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Online investors trading behavior and performance: Evidence ... Online investors trading behavior and performance: Evidence ... Document Transcript

  • Online investors’ trading behaviour and performance: Evidence from the Korean equity market NATALIE Y OH* Ψ University of New South Wales JERRY T PARWADA* University of New South Wales TERRY S WALTER* University of New South Wales 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: +61 2 9236 9118, Fax: +61 2 9231 5988 E-mail. natalie@sirca.org.au (Oh); jerry@sirca.org.au (Parwada); t.walter@unsw.edu.au (Walter)
  • Online investors’ trading behaviour and performance: Evidence from the Korean equity market 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. 1
  • 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. 2
  • 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 2003. 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. 3
  • 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 4
  • 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. 5
  • 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, 10th February 2001 (on the Korean market); “Online Trading May Trigger New Dimension in Volatility” New Straits Times Press, 19th October 2000 (Malaysia); and “Technology Boom Keeps Volatility Ticking”; Australian Financial Review, 28th August 2000 (Australia). 6 See Barber and Odean (2001) for a review of the experimental economics literature on asset bubbles. 6
  • 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 2001-2003 period. The sample period falls after the worldwide “tech boom” which started in 1999 and the subsequent “tech bust” which diminished in late 2000. 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. 7
  • 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. 3.2. 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 trading8. 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. 8
  • 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 2002. 9
  • 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 1998. 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 2002.10 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. 10
  • 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). 11
  • Purchasing Valueit − Selling Valueit NIFit = . (1) Purchasing Valueit + Selling Valueit NIFit 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 flow- return 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. 12
  • 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: p Z t = C + ∑ Φ j Z t− j + ε t (2) j =1 ⎡ Rt ⎤ ⎡α R ⎤ ⎡φ1,1, p φ1, 2 , p ⎤ ⎡ε R , t ⎤ where Zt = ⎢ ⎥ C = ⎢α ⎥ Φp = ⎢ εt = ⎢ ⎥ . ⎣ Ft ⎦ ⎣ F⎦ ⎣φ1, 2 , p φ2, 2, p ⎥⎦ ⎣ε F ,t ⎦ Zt is a 2×1 matrix of return, Rt, and flow, Ft (Purchases, Sales or NIF) for day lag j, C is the constant and εt is the 2×1 error matrix. 13
  • 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 R2 value for Purchases and Sales is relatively high, 14
  • 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 15
  • 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 16
  • 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 R2 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 17
  • 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): 3 3 Rett = α + ∑ βRt −i + ∑ χFlowt −i + ϖDdiv + πDint + τAspr + ξAxrate , (3) i =1 i =1 and 3 3 Flowt = η + ∑ φRt −i + ∑ ϕFlowt −i + νDdiv + γDint + ψAspr + ρAxrate (4) i =1 i=1 In the specifications, Flowt is measured by Purchases, Sales or NIF, Rt 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 18
  • 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 19
  • 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. 20
  • 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 21
  • 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: Ft = α + β Unc t + γInfVt + δFt −1 + ε t (5) where Ft denotes flow measurements as being Purchases, Sales, or NIF. InfVt is a vector of “information variables” (or market fundamentals as explained in section 4.2), ε t is the error term, and Unct 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 ∧ σ t = VAR(GK ) = 0.5[ LN ( High) − LN ( Low)]2 − [2 LN (2) − 1][ LN (Open) − LN (Close)]2 , 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. 22
