Pacific-Basin Finance Journal

Pacific-Basin Finance Journal 20 (2012) 1 23 Contents lists available at ScienceDirect Pacific-Basin Finance Journal journal homepage: www.elsevier.com/locate/pacfin Investor type trading behavior and trade performance: Evidence from the Thai stock market Suwipa Phansatan a, John G. Powell b, Suparatana Tanthanongsakkun c, Sirimon Treepongkaruna d, a Central Treasury Department, Kasikornbank PCL, Thailand b Independent, Wyevale, Ontario, Canada c Department of Banking and Finance, Chulalongkorn University, Thailand d UWA Business School, The University of Western Australia, Australia article info abstract Article history: Received 30 September 2010 Accepted 21 July 2011 Available online 28 July 2011 JEL classification: G12 G14 Keywords: Trading performance decomposition Trading behavior Investor types This paper examines the trading behavior and decomposes the trading performance of foreign, individual and institutional investors as well as proprietary traders in a dynamic emerging stock market, the Stock Exchange of Thailand. Foreign investors follow a positive feedback, momentum strategy and are good short term market timers but have poor security selection performance in poor markets, thus suggesting that they have a macro (market timing) but not a micro (security selection) informational advantage relative to local investors. Institutions and proprietary traders have poor security selection trading performance. Individuals display herding behavior and have fairly good security selection performance, but individual investors appear to compensate proprietary traders for the provision of short term liquidity by proprietary traders, so individuals' security selection gains are canceled out by market timing losses. 2011 Elsevier B.V. All rights reserved. 1. Introduction When analyzing developed stock markets, numerous studies have concluded that foreign and institutional investors tend to be better informed and financially sophisticated, whereas individual investors can be subject to psychological biases which limit their trading performance. A somewhat similar picture has also been painted for We would like to thank our discussant, Madhu Kalimipalli, an anonymous referee, Robert Brooks, and William Griffith for helpful comments. We would also like to thank the Stock Exchange of Thailand for providing the data used in this study. The corresponding author would also like to acknowledge funding from ARC discovery grant DP1093344. Corresponding author at: UWA Business School, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia. Tel.: +61 8 6488 7853; fax: +61 6488 1086. E-mail address: sirimon.treepongkaruna@uwa.edu.au (S. Treepongkaruna). 0927-538X/$ see front matter 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.pacfin.2011.07.004

2 S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 emerging markets where some studies have found that foreign investors follow information-based, momentum trading strategies, with foreign investment inflows foreshadowing good subsequent returns (Froot et al., 2001). The superior trading performance of foreign investors in emerging markets, presumably at the expense of (less sophisticated) individual investors who take the other sides of foreigners' trades, raises a number of questions as to the sources of the trading performance. Is the superior performance of foreign investors in emerging markets due to good market timing, superior security selection, or both? How do individual investors in emerging markets perform in terms of market timing, security selection, and (consequently) overall trading performance? How do other (presumably information-based) institutional investors and proprietary traders behave in emerging markets, and what is their market timing and security selection performance? This paper therefore examines in detail the trading behavior as well as the market timing and security selection performance of investor types in a dynamic emerging market, the Thai stock market. A detailed study of trading behavior and performance is made possible due to the availability of extensive, high-frequency trading data for the Stock Exchange of Thailand (hereafter, SET) for a sample period January 1999 through December 2004 (see also Pavabutr and Sirodom, 2010). The study examines the trading performance of four investor types in the Thai stock market, namely foreign, individual, and institutional investors as well as proprietary traders, using a new technique that employs trade-weighted measures of buy and sell volumes to decompose investors' trading gains into gains that are due to market timing versus security selection (see, e.g., Bae et al., 2006). Prior to testing investor type trading performance, the study identifies trading behavior such as momentum versus contrarian investing and herding behavior of the investor types using weekly aggregated buying and selling flows to calculate net investment flows (NIF) for each investor type. The intraday data contains all orders (volume and amount of trade) for each of the four investor types. Using intraday data uniquely suits the paper's objectives because the data set not only records buy and sell trades of different investor types but also enables the computation of trade-weighted average prices of both buy and sell trades for all investor types, with the trade-weighted average prices reflecting the selection of stocks by the investor types. Observing the trading behavior of an additional investor type, proprietary traders, is fairly unique to this study (see also Bae et al., 2006), and is potentially very informative, because proprietary traders are generally assumed to be information-based investors with potential informational advantages over other investor types. Proprietary traders can often have a liquidity provision role that they are required or endeavor to fulfill for either the market as a whole or their security house clients in particular, thus also raising questions as to whether their market timing and security selection trading performance is enhanced or harmed by the provision of liquidity. As in past studies (e.g., Richards, 2005), foreign investors in the Thai stock market follow positive feedback, momentum-like trading strategies that are positively correlated with current market conditions. Further analysis reveals that foreign investors trade against the positions of institutions and individuals, with their trading leading to good short term market timing performance. Surprisingly, however, foreign investors are found to have poor security selection trading performance in downward trending markets, thus indicating that foreign investors in the Thai stock market appear to have market timing (macro) informational advantages but no (micro) informational advantages over local investors with respect to security selection. This is an important finding since it helps to explain contrasting results in the literature as to whether foreign investors actually do have informational advantages in emerging markets (see, for example, Froot et al., 2001 versus Choe et al., 2005 and Dvor ák, 2005). Proprietary traders are found to follow persistent trading strategies which lead to good short term but poor long term market timing performance. The short term market timing ability of proprietary traders and foreign investors appears to come at the expense of individual investors, thus indicating that proprietary traders appear to fulfill, and profit from, their role of providing short term liquidity to individual investors. Individual investors display herding behavior and gain from security selection at the expense of all the other investor types but have poor market timing performance, with the two trading components thus canceling each other out. Institutions follow trading strategies that are negatively correlated with lagged market conditions, and trade against the positions of foreign investors. Somewhat surprisingly, the trading strategies of institutions, as well as of proprietary traders, lead to very poor security selection performance, thus further leading to poor overall trading performance. The remainder of this paper is organized as follows. Section 2 provides a brief review of the literature. Section 3 describes the paper's method and data set, along with a brief description of the Thai stock market. Section 4 presents empirical results, with Section 5 providing a brief conclusion.

S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 3 2. Literature review 2.1. Trading pattern Empirical studies document the differing trading patterns of each investor type. Foreign investors are found to follow momentum trading strategies, especially in emerging markets, with foreign inflows predicting positive future returns in the markets receiving the cash inflows (Froot et al., 2001). Lin and Swanson (2003) find that foreign investors in Taiwan employ momentum strategies whereby they buy past winners and sell past losers. Richards (2005) finds strong evidence that foreign investors engage in momentum trading in six Asian emerging equity markets. Choe et al. (1999), Grinblatt and Keloharju (2000), and Kamesaka et al. (2003) provide an information based explanation of the momentum trading pattern of foreign investors. Individual investors, on the other hand, tend to be contrarians. Odean (1998, 1999), for instance, studies the behavior of individual investors in the United States who trade using a large discount brokerage house, and finds that individual investors tend to hold on to their losers and sell their winners, which is consistent with individuals being contrarians. Other studies document individual investor contrarian behavior in non- U.S. markets as well, including Richards (2005) who finds that individual investors in six Asian emerging markets are contrarian investors; Choe et al. (1999) also documents that individual investors in Korea are contrarians, and Bae et al. (2002) report that Japanese individual investors follow contrarian trading patterns. Grinblatt and Keloharju (2000) find contrarian tendencies of individual investors in Finland while Jackson (2003) demonstrates that Australian individual investors are consistently contrarian in their investment decisions. Mixed results are found for the trading patterns of institutional investors. Lakonishok et al. (1992), Nofsinger and Sias (1999), Griffin et al. (2003), and Cai and Zheng (2004) find United States institutional investors follow momentum trading patterns, and further document a strong positive contemporaneous relation between institutional trading and stock returns. Karolyi (2002) and Kamesaka et al. (2003) find, however, that institutional investors follow contrarian trading strategies, and Grinblatt and Keloharju (2000) document that Finnish institutional investors utilize contrarian investment strategies. The literature is still somewhat unclear as to what trading patterns are displayed by proprietary traders, since very few studies have examined the trading behavior of proprietary traders. Bae et al. (2006) find weak evidence that Japanese proprietary traders follow momentum strategies, and do not reach a conclusion regarding their trading patterns. This study will therefore add to the literature by documenting the trading behavior of proprietary traders in a dynamic emerging market, the Thai stock exchange. Similarly, very few published studies document the trading patterns of the various investor types in the Thai stock market. In an unpublished working paper, Kamesaka and Wang (2004) investigate behavior of individual, institutional and foreign investors using VAR analysis of daily buying and selling flows for Thailand's equity market from January 3, 1996 to December 30, 1999. The results indicate that foreign investors follow a momentum strategy by increasing their net buying (buying minus selling flows) after the stock market increases for a few days, whereas individual investors follow a contrarian strategy whereby they buy after the market has fallen for a few days. A secondary contribution of this paper is therefore to document the trading patterns and performance of all investor types in a dynamic, emerging stock market (the Thai stock market). 2.2. Trading performance The trading patterns of investor types that are documented in the literature have been shown to lead to differences in trading performance. Froot and Ramadorai (2001) examine international portfolio flows into various countries, for instance, and find that foreign investors' trades predict future equity returns relatively well. Grinblatt and Keloharju (2000) document that foreign investors in the Finnish stock market, pursuing momentum strategies, generate superior investment performance. Kamesaka et al. (2003) find that foreign investors in the Japanese equity market have good market timing ability, a result that is similar to Bae et al.'s (2006) finding that foreign investors in the Japanese equity market consistently generate gains from trade due to good market timing.

