Wei-Kuang Chen Professor, Department of Money and Banking, National Chengchi University Ching-Ting Lin* Assistant professor, Department of Money and Banking, National Chengchi University Cheng-Yi Shiu Professor, Department of Finance, National Central University
Abstract Using a unique dataset composed of comprehensive transaction data from Taiwan futures markets, we classify all investors into individuals, domestic institutions, and foreigners, and examine the price discovery and price leadership for these three groups. We find that, despite the relatively low trading volume of futures contracts by foreigners, such trades make a significant contribution to price discovery. Moreover, intraday analysis shows that foreigners’ correlated trades can positively predict concurrent and future price movements of futures contracts. The empirical result indicates that foreigners have an information advantage in Taiwan futures markets. In contrast to foreigners, individuals make the least contribution to price discovery and their correlated trades negatively predict the following price movements, suggesting that individuals have an information disadvantage.
Keywords: Price discovery; Price leadership; Herding; Foreigners; Information role
*Corresponding author. Department of Money and Banking, College of Commerce, National Chengchi University, Taipei, Taiwan, R.O.C., Tel: +886-2-29393091 ext. 81248,
1. Introduction
Literature on the informational role of foreign investors expresses diverse views.
One tranche of literature asserts that domestic investors are more knowledgeable than foreign investors about the local environment or domestic firms. For example, Choe,
Kho, and Stulz (2005) analyze all trades in the Korean stock market and find that domestic investors are better able to select winners than foreigners. Dvorak (2005) uses transactions data from Indonesian stocks and shows that domestic investors have better trading performance than foreigners. In contrast, another tranche of literature argues that foreigners may have better technological, financial, or human expertise, experience, or resources than domestic investors. For example, Grinblatt and Keloharju (2000) use daily data and find that foreigners are better able to select winners in Finnish stocks than domestic individuals. Ferreira and Matos (2008) demonstrate that non-U.S. firms with higher foreign ownership have higher firm valuation and better operating performance.
Huang and Shiu (2009) investigate firm-level data and show that stocks with high foreign ownership outperform those with low foreign ownership in Taiwan stock markets. In addition to stock markets, the superior performance of foreigners has also been revealed in options markets (Chang, Hsieh, and Wang, 2010) and futures markets (Kuo, Chung, and Chang, 2015).
Although the existing literature remains divided on whether domestic or foreign investors have an information advantage, the measure of information advantage or disadvantage for a group of investors remains an interesting issue for academic research.
According to the efficient market hypothesis, when new information about the value of a security arrives in the market, it should be incorporated into the stock price quickly and correctly. However, market friction and imperfections, such as transaction costs, taxes, 2 regulations, or asymmetric information, can injure market efficiency by preventing new information from being impounded into stock prices quickly and correctly. In such an environment, it could happen that a specific group of investors may be better equipped to select winners while another specific group of investors may be better equipped to discover equilibrium prices. This brings one to the question of whether better trading performance equals a better contribution to information processing. It is necessary to examine the contribution to price discovery and trading performance simultaneously before one can make conclusions regarding the information advantage of a specific group of investors.
In this paper, we explore the issue of whether domestic or foreign investors have an information advantage by analyzing the transactions of all investors in the futures markets in an emerging country. Our unique dataset consists of the complete historical orders, investor records, and identities of all investors in the Taiwan Futures Exchange from January 2003 to December 2008, allowing us to perform a detailed analysis of transactions and prices of the futures contracts. According to investors’ identities, we categorize investors as individuals, domestic institutions, or foreigners. We then examine the contribution to price discovery and trading performance, respectively, of these three groups. We are particularly interested in understanding whether there are differences in the contribution to price discovery and trading performance among these three groups.
More specifically, to address the issue of whether domestic or foreign investors have an information advantage, we examine two measures for the three different investor groups: contribution to price discovery and trading performance.
For the measure of the contribution to price discovery, we follow Hasbrouck (1995) and Lien and Shrestha (2014) to calculate the information share (IS). Hasbrouck (1995) 3 assumes that shares of one security are traded in multiple markets. The transaction prices in each market are decomposed into unobservable permanent prices, which reflect the fundamental value of the security, and transitory errors. The IS of a market is the contribution of the permanent price changes in this market to the revelation of the common efficient prices. The framework of IS provides us with a method to measure the contribution to price discovery for different types of investors. In this paper, we assign transaction prices to three different investor groups based on the identity of the investor who initiates the trade. We then calculate IS for the three investor groups.
Although many studies have used IS to measure the information content of equity prices, this method suffers from two limitations: it can only be used in situations where each pair of prices is cointegrated with a one‐to‐one relationship, and it involves upper and lower bounds. Lien and Shrestha (2009) modify the IS method so that a unique measure of IS can be achieved. Lien and Shrestha (2014) further propose the generalized information share (GIS) method, which could be applied to cases where the prices are cointegrated but the cointegrating vector does not have to be a one‐to‐one relationship. As an application of the GIS proposed by Lien and Shrestha (2014), we analyze the price discovery process of the futures contracts traded by the three investor groups.
For the measure of trading performance, we examine the association of investors’ correlated trades and concurrent and future returns of the futures contracts. The relationship of a concurrent return with the correlated trades in a group of investors is related to whether the investors’ trades can move concurrent prices, while the future return predictability is the measure that is most likely to reflect differential information among investors. To investigate the investors’ correlated trades, we use the herding measure proposed by Lakonishok, Shleifer and Vishny (1992) (hereafter LSV) and buy- 4 sell imbalance.
The results are as follows. First, despite being the smallest group of investors in terms of trading volume, foreigners play the dominant role in the price discovery process in Taiwan futures markets. For example, in 2008, foreigners account for only 10.5 percent of trading volume for TXF, which is the most liquid market index futures contract, but contributes 69.5 percent of price discovery as measured by the GIS method. For comparison, individual and domestic institutions account for 66.7 percent and 22.8 percent of trading volume but only contribute 6.3 percent and 24.3 percent of price discovery, respectively.
Second, foreigners’ correlated trades have a clear, positive association with concurrent and future returns of futures contracts. The positive relationship is revealed in both LSV measures and buy-sell imbalances. In contrast, individuals’ correlated trades have a significant negative relationship with concurrent and future returns. The result indicates that foreign investors as a group have the best trading performance while individuals have the worst performance in the Taiwan Futures Exchange. Domestic institutions lie between foreigners and individuals.
This study contributes to the existing literature regarding the debate on whether domestic or foreign investors have an information advantage. Overall, our empirical results show that foreigners not only make the greatest contribution to price discovery but also have the best trading performance, suggesting that foreigners have an information advantage over domestic institutions and individuals in the Taiwan Futures Exchange.
Our result showing that foreigners have an information advantage is consistent with the findings in Grinblatt and Keloharju (2000), Ferreira and Matos (2008), and Huang and
Shiu (2009). Our finding showing that individuals are naïve, uninformed investors which 5 is also consistent with the empirical results from the Taiwan stock market documented in
Barber, Lee, Liu, and Odean (2009), Chiang, Hirshleifer, Qian, and Sherman (2011),
Chen, Chow, and Shiu (2015), and Gao and Lin (2015).