  • 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. 23
  • 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. 24
  • 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 25
  • 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 -0.037 and 0.054 during heavy sell and buy days, respectively. The market rises after a day of heavy buying and selling by Online in a statistically 26
  • 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 -0.395 and 0.330 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. 27
  • 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 Purchaset -1 − Salest −1 Cumulative return = ∑ ( ) Rt (6) t =1 Purchaset -1 + Salest −1 where Purchases and Sales are raw values and Rt is the market return. Equation (6) is estimated for each investor group in analyzing the performance over the entire sample period. 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 28
  • 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 − Sales where NIF is defined as for each investor type and lead/lag market Purchases + Sales returns Ret is the return of KOSPI index. 29
  • 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 30
  • and Stulz’s (2004) results based on data sampled during the Asian crisis for the Korean market. 6. Summary and conclusions In this study we utilize daily data from the highly active Korean stock market to investigate the trading behaviour and performance of online investors. We compare our online results with other investor types. Online investors do not exhibit distinct trading patterns at an aggregate level and show signs of noise trading whereas foreigners, institutions and securities companies exhibit some levels of intra-group consensus in the direction of their trades in response to market movements. In the main, aggregated flows from investors, with the exception of lagged local institutions’ trades, fail to explain changes in the market index but the market index has a significant impact on investment movements. Positive serial correlation is persistent for all investors which raises concerns over the results of investors’ actions. Serial correlation, when interpreted as herding, could push prices away from fundamental values, destabilising the market (Lakonishok, Shleifer and Vishny, 1992 and Wermers, 1999). This is more of a concern for online investors relative to other investors since they constitute the vast majority of trading activity, in both shares and value) on the KSE. Results on the risk-volume relationship are similar for all groups – online investors join other investor types in increasing their trading volumes indiscriminately during volatile 31
  • periods, suggesting that volatility is perceived as an opportunity. Our results based on the dispersion of investor beliefs, proxied by open interest on the derivatives market, suggest that only foreign investors discriminate between reductions in purchases and sales in response to rising uncertainty. Based on performance measures following periods of extreme imbalances in trading, online investors, together foreign investors, record superior performance only at the highest buying levels, but in aggregate their gains are likely neutralized by poor selling positions. In the longer run, foreigners are the clear winners with online investors the worst performers. Our study may also have important policy implications. We find that online investors’ trading decisions are related to one variable, the dividend yield, amongst the proxies for fundamental information that we adopt. Since this information is easily available even to fairly naïve investors, one is driven to conjecture whether improvements in the way online investors access quality information could instill more discipline in their trading behaviour. As D’Avolio, Gildor and Shleifer (2001) have outlined, accurate information must be available to be of use to investors. Because the trading community on the Korean stock market mainly comprises online investors, the so-called unsophisticated investors, there may be an incentive for information providers, who post better long term gains, to discriminate against this source of cheap liquidity. Solutions need to be carefully thought out, but the beginning point could involve greater efforts at investor education. 32