4 S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 Despite documentation in the literature of superior trading performance by foreign investors, some studies indicate that foreign investors do not necessarily have informational advantages over domestic investors. Choe et al. (2005), for instance, suggest that foreign investors' trade execution performance indicates that they do not have private information advantages over Korean individual investors. Dvor ák (2005) finds domestic investors in Indonesia have an information advantage over foreign investors on average, resulting in domestic investors actually having higher profits than foreign investors. Dvor ák (2005) also demonstrates that domestic clients of global brokerages have higher profits than do foreign clients of global brokerages, suggesting that the combination of local information and global expertise leads to higher profits. Individual traders are generally found to have relatively poorer trading performance. Barber and Odean (2000) evaluate the timing of individual investors' trades at a large United States discount brokerage firm. Barber and Odean (2000) use individual investors' portfolio returns and, when compared to various benchmarks, including the market portfolio and multifactor benchmarks, they find that individual investors earn poor net returns when adjusted for trading costs. They conclude that overconfidence can explain high trading levels and the resulting poor performance of individual investors. Barber et al. (2004) investigate the performance of individual investors in the Taiwanese stock market using trade data for all market participants during the five years ending in 1999, and find that individual investors under-perform. Kamesaka et al. (2003) demonstrate that Japanese individual investors have poor market timing performance. Kaniel et al. (2005), on the other hand, examine the investment choices of individual investors using a large cross-section of NYSE stocks, and find that individual buying predicts subsequent positive excess returns. Institutional investors are sometimes found to have informational advantages over other investor types, thus augmenting their trade performance. Barber et al. (2004), for instance, find that Taiwanese institutional investors, in the presence of information and trading cost advantages, profit from uninformed investors. Institutional investors can be classified into insurance firms, banks, mutual funds, security firms (proprietary traders), and non-financial corporations. Proprietary traders are generally classified as information-based investors and can therefore potentially have informational advantages over other investor types. Proprietary traders can have good firm-specific information through their dealings with companies, for instance, and can also have detailed share market supply and demand information through their dealings with investors. Only a few papers have, however, specifically investigated the trading performance of proprietary traders. Kamesaka et al. (2003) find that proprietary traders on the Taiwan Stock Exchange (TSE) are good market timers. Bae et al. (2006) demonstrate that the trading gains of proprietary traders on the Japanese market tend to increase when domestic investors' trading gains decrease, thus indicating the potential for interesting dynamics between proprietary traders and other investor types. These considerations are explored in detail below for the Thailand stock market once the paper's method and data source are outlined in the following section. 3. Method and data sample 3.1. Method 3.1.1. Trading patterns As a first step towards examining the trading behavior and trading performance of different investor types, the trading patterns of investor groups are examined using aggregated weekly data to determine whether net investment flows indicate the various investor groups employ positive or negative feedback trades. The net investment flow (NIF i,t ) for investor type i during week t depends on whether investor type i is a net buyer or seller during week t, and is calculated as follows: Buying Value i;t Selling Value i;t NIF i;t = ð1þ Buying Value i;t + Selling Value i;t NIF i,t is positive (negative) when investor type i buys more (less) equities than sells during week t, thus providing an indication of attempts to time the market should large net investment flows be observed in

S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 5 either direction. Large net buying (selling) can therefore signal that the investor group thinks the market is undervalued (overvalued). Further, positive NIF i,t autocorrelation due to either large net buying or large net selling from week to week can help to indicate that an investor type is following a positive feedback, momentum style investment strategy, whereas negative feedback trading is present when an investor type trades against the prior market trading and direction. 1 Correlations between current and lagged NIF i,t and lagged market returns can therefore provide an initial indication as to the presence of momentum or contrarian trading strategies by each investor type. 2 To specifically investigate the negative or positive feedback trading behavior of investor types, past market returns and net investment flows are used to explain the current weekly NIF i,t of each investor type. Positive feedback trading, also known as momentum trading, is indicated when the net investment flow is significantly positively related to lagged market returns and past investment flows, whereas contrarian traders employ negative feedback trading, as indicated by a significantly negative relationship between NIF i,t and past market returns. NIF i,t will therefore be potentially related to the lagged net investment flows of various investor types as well as past returns, so a Vector Auto Regressive (VAR) model is employed to take account of investment flow autocorrelation. The VAR model is utilized as follows: NIF i;t = p Σ β i;τ ; R t n + n =1 p Σ λ i;τ NIF i;t n + n =1 p Σ γ i;n NIF j;t n + ε i;t ; n =1 ð2þ where NIF i,t is the NIF of investor type i for week t, R t n is the SET index return for week t n, NIF j, t n is the NIF of investor type j for week t n (where i j), and i and j represent foreign investors, institutional investors, individual investors, or proprietary traders. The VAR system and its standard errors are estimated using maximum likelihood estimation (see Reinsel, 1997). The determination of the appropriate number of lags n is based on the Akaike Information Criterion (AIC). A significantly positive index return (R t n ) regression coefficient estimate, β i, n, indicates that investor type i is following a momentum trading strategy, since the investor buys more than sells when the stock market has already been rising. A significantly negative regression coefficient β i, n estimate indicates that investor type i is following a contrarian investment strategy since the investor tends to trade in the opposite direction to past market returns. An additional indication of feedback trading is provided by lagged NIF coefficient estimates, since a significantly positive regression coefficient estimate λ i, n indicates that investor type i's purchases increase following past purchases, presumably as the investor makes additional positive feedback, momentum trades through time. Herding behavior between two investor groups is indicated by a positive estimated regression coefficient γ i, n, since investor type i makes net purchases (NIF) in response to investor type j's net purchases. A negative regression coefficient estimate γ i, n indicates that investor type i goes against the crowd by buying when others have been selling, thus providing an indication of contrarian investing. 3.1.2. Trading performance To investigate the trading performance of different investor types in the Thailand stock market, the portfolio performance measure of Grinblatt and Titman (1993) is adapted to measure the performance of each investor type's net trades over various holding period horizons (see, e.g., Bae et al., 2006). Each investor type's net cash gains from trading are estimated using the weekly baht amount of buy and sell trades for each investor type, so the trading performance of each investor type depends on market timing (more net buying prior to market upturns) as well as good stock selection (buying rather than selling stocks that outperform the market during the holding period). As net cash gains from trading are estimated using trading volume data, direct comparability is obtained by standardizing the median weekly value of 1 Momentum investing refers to the purchase of past winners and the sale of past losers, and for the market as a whole to net purchasing when the market has been rising as well as selling when the market has been falling. Contrarian trading is the reverse (buying a stock, or the market as a whole, when it has been falling, and vice versa). 2 Univariate correlations do not control for other variables that can explain net investment flows whereas vector autoregressive analysis can potentially control for these additional explanatory variables, as indicated below. Correlations therefore provide an initial indication of the presence of momentum or contrarian strategies that can then be tested more formally using vector autoregressive analysis (see also the discussion in the Results section).

