Online investors trading behaviour and performance: Evidence from the Korean equity market

Transcription

1 Online investors trading behaviour and performance: Evidence from the Korean equity market NATALIE Y OH * Ψ University of New South Wales and SIRCA Limited JERRY T PARWADA * University of New South Wales and SIRCA Limited TERRY S WALTER * University of New South Wales and Capital Markets CRC Limited Abstract This paper investigates the trading behaviour and performance of online equity investors in comparison to other investors on the Korean stock market. We find that online traders are noise traders who provide liquidity to other investors. We also show that the aggregate trading activity of all investor types largely fails to explain market returns. However, returns on the index have a significant positive impact in changing online investment flows, compared to the negative feedback trading we find for other domestic investor types. Volatility is apparently perceived as an opportunity, and like all other investors, online traders increase their trading during volatile periods. In periods when there is uncertainty about the future direction of the market online investors reduce purchases and sales indiscriminately. Although some market timing ability characterizes online buy trades, the long run performance of online investors trading decisions is below that of other investor types. JEL classification: G10; G20; O33 Key words: Online trading, Performance, Investor Behaviour *We thank the Securities Industry Research Centre of Asia Pacific for financial support and acknowledge Mr Youngjin Kim from KSDA for valuable comments. Mr Kilhyun Ahn from the Korean Stock Exchange gets special thanks for providing data. Ψ Corresponding author. Address for correspondence: SIRCA Research, PO Box H58 Australia Square, NSW 1215, Australia. Telephone: , Fax: natalie@sirca.org.au (Oh); jerry@sirca.org.au (Parwada); t.walter@unsw.edu.au (Walter)

2 1. Introduction Recent developments in internet-based transaction technologies have allowed online investing to become an important, if not controversial, feature of financial markets. 1 Online trading has the potential to lower transaction costs and facilitate entry, resulting in increased trading volumes (D Avolio, Gildor, and Schleifer, 2002). Despite evidence that internet-based stock trading now accounts for a large proportion of securities trading, it is surprising that very few academic studies have been conducted of this rapidly expanding form of trading. Choi, Laibson, and Metrick (2002) and Barber and Odean (2002) are the exceptions. Choi et. al compare online traders with phone-based traders using a sample of 100,000 members of two large US pension funds, and find that the availability of internet trading increases transactions by 50 percent. Barber and Odean document increased trading activity and a higher propensity to speculate amongst investors who take up online trading. Importantly, these studies show that trading profits quickly deteriorate (Barber and Odean) or are non-existent (Choi, Liabson and Metrick) in the period after online trading is adopted. Online investing research is of interest from at least two perspectives. First, by demonstrating that online trading increases volume, these studies open up the possibility that online investing may bring price pressure to bear in markets. Second, by analyzing post trading performance, it can be deduced whether the phenomenal growth in volume is driven by information or cognitive biases on the part of the investors. 1 See (Barber and Odean, 2002) for US data and examples of adverse press coverage of online trading. 1

3 In this paper we examine the behaviour and performance of online investors, and compare this to other investor categories in the Korean equity market. We choose this market because the level of online investing in Korea may be characterized as being phenomenal. For example, online trading accounted for 65.3 percent of all stock trading on Korean Stock Exchange (KSE) in April We examine the behaviour of aggregated online trading to identify any systematic patterns that emerge relative to other investors. To the best of our knowledge this paper represents the first substantive study that directly compares online investors behaviour and performance to all other participants in the equity market. The study closest to ours is Jackson (2003). He uses individual client accounts at a sample of 56 Australian retail stockbroking firms, including nine internet brokers, to investigate whether discernible flow-return patterns exist for his dataset. The average trade size is $7,570 for internet brokerage clients, and $10,080 for the full-service brokers, suggesting that Jackson s study does not capture much of the institutional trading on ASX. We utilize a detailed database of daily trading volumes and values provided by the KSE. Importantly this database links volumes and values to each of the categories of investors originating the trades, including online investors, individual investors, foreign investors, local institutional investors, securities houses and unclassified institutional investors. Such data are not readily available in other markets. This makes our study a potentially useful supplement to the studies that have used proprietary firm level data. 2 Moreover, 2 Barber and Odean (2002) use a dataset consisting of clients of a discount brokerage firm; Choi, Laibson and Metrick (2002) utilise data on two US pension funds. 2

4 the dataset splits trades into purchases and sales allowing us to augment our approach with analysis based on trade imbalances, as aggregate trading activity could mask interesting trading traits. This paper is organized as follows. The next section summarizes the background to online trading and the related literature and Section 3 describes the data and market setting. Trading behaviour and performance are analyzed in Sections 4 and 5, respectively. Section 6 concludes. 2. Background to perceptions of online investors This section reviews the role of internet investing and the research literature relevant to this paper. The benefits for online trading have been documented in various industries. For example, Brown and Goolsbee (2002) suggest that the internet may significantly reduce search costs by enabling price comparisons online in the insurance market. However, it is not clear that informational advantages translate into superior return performance in equity markets. Barber and Odean (2002) investigate the performance of investors who switched from phone-based trading to internet trading. While those traders who opted for internet trading initially beat the market by about 2% prior to going online, their performance decreased afterwards, resulting in performance 3% below the market. Choi, Laibson and Metrick (2002) also report evidence of underperformance in the market 3