  • With regards to the implications of this study for future research, it is important to note that online investors dominate not only the equities market, but also the futures market. As such, the behaviour of online traders and their performance in the derivatives market is a potentially fruitful area for further research. 33
  • Appendix 1: Cumulative Performance Measure The performance measure developed by Grinblatt and Titman (1993), based on changes in portfolio holdings, captures the covariance between return on an asset and the change in proportional holding of an asset as follows: T N COV = ∑ ∑ ( w j ,t −w j ,t −1 ) R j ,t / T , t =1 j =1 where Rj is the return, wj is the weight of holding asset, N is the number of the assets and T is the estimating sample period. In this paper, we estimate this portfolio holding change measure for each investor i, using modified conditions adopted by Karolyi (2002) and Kamesaka, Nofsinger and Kawakita (2003). The modified condition assumes that there are only two assets, one is the stock market index and the other is the risk-free rate. The proxy for the daily or weekly market return is calculated using the market index, the KOSPI index, and the daily risk free rate is assumed to be zero. The modified measure is: T COV = ∑ ( wt −wt −1 ) Rt / T t =1 where Rt is the return on the market index during period t. Similar to Karolyi (2002) and Kamesaka, Nofsinger and Kawakita (2003) we replace the change in portfolio weight Purchases − Sales with net investment flows defined as . Purchases + Sales 34
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  • Table 1: Sample Descriptive Statistics This table presents summary statistics for daily equity sales and purchases for six investor types on the Korean Stock Exchange. Panel A presents volume statistics (in lots of 10,000 shares), and Panel B, values (1 million Korean won). ‘Raw’ reports the actual volume and value whereas ‘Ratio’ represents raw figures for that investor type divided by the total volume or value. The sample period is January 2001 to February 2003. Purchases Sales Panel A : Volume Raw Online Foreigners Individuals Institutions Securities Online Foreigners Individuals Institutions Securities Mean 538,237 12,882 621,573 13,799 4,306 525,993 13,352 615,771 17,313 4,621 median 481,978 11,291 556,194 13,202 3,506 463,274 11,898 551,274 14,606 3,854 std dev 306,923 7,485 327,395 5,050 3,243 302,428 7,193 327,452 13,813 3,387 Min 138,697 873 92,674 1,764 121 133,298 2,600 90,580 1,738 327 Max 2,173,568 75,906 2,341,094 34,660 34,255 2,145,120 57,871 2,342,724 222,297 40,038 Ratio Mean 0.80 0.02 0.94 0.02 0.01 0.78 0.02 0.93 0.03 0.01 median 0.80 0.02 0.94 0.02 0.01 0.78 0.02 0.93 0.03 0.01 std dev 0.06 0.01 0.05 0.01 0.01 0.06 0.01 0.05 0.02 0 Min 0.29 0 0.24 0 0 0.28 0 0.22 0 0 Max 0.92 0.07 0.99 0.1 0.07 0.91 0.11 0.99 0.2 0.04 Panel B: Value Raw Mean 1,429,624 282,158 1,749,835 260,423 79,334 1,414,701 274,172 1,754,300 262,967 81,824 median 1,271,775 259,484 1,552,106 239,803 67,698 1,273,767 250,602 1,576,952 234,458 73,272 std dev 615,412 136,156 766,741 123,260 49,310 614,422 133,303 778,433 125,894 47,943 min 485,852 11,022 226,597 16,560 1,826 514,907 34,233 194,352 15,781 2,933 max 3,813,982 923,406 4,767,107 846,906 361,240 3,546,988 806,597 4,442,588 821,838 454,606 Ratio mean 0.58 0.12 0.71 0.11 0.03 0.57 0.12 0.71 0.11 0.03 median 0.58 0.11 0.72 0.11 0.03 0.58 0.11 0.72 0.11 0.03 std dev 0.06 0.04 0.08 0.03 0.02 0.06 0.05 0.08 0.03 0.02 min 0.24 0 0.16 0.01 0 0.22 0.02 0.14 0.01 0 max 0.74 0.26 0.87 0.30 0.14 0.72 0.29 0.88 0.28 0.12 38