6 S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 buy trades so that it equals the median weekly value of sell trades for each investor type; the median rather than the mean is used because it is well known that the value of trades has a skewed distribution (see, e.g., Bae et al., 2006). The trading performance measure will therefore indicate that one investor type outperforms the others when the investor type allocates more net buy orders relative to the investor type's median net trade value (zero) at times when the selected shares subsequently perform very well. Overall net trading gains (π t ), representing the (median-adjusted) cash value implicitly generated by trades over an h-week trading interval from week t, are defined as "! 1=h! π t Y b t Y s p b 1=h # t + h t p s ; ð3þ t p s t + h p b t where Y t b =v t b p t b is the median-adjusted baht value of buy trades in week t, Y t s =v t s p t s is the median-adjusted baht value of sell trades in week t, v t b and v t s are trade-weighted buy and sell volumes, respectively, and p t b and p s t are trade-weighted buy and sell prices, respectively. It can therefore be noted that pb t + h p s and ps t + h t p b t are the inter-temporal relative spreads of trade-weighted average prices for (median-adjusted) share purchases and sales, respectively. π t is an implied measure of trade performance because it measures how well median-adjusted net trades would have done over the holding period (even though the net trades are not necessarily physically liquidated at the end of the particular holding period). Positive (negative) overall net trading gains π t N0(π t b0) indicate that share purchases outperformed (underperformed) the investor type's share sales either due to good (back) market timing prior to market upturns (downturns) or due to superior (inferior) selection of stocks to buy versus stocks to sell, as measured over a specific h week holding period. To identify the trading gains or losses for each investor type that are due to market timing versus stock selection, overall net trading gains π t can be divided into stock selection (π S t ) versus market timing (π T t ) components such that π t = π T t + π S t : ð4þ The market timing trade performance component π T t measures the extent to which net buying by each investor type benefits (hurts) the investor because the market as a whole rises (falls) over the subsequent h week holding period: π T t Yb t Ys t R M 1=h b t + h Y t Ys t R M 1=h b t + h = Y t Ys t R M 1=h; t + h ð5þ where R M t + h is the h-week holding period rate of return of the market index, (Y b t Y s t )(R M t + h ) 1/h is the net (median-adjusted) trading gain that is due to net market purchases, and the median buy versus sale values are Y b t = Y s t =1000 baht (by construction). A high π t T value represents good market timing performance since the investor buys (sells) before the market increases (decreases). When π t T N0, it implies that the investor executes net buys (sells) before the market increases (decreases), whereas if net gains arising from market timing ability are negative (π t T b0), the investor buys (sells) before a decline (increase) in the stock market index. Intuitively, the market timing component π t T of overall net trading gains π t sets a market indexing benchmark for the performance of security selection trades; the remaining stock selection component (π t S ) depends on an ability to outperform the market by trading specific stocks that perform better than the market index. The net trading gain in excess of the market benchmark that arises due to active security selection trades, π t S, is therefore defined as "! 1=h! π S t Y b t Y s p b 1=h # t + h t p s Y b t Y s t R M 1=h t + h ; ð6þ t p s t + h p b t where (again) R M t + h is the h-week holding period rate of return of the market index and (Y b t Y s t )(R M t + h ) 1/h is the trading gain that investors would receive if they traded the market index. The positive (negative) net gain arising from superior security selection trades, π t S N0(π t S b0), implies net trading gains (losses) arise

S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 7 A 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 - B Trading volume and value between Jan 1999 and Dec 2004 Volume Value 199901 199904 199907 199910 200001 200004 200007 200010 200101 200104 200107 200110 200201 200204 200207 200210 200301 200304 200307 200310 200401 200404 200407 200410 600 SET50 Index 140,000 130,000 120,000 110,000 100,000 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000-500 400 300 200 100 0 31/12/1998 31/12/1999 31/12/2000 31/12/2001 31/12/2002 31/12/2003 31/12/2004 Fig. 1. A: The profile of weekly aggregated trading volume and trading value for the SET50 index between January 1999 and December 2004. B: The weekly SET 50 Index between January 1999 and December 2004 (shading indicates the quiet market period 1998 through 2002). due to investors making specific security selection trades that outperform (underperform) the market index. [Note that π t S =0 if the investor trades the market index, so π t S can only differ from zero if the investor type's stock trade composition differs from the market index's weights.] To test whether the median of each of the three trading performance measurements is significantly different from zero, the non-parametric Wilcoxon signed-rank test is employed. Trading intervals (h) equal to 1, 4, 8, 13, 26, and 52 weeks are used for the tests. The correlations between overall trade performance (π t ), stock selection performance (π t S ), and market timing performance (π t T ) for each investor type are also estimated to examine whether and how net trading gains shift between one investor type and another. The

Table 1 Investor type and SET50 summary statistics. This table reports investor type trading characteristics and SET50 index summary statistics on the Stock Exchange of Thailand using stocks in the SET50 index. In Panels A and B, trades are aggregated by investor type. The full sample period is from January 1999 to December 2004. Average Market Capitalization is the market capitalization of each stock weighted by the value of trade for each investor type. Average Investor Type Turnover is weekly trade by each investor type divided by total shares outstanding of the companies the investor type trades. We also report the percentage of share trading by each investor type for each industry. We classify stocks into 9 industries based on the Industry Classification Benchmark. Numbers in parentheses in Panels B and C are q-statistics. Panel A: trading characteristics Average investor type market cap. (Mil. baht) Average investor type turnover Percentage of share trading by each investor type for each industry Basic materials Consumer goods Consumer services Financials Industrials Oil and gas Technology Telecommunications Utilities Foreign investors Buying 97,448.02 0.08% 6.80% 5.29% 7.99% 36.00% 14.68% 14.28% 3.79% 8.73% 2.44% Selling 100,948.64 0.08% 6.32% 4.67% 8.18% 34.96% 16.36% 14.89% 3.79% 8.32% 2.51% Institutional investors Buying 106,149.93 0.03% 8.55% 4.55% 5.75% 32.40% 18.15% 12.14% 5.59% 8.43% 4.44% Selling 96,129.72 0.04% 8.63% 5.30% 6.45% 33.60% 17.02% 10.77% 5.48% 8.19% 4.56% 8 S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 Individual investors Buying 65,488.14 0.39% 9.19% 3.05% 6.01% 45.75% 12.80% 7.61% 6.01% 7.47% 2.11% Selling 65,689.08 0.39% 9.36% 3.14% 5.89% 45.89% 12.45% 7.54% 6.00% 7.62% 2.11% Proprietary traders Buying 129,425.80 0.01% 8.49% 2.45% 4.00% 43.74% 11.13% 16.97% 5.27% 5.84% 2.11% Selling 129,003.27 0.01% 8.04% 2.62% 2.87% 44.99% 11.04% 16.94% 5.40% 6.28% 1.82%

Panel B: weekly trading by each investor type Mean (mil. baht) Standard deviation Maximum (mil. baht) Minimum (mil. baht) Lag 1 Foreign investors Buying 4,593.87 4,253.70 21,671.07 391.82 0.33*** (33.503) Selling 4,666.36 4,388.95 28,674.93 413.12 0.86*** (233.99) Net buying (selling) 72.49 2,194.83 7,414.08 9,143.24 0.46*** (67.505) Institutional investors Buying 1,702.56 1,780.25 10,586.58 153.95 0.86*** (233.84) Selling 1,569.72 1,599.56 9,159.94 105.71 0.82*** (210.89) Net buying (selling) 132.84 863.79 4,939.39 4,379.16 0.45*** (62.886) Individual investors Buying 13,795.29 14,147.44 95,413.08 1,090.20 0.87*** (239.6) Selling 13,850.98 14,110.18 89,770.37 903.52 0.85*** (227.77) Net buying (selling) 55.69 1,916.15 7,526.53 6,405.75 0.24*** (18.709) Proprietary traders Buying 415.09 519.16 3,098.41 12.73 0.86*** (230.76) Selling 419.76 524.97 3,163.92 14.49 0.86*** (234.41) Net buying (selling) 4.66 91.70 1,053.50 303.34 0.19*** (11.68) Panel C: SET50 index summary statistics SET50 index Mean Standard deviation Maximum Minimum Lag 1 Whole period (1999 2004) 1st difference of SET50 index 0.68 12.25 47.72 42.24 0.04 (0.43) Quiet market (1999 2002) 1st difference of SET50 index 0.14 11.88 47.72 42.24 0.001 (0.00) Active market (2003 2004) 1st difference of SET50 index 2.29 12.84 35.03 31.61 0.07 (0.57) S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 9