We also contribute to the literature on the price discovery and information advantage in derivatives markets. Using the same data base, Kuo, Chung, and Chang (2015) examine the future return predictability of buy-sell imbalance of futures contracts by foreigners, domestic institutions, and individuals in the Taiwan Futures Exchange during the period from January 1, 2002 to June 30, 2005. They demonstrate that trading by individuals tends to poorly forecast returns, whereas trading by institutions forecasts returns more accurately. Furthermore, the predictability of foreigners’ trades on futures contracts is stronger than that of domestic institutions’ trades. Generally, our results are consistent with the findings in Kuo, Chung, and Chang (2015), with a note that we provide more evidence including the contribution to price discovery and return predictability of buy-sell imbalances and herding by different types of investors.
In addition to the literature on the informational role of investors in return predictability, we also contribute to the literature on investors’ herding. Several early studies have demonstrated a moderate effect of institutional herding on stock return predictability (see Lakonishok, Shleifer and Vishny, 1992; Grinblatt, Titman, and
Wermers, 1995; Nofsinger and Sias, 1999; Wermers, 1999). However, because these studies use publicly available quarterly (or annual) institutional holdings data, they are neither able to examine the short-term price impact of herding nor able to detect the intra- quarter trades of investors, as documented by Puckett and Yan (2011). Using trade records of futures contracts, we find a stronger return predictability of trading by foreigners. More importantly, our evidence shows that herding by foreigners can be 6 considered information-based correlated trading.
We are not the first to examine the informational contribution of foreigners to derivatives markets. In examining the information content of index options open interest,
Chang, Hsieh, and Lai (2009) find that foreign investors are more likely to be better- informed than domestic institutions and individual investors. We contribute to this literature by providing evidence of the information advantages that foreign investors have in emerging futures markets.
The remainder of this paper is organized as follows. In Section 2, we introduce
Taiwan futures markets, and our data and sample. Price discovery methodology and empirical results are presented in Section 3. Investors’ correlated trades and performance is detailed in Section 4. Conclusions are presented in Section 5.
2. Introduction of Taiwan futures markets, data, and sample
The Taiwan Futures Exchange (TAIFEX) was established on July 21, 1998, and ranked nineteenth on a global scale in 2015 with a total trading volume of 265 million contracts traded1.
Capitalization Weighted Stock Index (TAIEX) Futures (hereafter TXF), which tracks
TAIEX, a stock index measuring the performance of the universe of all companies listed
Monday to Friday. The contract size of TXF is NT$ 200 per index point, approximately amounting to NT$ 1.4 million (US$ 45,000)2.
One year after the establishment of TAIFEX, two sector index futures were introduced: Taiwan Stock Exchange Electronic Sector Index Futures (hereafter EXF) and
Taiwan Stock Exchange Finance Sector Index Futures (hereafter FXF). EXF tracks the electronic sector index with a contract size of NT$ 4,000 per index point, approximately amounting to NT$ 1.1 million (US$ 35,000)3. FXF’s underlying index is Taiwan Stock
Exchange finance sector index, and its contract size is NT$ 1,000 per index point, approximately amounting to NT$ 0.9 million (US$ 29,000)4.
To attract more investors, TAIFEX launched Mini-TAIEX futures (hereafter MXF) contracts on April 9, 2001. MXF contracts are identical to TXF contracts except that the contract size of MXF contracts is NT$ 50 per index point, a quarter of the size of a TXF contract, reducing margin requirements and position limits accordingly. MXF contracts are not as active as TXF contracts. The average daily trading volume of TXF is 71,169 contracts versus 32,400 contracts for MXF contracts during 2008. The TAIFEX also introduced other derivatives, including TAIEX index options on December 24, 2001, and
2 In 2008, the average daily closing price of TAIEX futures for the nearest month contract is 7,106, ranging from 3,903 to 9,370. 3 In 2008, the average daily closing price of electronic sector index futures for the nearest month contract is 277, ranging from 143.8 to 353.95. 4 In 2008, the average daily closing price of finance sector index futures for the nearest month contract is 904, ranging from 434.8 to 1,238.4. 8 the Government Bond Futures on January 2, 2004. Among the above products, TXF and the two sector index futures are the most liquid and popular futures contracts traded on the TAIFEX5. Detailed descriptions of TXF and the two sector index futures contracts are reported in Table 1.
2.2 Data and sample
We use the database acquired from the TAIFEX that contains all trades of investors in the period from January 2003 to December 2008. The data includes detailed investor and transaction information, including investor identity, investor type, date, order type, order price, order quantity, order time, execution price, execution quantity, and execution time.
This unique dataset allows us not only to classify all investors into three groups (i.e., individuals, domestic institutions, and foreigners), but also to identify the initiators of each trade in the intraday period. We only consider the nearest contract month for the empirical study because the spot month is the most active and liquid.6
Table 2 reports summary statistics for trading volume of index futures. Daily trading volume is measured by the number of contracts traded on a round-trip basis to avoid double-counting. As Table 2 shows, TXF is the most active futures contracts in the
Taiwan futures markets. For example, in 2008, the average daily trading volume of TXF is 71,168 contracts, which is roughly fifteen times the volume of EXF and FXF. We
5 The TAIFEX cut its transaction tax twice in 2000 and 2006. The change in the transaction tax enhances the market quality of the TAIFEX and attracts more foreign trades. Chou and Lee (2001) indicate that trading volume on the TAIFEX increases after the transaction tax cut. The increase is significantly higher than the one of the Singapore Exchange which also provides trading on the Taiwan index futures but without a transaction tax. 6 The rollover day is the day before the last trading day. More specifically, we analyze the trades in the nearest contract to the two days before expiration and then switch to the trades in the next nearest contract. 9 further break down the trading volume as a percentage of individuals, domestic institutions, and foreigners. It shows that foreigners account for the least trading volume in the Taiwan futures markets. Specifically, foreigners only have 10.5 percent of trading volume of TXF in 2008. Similarly, only 18.3 percent of EXF and 17.8 percent of FXF’s trading volume is attributed to foreign traders in 2008. By contrast, individuals place the most orders, accounting for approximately three quarters of the total trading volume, followed by domestic institutions. Although foreign trades as a portion of TXF trading volume increases from 2.0 percent in 2003 to 10.5 percent in 2008, the importance of foreign investors in Taiwan futures market is still inferior to individuals and domestic institutions in term of trading volume.
< Insert Table 2 is inserted here>
3. Price discovery
We first discuss the contribution to price discovery for three different groups of investors. We follow Hasbrouck (1995) and Lien and Shrestha (2014) to calculate the information share (IS) and generalized information shares (GIS). The details of the information share measures are reviewed below, followed by empirical results.
3.1 Hasbrouck information share (IS)
Hasbrouck (1995) suggests that the information share in a specific market can be treated as the proportion of innovation variance. Assume Yt is an n1 vector of unit- root series and there are (n – 1) cointegrating vectors. Hence, vector error-correction
(VEC) is represented in the form
푘 푇 ∆푌푡 = ∏ 푌푡−1 + ∑푖=1 퐴푖∆푌푡−푖 + 휀푡 , ∏ = 훼훽 , (1) 10 where β and α are n (n 1) matrices of rank (n-1). β consists of (n – 1) cointegrating vectors and α are coefficients. A model as in equation (1) can be inverted into a vector moving average (VMA) representation
∆푌푡 = Ψ(L)휀푡, (2) where 휀푡 is a zero-mean vector of serially uncorrelated disturbances with covariance matrix Ω, and Ψ is a polynomial in the lag operator. Equation (2) can be written as
푘 ∗ 푌푡 = 푌0 + Ψ(1) ∑푖=1 휀푡 + Ψ (L)휀푡, (3)
* where Y0 is a constant n-vector and Ψ (L) is a matrix polynomial in the lag operator.