  • Table 2: Correlation Between Investor types: Volumes and Values This table reports Pearson correlation statistics between investor types for daily Purchases and Sales. Panel A reports correlations between the investor types for volume. Panel B reports the correlation for the value. P-values are in parentheses. The sample period is January 2001 – February 2003. Purchases Sales Panel A : Volume (‘000) Foreigners Individuals Institutions Securities Foreigners Individuals Institutions Securities Online 0.390 1.000 0.070 0.230 Online 0.290 1.000 0.100 0.160 (<.0001) (<.0001) (0.090) (<.0001) (<.0001) (<.0001) (0.020) (<.0001) Foreigners 0.400 0.340 0.320 Foreigners 0.290 0.050 0.130 <.0001 (<.0001) (<.0001) (<.0001) (0.230) (0.001) Individuals 0.120 0.250 Individuals 0.100 0.180 (0.010) (<.0001) (0.020) (<.0001) Institutions 0.560 Institutions 0.180 (<.0001) (<.0001) Securities Securities Panel B : Value Foreigners Individuals Institutions Securities Foreigners Individuals Institutions Securities Online 0.570 0.990 0.710 0.550 Online 0.520 0.990 0.690 0.490 (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) Foreigners 0.580 0.570 0.580 Foreigners 0.500 0.520 0.430 (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) (<.0001) Individuals 0.730 0.560 Individuals 0.700 0.500 (<.0001) (<.0001) (<.0001) (<.0001) Institutions 0.710 Institutions 0.650 (<.0001) (<.0001) Securities Securities 39
  • Table 3: Correlation Between Investor types: Net Investment Flows Pearson correlation coefficients are reported between each investor group’s NIF and market returns and also between investor types. Return is the daily return of the KOSPI index for the day of the investment flow (t=0) and the preceding five days to capture and report on the whole trading week. NIF is defined Purchase - Sales as . The sample represents 531 days of investment flows. Purchase + Sales Online Foreigners Institutions Securities Panel A: Flow-return correlations Return (t=0) -0.616 *** 0.374 *** 0.127 *** 0.193 *** Return (t=-1) -0.144 *** 0.408 *** -0.140 *** -0.117 *** Return (t=-2) -0.094 ** 0.059 0.037 0.169 *** Return (t=-3) -0.035 -0.011 -0.010 0.095 ** Return (t=-4) -0.017 0.030 -0.026 0.009 Return (t=-5) -0.009 0.054 -0.039 -0.032 Panel B: Investor type correlations Online Foreigners -0.472 *** Institutions -0.331 *** -0.372 *** Securities -0.463 *** -0.152 *** 0.289 *** *, **, *** denotes significance at 10, 5 and 1 percent levels. 40
  • Table 4: Vector Autoregressive Regression Analysis of Flows and Returns This table reports results from Vector Autoregressive Regression (VAR) of flows and returns for four different investor types. The three flows are NIF, Purchases and Sales based on daily trading value. NIF is Purchases − Sales defined as , and Purchases and Sales are normalized by dividing daily trading value by the Purchases + Sales 90-day moving average of the KOSPI index’s market capitalization. Return is the daily return on the KOSPI index. Five lags are chosen to capture up to one week effects. Daily number of observations for the sample period is 531. ***, **, * denotes significance at 1, 5, and 10 percent levels. The t-statistics are in italics. Panel A: Online Part1 Returns Part2 Flows NIF Purchase Sales NIF Purchase Sales C 0.000 -0.002 -0.002 0.006 0.000 0.000 0.108 -0.790 -0.881 3.814 *** 3.514 *** 3.113 *** Return (-1) -0.045 -0.013 -0.030 -0.083 0.014 0.009 -0.797 -0.280 -0.596 -0.982 5.958 *** 3.253 *** (-2) -0.063 -0.059 -0.077 -0.059 -0.005 -0.007 -1.115 -1.228 -1.523 -0.696 -2.036 ** -2.678 *** (-3) 0.028 -0.013 -0.006 -0.028 0.005 0.003 0.500 -0.271 -0.110 -0.333 1.934 * 1.235 (-4) 0.004 -0.046 -0.037 -0.049 0.006 0.005 0.081 -0.976 -0.738 -0.579 2.636 *** 1.973 * (-5) -0.010 -0.001 0.000 0.009 -0.001 -0.003 -0.176 -0.026 -0.008 0.114 -0.433 -1.081 Flow (-1) -0.043 0.469 0.925 0.121 0.576 0.603 -1.147 0.501 0.990 2.153 ** 12.492 *** 12.135 *** (-2) -0.018 0.308 0.468 0.061 0.180 0.180 -0.485 0.288 0.435 1.085 3.407 *** 3.144 *** (-3) 0.041 -0.057 -0.760 -0.002 0.123 0.068 1.095 -0.053 -0.706 -0.039 2.333 *** 1.193 (-4) 0.034 0.489 0.187 -0.039 0.002 0.031 0.915 0.468 0.178 -0.699 0.047 0.549 (-5) -0.035 -0.863 -0.425 0.030 0.038 0.040 -0.948 -0.970 -0.479 0.536 0.875 0.848 2 R 0.011 0.006 0.008 0.039 0.831 0.805 41