10 S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 trading behavior and trading performance of various investor types in the Thailand stock market are examined in the following section once the data set used in the study is outlined immediately below. 3.2. Data The trading patterns and performance of various investor types in Thailand's equity market are examined using the intraday data set from the Stock Exchange of Thailand (SET) that separates investors into four types: foreign investors, institutional investors, individual investors, and proprietary traders. As described in Pavabutr and Sirodom (2010), the SET is a continuous auction limit order driven market and implements a multiple tick size regime that benefits small investors in particular, thus catering to the individual retail investors who dominate the market. The SET utilizes a fully computerized trading system where orders can either be automatically implemented by brokers or else brokers can negotiate trades amongst themselves on behalf of clients. There has been a fixed commission rate of.25% of trade values, but market participants indicate that proprietary traders sometimes make trades on behalf of large retail clients to (surreptitiously) avoid the minimum fixed commission rate, thus benefiting clients and performing an additional proprietary trader role. The SET has a morning and afternoon trading session with a lunch-time break, and the automated trading system continuously matches non-negotiated buy and sell orders according to price and then trade arrival timing priority. The SET50 data set contains the volume (number of shares traded) and the value of trades in baht for both buy and sell trades for each investor type. Intraday volume and price data for the fifty component stocks of the SET50 index is then aggregated weekly for the sample period January 1999 to December 2004. 3 Fig. 1A illustrates the aggregated weekly trading volume and trading value during the sample period, and indicates that trading was quite subdued during 1999 through 2002 but picked up sharply and became quite volatile during 2003 and (to a lesser extent) 2004. Fig. 1B, which displays the level of the SET50 index during the sample period, further indicates that the active, volatile trading years of 2003 and 2004 corresponded to a general upward trend in the Thai stock market whereas the market had a slight average downtrend during the quiet trading years 1999 through 2002. The full sample is therefore also divided into an inactive, directionless market subsample period (1999 through 2002), labeled the quiet market period, and a volatile, upward trending market subsample period (2003 and 2004), labeled the active market period, in subsequent analysis to determine if the paper's results vary between subsamples (see the Results section). Panels A and B of Table 1 summarize the weekly aggregated buying and selling on the SET50 Index from January 1999 to December 2004 that is attributable to foreign investors, institutional investors, individual investors, and proprietary traders. Panel B of Table 1 indicates that, of all four investor types, individual investors are the most active traders and their trading activities are also the most volatile. On average, they purchased 13,795 million baht per week of stocks and sold 13,851 million baht per week during the sample period. This contrasts sharply with more developed markets, where individual investors do not tend to do the vast majority of trading (see, e.g., Bae et al., 2006). Panel A of Table 1 reveals that individual investors tend to purchase smaller sized companies, as measured by average market capitalization, and trade each week a much higher percentage of the shares outstanding of these stocks (see the Average Investor Type Turnover column) relative to other investor types (.39%). Panel B of Table 1 demonstrates that foreign investors rank second in terms of trading activity, followed by institutional and proprietary traders, respectively. During the sample period, proprietary traders conducted a relatively small amount of trading with only 415 and 420 million baht in weekly purchases and sales, respectively. It should be noted that the investment flow values for proprietary traders trading on their own accounts only, and do not include the commission trades of other investors. The lighter trading activity of proprietary traders is further reflected in the Average Investor Type Turnover column of Panel A of Table 1 where it is revealed that the weekly turnover of proprietary traders is very small relative to the shares outstanding of the companies they trade (.01%). Proprietary traders buy and sell relatively larger companies, as indicated by the Average Market Capitalization column of Panel A, with the average market capitalization of their holdings being roughly twice as large as those of individual traders 3 The SET 50 index is a market value weighted index calculated from the prices of the 50 largest stocks on the Stock Exchange of Thailand.

S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 11 (approximately 129 million baht versus 65 million baht). Panel A of Table 1 also outlines the industries of the companies each investor type tends to trade (see the final nine columns), with no major differences being apparent. Panel C of Table 1 reports summary statistics for the SET50 during the full sample and subsamples. Panel C reveals that SET50 returns are not significantly autocorrelated (see the final column of Panel C), but are negative on average during the quiet market subsample period 1999 through 2002 (the average first difference is.14) and positive otherwise (see the Mean column). The Panel C summary statistics therefore further justify the division of the sample into quiet and active subsamples. Fig. 2A and B presents the proportion of the yearly SET50 trading volume and trading value that is accounted for by foreign, institutional, individual, and proprietary trader investor types during the years 1999 through 2004. Fig. 2A and B indicates that individual investors are by far the major investor type on the SET50 since they account for 80% of the trading volume and 70% of the trading value. The other investor groups account for a relatively small proportion of trades. Although the trading portion of individual investors in Thailand is very high, the relative level of trading by individual investors decreased during the 2003 to 2004 active market period, as shown in Fig. 2A and B. In the quiet market period of 1999 to 2002, individual investors traded 81% of the volume and 70% of the value of trades, on average, whereas during the active market of 2003 to 2004 individual investor trades fell A 100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% B 100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Trading Volume by Investor type 1999 2000 2001 2002 2003 2004 Foreigners Institutions Individuals Proprietary Trading Value by Investor type 1999 2000 2001 2002 2003 2004 Foreigners Institutions Individuals Proprietary Fig. 2. A: The proportion of volume relative to total trading volume for four investor groups between January 1999 and December 2004 for the SET50 index. B: The proportion of trading value to total trading value of four investor groups between January 1999 and December 2004 for the SET50 index.

12 S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 Table 2 Summary statistics of weekly net investment flow. This table reports the descriptive statistics for each investor type's weekly Net Investment Flow (NIF). The NIF is computed as NIF it =(Buying Value it Selling Value it )/(Buying Value it + Selling Value it ) for each investor group i during week t. ***, **, and * denote significance at the 1%, 5% and 10% level. Mean (t-stat) Standard deviation Minimum Maximum Foreign Investors 0.0256 ( 2.76)*** 0.1645 0.4021 0.3572 Institutional Investors 0.0310 (2.59)** 0.2120 0.6156 0.6415 Individual Investors 0.0092 (2.58)** 0.0630 0.1832 0.2308 Proprietary Traders 0.0082 ( 1.20) 0.1214 0.4220 0.6181 to 76% of the volume and 66% of the value of trades, on average. The behavior and performance of individual investor trades as well as other investor types is examined in detail in the following section. 4. Empirical results 4.1. Trading patterns SET50 net investment flow (NIF) summary statistics for each investor type are provided in Table 2. A positive (negative) NIF for a particular week implies that investors buy more (less) equities than they sell during the week. Table 2 therefore indicates that institutional investors tended to buy shares from foreign investors, on average, during the sample period, but the minimum and maximum summary statistics indicate that all four investor types were heavy net sellers during some weeks and heavy net buyers of stocks at other times. Supporting this conclusion is the finding that the average NIF of foreign investors is 0.0256, but their NIF standard deviation of 0.1645 indicates that they ranked second among the four investor types, with NIF swinging from as low as a minimum of 0.4021 one week to large net buying of 0.3572 in another week. Individual investors, the largest trading group on the SET50 index, have a positive average NIF of 0.0092 and a relatively low standard deviation of 0.0630; additionally, measuring variation by using the minimum and maximum NIF shows that their weekly range from 0.1832 to 0.2308 is the lowest of the investor types. 4 Institutional investors are found to be net buyers with an average NIF of 0.0310. Their NIF swings the most among the four investor types, as indicated by a weekly NIF standard deviation of 0.2120. Proprietary traders are not significant net buyers or sellers during the sample period (their NIF of 0.0082 is close to zero) but they have a relatively high NIF standard deviation of 0.1214. This large variation in net investment flow could have an effect on the market if the potentially information-based trades of proprietary traders lead to herding type behavior by other investors (such as individuals), despite the small average value of their weekly trades (see Table 1). The first step towards identifying the trading behavior of each investor type in the Thai stock exchange is provided in Table 3 which reports the Pearson correlation coefficients between each investor type's weekly NIF and leads and lags of the weekly rate of return of the SET50 index (the weekly SET50 rate of return lags and leads extend from week t= 4 to week t=4). The sample represents 313 weeks of investment flow. This table is divided into three panels, with Panel A representing the whole sample period (1999 to 2004), Panel B representing a quiet market period from 1999 through 2002 and Panel C representing an active market period from 2003 through 2004. A positive (negative) correlation between NIF and lagged market returns during the previous weeks indicates that investors follow a positive (negative) feedback trading, with positive (negative) feedback trading representing momentum (contrarian) investing. Table 3 indicates that foreign investors appear to be momentum traders, especially during the quiet market period 1999 through 2002, since the correlation of.5568 (.5838) between their NIF and current market returns in Panel A (B) is highly significant, as is the correlation between their NIF and one week 4 Note that Panel B of Table 1 indicates that individual investors are net sellers, on average, but Table 2 indicates that they have a positive NIF, a result that is due to the correlation between the numerator of NIF (net trades in a week) and the denominator (total trades), i.e., individual investors tend to be net buyers when they are not doing a lot of overall trading (buying plus selling).