If the covariance matrix Ω is diagonal, the variance of long-run impact (ψet) is presented in the form
푇 푛 2 Var(ψ푒푡) = 휓Ω휓 = ∑푗=1 휓 Ω푗푗. (4)
Thus, the information share of a market j is defined as
휓2훺 푆 = 푗 푗푗. (5) 푗 휓Ω휓푇
On the other hand, if the covariance matrix Ω is not diagonal, information share of a market j is presented in the form
(|휓퐹| )2 푆 = 푗 , (6) 푗 휓Ω휓푇 where F is the Cholesky factorization of Ω. Due to the employment of Cholesky factorization, the IS depends on the ordering of the series.
In this paper, we use Hasbrouck’s (1995) IS to measure the contribution to price discovery of individuals, domestic institutions, and foreigners. To compute IS, we must construct the transaction price series of three different investor groups based on the identity of the investor who initiates the trade. The transaction price is attributed to a series of specific investor types according to the group membership of the investor who
11 initiates the trade. Since Taiwan Futures Exchange adopts the order-driven and continuous trading during market hours, we use the method suggested by Odders-White
(2000) to identify the trade’s initiator, whose order arrives later than their counterpart in this trade. We compute the IS of three investor groups for each calendar month and then take the average of the monthly measures as an annual measure. Table 3 reports the average lower and upper bounds, as well as the average middle measure for each index futures contract across individuals, domestic institutions, and foreigners in the years from
2003 to 2008.
< Insert Table 3 is inserted here>
As shown in Panel A of Table 3, we find that foreigners have the highest IS measures on TXF, with individuals having the lowest. Surprisingly, in 2003 foreigners contribute 90.2 percent of the price discovery (with lower bound of 90.0 percent and upper bound of 90.3 percent) despite their proportion of trading volume being only 2.0 percent (as reported in Table 2). Although the contribution to price discovery by foreigners gradually decreases to 70.7 percent in 2007, it remains at 71.0 percent in 2008, when the market is struck by the global financial crisis. In contrast to foreigners, individuals only contribute 0.8 percent of the price discovery, even though their proportion of trading volume is 84.6 percent in 2003. Similarly, domestic institutions’ contribution is much smaller than foreigners’: domestic institutions only contribute 9.0 percent of price discovery in 2003 and 24.8 percent in 2008. The baseline result indicates that foreigners play the primary role in price discovery on market index futures.
Panel B reports the IS measure of two sector index futures. Interestingly, the information share attributable to foreigners is lower on sector index futures than on market index futures. Foreigners as a group contribute 66.8 percent on EXF and 53.9 12 percent on FXF in 2003. These contributions decrease to 44.7 percent and 49.5 percent in
2008, respectively. On the other hand, domestic institutions’ contribution to the price discovery of EXF increases significantly from 30.9 percent to 47.0 percent in the corresponding period, and the contribution to EXF becomes similar to foreigners’ contribution. Not surprisingly, the contribution to price discovery by individuals on two sector index futures remains limited.
3.2 Generalized information share (GIS)
As discussed earlier, IS measure can only be used in the situation where each pair of prices are cointegrated with a one‐to‐one relationship, and it involves upper and lower bounds. Lien and Shrestha (2009) modify the IS method so that a unique measure of IS can be achieved. Lien and Shrestha (2014) further propose the generalized information share (GIS) method. In this subsection, we review the GIS method and present the empirical results for the application of the GIS method.
Lien and Shrestha (2014) propose the GIS method based on the following structure of the cointegrating vectors by matrix β:
1 −훾1 0 ⋯ 0 1 0 −훾 ⋯ 0 2 푇 1 0 0 ⋯ 0 훽 (푛−1)×푛 = [휄(푛−1): −훤(푛−1)] = , (7) ⋮ ⋮ ⋮ ⋱ ⋮ [1 0 0 ⋯ −훾(푛−1)] where 훤(푛−1) = 퐷푖푎푔(훾1, 훾2, … . , 훾(푛−1)) and 휄(푛−1) is represented as an (n-1) element column vector where all its elements are one. Equation (7) only requires all the n unit- root series driven by a single common stochastic trend. The long-term contribution of innovations on the ith series is presented in the form
푟 푟 −1 휓푖 휀푡 = 휓1 훾푖−1휀푡, 푖 = 1, … , 푛, (8)
13
푟 푟 where 훾0 = 1 and 휓1 = 훾푗−1휓푗 , 푗 = 2, … , 푛. Therefore, the contribution of the innovation of series j to the total variance of the long-run impact of innovation on the ith series is presented in the form
2 휓1푗훺푗푗 푆푗,푖 = 푟 푟푇, (9) 휓1훺휓1
푟 푟푇 where the variance of long-term impact on the ith series is described as 휓푖 훺휓푖 =
푛 2 −2 푛 2 ∑푗=1 휓푖푗훺푗푗 = 훾푖−1 ∑푗=1 휓1푗훺푗푗. This is the case of the IS where the innovations are independent. However, if the innovations are not independent, IS of the jth series can be presented in the general case as
퐺 2 퐺 (휓푗 ) 푆푗 = 푟 푟푇, (10) 휓1훺휓1
1 퐺 푟 푀 푀 − 푇 −1 −1 where 휓 = 휓1퐹 , and 퐹 = [퐺⋀ 2퐺 ⋀ ] . Thus, the GIS method is not only independent of the ordering which leads to a unique measure of price discovery, but also has the flexibility to measure the cointegrating vector which does not have to be a one-to- one relationship.
Accordingly, we compute the GIS measure to obtain a unique information share measure. In a manner similar to that for the IS measure, we take the average of monthly
GIS measure and report the annual GIS measure for the three types of investors in Table
4.
Similar to the results of the IS measure, the GIS measure estimation supports the evidence of the price contribution of foreigners on market index futures. For example, as reported in Panel A of Table 4, foreigners contribute 88.8 percent on TXF in 2003, whereas domestic institutions’ share is 8.8 percent followed by individuals at 2.3 percent.
The corresponding GIS measures are 69.5 percent, 24.3 percent, and 6.3 percent in 2008.
Panel B reports the GIS measures for the sector index futures. As shown, the information 14 share of foreigners on EXF is 65.4 percent in 2003 and this figure reduces to 43.7 percent in 2008, showing the contribution to price discovery from foreigners decreases in our sample period. At the same time, the contribution of domestic institutions increases significantly. In 2008, the information share of domestic institutions is 45.4 percent on
EXF and 42.5 percent on FXF, suggesting that they also play an important role in price discovery on sector index futures. Individuals remain the investor type that contributes the least to price discovery.
< Insert Table 4 is inserted here>
In sum, the GIS results reported in Table 4 are consistent with the IS findings in
Table 3. These findings demonstrate that, although foreigners as a group are the least active traders in terms of trading volume, they play the dominant role in price discovery on the index futures in our sample period, particularly on the market index futures.
Meanwhile, domestic institutions make a moderate contribution to price discovery, particularly on sector index futures. These findings are probably attributable to the fact that foreigners have better technological, financial, or human expertise, experience, or resources compared with domestic investors. On the other hand, local investors are better positioned to access and to interpret information regarding the local environment or domestic firms than foreigners; this can improve the contribution to price discovery for the domestic institutions on the sector index futures. Finally, individuals remain the investor type that contributes least to price discovery.