  • Panel B: Foreigners Part1 Returns Part2 Flows NIF Purchase Sales NIF Purchase Sales C 0.000 -0.002 -0.002 0.005 0.000 0.000 -0.069 -0.650 -0.637 0.621 6.197 *** 4.754 *** Return (-1) -0.029 -0.022 -0.006 2.787 0.005 -0.001 -0.596 -0.473 -0.135 7.032 *** 5.464 *** -1.297 (-2) -0.052 -0.045 -0.043 -0.819 0.000 0.002 -1.019 -0.936 -0.957 -1.953 ** 0.024 2.418 ** (-3) 0.015 0.016 0.002 -0.551 0.001 0.001 0.290 0.337 0.041 -1.314 0.824 1.878 * (-4) -0.022 -0.023 -0.028 -0.021 0.001 0.001 -0.427 -0.498 -0.626 -0.050 0.987 1.024 (-5) 0.012 -0.004 0.014 0.211 0.000 0.002 0.237 -0.091 0.314 0.510 0.171 2.032 ** Flow (-1) 0.008 2.895 -0.411 0.271 0.295 0.432 1.371 1.367 -0.156 5.610 *** 6.456 *** 9.814 *** (-2) -0.006 -2.683 0.353 0.094 0.023 0.076 -1.025 -1.227 0.123 1.899 * 0.498 1.587 (-3) 0.001 -0.147 0.698 0.013 0.036 0.035 0.209 -0.068 0.244 0.268 0.775 0.725 (-4) -0.002 -0.708 -0.470 0.058 0.081 0.095 -0.385 -0.327 -0.165 1.165 1.742 1.993 * (-5) 0.001 2.521 1.569 0.014 0.164 0.138 0.272 1.269 0.606 0.322 3.823 *** 3.190 *** 2 R 0.008 0.012 0.004 0.244 0.284 0.412 42
  • Panel C: Institutions Part1 Returns Part2 Flows NIF Purchase Sales NIF Purchase Sales C 0.000 -0.003 -0.001 -0.003 0.000 0.000 0.040 -1.252 -0.474 -0.473 4.170 *** 5.418 *** Return (-1) -0.010 -0.023 -0.005 -1.253 0.001 0.005 -0.215 -0.500 -0.123 -4.203 *** 1.408 5.402 *** (-2) -0.047 -0.046 -0.037 0.257 0.001 0.000 -1.034 -1.002 -0.799 0.848 1.530 0.399 (-3) 0.023 0.002 0.031 -0.077 0.000 0.001 0.523 0.034 0.679 -0.257 -0.568 0.976 (-4) -0.039 -0.024 -0.029 -0.053 0.001 0.002 -0.869 -0.540 -0.653 -0.179 0.927 1.982 * (-5) 0.010 -0.008 0.003 -0.357 0.000 0.001 0.226 -0.168 0.063 -1.200 0.054 1.602 Flow (-1) 0.006 3.640 2.238 0.123 0.301 0.345 0.906 1.416 0.952 2.771 *** 6.680 *** 7.849 *** (-2) 0.020 -0.703 -7.078 0.153 0.120 0.079 3.058 *** -0.265 -2.852 *** 3.425 *** 2.585 *** 1.700 (-3) -0.008 -2.124 2.552 0.097 0.082 0.120 -1.191 -0.800 1.026 2.152 ** 1.765 * 2.586 *** (-4) -0.004 -1.209 -0.243 -0.053 0.123 0.036 -0.664 -0.460 -0.098 -1.184 2.679 ** 0.784 (-5) 0.004 3.752 3.694 0.010 0.172 0.153 0.529 1.488 1.614 0.219 3.898 *** 3.571 *** 2 R 0.026 0.014 0.022 0.081 0.428 0.422 43
  • Panel D: Securities Part1 Returns Part2 Flows NIF Purchase Sales NIF Purchase Sales C 0.000 -0.001 -0.004 -0.027 0.000 0.000 -0.381 -0.426 -1.614 -2.385 ** 6.069 *** 5.886 *** Return (-1) 0.005 -0.014 -0.010 -1.561 0.000 0.001 0.109 -0.297 -0.229 -2.871 *** 0.529 3.694 *** (-2) -0.053 -0.036 -0.051 1.719 0.001 0.000 -1.168 -0.788 -1.151 3.142 *** 3.905 *** -0.789 (-3) 0.019 0.015 -0.002 1.052 0.001 0.000 0.425 0.316 -0.048 1.906 * 1.717 * -0.586 (-4) -0.019 -0.033 -0.039 0.113 0.001 0.001 -0.416 -0.720 -0.873 0.206 1.710 2.513 *** (-5) 0.012 0.009 -0.006 -0.574 0.000 0.001 0.266 0.202 -0.135 -1.046 -0.822 2.049 ** Flow (-1) -0.005 3.595 5.186 0.020 0.289 0.194 -1.269 0.643 0.968 0.456 6.353 *** 4.467 *** (-2) 0.000 -2.170 -3.482 0.108 0.094 0.114 0.088 -0.374 -0.643 2.400 ** 1.985 * 2.586 *** (-3) -0.005 -8.337 3.841 -0.010 0.072 0.090 -1.389 -1.441 0.710 -0.222 1.534 2.052 ** (-4) -0.005 4.588 6.316 -0.094 -0.015 0.070 -1.301 0.803 1.170 -2.136 ** -0.318 1.595 (-5) 0.002 5.505 0.987 -0.032 0.170 0.144 0.529 1.013 0.188 -0.739 3.849 *** 3.391 *** 2 R 0.014 0.010 0.012 0.068 0.268 0.223 44
  • Table 5: Granger Causality Tests in the Presence of Instrumental Variables This table reports the results of Granger causality tests of three different types of equity flows and market return incorporating fundamental variables including dividends, interest rate, risk premia and exchange rate. 3 3 Flowt = η + ∑ φRt −i + ∑ ϕFlowt −i + νDdiv + γD int + ψAspr + ρAxrate …(1) i=1 i=1 3 3 Ret t = α + ∑ β Rt −i + ∑ χFlowt −i + ϖDdiv + πD int + τAspr + ξAxrate …(2) i=1 i=1 The three flows are NIF, Purchases and Sales based on daily trading value. NIF is defined Purchases − Sales as , and Purchases and Sales are normalized by dividing daily trading value by the 90-day Purchases + Sales moving average of the KOSPI index’s market capitalization. Ret is the daily return of the KOSPI index. Ddiv is the average of the preceding three days’ dividend yield, differenced to meet stationarity requirements. Dint is the average of the preceding three days 90-day commercial paper rate, differenced to meet stationarity requirement. Aspr is a proxy for the risk premia and calculated by differencing 3 year government bond and 3 year corporate bond rate and then taking the average of the result for the past three days. Axrate is the average change in exchange rate (USD/Won) for the preceding three days. The results are reported in panels (A-D) by investor type. For each panel, the null hypothesis for Part (1) is that stock market returns do not Granger-cause flows in the presence of market fundamentals and the null hypothesis for Part (2) is flows do not Granger-cause stock market returns in the presence of market fundamentals. The daily number of observations is 531. ***,**,* denotes significance level at 1. 5, 10 percent levels. The figures are Newey-West heteroskedasticity and autocorrelation adjusted. Panel A: Online NIF Purchases Sales Variable Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Panel A (Part 1): H0 - stock market returns do not Granger cause flows Intercept 0.016 3.224 *** 0.000 1.843 * 0.000 1.585 Flowt-1 0.106 1.827 * 0.584 10.415 *** 0.612 9.847 *** Flowt-2 0.056 1.004 0.168 3.183 *** 0.174 2.771 *** Flowt-3 -0.013 -0.251 0.168 3.831 *** 0.132 2.580 ** Rett-1 -0.084 -1.172 0.014 3.054 *** 0.009 2.110 ** Rett-2 -0.062 -0.792 -0.005 -1.742 * -0.007 -2.277 ** Rett-3 -0.024 -0.317 0.004 1.401 0.002 0.698 Ddiv 0.003 0.066 0.001 0.277 0.001 0.414 Dint -0.005 -0.097 0.002 0.845 0.002 0.811 Aspr -0.010 -2.451 ** 0.000 1.085 0.000 1.336 Axrate 0.148 0.335 -0.019 -1.039 -0.012 -0.614 2 R 0.053 0.830 0.805 Panel A (Part 2): H0 - flows do not Granger cause market returns Intercept -0.004 -1.455 -0.006 -1.770 * -0.006 -1.823 * Flowt-1 -0.037 -0.943 0.515 0.456 0.910 0.842 Flowt-2 -0.015 -0.391 0.309 0.267 0.553 0.509 Flowt-3 0.041 1.259 -0.523 -0.546 -1.131 -1.238 Rett-1 0.013 0.179 0.041 0.725 0.027 0.431 Rett-2 0.004 0.042 0.009 0.127 -0.009 -0.117 Rett-3 0.093 1.197 0.063 0.934 0.072 1.085 Ddiv 0.105 1.254 0.111 1.391 0.113 1.415 Dint 0.000 0.002 -0.007 -0.159 -0.007 -0.165 Aspr 0.004 1.601 0.004 1.519 0.004 1.501 Axrate 0.468 1.348 0.510 1.481 0.499 1.453 2 R 0.019 0.017 0.020 45
  • Panel B: Foreigners NIF Purchases Sales Variable Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Panel B (Part 1) : H0 - stock market returns do not Granger cause flows Intercept -0.060 -2.669 *** 0.001 5.927 *** 0.000 6.850 *** Flowt-1 0.261 6.326 *** 0.336 6.007 *** 0.461 9.190 **** Flowt-2 0.083 1.704 * 0.038 0.732 0.095 1.951 * Flowt-3 0.018 0.455 0.117 2.563 *** 0.102 2.192 ** Rett-1 2.925 5.082 *** 0.004 2.405 ** -0.001 -1.192 Rett-2 -0.676 -1.431 -0.002 -1.104 0.001 1.340 Rett-3 -0.463 -1.045 -0.001 -0.511 0.001 1.027 Ddiv 0.293 0.624 -0.002 -0.701 -0.001 -1.021 Dint 0.109 0.273 0.001 1.159 0.000 0.295 Aspr 0.064 2.968 *** 0.000 -0.491 0.000 -2.835 *** Axrate -2.619 -1.028 0.004 0.626 0.001 0.181 2 R 0.254 0.249 0.392 Panel B (Part 2) : H0 - flows do not Granger cause market returns Intercept -0.004 -1.576 -0.006 -1.642 -0.008 -1.888 * Flowt-1 0.007 1.286 3.760 2.118 ** 0.570 0.295 Flowt-2 -0.007 -1.171 -1.794 -0.791 1.073 0.383 Flowt-3 0.000 -0.046 -0.120 -0.062 1.339 0.537 Rett-1 0.030 0.483 0.030 0.542 0.053 0.911 Rett-2 0.018 0.227 0.009 0.138 0.026 0.356 Rett-3 0.090 1.217 0.077 1.151 0.074 1.026 Ddiv 0.110 1.342 0.117 1.439 0.114 1.355 Dint 0.005 0.123 -0.002 -0.038 -0.006 -0.132 Aspr 0.004 1.616 0.004 1.730 * 0.005 2.040 ** Axrate 0.435 1.241 0.461 1.356 0.461 1.314 2 R 0.019 0.021 0.017 46
