Gavriilidis, Kallinterakis and Ferreirac, 2013; Celiker, Chowdury and Sonaer, 2015). Voronkova and Bohl (2005) found a higher degree of industry herding in relation to metal production, banking and computer services by Polish pension fund managers from 1999 to 2002. Choi and Sias (2009) identified institutional industry herding in the US market from 1983 to 2005. Chen, Yang and Lin (2012) found that foreign institutional investors herd in industries in the Taiwan market from January 2002 to January 2009. Gavriilidis, Kallinterakis and Ferreirac (2013) found that mutual funds herding in industries under examination underperform, and exhibited high volatility and high volume using the Spanish market from June 1995 to September 2008. Celiker, Chowdury and Sonaer (2015) 136 Tung-Yueh Pai and Yen-Hsien Lee found mutual funds herding in industries using mutual funds in the US market from 1980 to 2013. Our data are generally non-stationary, daily returns defined as: R t = (lnPt − lnPt−1 ) × 100 (1) where Pt is the Brent oil price at time t, with 𝑡 = 1,2, … , 𝑇, and ln is the natural logarithm. Kremer and Nautz (2013) defined herding as the tendency of traders to accumulate on the same side of the market in specific stocks at the same time. This study applies the measure of herding proposed by Lakonishok, Shleifer and Vishny (1992) to estimate the herding behavior of foreign institutional investors in Taiwan’s stock market. The herding for a given stock in a given time t is defined as follows: HM𝑖,𝑡 = |𝑄𝑖,𝑡 − 𝐸(𝑄𝑖,𝑡 )| − 𝐸|𝑄𝑖,𝑡 − 𝐸(𝑄𝑖,𝑡 )| (2) where the first term captures the deviation of the buyer ratio in industry i at t from the overall buy probability at time t. 𝑄𝑖,𝑡 is the proportion of buy transactions out of foreign institutional investors in industry i during t. 𝑄𝑖,𝑡 = 𝐵𝑖,𝑡 /(𝐵𝑖,𝑡 + 𝑆𝑖,𝑡 ), where 𝐵𝑖,𝑡 is the number of foreign institutional investors who increase their holdings in the industry in the time (net buyers), and 𝑆𝑖,𝑡 is the number of foreign institutional investors who decrease their holdings (net sellers). E(𝑄𝑖,𝑡 ) is the average proportion of foreign institutional investors buying in time t relative to the number of active buyers. The second term E|𝑄𝑖,𝑡 − 𝐸(𝑄𝑖,𝑡 )| is an adjustment factor. However, HM𝑖,𝑡 measures herding without considering the direction of the trade. Moreover, Wermers (1999) modifies the LSV model by dividing it into buy-side herding (BHM) and sell-side herding (SHM): BHMi,t = HMi,t |𝑄𝑖,𝑡 > 𝑄𝑡 (3) SHMi,t = HMi,t |𝑄𝑖,𝑡 < 𝑄𝑡 (4) where BHMi,t is the measure of herding for foreign institutional investors on the buy-side, and SHMi,t is the measure of herding for foreign institutional investors on the sell-side. 3.2 Measuring the Spillover Index Considering covariance, the stationary N=13 industry herding variables VAR(𝑝) model is set as follows: 𝑝 𝐻𝑡 = ∑𝑖=1 Φ𝑖 𝐻𝑡−𝑖 + 𝜀𝑡 ,t = 1,2, … , T (5) where 𝐻𝑡 = (𝐻1𝑡 , 𝐻2𝑡 , … , 𝐻𝑁𝑡 )′ is a(𝑁 × 1) vector of endogenous variables, Φ𝑖 is a (𝑁 × 𝑁) parameter matrix, 𝜀𝑡 is the vector of error with zero mean and the covariance matrix ∑. Assuming 𝐻𝑡 is covariance stationary, then there exists a moving average representation, which is given by 𝐻𝑡 = ∑∞ (6) 𝑖=0 𝐴𝑖 𝜀𝑡−𝑖 ,t = 1,2, … , T where the (𝑁 × 𝑁) coefficient matrices 𝐴𝑖 obey a recursion of the form 𝐴𝑖 = Φ1 𝐴𝑖−1 + Φ2 𝐴𝑖−2 + ⋯ + Φ𝑝 𝐴𝑖−𝑝 ,i = 1,2, … (7) Industry