5 timing of online traders in a 401(k) plan. 3 Thus, access to wider information sources on the internet does not seem to imply higher return performance. Whilst online investing facilities may have reduced the costs of trading, there is a downside. First, the detrimental effects of high portfolio turnover have been shown to reduce performance (Barber and Odean, 2000, 2002 and Choi Laibson and Metrick, 2002). 4 In contrast, additional trading increases liquidity and this may induce even higher volumes, possibly creating a winner s curse. Second, trading volume bubbles in online trading may result in another detrimental feature - low information revelation. Third, online trading may also increase noise as information sources such as discussion groups (dominated by unsophisticated investors) become an avenue for spreading inaccurate information (Madhavan, 2000). According to D Avolio, Gildor and Shleifer (2001), A well functioning securities market relies on the availability of accurate information, a broad base of investors who can process this information, legal protection of these investors rights, and a liquid secondary market unencumbered by excessive transaction costs and constraints. The reliance of securities markets on a broad base of investors who can process information has been considered questionable in the case of online investors. Are online 3 401(k) plans are the primary vehicle for retirement savings in the United States. According to Choi, Laibson and Metrick (2002), in 1999, 401(k) plans held $1.6 trillion in assets, 72% of which represent equity holdings. 4 See also Carhart (1997) on the negative relation between portfolio turnover and net returns in mutual funds. 4

6 investors able to process information correctly and thus contribute to a well-functioning market? Recently, online trading has been criticized in the financial press as being a conduit for introducing unsophisticated individuals into the market, thus making the market too speculative in ways synonymous with a gambling arena. 5 The justification behind this accusation is that it is believed the so-called inexperienced online traders have access to an overwhelming amount of information through the internet, which creates an illusion of knowledge and brings about overconfidence in trading (Barber and Odean, 2002). Further, lower transaction costs with online trading have allowed online investors to engage in speculative trading more cheaply, thus creating speculative bubbles, inducing higher volatility, asset mispricing and poor trading performance. 6 It is surprising, given the contentious perceptions of online investors behaviour, that there has not been any systematic analysis of the aggregate behaviour and trading returns for online investors. We provide empirical evidence on these issues and, perhaps more importantly, compare online investors behaviour with other investors to identify whether internet-based traders indeed behave in a manner that warrants such criticism. 5 See for example, Digital Manipulation The Economist, 10 th February 2001 (on the Korean market); Online Trading May Trigger New Dimension in Volatility New Straits Times Press, 19 th October 2000 (Malaysia); and Technology Boom Keeps Volatility Ticking ; Australian Financial Review, 28 th August 2000 (Australia). 6 See Barber and Odean (2001) for a review of the experimental economics literature on asset bubbles. 5

7 3. Data and market background 3.1. Data This paper utilizes daily data on online trading volumes obtained from the KSE. The data identify trading volume (number of shares traded) and value (Korean won) for both purchases and sales over the period. The sample period falls after the worldwide tech boom which started in 1999 and the subsequent tech bust which diminished in late The dataset provides information on aggregate online trading activity that is not readily available in other equity markets. The dataset also contains trading volumes and values for other market participants. These include foreign investors (hereafter denoted Foreigners), individual investors (Individuals), local institutional investors (Institutions), securities houses (Securities) and unclassified institutions (Others). 7 Institutions comprise insurance, investment trust companies, commercial banks, merchant banks and pension funds. Securities are the local securities companies that trade on their own behalf (principal trading). Others in the dataset are mainly government agencies. We exclude Others from this study because they trade infrequently. Others have also been 7 The foreign investor category includes both institutional and individual investors, but most trades are from institutions. It is possible that trades we identify as foreign trades are actually trades by Korean investors who set up a foreign nominee company to trade on the KSE. This limitation has also been identified by Choe, Kho and Stultz (1999). Prior to May 1998, foreigners had limited access to ownership of Korean stocks. The restrictions that remain for foreigners apply to the ownership of public corporations that are deemed to be of national strategic importance, including electricity, telecommunications, and airline companies. 6