  • Panel C: Institutions NIF Purchases Sales Variable Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Panel C (Part 1) : H0 - stock market returns do not Granger cause flows Intercept 0.013 0.623 0.000 3.985 *** 0.000 4.848 *** Flowt-1 0.112 2.294 ** 0.365 5.234 *** 0.377 6.476 *** Flowt-2 0.142 3.035 *** 0.174 4.327 *** 0.107 2.379 ** Flowt-3 0.088 2.115 ** 0.180 4.436 *** 0.192 4.612 *** Rett-1 -1.274 -3.304 *** 0.000 0.004 0.004 2.114 ** Rett-2 0.148 0.454 0.000 -0.122 -0.001 -0.638 Rett-3 -0.161 -0.443 -0.002 -1.535 -0.001 -0.473 Ddiv -0.171 -0.508 -0.001 -0.975 -0.001 -0.690 Dint -0.494 -1.630 0.001 1.221 0.002 1.729 * Aspr -0.018 -0.935 0.000 0.603 0.000 0.923 Axrate -0.368 -0.155 -0.002 -0.340 0.000 0.015 2 R 0.081 0.393 0.402 Panel C (Part 2) : H0 - flows do not Granger cause market returns Intercept -0.005 -1.826 * -0.007 -2.066 ** -0.004 -1.276 Flowt-1 0.006 0.937 4.603 2.164 ** 2.974 1.447 Flowt-2 0.021 2.938 *** -0.024 -0.011 -6.044 -2.136 ** Flowt-3 -0.006 -0.914 -1.593 -0.807 3.058 1.214 Rett-1 0.048 0.851 0.031 0.564 0.050 0.875 Rett-2 0.015 0.215 0.013 0.198 0.023 0.325 Rett-3 0.095 1.357 0.066 0.996 0.083 1.247 Ddiv 0.118 1.437 0.117 1.422 0.109 1.304 Dint 0.015 0.382 -0.011 -0.239 0.004 0.090 Aspr 0.005 1.926 * 0.004 1.575 0.004 1.601 Axrate 0.425 1.240 0.479 1.403 0.458 1.335 2 R 0.037 0.022 0.027 47
  • Panel D: Securities NIF Purchases Sales Variable Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Panel D (Part 1) : H0 - stock market returns do not Granger cause flows Intercept -0.004 -0.115 0.000 5.978 *** 0.000 4.969 *** Flowt-1 0.023 0.461 0.296 4.694 *** 0.227 6.549 *** Flowt-2 0.093 2.227 ** 0.124 2.751 *** 0.133 3.295 *** Flowt-3 -0.026 -0.629 0.108 1.984 ** 0.150 3.531 *** Rett-1 -1.605 -2.161 ** 0.000 -0.247 0.001 1.461 Rett-2 1.692 2.277 ** 0.001 1.675 * -0.001 -1.611 Rett-3 1.068 1.384 0.000 0.300 -0.001 -0.993 Ddiv -0.347 -0.421 -0.001 -0.839 -0.001 -1.097 Dint -0.112 -0.202 0.001 1.060 0.001 1.425 Aspr -0.020 -0.635 0.000 -0.125 0.000 0.214 Axrate 0.123 0.027 0.000 -0.044 0.002 0.679 2 R 0.061 0.249 0.183 Panel D (Part 2) : H0 - flows do not Granger cause market returns Intercept -0.004 -1.562 -0.004 -1.346 -0.008 -2.019 * Flowt-1 -0.005 -1.171 5.211 0.745 7.312 1.306 Flowt-2 0.000 -0.064 -0.227 -0.042 -1.513 -0.311 Flowt-3 -0.005 -1.287 -4.885 -0.865 5.294 1.087 Rett-1 0.058 0.953 0.040 0.672 0.048 0.868 Rett-2 0.007 0.098 0.020 0.271 0.010 0.145 Rett-3 0.087 1.277 0.072 1.074 0.069 1.047 Ddiv 0.104 1.235 0.114 1.333 0.113 1.410 Dint -0.001 -0.029 0.002 0.054 -0.015 -0.325 Aspr 0.004 1.529 0.004 1.660 * 0.004 1.673 * Axrate 0.536 1.593 0.459 1.330 0.544 1.620 2 R 0.022 0.017 0.022 48
  • Table 6: Determinants of Flow This table presents the regression analysis of the determinants of flows including the uncertainty variable as an independent variable. The functional specification is Ft = α + βUnct + γInfVt + δFt −1 + ε t . Ft represents NIF, Purchases and Sales where Purchases and Sales are normalized flows and NIF is defined as Purchase - Sales . Unct is an uncertainty variable proxied by the log of return squared [ln(Ret2)] - volatility in Purchase + Sales Panel A. Ret is the market return. For Panel B, standardized open interest for the KOSPI 200 index futures is used as a proxy for dispersion of beliefs. Ddiv is the dividend yield; Dint is the 90-day commercial paper rate; Dspr is a proxy for the risk premium calculated by differencing the 3 year-government bond and corporate bond rate; Xrate is the change in daily exchange rate (USD/Won). Ddiv, Dint and Dspr are differenced to meet stationarity requirements. The figures are Newey-West heteroskedasticity and autocorrelation adjusted. Observations are daily in frequency and the sample period is 1991-1993. *, **, *** denotes significance at 10, 5 and 1 percent levels. All the coefficients are multiplied by a factor of 100 for ease of presentation. Panel A: Uncertainty Variable - Volatility NIF Purchases Sales Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Online Intercept 1.114 2.353 ** 0.151 7.114 *** 0.154 7.324 *** Unc 0.061 1.320 0.012 6.065 *** 0.011 6.027 *** Xrate -7.568 -0.240 -1.734 -1.584 -1.169 -0.963 Ddiv 34.595 4.681 *** -0.627 -5.185 *** -1.010 -6.527 *** Dint -1.357 -0.448 0.139 0.755 0.165 0.998 Dspr 3.347 0.905 -0.132 -1.054 -0.197 -1.546 Flowt-1 16.158 3.931 *** 65.143 11.547 *** 67.560 13.225 *** Flowt-2 5.862 1.375 11.590 2.038 ** 8.264 1.547 Flowt-3 2.152 0.601 15.635 3.756 *** 15.255 4.032 *** 2 R 0.280 0.837 0.837 Foreigners Intercept 2.342 0.763 0.078 7.641 *** 0.053 7.634 *** Unc 0.203 0.636 0.003 3.066 *** 0.002 3.300 *** Xrate 9.693 0.050 -0.534 -1.050 -0.312 -1.164 Ddiv -114.185 -4.374 *** -0.258 -4.136 *** 0.081 2.099 ** Dint -4.540 -0.129 0.205 3.060 *** 0.163 2.360 ** Dspr -35.185 -1.430 -0.091 -1.512 -0.055 -1.048 Flowt-1 38.156 9.448 *** 39.891 7.549 *** 49.394 10.158 *** Flowt-2 1.605 0.287 2.635 0.494 6.816 1.404 Flowt-3 1.132 0.277 8.348 1.952 * 11.427 2.685 *** 2 R 0.229 0.288 0.393 49