Herding, Spillover Index and Investment Strategy 137 with 𝐴0 = 𝐼𝑛 and if 𝐴𝑖 = 0 for i < 0. Diebold and Yilmaz (2012) use the KPPS Z-step-ahead forecast error variance decomposition, which is computed as 𝑔 𝜃𝑖𝑗 (𝑆) = −1 ∑𝐻−1 ′ 𝜎𝑖𝑖 ℎ=0 (𝑒𝑖 𝐴ℎ ∑ 𝑒𝑗 ) ′ ′ ∑𝐻−1 ℎ=0 𝑒𝑖 𝐴ℎ ∑ 𝐴ℎ 𝑒𝑖 ,i, j = 1,2, … , N (8) where Σ is the variance matrix for the error vector ε. σii is the standard deviation of the error term of the ith industry, and ei is an (N × 1) vector with one as the ith element and 0 elsewhere.3 Diebold and Yilmaz (2012) define “own variance shares” which are indicated by the fraction of the Z-step ahead forecast error variances in forecasting 𝐻𝑖 due to shocks in 𝐻𝑖 , for i=1,2,…,N, and “cross variance shares”, or spillovers, to be a fraction of the Z-step ahead error variances in forecasting 𝐻𝑖 due to shocks to 𝐻𝑗 , for (i ≠ j).4 Diebold and Yilmaz (2009) present three spillover indices, (total spillover, directional spillover and net spillover). The total spillover index is constructed as follows: ̃g ∑N i.j=1 θij (Z) i≠j ̃g i,j=1 θij (Z) S g (Z) = ∑N ̃g ∑N i,j=1 θij (Z) × 100 = i≠j × 100 N (10) where the total index measures the contributions from the spillovers of shocks across herding variables on industries to the total forecast error variance. Second, directional spillover allows us investigate both the magnitude and direction of the spillover. Directional spillover is defined as: g g ̃ ∑N j=1 θij (Z) g i≠j Sj→i (Z) = ∑N g g ̃g j=1 θij (Z) × 100 and g ̃ ∑N j=1 θij (Z) i≠j Si→j (Z) = ∑N ̃g j=1 θij (Z) × 100. (11) where Sj→i (Si→j ) is the directional spillover received (transmitted) by variable i (j) from all other variables j (i). Third, net spillover is the difference between the g g gross volatility shocks transmitted to Si→j and those received Sj→i from all other industries. The net spillover is defined as: g g g Si (Z) = Si→𝑗 (Z) − Sj→i (Z) (12) g g where Si > 0 (Si < 0)defines i industry as a net sender (receiver). 3.3 Granger causality test between returns and spillover indices We then use the Granger causality test to identify the nature of causality between industry herding spillover and stock returns, i.e. to see if it is stock returns that cause industry herding spillover or if it is industry herding spillover 3 To obtain a unit sum of each row of the variance decomposition, each entry of the variance decomposition matrix is normalized, so that the construction of the decomposition, including own shocks in each market, is equal to one. According g to the characteristics of generalized VAR,∑𝑁 𝑗=1 θij (Z) ≠ 1, normalize each entry of the variance decomposition matrix by g g g 𝑁 𝑁 ̃g ̃g the row, as follows θ̃ij (Z) = θij (Z)⁄∑𝑗=1 θij (Z), where ∑𝑁 𝑗=1 θij (Z) = 1 and ∑𝑖,𝑗=1 θij (Z) = N. 4 This study uses 13 industry herding variables; the optimal lag of the VAR model is based on AIC and SBC and 10-step-ahead forecasts. 138 Tung-Yueh Pai and Yen-Hsien Lee that causes stock returns, using the regressions relating industry herding spillover and stock returns as follows: i R t = α0 + ∑np=1 αp R t−p + ∑nq=1 βq Spillovert−q + εt (13) n n i i Spillovert = θ0 + ∑p=1 θp R t−p + ∑q=1 πq Spillovert−q + εt (14) i HM BHM where R t is stock index return; Spillovert =(Spillovert , Spillovert and SHM Spillovert ) is the change of spillover index (spillover index of herding, buying herding and selling herding). If βq ≠ 0 and θp = 0 (θp ≠ 0 and βq = 0), this means that the Spilloverti (R t ) will affect R t (Spilloverti ). Second, βq ≠ 0 and θp ≠ 0 refer to the feedback relationship between the two series. Finally, if βq = 0 and θp = 0, then there is a non-causal relationship between the two series. 