8 excluded in the prior research due to insignificant trading (e.g. Choe, Kho and Stulz, 2004). In line with the notation adopted for identifying different investor types, we also refer to online investors simply as Online. The daily market data utilized in this study are also sourced from the KSE. The data include the dividend yield, the 90-day commercial paper rate, the foreign exchange rate (US dollar/korean won), the 3-year government bond and the 3-year corporate bond rate Market structure and descriptive statistics The KSE is an order driven market with trading facilitated by the Automated Trading System (ATS). There are no designated market makers or specialists. Stock trade orders are placed through stockbrokers. Whilst a few dedicated discount online brokers exist, almost all the full service brokers also operate systems that facilitate online trading 8. Following the authorization of online trading in late 1997, internet-based trading was not immediately popular and the onset of the Asian economic crisis further reduced investors willingness to adopt the new technology. It was not until late 1998, when the economy stabilized and the tech boom had swept the Korean market, that investors truly embraced online trading, primarily due to heavily discounted online trading commissions and the fierce competition between providers of the service that pushed the costs even lower. The average commission rate of online trading in 1997 was 0.5 percent, the same 8 To date no foreign brokerage firms provide online trading services. All online trading is provided by local brokerages. 7

9 level as for traditional methods, but in 1999 it dropped to 0.14 percent, further dropping to 0.07 percent by 2001 (Byun, 2002). The new technology also brought speedy information dissemination that made trading more accessible for existing and new investors. A combination of lower transaction costs and easy access to information not only encouraged new investors to enter the market but apparently also increased trading frequency and the participation of day traders. According to Korean Securities and Derivatives Association (KSDA) figures, in 2001 day trading was responsible for 46.6 percent of the total stock trading, an increase from 38.7 percent in 2000 (see KSDA, 2003). The KSE is amongst the most actively traded exchanges in the Asia-Pacific region. The total value of share trading on the KSE stood at US$597 billion in 2002, a considerable amount when compared with the largest neighboring exchanges (e.g. Tokyo, US$1,66 trillion; Taiwan, US$541 billion; Australia, US$244 billion, and Shanghai, US$291 billion). 9 As reported in Table 1, share trading in Korea is dominated by individual investors. The trading frequency (volume) of individual investors is phenomenal in comparison to most other equity markets, in which institutions are the dominant investor category. Individual trading on average is above 90 percent for both purchases and sales of KSE volumes. When total trading value is considered, individuals have a lower presence but remain the dominant group, standing at 70 percent, whilst value of trading for other investor types such as foreigners and institutions, are about 10 percent. The correlation between individual investors and online investors is almost perfect, although the percentage of trading done by individuals in volume and value terms outweighs 9 Data obtained from World Federation of Exchanges Annual Statistics

10 online investors (see Table 2). Because of this almost perfect correlation between individuals and online traders, we exclude a comparison of individual investors and online investors. As reported in Panel A of Table 1 mean and median online trading volumes are 80 percent of total trading volume. Reports in the popular press suggest that these volumes are all made up of small parcels with little information content, creating bubbles or noise in the market place. Figure 1 shows the extent of the increase in online trading since January Clearly, the value of non-online investing has decreased. Total on-line trading (including stocks, futures and options) increased 146 times from 22.5 trillion won in 1998 to 3,293 trillion won in This signifies that many investors have switched from traditional methods of transacting to online trading. 4. Stock market effects of the trading behaviour of online traders This section investigates whether online investor flows move stock prices. If so, is this in ways that are different from the impact of other investors trades? Studies beginning with Kraus and Stoll (1972a, b) document temporary price pressures from trades. Dealing with a homogeneous and dominant group of investors such as online investors may give rise to expectations that aggregate flows from this section of the market can exert pressure on market prices. We also consider whether shifts in returns earned by the different investors are information driven, or a consequence of price pressure. If trades 10 On-line stock trading has been declining since 2002, but on-line derivatives trading shows a consistent increase. 9

11 are information driven then they could be reasonably expected to result in high performance; if trading is due to cognitive biases, poor performance would more likely follow. To facilitate comparison between online and other investors, we investigate, for each investor type, at an aggregate level, whether the demand curve for each investor type is horizontal or downward sloping to understand the nature of online investors demand. This is done by examining the relationship between flow and return - whether flow Granger causes return or vice versa. Further, if flow contains information above market fundamentals, then there is price pressure being exerted by that investor type, hence rejecting the traditional assumption that the demand curve is horizontal. The latter issue is tested by adding market fundamentals to the estimated regression equations following Cha and Lee (2001). The relationship between flow and market return also enables the study to conclude whether investors are in aggregate positive or negative feedback traders. Herding and feedback trading have been extensively studied for many markets and for different investor types. According to Grinblatt, Titman and Wermers (1995), a trade imbalance by an investor type that is correlated with past returns can be considered feedback trading. In many studies, large trading imbalances are interpreted to indicate investor herding. 11 Hence we investigate investor behaviour by calculating each day s trade imbalance for that investor group. We compute trade imbalance or Net Investment Flow (NIF) as follows: 11 Herding is defined as a group of investors buying or selling during the same time interval (Nofsinger and Sias, 1999). 10