4. Investors’ correlated trade and index futures return
In this section, we evaluate investors’ trading performance by analyzing investors’ correlated trades with index futures returns. Two measures are applied: herding measure 15 and buy-sell imbalance. We investigate how investors’ herding level and buy-sell imbalance are associated with index returns, including prior, contemporaneous, and subsequent returns. Both herding level and buy-sell imbalance of a specific investor type can measure the group’s correlated trades. The major difference between the measures is that herding counts the ratio of the number of investors who are in the same trade direction while buy-sell imbalance measures the ratio of the number of shares which are traded in the same direction. The two measures predominantly applied in the literature are briefly reviewed first, followed by empirical results.
4.1 Herding measure
Proposed by Lakonishok, Shleifer and Vishny (1992), the LSV herding measure is widely used in literature to examine the investors’ correlated trade. The LSV herding measure is defined as the tendency of a group of investors to buy or sell specific securities at the same time, relative to what would be expected if they traded independently. The measure of herding 퐻푀푖,푡 for futures contract i at time t defined by
LSV is given by
퐻푀푖,푡 = |푝푖,푡 − 푝푖̅ | − 퐸|푝푖,푡 − 푝푖̅ |, (11)
퐵푖,푡 where 푝푖,푡 = . 퐵푖,푡 and 푆푖,푡 are the number of investors in the group who buy 퐵푖,푡+푆푖,푡
and sell futures contract i at time t, respectively. pi is the average of Pi ,t for futures contract i across time t. 퐸|푝푖,푡 − 푝푖̅ | is the adjustment factor that the expectation of
|푝푖,푡 − 푝푖̅ |under the null hypothesis of no herding. The adjustment factor is expressed by
푛 푛 푗 푖,푡 푖,푡 푗 푛푖,푡−푗 퐸|푝푖,푡 − 푝푖̅ | = ∑ [퐶 (푝푖̅ ) (1 − 푝푖̅ ) × | − 푝푖̅ |], (12) 푗=0 푗 푛푖,푡 16 where 푛푖,푡 represents the total number of investors in a group buying futures contract i during time t.
In this paper, we calculate the LSV herding measure on 15-minute intervals throughout the trading hours. A higher herding measure represents a stronger tendency of a group of investors to buy or sell specific futures contracts at the same time. To distinguish the tendency of buyer or seller herding, Wermers (1999) extends the LSV model and defines buy herding measures (BHM) for buy-herding securities and sell herding measures (SHM) for sell-herding securities, expressed as
BHM푖,푡 = 퐻푀푖,푡|푝푖,푡 > 푝푖̅ , (13)
SHM푖,푡 = 퐻푀푖,푡|푝푖,푡 < 푝푖̅ . (14)
We begin by creating herding portfolios, which are formed independently across individuals, domestic institutions, and foreigners. Trading hours are divided into 19 15- minute intervals from 8:45 a.m. to 1:45 pm. In each 15-minute interval, futures contracts traded by at least 10 specific investors are divided into buy-herding (buyer proportion is greater than 50 percent) and sell herding (seller proportion is greater than 50 percent) groups. Quartile portfolios of buy-herding (sell-herding) futures contracts are formed by the LSV measure where Portfolio B1 (Portfolio S1) is the quartile of futures contracts with the highest buy-herding (sell-herding) measures, and Portfolio B4 (Portfolio S4) is the quartile of futures contracts with the lowest buy-herding (sell-herding) measures. We form equal-weighted portfolios and calculate the specific index futures returns within the period of R[0] and R[x,y], where R[0] is the index futures return within the portfolio formation interval and R[x,y] is the cumulative index futures return from interval x to interval y. Returns earned by these portfolios are computed over horizons of [-20,-6], [-
5,-1], 0, [1,5], and [6,20]. The above steps are repeated for TXF, EXF, and FXF. 17
Table 5 shows the intensity of herding and the average 15-minute index futures returns. The mean return of each portfolio, and the return difference of the highest buy- herding and the highest sell-herding portfolios on market index futures (Panel A) and sector index futures (Panel B) are reported.
< Insert Table 5 is inserted here>
We start from the discussion of foreigners. As shown in Panel A, foreigners follow a positive-feedback investment strategy that their highest buy-herding (sell-herding) portfolios are associated with positive (negative) prior returns. The result is consistent with the existing research on foreign investor trading strategy (Froot, O'Connell, and
Seasholes, 2001; Richards, 2005; Chen, Chow, and Shiu, 2015). Moreover, the results on the formation interval indicate that foreigners’ herding can move index futures prices.
Specifically, the contemporaneous futures return of the intense buying portfolio
(hereafter high BHM) is 0.048 percent and of intense selling portfolio (hereafter high
SHM) is -0.057 percent. The return difference is 0.105 percent – a difference that yields a profit of NT$ 1,384 (approximately US$ 42.18) for one TXF contract in 15 minutes7.
Investors trade on information if the buy-herding portfolio outperforms the sell- herding portfolio in the post-herding period. The results of subsequent outperformance for foreigners’ buy-herding and underperformance of sell-herding illustrate that foreigners’ herding is attributable to information. Specifically, the return difference between the high BHM and the high SHM is 0.112 percent over the horizon of [1,5] and
0.234 percent over the horizon of [6,20]. These abnormal returns equal a profit of NT$
1,476 (US$ 44.99) in the 75 minutes following the portfolio formation session and
7 The profit is calculated as the average daily closing price between 2003 and 2008 times NT$ 200 for TXF contract and then multiples 0.105 percent. The average daily closing price of TXF is 6591.42. The average exchange rate of US$/NT$ over the same period is 32.82. 18 another NT$ 3,084 (US$ 93.99) in the subsequent 225-minute period. The results show that foreigners exhibit excellent prediction ability of price movement on market index futures.
We did not, however, find any significant result for foreigners’ price prediction ability on sector index futures, which differs from the evidence shown on market index futures.
As shown in Panel B, for both EXF and FXF, foreigners’ return difference of the high
BHM and high SHM portfolios are insignificant over the prior herding period, on formation interval, and post herding period. The findings indicate that foreigners’ correlated trades has an influence on market index futures but not on sector index futures.
We now turn to the results of domestic institutions. As shown in Panel A, domestic institutions also follow a positive-feedback investment strategy on the TXF that their high
BHM portfolios chase a positive prior return and their high SHM portfolios chase a negative prior return. On the formation interval, the return difference of the high BHM portfolio and high SHM is a significant 0.094 percent, a profit of NT$ 1,239 (US$ 37.76) for one TXF contract in 15 minutes. The evidence indicates that domestic institutions’ herd can moderately move the price of market index futures. After the herding formation interval, the high BHM portfolio keeps earning a positive return and the high SHM portfolio keeps earning a negative return. However, the outperformance of the high BHM portfolio relative to the high SHM portfolio is only significant in the first five 15-minute intervals following the portfolio formation. The return difference reverses to become insignificantly negative thereafter. The finding indicates that, unlike foreigners who keep outperforming post herding formation intervals, domestic institutions’ herding does not have a strong influence on the following returns of market index futures.
In terms of sector index futures, we do not find consistent results for domestic 19 institutions’ correlated trades prior to the formation interval. For example, the return difference of their high BHM and high SHM is a significantly positive 0.147 percent on
EXF prior to the formation interval. However, the return difference is a significantly negative of -0.342 percent on FXF over the horizon of [-20,-6] but an insignificant positive return on [-5,-1]. The results on formation interval indicate that domestic institutions’ correlated trades are positively associated with the concurrent return of EXF and FXF. Specifically, the return difference of the high BHM portfolio and the high SHM portfolio is a significantly positive 0.085 percent on EXF and 0.139 percent on FXF, respectively. However, the return differences of the high BHM portfolio and the high
SHM portfolio in post herding periods are insignificantly positive, indicating that the association of domestic institutions’ correlated trades with future returns of sector index futures is weak.