3.4 Industry momentum returns and Zero-cost momentum strategies at the level of industry herding This paper investigates the industry momentum strategies and zero-cost momentum strategies at different industry herding levels in the Taiwanese stock market. As evidence for industry momentum strategies, we sort industries into five groups from higher return to lower return industries based on their past 60 daily returns i.e. t through t-60. Industries are then defined as winner (loser) industries if their past 60 returns are highest (lowest) across all industries. We calculate the portfolios return spread between winner and loser industry portfolios in subsequent 10, 20, 40 and 60 days, respectively. The portfolios return spread has a significant positive spread between winner and loser industry portfolios, implying the presence of industry momentum. Second, there is evidence for zero-cost industry momentum strategies for high and low herding levels. Independently, industry herding is also sorted into high (33.3%), intermediate (33.3%) and low (33.3%) groups over the most recent 3-month period. This study investigates whether subsequent returns are different between high and low herding industries in winner and loser portfolios. Finally, we establish four zero-cost industry momentum strategies in subsequent 10, 20, 40 and 60 days to examine whether the profitability of zero-cost industry momentum strategies depends on the level of industry herding. 4 Data and Empirical Results 4.1 Data Description, Summary Statistics and Unit Root Test The data employed in this study include the daily industries index prices and foreign institutional holding data from the Taiwan Economic Journal (TEJ) during the period January 2, 2004 through December 31, 2014. Industries are classified in this paper using the industry specifications of the Taiwan Stock Exchange. Appendix 1 presents the proportion of foreign institutional holdings on industry; Industry Herding, Spillover Index and Investment Strategy 139 we select a proportion of total market value for foreign institutions holding at least higher than 1%. Given this, there are thirteen industries in our sample. Those thirteen take up 92 of the proportion of total foreign institutions holding value, the proportions ranging from high to low are Semiconductor (38.89%), Finance (9.58%), Other Electronic (7.53%), Computer & Per. (6.75%), Elec. Parts (4.73%), Plastics (4.4%), Optoelectronic (4.02%), Comm. Internet (3.45%), Others (3.08%), Trading & Cons. (1.62%), Foods (1.47%), Elec. Machinery (1.24%) and Automobile (1.22%). This study uses this sample to compute herding measures, buy-side herding measures and sell-side herding measures, as well as analyze herding spillovers on industries in Taiwan. In the case of returns on Table 1, the average return ranges from a low of -0.0266 for the Optoelectronic industry (M2326) to a high of 0.0752 for the Foods industry (M1200), and the Optoelectronic industry (M2326 =1.9709) has the