12 Purchasing Value NIF it = Purchasing Value it it Selling Value + Selling Value it it. (1) NIF it is a proxy for ownership data which enables us to identify net purchases by investor type i at time t. This net measure is sometimes considered to be indicative of when the market is under or over-valued, hence reflecting the market timing ability of different investor types. We also analyze the behaviour of each investor type by considering stock purchases and sales separately. We normalize purchases and sales flow measures to counteract the upward trend in market trading volumes and valuations. In line with common practice, we normalize flow by the 90-day moving average of market capitalization for value flow (see Warther, 1995, Goetzmann and Massa, 2003). 4.1 Simple measures of relatedness To provide a preliminary view of trading behaviour, Table 3 reports correlations between NIF and return and correlations among investor types. Panel A reports a negative correlation between Online and contemporaneous return, suggesting that when Online are net buyers the market goes down. Based on results for one-day lagged returns, the flowreturn correlation patterns are suggestive of all domestic investors engaging in negative feedback trading whilst Foreigners are positive feedback traders. Foreigners, Institutions and Securities exhibit a positive correlation with contemporaneous return raising the possibility that, in terms of market timing, Online are poor performers whereas Foreigners, Institutions and Securities are better market timers. 11

13 Panel B reports the relationship between investor types. Foreigners and all other investor flows are negatively related. Combining this result with those of the flow return correlations suggesting Foreigners are good market timers raises the specter that domestic market participants, especially online investors, are liquidity providers to foreigners. However, we regard these results as preliminary, and we conduct further tests in which we control for other possible determinants in the sections below. 4.2 Detecting feedback trading Feedback trading analysis based on correlations alone may give erroneous conclusions due to possible multicollinearity. We use a bivariate vector autoregressive regression (VAR) model to analyze whether online investors engage in feedback trading, following Seasholes (2000) and Froot, O Connell and Seasholes (2001). Econometrically, a VAR model and its transformed representation constitute a useful tool which allows us to effectively measure the impact of one variable on fluctuations of other variables in the model. The VAR model specification is: Z t p = C + Φ j= 1 j Z t j + ε t (2) Rt where Z t = C = F t α α R F Φ p φ = φ 1,1, p 1,2, p φ φ 1,2, p 2,2, p ε R ε = t ε F, t, t. Z t is a 2 1 matrix of return, R t, and flow, F t (Purchases, Sales or NIF) for day lag j, C is the constant and ε t is the 2 1 error matrix. 12

14 The VAR analysis constitutes estimates of reduced form equations with uniform sets of lagged dependent variables from all equations as regressors. Using the Schwartz and Alkaike criteria, we find that two lags of each variable are enough to capture the linear interdependencies in the system. However, a five day lag is chosen to capture, and report on, trading patterns over the preceding week. Table 4 reports results from a bivariate VAR (5) model for all investors. Panel A, Part 1 reports the results for Online with Return as the dependent variable. All three flow types (Purchases, Sales and NIF) and past returns fail to show any significant impact on returns. This indicates that aggregate online flows do not move the market index. The absence of a significant relationship between past returns and contemporaneous return demonstrates that KSE is at least weak-form efficient. With Flow as a dependent variable (Part 2), both market returns and past Online flows exhibit some significant relationship to Online flows. For both Purchases and Sales flow measures, the market return impact lasts up to four lags. It is interesting to observe that Purchases and Sales move in the same direction. Yesterday s return has a positive correlation with today s flow for both Purchases and Sales. Two day lagged returns are negatively correlated with both Purchases and Sales. Hence, it is not surprising that we do not observe a significant impact of market returns on NIF because it seems that Online as a group, do not share a consensus on market movements and are likely to be just noise trading. It is therefore hard to predict whether online investors are positive feedback traders (momentum traders) or negative feedback traders (contrarians) at an aggregate level since there is no distinct pattern of behaviour. The R 2 value for Purchases and Sales is relatively high, 13

15 consistent with periods of boom and bust in trading levels, but low for NIF, consistent with there being little information in the net opinion of online traders. Online exhibit strong positive serial correlations in all three flows in that Online buys and sells tend to lead other Online investors to buy and sell, demonstrating herding behaviour. This also suggests that lagged online flow is a good indicator of contemporaneous flow. Our results on Online flow s relations with past returns differ from those of Jackson (2003) who finds evidence of negative feedback trading by internet brokers driving the overall observed relationships for a sample including full service brokers. Our results also contrast with the findings on Finnish and US individual investors net flows by Grinblatt and Keloharju (2001) and Odean (1998), respectively. Similar to Online, results reported in Table 4 Panel B indicate that Foreigners past flows and returns alike fail to explain changes in market return. However, past market returns and flows do have some significant impacts for the three flows (Part 2). Unlike Online, Foreigners do have a distinct pattern in their behaviour. Yesterday s return has a significant positive correlation on today s Purchases but no significant impact for Sales, a position confirmed by the significant positive impact on NIF. Similarly, returns posted two days ago have no impact on today s Purchases but are significantly positively correlated to Sales, resulting in a significant negative relationship with NIF. This illustrates that Foreigners as a group have more consensus (than Online) on the direction of market movements. Part 2 also reports a significant sign reversal between one day lagged returns and two day lagged returns for NIF flows. This may suggest that 14