As for individuals, they adopt the opposite approach to foreigners and domestic institutions. As shown in Panel A, individuals use a negative-feedback investment strategy that they tend to hold long (short) positions when the prior return of market index futures is negative (positive). On the formation interval, their correlated trades negatively move the price. The mean difference of the high BHM portfolio and the high
SHM portfolio is -0.175 percent, which is significantly different from zero, and yields a loss of NT$ 2,307 (US$ 70.29) for one TXF contract in 15 minutes. Moreover, the BHM portfolios underperform and SHM portfolios outperform during post-herding periods, indicating that individuals’ correlated trades negatively predict the following price movements. This evidence suggests that individuals are uninformed investors.
The information disadvantage of individuals is also shown in the sector index futures markets in that they lose money from their correlated trades on EXF and FXF, as shown 20 in Panel B. The returns for individuals’ portfolios prior to the formation interval are mixed and do not have consistent patterns for BHM and SHM. However, the returns on their high BHM portfolios are negative but the returns on the high SHM portfolios are positive in the post-herding periods, such that the return difference of the two portfolios yields a significant negative returns. For example, the return differences of EXF (FXF) are significant -0.069 percent (-0.070 percent) and -0.100 percent (-0.151 percent) over the horizons of [1,5] and [6,20], respectively. Chen, Chow, and Shiu (2015) examine large and small individual investors’ correlated trades in the Taiwan stock market. They document that the correlated trades by small retail investors are negatively related to concurrent and future stock returns, and are more passive than the other types of investors when small retail investors herd. Our findings are consistent with findings documented in
Chen, Chow, and Shiu (2015) and support the view that small retail investors are naïve and uninformed traders.
4.2 Buy-sell imbalance
Although the LSV herding measure is widely used to estimate herding tendency, it is sensitive to the number of investors. Specifically, our sample includes a large number of individual investors which may bias the results of the LSV herding measure calculation.
Moreover, the LSV herding measure counts the number of investors that trade in the same direction; it is not necessarily correlated to buy and sell volume, which can directly influence the transaction price. To achieve a robust result, we use buy-sell imbalance as an alternative proxy for investors’ correlated trades.
Similar to the LSV herding measure, we construct a series of 15-minute buy-sell imbalances. Following Kaniel, Saar, and Titman (2008), we subtract the number of 21 futures contracts sold by investors in a group from the number of contracts bought, and we standardize the measure by the average 15-minute dollar volume in the year.
Specifically, the 15-minute buy-sell imbalance of futures contract i for investors in a group at time t, 퐼푀퐵푖,푡, is calculated as
퐵푢푦푖,푡−푆푒푙푙푖,푡 퐼푀퐵푖,푡 = , (15) (퐵푢푦푖,푡+푆푒푙푙푖,푡)⁄2 where 퐵푢푦푖,푡 (푆푒푙푙푖,푡) is the futures contract i’s buy (sell) dollar volume by investors in a group.
We form quartile net-buy portfolios and quartile net-sell portfolios based on the buy- sell imbalance measure, where Portfolio B1 (Portfolio S1) is the quartile of futures contracts with the highest buy imbalance (sell imbalance), and Portfolio B4 (Portfolio S4) is the quartile of futures contracts with the lowest buy imbalance (sell imbalance). We form equal-weighted portfolios and calculate the specific futures index returns over horizons of [-20,-6], [-5,-1], 0, [1,5], and [6,20].
Table 6 shows the results of the intensity of the buy-sell imbalance measure and the average 15-minute index returns. Panel A reports the results for TXF, and Panel B reports for EXF and FXF. The results are generally in line with the findings of the LSV herding measure. Foreigners employ a positive-feedback investment strategy and their correlated trade can move the concurrent prices. Moreover, their correlated trade can predict future returns on market index futures (in Panel A). For domestic institutions, their correlated trade can positively move the price on formation interval. Interestingly, they can predict future price movement on FXF, such that the return difference is a significantly positive
0.168 percent. However, we do not find any significant result on TXF and EXF for domestic institutions.
22
< Insert Table 6 is inserted here>
Finally, for individuals, they tend to buy losers and sell winners. Not surprisingly, their correlated trades negatively move the price. Moreover, they exhibit poor predictive power for price movements and they lose money from trading futures contracts. This evidence suggests that individuals are uninformed and naïve investors in the Taiwan
Futures Exchange.
5. Conclusion
Foreign investment plays an important role in emerging markets in terms of cross border investment, yet the debate on whether domestic or foreign investors have an information advantage persists. Literature on the informational role of foreign investors often argues that foreign investors are more knowledgeable than domestic investors.
However, the question of whether better trading performance equals a better contribution to information processing remains unanswered. The central issues of this controversy can be articulated by examining the contribution to price discovery and trading performance of specific investor groups simultaneously, which we do in this study.
We document that, despite being the smallest group of investors in term of trading volume, foreigners play the dominant role in the price discovery process in Taiwan futures markets. By contrast, individuals make the smallest contribution to price discovery. Moreover, the results of correlated trades indicate that foreign investors as a group have the best trading performance, while individuals have the worst performance.
Domestic institutions lie between foreigners and individuals in terms of contribution to price discovery and trading performance.
Overall, our empirical results show that foreigners not only make the greatest 23 contribution to price discovery, but also have the best trading performance, suggesting that foreigners have an information advantage over domestic institutions and individual investors in the Taiwan Futures Exchange. Although we are not the first to examine the informational contribution of foreigners in derivatives markets, we contribute to the literature by providing evidence of the information advantages that foreign investors have in emerging futures markets.
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Grinblatt, M., Titman, S., Wermers, R., 1995, Momentum investment strategies, portfolio performance, and herding: A study of mutual fund behavior. American Economic Review, 85, 1088-1105. Hasbrouck, J., 1995, One security, many markets: Determining the contributions to price discovery. Journal of Finance, 50, 1175-1199. Huang, R. D., Shiu, C. Y., 2009, Local effects of foreign ownership in an emerging financial market: Evidence from qualified foreign institutional investors in Taiwan. Financial Management, 3, 567-602. Kaniel, R., Saar, G., Titman, S., 2008, Individual investors trading and stock returns. Journal of Finance, 63, 273–310. Kuo, W.H., Chung, S.L., Chang, C.Y., 2015, The impacts of individual and institutional trading on futures returns and volatility: Evidence from emerging index futures markets. Journal of Futures Markets, 35, 222-244. Lakonishok, J., Shleifer, A., Vishny, R.W., 1992, The impact of institutional trading on stock prices. Journal of Financial Economics, 32, 23–43. Lien, D., Shrestha, K., 2009, A new information share measure. Journal of Futures Markets, 29, 377-395. Lien, D., Shrestha, K., 2014, Price discovery in interrelated markets. Journal of Futures Markets, 34, 203-219. Nofsinger, R.W., Sias, R.W., 1999, Herding and feedback trading by institutional and individual investors, Journal of Finance, 54, 2263-2295. Odders-White, E.R., 2000, On the occurrence and consequences of inaccurate trade classification. Journal of Financial Markets, 3, 259-286. Puckett, A., Yan, X.M., 2011, The interim trading skills of institutional investors. Journal of Finance, 66, 601-633. Richards, A., 2005. Big fish in small ponds: the trading behavior and price impact of foreign investors in Asian emerging equity markets. Journal of Financial and Quantitative Analysis, 40, 1–27. Wermers, R., 1999, Mutual fund herding and the impact on stock prices. Journal of Finance, 54, 581–622.