S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 13 Table 3 Correlation of weekly net investment flow of Thai equity investors. This table reports Pearson correlation coefficients between NIF and market return for each investor type. The NIF is computed as NIF it =(Buying Value it Selling Value it )/(Buying Value it +Selling Value it ) for each investor type i during week t. ***, **, and * denotes significance at the 1%, 5% and 10% level. Foreigners Institutions Individuals Panel A: trading performance for all years (1999 2004) Institutions 0.4013*** 1 Individuals 0.8492*** 0.0111 1 Proprietary 0.0821 0.0503 0.1245** Return (t= 4) 0.0483 0.0537 0.0073 Return (t= 3) 0.0188 0.0184 0.0059 Return (t= 2) 0.0843 0.0102 0.1223** Return (t= 1) 0.2973*** 0.198*** 0.2332*** Return (t=0) 0.5568*** 0.0409 0.6149*** Return (t=+1) 0.1644** 0.0679 0.0918 Return (t=+2) 0.0743 0.022 0.0624 Return (t=+3) 0.0086 0.0607 0.0223 Return (t=+4) 0.0981* 0.07 0.1367** Panel B: trading performance for the quiet market period (1999 2002) Institutions 0.3533*** 1 Individuals 0.8374*** 0.0854 1 Proprietary 0.0472 0.0843 0.0546 Return (t= 4) 0.0311 0.0369 0.0234 Return (t= 3) 0.0075 0.0177 0.0148 Return (t= 2) 0.1253* 0.0454 0.1715** Return (t= 1) 0.3381*** 0.1674** 0.2736*** Return (t=0) 0.5838*** 0.0552 0.6324*** Return (t=+1) 0.1327* 0.0488 0.1023 Return (t=+2) 0.0419 0.0477 0.0433 Return (t=+3) 0.0013 0.0544 0.018 Return (t=+4) 0.1333* 0.0773 0.1716** Panel C: trading performance for the active market period (2003 2004) Institutions 0.5397*** 1 Individuals 0.8698*** 0.1836* 1 Proprietary 0.2046** 0.0857 0.3668*** Return (t= 4) 0.1202 0.1009 0.0664 Return (t= 3) 0.114 0.0361 0.0938 Return (t= 2) 0.045 0.1292 0.0368 Return (t= 1) 0.1838* 0.3286*** 0.1066 Return (t=0) 0.5027*** 0.0243 0.5872*** Return (t=+1) 0.0556 0.1538 0.0339 Return (t=+2) 0.1426 0.0825 0.0841 Return (t=+3) 0.0065 0.0671 0.003 Return (t=+4) 0.0298 0.062 0.0817 lagged returns (.2973 (.3381) in Panel A (B)). Institutional investors and individual traders appear to be contrarian traders, with significantly negative correlations between their NIFs and either contemporaneous or one week lagged market returns (see the second and third columns of Table 3). When the market falls (rises), foreign investors therefore tend to sell to (buy from) individual and institutional investors. The large negative correlations between the NIFs of foreign investors and institutional as well as individual investors reported in all panels of Table 3 (.4013 and.8492, respectively, for the overall sample) provide further support for this interpretation. The negative correlation between the NIFs of individual investors and

14 S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 proprietary traders during the complete and active period samples, reported as.1245 and.3668 in Panels A and C of Table 3, could indicate that proprietary traders step in to fulfill a market liquidity duty by meeting the instantaneous liquidity needs of individual investors (and perhaps step away from this role when it is more risky to do so during downwards trending markets!). Contemporaneously, as mentioned above, foreign investors' momentum trades are strongly positively correlated with returns during the same week. The strong negative correlation between individual investors' trades and the same week's market returns indicates that individuals go against the trend (are contrarian investors) on the Thai stock market (e.g.,.6149 in the t=0 row of Panel A of Table 3). The correlation analysis reported in Table 3 does not provide a consistent picture as to whether proprietary traders follow a contrarian or momentum trading strategy (see the positive and negative correlations between proprietary traders' NIFs and contemporaneous plus lagged market returns in the final column). Interestingly, the short term correlation between foreign traders' weekly NIF and the subsequent week's SET50 market return of.1164 (see Panel A of Table 3) indicates that foreigners' trades positively forecast the subsequent week's returns. Longer term, the trades of individual (proprietary) traders positively (negatively) forecast returns four weeks later (see the bottom row of Panel A of Table 3). When the trading behavior of Thai stock market investor types is tested more formally in Table 4 using VAR estimation (see regression Eq. (2)), the Table 3 results are greatly clarified. In Table 4, the λ i coefficient estimates represent the ability of lagged NIF (t= 1, 2, 3, and 4) of investor type i to explain investor type i's current (t=0) NIF (e.g., evidence of NIF persistence) whereas the γ i coefficient estimates represent the ability of lagged NIF (t= 1, 2, 3, and 4) of investor type i to explain investor type j's current (t=0) NIF (e.g., evidence of herding if one group's purchases follow another group's lagged NIF). The (β i ) coefficient estimates in the final rows of each panel represent the ability of lagged SET50 rates of return (t= 1, 2, 3, and 4) to explain investor type i's current (t=0) NIF, thus identifying momentum or contrarian investing. 5 Table 4 indicates that the momentum-like trading behavior of foreign investors, reported in Table 3, can be explained by their reaction to the lagged trades of other investor groups as well as the persistence properties of their own trades. In the overall sample as well as during quiet market periods, foreign investor trades are very persistent (the significant λ i coefficient estimate for a one (three) week lag is.2962 (.3735) in Panel A and is.5245 for a one week lag in Panel B). This result reinforces the implication from Table 3 that foreign investors are positive feedback, momentum investors, since Table 3 indicates that they buy (sell) when the market is currently rising (falling) and Table 4 further indicates that they continue to trade in the same direction in the following weeks. Once again, however, Table 4 demonstrates that foreign investors are not strong momentum traders in active market conditions. Foreign investors do, however, trade against the lagged NIFs of institutional or proprietary investors in all market conditions, displaying a significantly negative γ i coefficient estimate for one or the other of these investor types in each panel of Table 4. Once account is taken of investor types' lagged trades in Table 4, the interpretation from Table 3 that foreign investors trade against (lagged) institutional and proprietary traders' positions is therefore reinforced. Table 4 further indicates that institutions tend to respond in kind, by trading against the lagged positions of foreigners (for instance, the significantly negative one week lagged γ i coefficient estimate in the Institution column for foreign investors is.3494 in Panel A and.7524 in Panel B), especially when market conditions are weak. Individual traders are no longer identified as contrarians in Table 4. Interestingly, however, proprietary traders' and institutions' four week lagged NIFs significantly explain subsequent net trades by individual investors (for instance, in the Individual column of Panel A of Table 4 the full sample, four week lagged γ i coefficient estimate for Institutions is.0447 and for Proprietary Traders is.0771). As mentioned above, this pattern of estimated relationship provides evidence of herding behavior by individual investors (see, e.g., the discussion following Eq. (2)). The distinguishing feature of the NIF of proprietary traders is that they are very persistent (see the NIF: Proprietary rows of the final column of all panels in Table 4 which report one week lagged λ i coefficient estimates of.1802 for the full sample and.1564 (.2791) for the quiet (active) 5 The results reported in Table 4 are not significantly affected by the use of a Heteroskedastic and Autocorrelation Consistent regression error correction (results not reported). Non-HAC standard error estimates are reported in Table 4 because they are actually slightly more conservative (very slightly less significant), so the more conservative standard error estimates are used (thus also avoiding the complications involved with implementing HAC standard error estimation in a VAR framework).