16 Foreigners are profit seekers or bargain hunters who buy in up markets and sell the following day, as it were to take profits. However, for a market that has been shown to be at least weak form efficient, a more plausible interpretation is perhaps that foreigners, as net sellers after market rises, are contrarians, and that gains posted in periods of temporary price pressure reverse shortly afterwards due to the actions of other investors. A significant positive coefficient is observed for Foreigners one day lagged flow and current flow, which suggests that herding behaviour amongst Foreigners is also evident. Table 4 Panel C (Part 1) reports results for Institutions with Return as the dependent variable, and indicates that the coefficients on past returns are not significantly different from zero. Unlike any other investors, Institutions two day lagged flows (NIF and Sales) display a significant impact on returns suggesting the positive NIF impact on Return is driven by negative Sales flow. In terms of results on Flow in Panel C (Part 2), Institutions exhibit negative feedback trading. Yesterday s return induces significantly higher Sales and no clear impact on Purchases, thus leaving a negative NIF. The result that Korean institutions are negative feedback traders contradicts findings for US institutions which suggest positive feedback trading (Grinblatt, Titman and Wermers, 1995; Wermers, 1999; and Nofsinger and Sias, 1999) but portrays similarities to Japanese institutions (Kim and Nofsinger, 2002). Since Institutions are net sellers and Foreigners net buyers, it is possible that these two investor types interact. Positive serial correlation is detected for up to a five day lag for Purchases and Sales but only up to a three day lag 15

17 for NIF. This result indicates possible herding behaviour by Institutions - yesterday s trading activity spurs other institutional investors to trade today. Finally, Table 4 Panel D reports results for Securities. Analogous to Online, Foreigners, and Institutions, past flows and returns for Securities display an insignificant impact on Returns. However, both past returns and flows show some significant results for all three flows. Securities exhibit significant net selling activity for one day lagged returns and net buying activity for two day lagged returns which is the exact opposite of Foreigners conduct, but in line with the behaviour of Institutions. This suggests that Securities, like Institutions, interact with Foreigners. Positive serial correlation is evident for flows for up to five days. In summary, the R 2 statistics reported for all investors in Table 4 indicate that past market returns are an important variable in explaining flows whilst the explanatory power of past flow measures on market returns is low. Past market returns do not have any significant impact on current market returns. As well, significant positive serial correlations are noted for Online, as with all other investor types, which suggest that there is significant herding behaviour evident in the Korean market. All in all, online investors, like other investor types, do not exhibit trading patterns that suggest particular skill in trading. 4.3 Is there information in flows? Our analysis of the flow-return relationship is augmented in this section by further tests of whether, beyond simply reacting to market returns, online investors flows contain any 16

18 information about market returns. The study by Cha and Lee (2001) conjectures that flows may affect returns through either price pressure, or their effect on aggregate market revisions of fundamentals about stock prices. Cha and Lee devise a method of disentangling the price pressure and information effects of investor flows. Whilst it is difficult to pinpoint the appropriate market fundamentals for purposes of our study, there is reason to expect that, at the market level, dividends are important drivers of prices, as too would be the risk premium. At the macroeconomic level interest rates and, for a fairly open market like Korea, foreign exchange rates are further possible candidates. We conduct the following regression analyses, loosely based on Cha and Lee (2001): Ret and Flow 3 3 t t i t i + i= 1 i= 1 = α + βr + χflow + ϖ Ddiv + πdint + τaspr ξaxrate, (3) 3 3 t t i t i + i= 1 i= 1 = η + φr + ϕ Flow + νddiv + γdint + ψaspr ρaxrate (4) In the specifications, Flow t is measured by Purchases, Sales or NIF, R t is market return calculated by the natural logarithm of the difference between the level of the market index on day t and day t-1; the market fundamentals are Ddiv, the dividend yield; Axrate, the change in the foreign currency exchange rate (US dollar / Korean won); Aspr, the spread, proxied by the difference between the 3-year government bond and 3-year corporate bond rate; and Dint, the 90-day commercial paper rate. Since the frequency of the data is daily and the market fundamentals do not fluctuate much on a daily basis, with the exception of the foreign exchange rate, the variables are averaged over the past three 17