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Table 1 Descriptive Statistics of Index Futures Contracts in the TAIFEX
Index futures contracts TAIEX futures Electronic sector index Finance sector index futures futures Underlying index Taiwan Stock Taiwan Stock Taiwan Stock Exchange Exchange Electronic Exchange Finance Capitalization Sector Index Sector Index Weighted Stock Index (TAIEX) Ticker symbol TX TE TF Delivery months Spot month, the next calendar month, and the next three quarterly months. Last trading day The third Wednesday of the delivery month of each contract. Trading hours 08:45AM-1:45PM Taiwan time Monday through Friday. Contract size NT$ 200 x index point NT$ 4,000 x index NT$ 1,000 x index point point Tick size One index point 0.05 index point 0.2 index point Daily price limit +/- 7% of previous day's settlement price Daily settlement price The daily settlement price is the volume weighted average price within the last one minute. Settlement Cash settlement Source: Taiwan Futures Exchange
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Table 2 Summary Statistics for Trading Volume of Index Futures This table provides summary statistics on the average daily trading volume and trading percentage of individuals, domestic institutions, and foreigners over the period from January 2003 to December 2008. The sample covers all trade records in Taiwan futures markets. Panel A reports the statistics for market index futures, TXF, and Panel B reports the statistics for electronic sector index futures, EXF, and finance sector index futures, FXF. Trading volume is measured by the number of contracts traded on a round-trip basis to avoid double-counting. Trading volume percentage is trading volume of a specific investor type divided by total trading volume of an index futures contract.
Percentage by Investor Types Domestic Average Daily Volume Individuals Foreigners Institutions Panel A: Market Index Futures (TXF) 2003 24,160.14 0.846 0.133 0.020 2004 32,287.76 0.780 0.179 0.042 2005 24,831.12 0.709 0.228 0.064 2006 35,893.34 0.703 0.235 0.062 2007 42,530.07 0.670 0.222 0.108 2008 71,168.91 0.667 0.228 0.105 Panel B: Sector Index Futures EXF 2003 3,616.27 0.842 0.113 0.045 2004 5,575.37 0.808 0.109 0.083 2005 4,217.87 0.722 0.177 0.101 2006 5,168.17 0.696 0.180 0.124 2007 3,466.13 0.657 0.177 0.167 2008 4,776.56 0.624 0.193 0.183 FXF 2003 4,062.59 0.877 0.064 0.059 2004 7,920.64 0.851 0.083 0.067 2005 3,071.23 0.703 0.168 0.129 2006 2,733.67 0.650 0.187 0.163 2007 3,139.63 0.689 0.145 0.167 2008 4,533.68 0.662 0.160 0.178
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Table 3 Estimation of Hasbrouck Information Share Measure This table provides estimation of the Hasbrouck information share measure of individuals, domestic institutions, and foreigners over the period from January 2003 to December 2008. The sample covers all trade records in Taiwan futures markets. Panel A reports the statistics for market index futures, TXF, and Panel B reports the statistics for electronic sector index futures, EXF, and finance sector index futures, FXF. Contribution to price discovery is attributed to a specific investor type if the trade is initiated by an investor who belongs to the specific investor type. Following Odders-White (2000), the later arriving order of a trade is treated as the initiating side. The calendar monthly IS measure is calculated then we take the average of the monthly measure as annual one. The average of lower and upper bounds and the middle of the information share measure are presented.
Individuals Domestic Institutions Foreigners Lower Middle Upper Lower Middle Upper Lower Middle Upper Panel A: Market Index Futures (TXF) 2003 0.007 0.008 0.011 0.089 0.090 0.091 0.900 0.902 0.903 2004 0.012 0.015 0.021 0.149 0.150 0.153 0.830 0.835 0.838 2005 0.023 0.028 0.040 0.218 0.222 0.228 0.742 0.749 0.757 2006 0.029 0.035 0.048 0.190 0.194 0.201 0.762 0.770 0.779 2007 0.038 0.045 0.061 0.241 0.247 0.256 0.697 0.707 0.718 2008 0.034 0.041 0.057 0.243 0.248 0.257 0.700 0.710 0.721 Panel B: Sector Index Futures EXF 2003 0.019 0.024 0.033 0.306 0.309 0.313 0.662 0.668 0.673 2004 0.021 0.026 0.036 0.388 0.391 0.396 0.575 0.583 0.590 2005 0.036 0.045 0.062 0.412 0.419 0.428 0.524 0.536 0.549 2006 0.040 0.049 0.067 0.360 0.366 0.375 0.573 0.585 0.597 2007 0.066 0.079 0.104 0.464 0.474 0.487 0.431 0.447 0.463 2008 0.070 0.084 0.110 0.460 0.470 0.483 0.430 0.447 0.463 FXF 2003 0.018 0.023 0.032 0.435 0.438 0.442 0.533 0.539 0.545 2004 0.019 0.023 0.032 0.396 0.399 0.403 0.571 0.578 0.584 2005 0.044 0.054 0.075 0.501 0.509 0.520 0.424 0.436 0.449 2006 0.067 0.080 0.107 0.453 0.463 0.476 0.440 0.457 0.474 2007 0.057 0.068 0.091 0.420 0.429 0.441 0.487 0.503 0.519 2008 0.054 0.065 0.088 0.432 0.439 0.451 0.481 0.495 0.510
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Table 4 Estimation of Generalized Information Share Measure This table provides estimation of generalized information share measure of individuals, domestic institutions, and foreigners over the period from January 2003 to December 2008. The sample covers all trade records in Taiwan futures markets. Panel A reports the statistics for market index futures, TXF, and Panel B reports the statistics for electronic sector index futures, EXF, and finance sector index futures, FXF. Contribution to price discovery is attributed to a specific investor type if the trade is initiated by an investor who belongs to the specific investor type. Following Odders-White (2000), the later arriving order of a trade is treated as the initiating side. The calendar monthly GIS measure is calculated then we take the average of the monthly measure as annual one.
Individuals Domestic Institutions Foreigners Panel A: Market Index Futures (TXF) 2003 0.023 0.088 0.888 2004 0.034 0.147 0.819 2005 0.051 0.217 0.733 2006 0.056 0.190 0.753 2007 0.067 0.241 0.692 2008 0.063 0.243 0.695 Panel B: Sector Index Futures EXF 2003 0.046 0.300 0.654 2004 0.050 0.380 0.570 2005 0.071 0.405 0.524 2006 0.074 0.355 0.571 2007 0.104 0.458 0.438 2008 0.109 0.454 0.437 FXF 2003 0.047 0.425 0.527 2004 0.047 0.388 0.565 2005 0.081 0.492 0.427 2006 0.107 0.446 0.447 2007 0.093 0.415 0.492 2008 0.091 0.425 0.484
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Table 5 LSV Herding Measure and Index Return This table provides the index returns sorted by the LSV herding measure of individuals, domestic institutions, and foreigners over the period from January 2003 to December 2008. The sample covers all trade records in Taiwan futures markets. Panel A reports the statistics for market index futures, TXF, and Panel B reports the statistics for electronic sector index futures, EXF, and finance sector index futures, FXF. Trading hours are divided in to 19 15-minute intervals from 8:45 a.m. to 1:45 pm. In each 15-minute interval, futures contracts traded by at least 10 specific investors are divided into buy- herding (buyer proportion is greater than 50 percent) and sell herding (seller proportion is greater than 50 percent) groups. Quartile portfolios of buy- herding (sell-herding) futures contracts are formed by the LSV measure where Portfolio B1 (Portfolio S1) is the quartile of futures contracts with the highest buy-herding (sell-herding) measures, and Portfolio B4 (Portfolio S4) is the quartile of futures contracts with the lowest buy-herding (sell- herding) measures. We form equal-weighted portfolios and calculate the specific futures index returns within the period of R[0] and R[x,y], where R[0] is the index return within portfolio formation interval and R[x,y] is the cumulative index return from interval x to interval y. The mean return differences between Portfolio B1 and Portfolio S1 are highlighted in bold-faced type if p-values are 0.10 or less. Stand errors are shown in the parentheses.