S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 15 Table 4 Vector autoregressive model of NIF and SET50. This table reports bivariate VAR (4) model estimates for investor type for the variables p weekly NIF using the following equation with p lags: NIF i;t = Σ p β i R t τ + Σ p λ i NIF i;t τ + Σ γ i NIF j;t τ + ε i;t. ***, **, and * denote τ =1 τ =1 τ =1 significance at the 1%, 5%, and 10% level, respectively. Foreigners Institutions Individuals Proprietary Panel A: trading performance for all years (1999 2004) NIF: foreigners t= 1 0.2962* 0.3494* 0.0197 0.0615 t= 2 0.1042 0.0491 0.0104 0.1289 t= 3 0.3735** 0.4454** 0.1046 0.0087 t= 4 0.1857 0.0557 0.069 0.2444* NIF: institutions t= 1 0.1058 0.2783*** 0.0184 0.0174 t= 2 0.0637 0.0363 0.0197 0.0565 t= 3 0.1239* 0.0652 0.0363 0.0164 t= 4 0.1522** 0.0936 0.0447* 0.0646 NIF: individuals t= 1 0.1216 0.4073 0.239 0.2578 t= 2 0.0563 0.4119 0.1805 0.1175 t= 3 0.7278* 1.0206** 0.204 0.0507 t= 4 0.1181 0.274 0.0673 0.4438 NIF: proprietary t= 1 0.0934 0.0481 0.0227 0.1802*** t= 2 0.035 0.063 0.0022 0.0335 t= 3 0.0657 0.0643 0.0253 0.0056 t= 4 0.2015*** 0.0143 0.0771*** 0.0001 NIF: return t= 1 0.5002* 0.6591* 0.1074 0.501** t= 2 0.4814* 0.2745 0.1155 0.0956 t= 3 0.4184 0.146 0.1048 0.1737 t= 4 0.1592 0.1768 0.0952 0.0811 Panel B: trading performance for quiet market period (1999 2002) NIF: foreigners t= 1 0.5245** 0.7524** 0.0417 0.093 t= 2 0.109 0.022 0.0248 0.0352 t= 3 0.3178 0.4348 0.1421 0.0156 t= 4 0.1731 0.0623 0.074 0.3764* 1 NIF: institutions t= 1 0.0047 0.1654 0.0039 0.0186 t= 2 0.0534 0.0266 0.0137 0.0289 t= 3 0.1216 0.0857 0.045 0.014 t= 4 0.176** 0.1021 0.0585* 0.084 NIF: individuals t= 1 0.4371 1.6113** 0.2787 0.3691 t= 2 0.0763 0.3 0.1441 0.0941 t= 3 0.2893 0.9409 0.1857 0.1018 t= 4 0.1147 0.3865 0.0322 0.737 NIF: proprietary t= 1 0.091 0.1441 0.0024 0.1564*** t= 2 0.0068 0.0486 0.0086 0.0219 t= 3 0.1067 0.1116 0.0433 0.0326 t= 4 0.1806** 0.0038 0.0688*** 0.0439 NIF: return t= 1 0.6133** 0.6513 0.098 0.4616 t= 2 0.2925 0.3786 0.0509 0.00687 t= 3 0.5187 0.0614 0.1641 0.3457 t= 4 0.2506 0.4936 0.0852 0.1161 Panel C: trading performance for active market period (2003 2004) NIF: Foreigners t= 1 0.4582 0.2874 0.2508* 0.0287 (continued on next page)

16 S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 Table 4 (continued) Foreigners Institutions Individuals Proprietary t= 2 0.4238 0.1551 0.1634 0.3201 t= 3 0.281 0.0657 0.0394 0.203 t= 4 0.1056 0.2567 0.0339 0.0532 NIF: institutions t= 1 0.4463*** 0.4374** 0.1528** 0.0051 t= 2 0.1906 0.0159 0.0643 0.1737* t= 3 0.0899 0.1777 0.0286 0.0771 t= 4 0.045 0.0281 0.0016 0.0517 NIF: individuals t= 1 1.7399** 1.6074* 0.584* 0.0414 t= 2 0.1752 0.5054 0.0061 0.3751 t= 3 1.045 0.6444 0.2114 0.4699 t= 4 0.2997 0.6931 0.1584 0.0244 NIF: proprietary t= 1 0.1115 0.4178* 0.0614 0.2791*** t= 2 0.0164 0.0366 0.0234 0.2037 t= 3 0.0229 0.0008 0.0162 0.0071 t= 4 0.0971 0.1574 0.0005 0.113 NIF: return t= 1 0.326 0.8906 0.2217 0.6597* t= 2 1.7452** 0.4407 0.6356** 0.2704 t= 3 0.2706 0.3035 0.1534 0.0896 t= 4 0.1955 0.9165 0.1798 0.2146 market subsamples). Correctly taking account of the persistence of net investment flows using VAR analysis has therefore refined the estimation of the trading behavior of each investor type. In summary, Tables 3 and 4 indicate that foreign investors exhibit positive feedback (momentum) trading in quiet market conditions, and trade against the positions of other investor types. Foreign investors therefore appear to buy when other investor types are selling. Institutions respond in kind, by subsequently trading against the positions of foreign investors. The NIFs of foreign investors as well as proprietary traders are also found to be very persistent. Individual investors display herding behavior, since they follow the position-taking of other investor types. The following subsection tests the extent to which each investor type's trading behavior affects the performance of their trades. 4.2. Trading performance The trading performance of each investor type in the Thai stock market is examined in Table 5 where the median value of the overall trade performance measure (π t ) as well as the market timing (π t T ) and security selection or spread (π t S ) components of trading gains of each investor type are reported for trading intervals of length (h)=1, 4, 8, 13, 26, and 52 weeks. Full sample period results (1999 through 2004) are reported in Panel A of Table 5 as well as results for a quiet market (1999 to 2002) and an active market (2003 through 2004) subsample in Panels B and C, respectively. The baht amount of buy and sell trades for each investor type is adjusted to have the same median values of 1000 baht. Note that the sum of the market timing and security selection (spread) components of trading gains do not equal the overall trade performance measure for each investor type because each component is reported as the investor type median for the sample. Table 5 indicates that the momentum-like trading strategy of foreign investors consistently provides significant short term (one week) market timing gains (π t T ) of 2.79993 in the full sample and 2.2632 (6.714) in the quiet (active) market subsamples, respectively. This evidence is consistent with the findings of Fu and Qian (2010) that momentum traders in the Singapore residential presale market are good short term market timers. Foreign investors' very good one week security selection (spread) performance (π t S )in active markets (342.5044 in Panel C of Table 5) does not appear to make up for poor security selection in quiet markets ( 56.3401 in Panel B), however, thus resulting in insignificant overall trading performance (π t ) for the entire sample (an insignificant 12.1755, as reported in Panel A). This result is consistent with

S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 17 Table 5 Source of trade performance. This table reports the sources of performance for different types of investors, where Π t =π s T 2 t +π t. The! p s 1=h! 3 overall net trading gains, π t, over h-week the trading interval from week t is π t Yt b t + h p b 1=h 4 Y s t + h 5 p b t p s where Y b t =v b b t p t t t (Y s t =v s t p s t ) is the baht amount of buy (sell) trades in week t, p b t and p s t are trade-weighted buy and sell prices, and v b t and v s t are buy and sell volumes, respectively. 2 The price spread, π s is the trading gains that arises from security selection in excess of the market benchmark and is! defined as πt S p s 1=h! 3 Yb t + h p b 1=h h i 4 t Y s t + h p b t 5 p s Yt b Ys t R M 1=h t + h where R M t + h is one plus the h-week holding period return of the t t market index and (Y b t Y s t )(R M t + h ) 1/h is the net trading gains when investors trade the market index. The market timing measure, π s,isthe measure of timing ability in relation to the market index. Since we standardize the baht amount of buy and sell trades to have the equal median values (Y b t = Y s t =1000 baht), the net buy trade (buy minus sell trade) for the observation period is zero. πt can be defined as follows: πt T Yb t Ys t R M 1 = h b t + h Y t Y s t R M 1=h t + h = Y b t Yt s R M 1=h. t + h We apply the trading interval (h)=1, 4, 8, 13, 26, and 52 weeks for each measure. ***, **, and * denote significance at the 1%, 5% and 10% level, respectively. Trading frequency Foreigners Institutions Individuals Proprietary traders Panel A: trading performance for all years (1999 2004) 1 week Overall 12.1755 173.607*** 1.5269 12.4355** Timing 2.7993*** 0.3449 1.0434*** 0.3017*** Spread 12.311 175.649*** 3.8394* 14.9494** 4 week Overall 9.399 51.875*** 0.7531 32.305*** Timing 10.336 15.6463 0.0305 19.6925*** Spread 3.8852 53.953*** 2.4273** 3.1951** 8 week Overall 14.8439 27.416* 0.5056 24.12*** Timing 17.1491 13.4428 0.3314 21.073*** Spread 0.926 24.5382*** 1.1054** 0.8696 13 week Overall 12.7893 44.934* 0.0886 15.579*** Timing 14.8061 1.8776 1.0184 19.854*** Spread 1.0889 23.532*** 1.3494*** 1.1371 26 week Overall 22.7849 14.762 1.3494*** 22.634*** Timing 19.401 7.7945 3.51 17.982*** Spread 1.0611 7.0263*** 0.214 0.6316* 52 week Overall 10.0602 13.2744 2.9906 9.2708** Timing 13.028 20.0074 3.5547 9.9527** Spread 0.554 2.9399*** 0.2366*** 0.1099 Panel B: trading performance for quiet market period (1999 2002) 1 week Overall 56.0341*** 125.086*** 3.7244** 3.8054 Timing 2.2632*** 0.3822 0.8237*** 0.0971** Spread 61.4865*** 125.631*** 6.9618*** 5.3776 4 week Overall 7.112 23.9038 5.0749 11.7912*** Timing 10.4888 24.0772 5.7345 8.9269*** Spread 13.2392*** 40.6432*** 2.3776*** 0.4952 8 week Overall 10.9054 4.1281 7.1754 14.2546*** Timing 14.8817 29.6671 7.0928 10.3183*** Spread 6.4645*** 18.2163*** 1.1938*** 0.1792 13 week Overall 10.5618 3.823 7.973 12.47*** Timing 17.218 32.837* 7.0405 10.6044*** Spread 6.2499*** 16.7549*** 1.1921*** 1.163 26 week Overall 8.6467 40.5435** 7.6239 18.2587*** Timing 10.9613 46.043** 7.7847 12.0171*** (continued on next page)