19 days. Short-term interest rates and dividend yields are differenced to meet stationarity conditions. Table 5 presents results of tests of Granger causality between flows and returns in the presence of market fundamentals. The most interesting result in Panel A for Online is that our specifications reject the null of no causality between net flows and returns whilst failing to reject the null of causality in the opposite direction. The lack of causality from flows to returns suggests the absence of any information in Online flows concerning market returns. Strikingly, the significant impact (negative in sign) of two day lagged returns on both Online Purchases and Sales followed by a positive reaction to returns the following day, as reported in Panel A (Part 2), is consistent with the directionless nature of online investor trades shown in Table 4. Online flows clearly contain information about the following day s flows, particularly in the case of disaggregated flows; however only a low level of statistical significance applies to net flows lagged one day (again emphasizing the absence of a clear trading strategy by online investors). We contrast the flow-returns relations observed for Online with those for other investor types. Generally net flows from Foreigners, Institutions and Securities (see Panels B (Part 2), C (Part 2) and D (Part 2)) do not contain information about market returns in the presence of market fundamentals. The exception is for Institutions flows lagged by two days where we reject the null of causality from these flows to returns. This result suggests domestic institutional investors are privy to some private information about 18

20 returns. 12 This may be the signal used by Foreigners to purchase prudently, along with Institutions themselves, the following day as their one day lagged purchases seem to contain information about market returns even after incorporating market fundamentals (possibly with Online and Securities being the providers of the necessary liquidity). The non-online traders all appear to be influenced by past returns in their investment strategies. The signs on the coefficients for one-day lagged returns suggest positive feedback trading behaviour for Foreigners, and negative feedback trading by Institutions and Securities. (See Panels B (Part 1), C (Part 1) and D (Part 1), respectively). The difference in the behaviour of Foreigners compared to other non-online investors raises questions about whether they act in a manner that exploits the trends observed for local investors. However, this question is beyond the scope or our paper. In possible vindication of online investors, it is interesting to note that all other investors flows are also subject to positive serial correlation. The levels of severity differ though for instance, for Institutions all lags of the various flow measures reveal strong positive serial correlation; for Securities this phenomenon applies more significantly in the case of disaggregated flows; and for Foreigners it is generally restricted to one lag of daily flows. These results in part deflect the criticism of online investors to the extent that their trading behaviour seems to be no more affected by past trading trends instead of market fundamentals than other investor types. 12 Although we do not investigate this issue we note that the Korean corporate market is dominated by cross-holding structures, Chaebols, of which the Institutions in our study often are part. 19

21 In summary, since for most investor types all three flows do not contain additional information about returns over and above market fundamentals, it is likely that the price pressure hypothesis does not hold. Flows from online investors, like those of other investor types, simply respond to changes in market returns. In this regard, these findings are consistent with results for US institutions (Cha and Lee, 2001), and imply that a horizontal market demand curve for equities holds at an aggregate level. The presence of strong positive serial correlation for all investors demonstrates that previous trading activity by the same investor class has a significant impact on current flows. That this phenomenon is shown to exist in the presence of market fundamentals emphasizes the possible influence of herding on trading patterns amongst investors in the Korean stock market at the expense of fundamental information. 4.4 Risk, uncertainty and market fundamentals as determinants of flows According to finance theory investors decisions are based not only on expectations about return but also on the uncertainty of those returns. What bearing does risk have on the determinants of flow for online investors and how does this compare with other investor types? In a classical CAPM model it is known that risk has negative utility effects on investors and therefore a negative relationship with demand is expected. But if online investors are unsophisticated and increase their comparative presence during periods of market uncertainty, allegations about the detrimental effects of online trading are likely justified. This is because it is usually speculators who enter the market and increase their presence during periods of uncertainty, hoping to reap profits from volatility, driving prices away from fundamentals (Goetzmann and Massa 2003). In the previous section 20

22 we investigated the relationship between flow and returns. In this section we examine investors risk perceptions and behaviour and the role of risk in determining the volume or demand for each investor type. We investigate the role of uncertainty in determining flows using the following regression equation: F t = α + βunc + γinfv + δf 1 + ε (5) t t t t where F t denotes flow measurements as being Purchases, Sales, or NIF. InfV t is a vector of information variables (or market fundamentals as explained in section 4.2), ε t is the error term, and Unc t represents the measure of uncertainty under consideration. The specification is similar to that use by Goetzmann and Massa (2003). Two proxies for uncertainty are considered. First, volatility is measured as the square of the natural logarithm of return. 13 Since anecdotal evidence suggests Korean investors are mostly day-traders, intra-day volatility based on the Garman and Klass (1980) measure is also used for robustness. 14 Second, as an estimate of the dispersion of investor beliefs, KOSPI 200 futures open interest, standardized by dividing daily open interest of KOSPI 13 Bae, Chan and Ng (2004) use this measure to capture the volatility in their study for all the emerging markets which includes Korea. 14 Garman and Klass (1980) investigate the relative efficiency of various measures of volatility and identify a volatility measure with the highest efficiency. In the so-called GK measure, written as 2 2 σ t = VAR( GK) = 0.5[ LN( High) LN( Low)] [2LN (2) 1][ LN( Open) LN( Close)], VAR(GK) is the variance using the Garman-Klass (1980) method, LN denotes the natural logarithm, and High, Low, Open, Close are the high, low, open, and closing prices of the day to determine the volatility. 21