Individuals Domestic Institutions Foreigners R[-20,-6] R[-5,-1] R[0] R[1,5] R[6,20] R[ -20,-6] R[-5,-1] R[0] R[1,5] R[6,20] R[ -20,-6] R[-5,-1] R[0] R[1,5] R[6,20] Panel A: Market Index Futures TXF Portfolio B1 (High BHM) -0.074 -0.341 -0.149 -0.074 -0.052 0.064 0.186 0.094 0.035 -0.003 -0.067 0.083 0.048 0.044 0.005 Portfolio B2 0.041 -0.217 -0.097 -0.036 -0.069 0.013 0.099 0.036 0.025 0.057 -0.238 -0.017 -0.011 0.053 -0.190 Portfolio B3 0.039 -0.122 -0.059 -0.015 -0.020 0.055 0.062 0.024 -0.002 -0.019 -0.110 -0.012 -0.018 0.028 -0.002 Portfolio B4 (Low BHM) 0.018 -0.044 -0.019 -0.020 -0.019 0.035 0.026 -0.005 0.004 0.001 -0.239 -0.072 -0.002 0.002 -0.095 Portfolio S4 (Low SHM) -0.004 0.339 0.156 0.086 0.130 -0.076 -0.184 -0.088 -0.020 -0.020 0.056 -0.061 -0.024 -0.107 -0.193 Portfolio S3 0.047 0.227 0.088 0.052 0.046 -0.004 -0.102 -0.039 -0.005 0.003 -0.042 -0.086 -0.013 -0.081 -0.028 Portfolio S2 0.006 0.133 0.050 0.019 0.011 -0.001 -0.035 -0.024 0.003 0.026 -0.091 -0.152 -0.018 -0.089 -0.101 Portfolio S1 (High SHM) 0.054 0.031 0.026 0.029 0.055 0.006 -0.019 0.000 -0.012 0.033 -0.224 -0.055 -0.057 -0.068 -0.229
B1 minus S1 -0.128 -0.372 -0.175 -0.103 -0.107 0.058 0.205 0.094 0.047 -0.036 0.157 0.138 0.105 0.112 0.234 (s.e.) (0.031) (0.018) (0.008) (0.018) (0.031) (0.031) (0.019) (0.008) (0.018) (0.033) (0.087) (0.057) (0.028) (0.052) (0.091) Panel B: Sector Index Futures EXF Portfolio B1 (High BHM) -0.083 -0.269 -0.148 -0.049 -0.100 -0.049 0.174 0.077 0.043 -0.026 -0.386 -0.159 -0.053 -0.241 -0.093 Portfolio B2 0.035 -0.174 -0.107 -0.020 -0.008 -0.065 0.103 0.098 0.039 0.007 -0.412 -0.229 -0.081 -0.493 -0.398
Portfolio B3 0.008 -0.117 -0.063 0.008 -0.047 -0.074 0.031 0.025 -0.053 -0.096 -0.373 0.472 0.059 0.092 -0.414 Portfolio B4 (Low BHM) 0.006 -0.045 -0.021 0.015 0.034 0.019 0.089 0.031 -0.019 -0.012 -0.768 0.153 0.022 -0.043 -0.453 Portfolio S4 (Low SHM) 0.007 0.295 0.136 0.020 0.080 0.058 -0.139 -0.092 -0.049 0.078 -0.650 -0.536 -0.045 -0.257 -0.599 Portfolio S3 0.048 0.207 0.113 0.028 0.077 -0.047 -0.090 -0.069 -0.015 -0.071 -0.879 -0.179 -0.056 -0.126 -0.485 Portfolio S2 0.025 0.122 0.074 0.032 0.042 -0.051 -0.074 -0.055 0.008 0.010 -0.636 -0.401 0.006 -0.200 0.185 Portfolio S1 (High SHM) 0.055 0.047 0.030 0.020 0.000 -0.035 0.027 -0.008 -0.018 -0.036 -1.012 -0.572 -0.086 -0.013 0.277
B1 minus S1 -0.138 -0.316 -0.178 -0.069 -0.100 -0.014 0.147 0.085 0.061 0.010 0.626 0.413 0.033 -0.228 -0.370 (s.e.) (0.034) (0.02) (0.009) (0.02) (0.036) (0.069) (0.049) (0.026) (0.04) (0.07) (0.596) (0.423) (0.148) (0.355) (0.521) FXF Portfolio B1 (High BHM) 0.011 -0.270 -0.107 -0.025 -0.074 -0.172 0.112 0.102 0.038 0.138 0.329 0.997 0.117 -0.201 -0.482 Portfolio B2 0.018 -0.213 -0.088 -0.037 0.025 0.120 0.181 0.081 0.095 0.122 -0.791 0.122 0.181 0.097 -0.126 31
Portfolio B3 0.058 -0.086 -0.060 -0.040 -0.070 -0.100 0.155 0.049 0.042 -0.071 0.863 0.011 -0.194 -0.066 -0.610 Portfolio B4 (Low BHM) 0.009 -0.023 -0.024 0.031 -0.007 0.050 0.032 0.030 0.007 0.150 0.191 0.768 0.203 0.025 0.009 Portfolio S4 (Low SHM) -0.057 0.293 0.119 0.010 0.026 0.123 -0.109 -0.142 0.057 0.035 -0.156 -0.231 -0.320 0.053 -0.448 Portfolio S3 -0.089 0.237 0.115 0.038 0.067 0.171 -0.004 -0.128 -0.050 -0.157 -0.044 -0.254 -0.097 0.200 -0.118 Portfolio S2 0.037 0.115 0.062 -0.005 0.043 0.045 -0.024 -0.062 -0.042 -0.145 0.644 0.122 -0.111 0.109 0.839 Portfolio S1 (High SHM) 0.087 0.047 0.028 0.045 0.077 0.170 -0.008 -0.037 -0.043 -0.066 -0.039 0.032 -0.007 -0.209 -0.040
B1 minus S1 -0.076 -0.317 -0.135 -0.070 -0.151 -0.342 0.120 0.139 0.081 0.204 0.368 0.965 0.124 0.008 -0.442 (s.e.) (0.061) (0.035) (0.015) (0.037) (0.067) (0.115) (0.094) (0.05) (0.069) (0.127) (0.807) (0.774) (0.172) (0.31) (0.505)
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Table 6 Buy-Sell Imbalance and Index Return This table provides the index returns sorted by the buy-sell imbalance of individuals, domestic institutions, and foreigners over the period from January 2003 to December 2008. The sample covers all trade records in Taiwan futures markets. Panel A reports the statistics for market index futures, TXF, and Panel B reports the statistics for electronic sector index futures, EXF, and finance sector index futures, FXF. Trading hours are divided in to 19 15- minute intervals from 8:45 a.m. to 1:45 pm. The 15-minute buy-sell imbalance of a future contract is defined as buy dollar volume minus sell dollar volume for a specific investor type scaled by the future contract’s average 15-minute dollar volume in the year. In each 15-minute interval, futures contracts are divided into net buy (buy dollar volume is greater than sell dollar volume) and net sell (sell dollar volume is greater than buy dollar volume). Quartile portfolios of net buy (net sell) futures contracts are formed by buy-sell imbalance ranking where Portfolio B1 (Portfolio S1) is the quartile of futures contracts with the highest buy imbalance (sell imbalance), and Portfolio B4 (Portfolio S4) is the quartile of futures contracts with the lowest buy imbalance (sell imbalance). We form equal-weighted portfolios and calculate the specific futures index returns within the period of R[0] and R[x,y], where R[0] is the index return within portfolio formation interval and R[x,y] is the cumulative index return from interval x to interval y. The mean differences between Portfolio B1 and Portfolio S1 are highlighted in bold-faced type if p-values of 0.10 or less. Stand errors are shown in the parentheses.