18 S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 Table 5 (continued) Trading frequency Foreigners Institutions Individuals Proprietary traders 26 week Spread 1.8813*** 5.1024*** 0.2991** 0.5347 Panel B: trading performance for quiet market period (1999 2002) 52 week Overall 28.5599 43.7676** 11.85* 19.6031** Timing 30.2737 47.3644** 11.9446* 19.0548** Spread 0.8629*** 2.0214*** 0.0744 0.0694 Panel C: trading performance for active market period (2003 2004) 1 week Overall 342.5044*** 648.599*** 4.7951 102.93** Timing 6.714*** 0.0074 3.1605*** 1.7596*** Spread 323.5744*** 680.934*** 2.8129 106.425** 4 week Overall 15.833 160.959*** 32.8763 196.296*** Timing 22.4079 15.26 18.4045 111.638*** Spread 71.4871*** 162.079*** 1.6728 29.552** 8 week Overall 44.4954 155.145*** 19.5066 135.862*** Timing 4.1354 56.053 19.8632 119.862*** Spread 32.7781*** 85.861*** 0.6474 12.496 13 week Overall 45.5683 172.693*** 22.7656 85.935*** Timing 8.2108 71.12* 19.1671 100.301*** Spread 25.4203*** 52.072*** 2.4709 7.2195 26 week Overall 46.9264 178.887*** 16.3532 82.566** Timing 41.0778 170.061** 19.453 86.125** Spread 9.5278*** 20.7871*** 0.8846 5.8081** 52 week Overall 20.8248 37.2727 20.9199 13.499 Timing 29.3545 38.291 20.1625 18.962 Spread 3.199** 8.7078*** 1.1952** 2.1483 the Korean market findings of Choe et al. (2005) and the Indonesian findings of Dvor ák (2005) that foreign investors do not have an information advantage. The interpretation can be greatly refined due to dividing up trading gains into timing and stock selection components, however; foreign investors do not appear to consistently have an information advantage with respect to (micro) security selection trades, but they are consistently good short term market timers and thus appear to have a (macro) information advantage for short run market timing. Table 5 further indicates that the persistent trading strategies of proprietary traders lead to good short term but poor long term market timing performance. For instance, in the overall sample (Panel A), proprietary traders' market timing performance is π t T =.3017 at the one week horizon but it is negative at all other longer time horizons. The short term market timing ability of proprietary traders and foreign investors appears to come at the expense of individual investors (the Individual Investor π t T = 1.0434 at the one week horizon for the overall sample in Panel A of Table 5), thus reinforcing the prior subsection's conclusion that proprietary traders appear to fulfill their role of providing short term liquidity to individual investors, and make short term gains from the provision of immediate liquidity. Somewhat surprisingly, the trading strategies of proprietary traders and institutions lead to very poor security selection (spread) performance (π t S ), thus further leading to poor overall performance in the full sample and especially so in strong markets (Institutions' one week π t S = 175.649 ( 680.934) in Panels A and C of Table 5 and Proprietary Investors' one week π t S = 14.9494 ( 106.425) in Panels A and C of Table 5). Individual investors gain from security selection (π t S ) at the expense of all the other investor types but, as mentioned above, they have poor market timing performance and thus have overall trading gains in the full sample that are statistically indistinguishable from zero (for instance, Individuals' one week

S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 19 π t S =3.894 and π t T = 1.0434 so their overall performance π t of 1.5269 is indistinguishable from zero in Panel A of Table 5). Interesting institutional features of the Thai stock market can help to explain why foreign investors, institutions, and proprietary traders perform very poorly with respect to stock selection. Foreign investors only tend to trade the larger stocks, so they do not have as many choices in terms of security selection (see Panel A of Table 1). Institutions are mostly mutual funds and also tend to hold larger stocks, with one manager looking after many funds. They do not trade very frequently. Proprietary traders buy and sell the largest average sized companies, and often trade on behalf of customers in order to reduce the commissions that customers pay (see the discussion at the beginning of Section 3.2), so their focus is often on factors other than security selection when buying and selling. All of these factors can limit the security selection performance of other investor types relative to individual investors. Table 6 provides a further indication of the sources of security selection gains and overall performance for each investor type by examining the average trade-weighted market capitalization and weekly imputed holding period returns of the stocks that investors buy and sell. Individual investors trade much smaller sized financial stocks (49 billion baht average market capitalization) which perform poorly in the short term (.05% per week for buys), but are very slight net sellers on average of these poorly performing stocks. Foreign investors select the highest performing financial stocks (.24% per week for buys), whereas institutions and proprietary investors hold larger capitalization financial stocks that perform poorly (.06% per week, on average, for buys) and are net sellers of financial stocks. Financial stocks account for at least a third of each investor type's buying and selling activity (see Panel A of Table 1); two other industries account for more than 10% of investor type trades, on average, and these are industrials as well as oil and gas; Table 6 indicates industrials perform very poorly during the sample period (roughly a zero return), whereas the relatively large stocks comprising the energy industry perform very well (close to 1% return per week). Individual investors trade relatively large oil and gas stocks (over 314 billion baht capitalization, on average), whereas foreign investors trade smaller oil and gas stocks (a quarter trillion baht capitalization, on average). The remaining industries tend to contain relatively smaller capitalization stocks, and display considerable variability in the average returns obtained by investor types. Table 6 therefore provides insights into some of the sources of investor types' security selection returns, although it can be noted that the market timing of trades in these industries also helps to determine (must be subtracted to obtain) security selection performance. Table 7 reports the correlations of overall net trading gains (π t ), gains from security selection (π t S ), and gains from market timing (π t T ) for various investor types for trading intervals of (h)=1, 8, and 52 weeks (short, medium, and long-term). The results of Table 7 reinforce the Table 5 findings which imply that proprietary traders appear to make short term market timing gains from the provision of immediate liquidity to individual investors, since proprietary traders' market timing gains are negatively correlated with those of individual investors (for instance, the one week market timing correlation is.2683 for the full sample in Table 7). Table 7 further indicates that the overall trading gains (π t ), market-timing performance (π t T ), and security selection (spread) performance (π t S ) of foreign investors tend to increase when the overall net trading gains (π t ), market-timing performance (π t T ), and security selection performance (π t S ) of institutional and individual investors decrease (since all the foreign investor trading performance correlations with institutional and individual investors are significantly negative in Table 7). This finding further reinforces the conclusion that foreign investors have informational disadvantages with respect to security selection, with Table 7 further indicating that these security selection informational disadvantages are periodically exploited by institutions and individual investors. 5. Conclusion This paper utilizes a unique data set of weekly aggregated purchases and sales over a 6-year period on the Stock Exchange of Thailand (SET) and a new trading performance decomposition method to examine two areas of investor behavior: the trading patterns and trade performance of four investor types on the SET. Foreign investors, as in past studies, are found to follow positive feedback, momentum trading strategies, but their trading strategies lead to superior short term market timing performance only, whereas their security selection performance is very poor, thus canceling out overall net trading gains. An important contribution of the paper is therefore the conclusion that foreign investors have a short term