23 200 futures by the trading volume on KOSPI 200 futures contract on the same day, is utilized. The results, reported in Table 6 Panel A show that a strong positive relationship exists between Unc (volatility) and disaggregated Online flows but not net flows (NIF). This shows that volatility is an important contributor to Online flow even though the weak relationship between volatility and NIF may demonstrate that Online increase their presence in the market indiscriminately during volatile periods. Increasing both Purchases and Sales during volatile periods may further increase volatility. 15 Hence, Online trading could be seen as speculation in the market during volatile periods, possibly creating bubbles and a winner s curse. 16 Similar to our results for earlier tests, a strong positive serial correlation persists for up to three days which may intensify the creation of bubbles. One market fundamental, Ddiv, is significantly related to all three flow measures. Remarkably, the positive relationship for NIF reported for Online in Table 6 defies the trend of negative relations for all other investor types net flows. This may indicate that online investors do take into consideration some fundamental information, particularly easily accessible variables such as dividend yield, in 15 Perhaps this is the reason why online investors have been blamed for increasing volatility and destabilizing the market. This is because during periods of higher volatility it is expected that speculators would enter the market attracted by the possibility of higher gains, pushing prices away from the fundamentals. 16 Speculative bubbles can also be manifestations of the winners curse. The winners curse is more likely when there are more bidders and when the dispersion of opinions about the value of whatever is being auctioned is greater. 22

24 determining their trade decisions, but they are still unable to process the information in a manner that results in astute trading strategies. This supports the perception of online investors as being naive market participants. The other investor types, Foreigners, Institutions, and Securities all display a significant positive relationship between volatility and Purchases and Sales in the presence of market fundamentals but fail to show a significant relationship between volatility and NIF. Clearly Online are not the only class of investors to increase Sales and Purchases volume during volatile periods. This suggests that the market as a whole perceives volatility as an opportunity to gain as suggested by Goetzmann and Massa (2003). For non-online investors, the Dint market variable bears a significant positive influence on disaggregated flows not borne out in net flow terms. On the alternative volatility measure we adopt, the above results are qualitatively similar for regressions incorporating the Garman and Klass intraday volatility computation. 17 Another risk measure adopted in the model is dispersion of beliefs. Dispersion of beliefs amongst investors is proxied for by the open interest of derivative contracts, the Korean futures contracts. The relationship between level of dispersion and demand (Purchases and Sales) is significantly negative for Online (see Table 6 Panel B) but, once again, the relationship is insignificant for NIF. This indicates that during periods when there is 17 Results available from the authors on request. 23

25 disagreement about the future Online investors withdraw from the market indiscriminately. The same results are evident for Institutions and Securities. Foreigners, however, show a significant negative relationship for all three flows, including NIF. The results indicate that Foreigners are more informed about the level of dispersion of beliefs and withdraw from the market more discerningly. This is in line with past research documenting that increases in the level of disagreement about the future course of the market by sophisticated investors (those who use derivatives) induces investors to be cautious (Goetzman and Massa, 2003). For Foreigners, clarity about the future is an important determinant of the level of investor trading activity. The interpretation of the rest of the explanatory variables remains unchanged for regressions incorporating the dispersion of beliefs as a risk measure. 5. Trading performance This section investigates whether each investor type s trading is based on information rather than cognitive biases. This is examined by analyzing post trading performance. Specifically, more rigorous tests of market timing ability are carried out to examine the extent to which trades affect prices in subsequent periods. Online investors have been alleged to be poor performers or market timers and the evidence we have presented so far appears to point in this direction. Hence this section shows, in comparison to other investor types, whether online investors are indeed the losers in the market. Due to the absence of portfolio holdings data, a direct estimate of investor group performance cannot be implemented. However, following the work of Kamesaka, Nofsinger and 24

26 Kawakita (2003), this study utilizes daily purchase and sale flows to characterize the market timing ability of these investor groups, which serves the purpose of proxying for ownership or portfolio holdings when examining market returns after each trading day. We firstly examine market performance after those days when investors conducted particularly heavy buying or selling. We then estimate the cumulative return due to the daily changes in investment flow and the subsequent market return for each investor group. 5.1 Market timing ability To examine whether the trading observed is motivated by information or cognitively biased behaviour, the performance of investor groups after heavy buying and selling days is measured. We conjecture that there is a stronger motivation behind heavy buying and selling and it is on these days we expect to capture clearer evidence of information driven or cognitively biased trading. After sorting each investor group s data by level of NIF into five equal sets, we class the highest positive NIF quintile as the buying days, and the largest negative NIF the selling days. Daily, weekly and monthly market returns following heavy buying and selling trading days are calculated. These give an indication of market timing ability. The results for performance after heavy buying and selling days in terms of value are reported in Table 7. The imbalance of the buy and sell days is low for Online. Online, on average, have NIFs of and during heavy sell and buy days, respectively. The market rises after a day of heavy buying and selling by Online in a statistically 25