Individuals Domestic Institutions Foreigners R[-20,-6] R[-5,-1] R[0] R[1,5] R[6,20] R[ -20,-6] R[-5,-1] R[0] R[1,5] R[6,20] R[ -20,-6] R[-5,-1] R[0] R[1,5] R[6,20] Panel A: Market Index Futures TXF Portfolio B1 (Intense Buy) -0.010 -0.203 -0.116 -0.051 -0.045 0.021 0.215 0.126 0.042 0.014 -0.058 0.013 0.026 0.038 0.044 Portfolio B2 -0.007 -0.059 -0.029 -0.028 -0.043 0.071 0.108 0.027 0.018 -0.003 0.012 0.005 -0.012 0.031 0.034 Portfolio B3 0.001 -0.038 -0.004 -0.028 0.021 0.051 0.030 0.012 0.033 0.035 0.021 -0.007 -0.008 0.023 0.058 Portfolio B4 -0.036 -0.014 0.003 -0.009 -0.015 0.032 0.006 0.004 -0.025 0.073 0.025 0.005 0.012 -0.003 0.029 Portfolio S4 0.022 0.004 0.000 0.015 0.037 0.005 -0.023 -0.006 -0.004 -0.067 0.083 0.025 0.004 -0.002 0.021 Portfolio S3 0.033 0.041 0.003 0.039 0.057 -0.043 -0.031 -0.019 -0.010 0.001 0.030 0.014 0.003 -0.012 -0.002 Portfolio S2 0.053 0.092 0.022 0.045 0.016 -0.020 -0.089 -0.026 -0.003 -0.004 -0.004 0.007 0.006 -0.013 -0.080 Portfolio S1 (Intense sell) 0.023 0.209 0.128 0.044 0.054 -0.043 -0.199 -0.118 -0.028 0.027 -0.018 -0.039 -0.032 -0.064 -0.072
B1 minus S1 -0.032 -0.412 -0.244 -0.095 -0.099 0.064 0.413 0.245 0.069 -0.013 -0.041 0.052 0.058 0.101 0.116 (s.e.) (0.029) (0.02) (0.01) (0.016) (0.028) (0.028) (0.019) (0.01) (0.016) (0.028) (0.032) (0.021) (0.01) (0.018) (0.03) Panel B: Sector Index Futures EXF Portfolio B1 (Intense Buy) -0.045 -0.158 -0.093 -0.024 -0.033 -0.088 0.120 0.048 0.012 0.060 0.030 0.112 0.048 -0.004 -0.004 Portfolio B2 -0.031 -0.096 -0.044 -0.002 -0.042 0.015 0.090 0.018 0.021 0.002 0.095 0.051 0.011 0.012 -0.008
Portfolio B3 -0.011 -0.074 -0.010 -0.021 -0.009 -0.004 0.003 0.009 0.010 0.006 0.056 0.005 0.008 0.028 0.079
Portfolio B4 -0.079 -0.034 0.006 -0.006 0.021 0.024 -0.033 0.004 0.009 0.002 0.042 0.037 0.014 0.012 0.058 Portfolio S4 0.040 0.008 -0.004 0.002 0.050 -0.004 -0.013 0.003 0.014 0.015 0.031 -0.060 -0.009 0.005 -0.015 Portfolio S3 0.046 0.038 0.018 0.035 -0.005 0.021 -0.018 0.001 -0.007 0.021 -0.025 -0.039 -0.018 -0.034 -0.021 Portfolio S2 0.112 0.120 0.034 0.012 0.010 0.024 -0.051 -0.024 -0.018 -0.060 -0.077 -0.097 -0.033 -0.012 -0.015 Portfolio S1 (Intense sell) 0.025 0.225 0.102 0.022 0.067 0.063 -0.083 -0.055 -0.025 0.010 -0.222 -0.118 -0.068 -0.011 -0.079
B1 minus S1 -0.070 -0.383 -0.195 -0.047 -0.100 -0.151 0.203 0.103 0.037 0.050 0.252 0.231 0.115 0.007 0.075 (s.e.) (0.033) (0.023) (0.011) (0.019) (0.034) (0.034) (0.023) (0.011) (0.02) (0.033) (0.036) (0.024) (0.011) (0.022) (0.037) 33
FXF Portfolio B1 (Intense Buy) 0.097 -0.149 -0.088 -0.023 -0.160 -0.046 0.117 0.076 0.044 0.060 0.011 0.094 0.026 0.001 -0.083 Portfolio B2 0.006 -0.105 -0.016 -0.049 -0.013 -0.082 0.074 0.018 0.041 0.029 0.062 0.051 0.006 -0.016 0.098
Portfolio B3 -0.002 -0.076 -0.004 0.005 0.079 0.044 0.016 0.010 0.006 0.045 -0.014 0.015 0.003 -0.009 0.027 Portfolio B4 0.031 -0.018 0.005 0.026 0.025 -0.006 0.031 -0.005 -0.013 0.095 0.006 0.072 0.011 0.066 0.180 Portfolio S4 0.031 0.045 0.001 0.043 0.098 -0.004 -0.053 -0.007 0.022 -0.064 0.003 -0.032 0.014 -0.008 0.014 Portfolio S3 -0.009 0.029 0.008 -0.007 0.019 0.067 -0.070 -0.004 -0.044 0.051 0.021 -0.053 -0.018 -0.034 -0.094 Portfolio S2 0.019 0.072 0.030 -0.007 0.070 0.035 -0.043 -0.035 -0.016 -0.041 -0.067 -0.081 -0.001 -0.020 -0.010 Portfolio S1 (Intense sell) -0.070 0.230 0.071 0.037 -0.039 0.105 -0.060 -0.054 -0.020 -0.109 0.079 -0.104 -0.049 0.035 -0.106
B1 minus S1 0.167 -0.379 -0.158 -0.059 -0.121 -0.151 0.177 0.130 0.064 0.168 -0.068 0.198 0.075 -0.034 0.023 (s.e.) (0.068) (0.046) (0.023) (0.042) (0.076) (0.067) (0.045) (0.022) (0.038) (0.07) (0.072) (0.047) (0.023) (0.043) (0.078)
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