highest volatility value while the Others industry (M9900=1.1438) has the lowest volatility. In the case of herding, the average herding ranges from a low of 5.3568 for the Computer & Per. industry (M2325) to a high of 8.6496 for the Automobile industry (M2200), and the Finance industry (M2800=7.4445) has the highest volatility value while the Computer and peripheral industry (M2325=4.5000) has the lowest volatility. In the case of buy-side herding in Table 2, the average buy-side herding ranges from a low of 4.8930 for the Others industry (M9900) to a high of 8.6611 for the Automobile industry (M2200), and the Finance industry (M2800=7.1264) has the highest volatility value while the Others industry (M9900=4.3082) has the lowest volatility. In the case of sell-side herding, the average sell-side herding ranges from a low of 7.7977 for the Finance industry (M2800) to a high of 8.6496 for the Automobile industry (M2200), and the Finance industry (M2800=7.4445) has the highest volatility value while the Computer & Per. industry (M2325= 4.5000) has the lowest volatility. 140 Tung-Yueh Pai and Yen-Hsien Lee Table 1: Descriptive statistics of returns and HM Panel A: Return 𝑅𝑡 Industry Mean Std. Dev. Max. Min. Skewness kurtosis M2324 0.0254 1.5912 6.8979 -6.9060 0.0227 2.7329 M2800 0.0228 1.6429 6.8646 -6.8400 -0.0170 3.3548 M2331 0.0102 1.9420 6.8233 -6.7854 -0.0206 1.7882 M2325 0.0165 1.5440 6.8466 -6.3904 -0.1516 2.3424 M2328 0.0081 1.5736 6.7230 -6.4675 -0.3508 2.2938 M1300 0.0292 1.4069 6.9335 -6.8186 0.0681 3.2689 M2326 -0.0266 1.9709 6.7278 -6.8938 -0.2302 1.2477 M2327 0.0176 1.1752 6.1037 -6.3302 -0.2004 2.4815 M9900 0.0433 1.1438 6.1691 -6.8424 -0.3319 3.5555 M2900 0.0611 1.4939 6.5851 -6.8137 0.0154 2.2073 M1200 0.0752 1.6834 6.7201 -6.7682 -0.0293 2.3832 M1500 0.0383 1.2890 6.1808 -6.7054 -0.5557 3.0028 M2200 rtindex 0.0498 1.7205 6.8810 -6.8993 0.1253 1.9902 0.0264 1.2610 6.7422 -6.6789 -0.3066 3.5834 Panel B: Herding (HMt ) Industry Mean Std. Dev. Max. Min. Skewness kurtosis M2324 6.2397 5.2043 41.1125 0.0122 1.4205 2.6697 M2800 8.1428 7.4445 65.9531 0.0022 2.2283 8.3941 M2331 6.5278 5.5015 44.6667 0.0034 1.4137 2.7260 M2325 5.3568 4.5000 33.7598 0.0084 1.6027 3.8982 M2328 5.7417 5.0439 37.7228 0.0059 1.6418 3.6784 M1300 6.0773 5.2403 35.4102 0.0011 1.4992 2.7405 M2326 6.3254 5.2202 38.0175 0.0003 1.4124 2.6045 M2327 6.4645 5.2966 40.9673 0.0121 1.3340 2.1791 M9900 5.2948 4.7031 48.7318 0.0054 1.7446 5.2466 M2900 7.4662 6.2969 45.1138 0.0004 1.5271 3.1997 M1200 7.6963 6.6450 44.2127 0.0017 1.5156 2.8833 M1500 5.8780 4.9090 39.7850 0.0020 1.5520 3.6739 M2200 8.6496 6.8605 45.9410 0.0032 1.3192 2.4808 Note: M2324 is the code of Semiconductor, M2800 is the code of Finance, M2331 is the code of Other Electronic, M2325 is the code of Computer & Per., M2328 is the code of Elec. Parts, M1300 is the code of Plastics, M2326 is the code of Optoelectronic, M2327 is the code of Comm. Internet, M9900 is the code of Others, M2900 is the code of Trading & Cons., M1200 is the code of Foods, M1500 is the code of Elec. Machinery, M2200 is the code of Automobile. R t is stock index return. HMt is thee measure of herding by Lakonishok, Shleifer and Vishny (1992) to estimate the herding behavior of foreign institutional investors in Taiwan stock market. T=2735 (2004/1/2–2014/12/31).