27 significant sense. But the market falls after a week and a month of heavy buying and selling. This indicates that Online are relatively good at market timing for buys on a daily basis and good market timing for sells on weekly and monthly basis. A comparison of average trading imbalance between Online and the rest of traders on the KSE, reveals that Online generally do not take extreme buy or sell positions. This may confirm that Online are not well informed about the market and therefore do not have confidence to take extreme trading positions. Foreigners show relatively high buy and sell NIF imbalances. The market rises for up to a week after heavy buying from Foreigners which shows Foreigners have good market timing ability for buys. But after heavy sells from the Foreigners the market fails to show significant declines, in fact there are significant increases. We conclude from this asymmetry that Foreigners are therefore good at timing their buys but not their sells. This result is similar to Online. After a day of heavy buying and selling by Institutions, the market rises and declines, albeit our results in this regard are without statistical significance. The t-statistics fail to show any significance for post-trading performance for Institutions trades. Securities on the other hand have the highest trading imbalance. The NIFs for heavy buy and sell weeks are and respectively. The post-trading performance of Securities is poor. The market rises after heavy sell days and the market declines after heavy buy days. This poor performance persists for up to a month. 26

28 5.2 Cumulative performance To investigate who are the ultimate winners and losers on the Korean Stock Exchange the relative market timing ability of the investor groups based on the entire sample period, rather than just heavy buying and selling days as in the previous section, is examined. The performance measure used in this section was originally developed by Grinblatt and Titman (1993) in their study based on the change in the portfolio holdings and later modified by Karolyi (2002) and Kamesaka, Nofsinger and Kawakita (2003) by utilizing net investment flows in the place of portfolio holdings. For a full description of the derivation of the performance measure refer to Appendix 1. The following empirical specification estimates the cumulative return due to the daily changes in investment flow and following market returns: T Purchase Cumulative return = ( t= 1 Purchase t-1 t-1 Sales + Sales t 1 t 1 ) R (6) where Purchases and Sales are raw values and R t is the market return. Equation (6) is estimated for each investor group in analyzing the performance over the entire sample period. t Daily cumulative performance for the duration of sample period is graphed in Figure 2. Consistent with performance based on heavy buy and sell days, cumulative performance shows that Foreigners and Institutions attain the best returns. Online and Securities are the losers but the extent of Securities losses is quite extreme compared to Online. Since Securities have extreme positions (imbalances) which is a sign of herding (Nofsinger and Sias, 1999), the inability to time the market would have devastating effects on their 27

29 performance at an aggregate level. On the other hand, because Online investors net position is more or less balanced, their losses are probably mitigated. 5.3 Robustness tests The results for Securities are surprising given that they are commonly presumed to be more informed than other domestic traders. As such, we conduct a robustness check by looking at weekly rather than daily performance. The weekly aggregation follows convention as laid out in Lo and Wang (2000). The weekly cumulative performance is graphed in Figure 3. The results are consistent for weekly as well as daily flows for Foreigners, Institutions and Online but the opposite result to the daily returns is recorded for Securities. Based on weekly returns Securities have positive returns. Foreigners are a clear winner on the Korean stock exchange. Consistent with daily return results, Online at an aggregate level are losers. As a further robustness test, past, contemporaneous and lead return is regressed against NIF for each investor type to assess whether they are good market timers and also to investigate whether their flows affect future returns at daily and weekly frequencies. The functional specification is: 5 NIF = α + β i Ret t + i + ε, (7) 5 Purchases where NIF is defined as Purchases + Sales Sales for each investor type and lead/lag market returns Ret is the return of KOSPI index. 28

30 Table 8 shows the relation between NIF for each investor type and lead/lag market returns on daily and weekly basis, respectively. From observing daily past returns Online investors are negative feedback traders whereas Foreigners are positive feedback traders up to three day lag returns. Institutions and Securities also show negative feedback trading at daily level. No significant results are present for weekly past returns. The results from contemporaneous daily return show that Foreigners, Institutions and Securities are good market timers, whereas Online display signs of naivety, increasing their NIF on days when the market return is negative. In the results for weekly contemporaneous return Foreigners continue to be winners, and Online remain as losers. No significant results are evident for Institutions and Securities at weekly frequency. To summarize our results on investor performance, Online show some superior market timing ability during extreme buying positions up to a week. But looking at longer horizons and taking into consideration all rather than just extreme cases, Online perform poorly. This suggests that Online at an aggregate level are purely liquidity providers who lose out to superior investors, although there exists some marginal online investors who display superior performance as shown for extreme buying positions. Foreign investors, in both general and extreme cases, record superior performance which supports the view that foreigner trading is information driven (Seasholes, 2000, Froot, O Connell and Seasholes, 2001, Kamesaka, Nofsinger and Kawakita, 2003) and confirms that they are the winners on the Korean stock exchange. However this result is contrary to Choe, Kho 29