Table 6 Holding characteristics by industry. This table reports the average market capitalization and average one-week holding returns for each industry for each investor type. For each industry, the average market capitalization weighted by investor type trading value for buy and sell transactions are calculated. The average one-week holding returns are value-trade weighted returns for a one week holding period for each investor type for each industry; buy returns are therefore imputed holding returns, and sell returns are therefore the imputed return if the investor had not sold. We classify stocks into 9 industries based on the Industry Classification Benchmark. Bold in the buy (sell) row indicates the investor type bought (sold) more of the stocks in the industry, on average, during the sample period (see Panel A of Table 1). Basic materials Consumer goods Consumer services Financials Industrials Oil and gas Technology Telecommunications Utilities Foreign investors Buy avg. cap. (mil. bt.) 31,081.25 46,577.51 48,049.75 65,362.33 85,232.26 250,721.99 77,015.72 153,535.04 35,332.94 Sell avg. cap. (mil. bt.) 30,268.60 44,220.82 48,186.04 66,503.18 91,831.00 256,515.18 73,684.16 154,819.09 35,249.82 Buy avg. return 1.57% 0.12% 0.02% 0.24% 0.20% 0.80% 0.71% 0.19% 0.56% Sell avg. return 0.78% 0.27% 0.15% 0.06% 0.06% 0.93% 1.09% 0.27% 0.10% Institutional investors Buy avg. cap. (mil. bt.) 34,139.75 43,166.47 50,353.36 82,012.73 118,172.97 269,883.50 64,026.96 146,893.03 36,727.54 Sell avg. cap. (mil. bt.) 32,920.61 46,307.60 41,887.54 73,611.86 106,589.44 260,086.39 60,966.21 150,075.60 35,471.23 Buy avg. return 0.69% 0.36% 0.24% 0.06% 0.06% 0.88% 0.91% 0.65% 0.23% Sell avg. return 0.78% 0.35% 0.44% 0.18% 0.18% 0.84% 0.74% 0.13% 0.11% Individual investors Buy avg. cap. (mil. bt.) 30,125.25 31,714.10 28,652.36 49,022.65 53,788.69 314,560.17 44,132.74 44,967.60 36,785.19 Sell avg. cap. (mil. bt.) 30,429.19 32,930.86 29,528.02 49,615.42 52,560.93 314,450.30 45,268.78 46,717.57 37,220.75 Buy avg. return 0.96% 0.15% 0.47% 0.05% 0.47% 0.97% 0.62% 0.50% 0.33% Sell avg. return 1.13% 0.07% 0.45% 0.03% 0.45% 0.87% 0.55% 0.40% 0.67% Proprietary traders Buy avg. cap. (mil. bt.) 36,297.54 43,327.02 54,473.26 85,758.39 106,450.42 361,481.38 65,151.93 138,643.27 40,592.62 Sell avg. cap. (mil. bt.) 37,612.48 44,840.99 42,211.19 84,485.82 104,759.86 359,560.71 65,571.50 140,456.17 41,009.09 Buy avg. return 1.03% 0.03% 0.18% 0.06% 0.30% 0.83% 0.42% 0.24% 1.33% Sell avg. return 1.14% 0.05% 0.10% 0.04% 0.15% 0.85% 0.58% 0.06% 1.16% 20 S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23

S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 21 Table 7 Correlation of overall net trading gains and its components. This table reports the correlations of overall net trading gains (π t ), gains from security selection (π S ), and gains from market timing (π T ) for various investor types, where Π t =π t S +π t T. The correlation coefficients are calculated for the trading interval (h) of 1, 8, and 52 weeks. ***, **, and * denotes significance at the 1%, 5%, and 10% level, respectively. Table 7.3: Correlation of trading performance between various investor types, h=52 Foreigners Institutions Individuals Proprietary Panel A: correlation of overall trading performance π t Institutions 0.6028*** 1 Individuals 0.7277*** 0.3208*** 1 Proprietary 0.0967* 0.0577 0.0311 1 T Panel B: correlation of market timing performance π t Institutions 0.267*** 1 Individuals 0.9366*** 0.0829 1 Proprietary 0.136** 0.2199*** 0.2683*** 1 S Panel C: correlation of security selection performance π t Institutions 0.5994*** 1 Individuals 0.7273*** 0.313*** 1 Proprietary 0.0973* 0.0523 0.0362 1 Table 7.2: correlation of trading performance between various investor types, h=8 Foreigners Institutions Individuals Proprietary Panel A: correlation of overall trading performance π t Institutions 0.5245*** 1 Individuals 0.9158*** 0.1503*** 1 Proprietary 0.0849 0.1676*** 0.2284*** 1 T Panel B: correlation of overall trading performance π t Institutions 0.4693*** 1 Individuals 0.9225*** 0.0956* 1 Proprietary 0.1258** 0.1443** 0.2495*** 1 S Panel C: correlation of overall trading performance π t Institutions 0.4038*** 1 Individuals 0.7379*** 0.1453** 1 Proprietary 0.2314*** 0.1422** 0.2213*** 1 Table 7.3: Correlation of trading performance between various investor types, h=52 Foreigners Institutions Individuals Proprietary Panel A: correlation of overall trading performance π t Institutions 0.2357*** 1 Individuals 0.9271*** 0.1384** 1 Proprietary 0.1486** 0.2311*** 0.3*** 1 T Panel B: correlation of overall trading performance π t Institutions 0.2269*** 1 Individuals 0.9306*** 0.1383** 1 Proprietary 0.1487** 0.2314*** 0.2986*** 1 (continued on next page)

22 S. Phansatan et al. / Pacific-Basin Finance Journal 20 (2012) 1 23 Table 7 (continued) Table 7.3: Correlation of trading performance between various investor types, h=52 Foreigners Institutions Individuals Proprietary S Panel C: correlation of overall trading performance π t Institutions 0.4818*** 1 Individuals 0.2117*** 0.1792*** 1 Proprietary 0.0675 0.1375** 0.2238*** 1 market timing (macro) information advantage in the Thai market, but do not have superior security selection (micro) informational advantages over local investors. This conclusion can explain why many studies can find that foreign investors have informational advantages in numerous markets (presumably where macro, market timing information is important), but not in other emerging markets where local investors might have superior security selection information. The persistent trading strategies of proprietary traders lead to good short term but poor long term market timing performance, with proprietary traders apparently profiting from the their liquidity provision role to the markets via short term market trading gains that are at the expense of individual investors. An unexpected result is the finding that the trading strategies of institutions and proprietary traders lead to very inferior security selection, and thus very poor overall trading performance. Individual investors' herding behavior leads to gains from security selection at the expense of all the other investor types, but their poor market timing cancels out these gains. References Bae, K., Ito, K., Yamada, T., 2002. Who profits from whom? The interaction of investor trades in a stock market. Working paper, Korea University, Nomura Securities, and Hong Kong University of Science and Technology. Bae, K., Yamada, T., Ito, K., 2006. How do individual, institutional, and foreign investors win and lose in equity trades? Evidence from Japan. International Review of Finance 6, 129 155. Barber, B., Lee, Y.T., Liu, Y.J., Odean, T., 2004. Who gains from trade? Evidence from Taiwan. Working Paper, University of California, and National Chengchi University. Barber, B., Odean, T., 2000. Trading is hazardous to your wealth: the common stock investment performance of individual investors. Journal of Finance 55, 773 806. Cai, F., Zheng, L., 2004. Institutional trading and stock returns. Working Paper, Federal Reserve Board, and University of Michigan Business School. Choe, H., Kho, B.C., Stulz, R., 1999. Do foreign investors destabilize stock markets? The Korean experience in 1997. Journal of Financial Economics 54, 227 264. Choe, H., Kho, B.C., Stulz, R., 2005. Do domestic investors have an edge? The trading experience of foreign investors in Korea. Review of Financial Studies 18, 795 829. Dvor ák, T., 2005. Do domestic investors have an informational advantage? Evidence from Indonesia. Journal of Finance 60, 817 839. Froot, K.A., O'Connell, P.G.J., Seasholes, M.S., 2001. The portfolio flows of international investors. Journal of Financial Economics 59, 151 193. Froot, K.A., Ramadorai, T., 2001. The information content of international portfolio flows. Working Paper, Harvard University. Fu, Y., Qian, W., 2010. A closer look at short term speculation on the street: evidence from a residential presale market. Working Paper, National University of Singapore. Griffin, J.M., Harris, J.H., Topaloglu, S., 2003. The dynamics of institutional and individual trading. Journal of Finance 58, 2285 2320. Grinblatt, M., Keloharju, M., 2000. The investment behavior and performance of various investor-types: a study of Finland's unique data set. Journal of Financial Economics 55, 43 67. Grinblatt, M., Titman, S., 1993. Performance measurement without benchmarks: an examination of mutual fund returns. Journal of Business 66, 47 68. Jackson, A., 2003. The aggregate behaviour of individual investors. Working Paper, London Business School. Kamesaka, A., Nofsinger, J.R., Kawakita, H., 2003. Investment patterns and performance of investor groups in Japan. Pacific-Basin Finance Journal 11, 1 22. Kamesaka, A., Wang, J., 2004. The asian crisis and investor behavior in Thailand's equity market. Working Paper, Ryukoku University, and University of New South Wales. Kaniel, R., Saar, G., Titman, S., 2005. Individual investor trading and stock returns. Journal of Finance 63, 273 310. Karolyi, A.G., 2002. Did the Asian financial crisis scare foreign investors out of Japan? Pacific Basin Finance Journal 10, 411 442. Lakonishok, J., Shleifer, A., Vishny, R.W., 1992. The impact of institutional trading on stock prices. Journal of Financial Economics 32, 23 43.

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