  • Panel A (Contd) Institutions Intercept 0.137 0.052 0.063 8.203 *** 0.068 9.105 *** Unc 0.055 0.209 0.003 6.269 *** 0.004 5.596 *** Xrate 176.697 1.459 -0.012 -0.036 -0.439 -1.249 Ddiv -27.685 -1.474 -0.172 -2.956 *** -0.118 -2.571 ** Dint -15.998 -0.743 0.147 2.400 ** 0.188 2.017 ** Dspr -5.277 -0.258 -0.056 -0.969 -0.073 -1.110 Flowt-1 8.574 1.800 * 37.031 5.305 *** 43.494 7.420 *** Flowt-2 14.200 3.005 *** 18.758 5.053 *** 9.188 2.012 *** Flowt-3 7.568 1.787 * 14.685 4.295 *** 15.778 3.582 *** 2 R 0.056 0.448 0.415 Securities Intercept 0.256 0.057 0.025 6.457 *** 0.023 7.052 *** Unc 0.290 0.629 0.001 3.770 *** 0.001 2.855 *** Xrate 456.260 1.967 ** 0.004 0.027 -0.387 -2.442 ** Ddiv -98.914 -2.066 ** -0.090 -2.183 ** -0.020 -1.296 Dint -32.455 -0.672 0.056 1.636 0.064 2.197 ** Dspr -49.543 -1.630 -0.056 -3.074 *** -0.011 -0.475 Flowt-1 0.205 0.046 30.688 4.929 *** 23.239 7.040 *** Flowt-2 12.424 3.006 *** 17.775 4.377 *** 12.638 3.173 *** Flowt-3 -0.711 -0.177 8.458 1.464 14.347 3.436 *** 2 R 0.056 0.300 0.176 50
  • Panel B: Uncertainty Variable – KOSPI200 futures Open Interest NIF Purchases Sales Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Online Intercept 0.534 1.145 0.122 4.799 *** 0.129 5.299 *** Unc 0.035 0.032 -0.187 -4.104 *** -0.191 -4.403 *** Xrate -7.095 -0.228 -1.611 -1.432 -1.076 -0.869 Ddiv 34.648 4.692 *** -0.612 -5.121 *** -0.999 -6.315 *** Dint -1.258 -0.417 0.189 1.072 0.216 1.409 Dspr 3.676 0.998 -0.073 -0.563 -0.134 -0.984 Flowt-1 15.755 3.829 *** 61.369 11.188 *** 65.062 12.671 *** Flowt-2 6.465 1.540 12.411 2.103 ** 7.031 1.271 Flowt-3 1.874 0.523 17.811 3.907 *** 18.305 4.356 *** 2 R 0.278 0.830 0.832 Foreigners Intercept 5.946 2.000 ** 0.095 7.363 *** 0.058 7.246 *** Unc -13.698 -1.898 ** -0.091 -4.376 *** -0.052 -3.841 *** Xrate 9.025 0.047 -0.542 -1.068 -0.272 -1.013 Ddiv -114.027 -4.412 *** -0.257 -4.114 *** 0.083 2.073 ** Dint -3.417 -0.097 0.222 3.736 *** 0.173 2.601 *** Dspr -34.428 -1.390 -0.081 -1.402 -0.045 -0.886 Flowt-1 37.558 9.162 *** 36.886 7.015 *** 47.485 9.710 *** Flowt-2 1.817 0.328 3.624 0.689 7.384 1.546 Flowt-3 0.487 0.121 6.705 1.586 11.412 2.650 ** 2 R 0.234 0.304 0.396 Institutions Intercept -2.286 -1.045 0.060 5.814 *** 0.067 6.776 *** Unc 4.752 0.901 -0.070 -5.043 *** -0.076 -4.314 *** Xrate 175.942 1.437 0.021 0.063 -0.440 -1.164 Ddiv -27.697 -1.472 -0.170 -2.733 *** -0.118 -2.420 ** Dint -16.197 -0.760 0.164 2.851 *** 0.210 2.348 ** Dspr -5.007 -0.247 -0.038 -0.656 -0.057 -0.897 Flowt-1 8.480 1.802 * 34.437 5.377 *** 38.190 6.560 *** Flowt-2 13.941 2.960 *** 18.023 4.915 *** 12.717 2.847 *** Flowt-3 7.095 1.677 * 16.497 4.728 *** 14.125 3.231 *** 2 R 0.057 0.440 0.408 Securities Intercept -2.641 -0.595 0.028 8.023 *** 0.037 7.822 *** Unc 0.483 0.045 -0.034 -4.839 *** -0.046 -4.913 *** Xrate 458.778 1.975 ** 0.015 0.100 -0.369 -2.421 ** Ddiv -98.596 -2.042 ** -0.089 -2.074 ** -0.020 -1.392 Dint -31.835 -0.662 0.064 2.068 ** 0.075 2.603 *** Dspr -48.086 -1.611 -0.051 -2.892 *** -0.010 -0.465 Flowt-1 0.392 0.088 27.559 4.447 *** 19.474 5.979 *** Flowt-2 12.322 2.990 ** 17.078 4.222 *** 11.598 2.887 *** Flowt-3 -0.725 -0.179 8.727 1.520 11.121 2.948 *** 2 R 0.055 0.308 0.223 51
  • Table 7: Market Performance after Heavy Buying and Selling Purchases − Sales This table reports the performance of different investor types at extreme ends of NIF. NIF is defined as for each investor-group is sorted into Purchases + Sales five equal sets. Periods of the highest positive NIF are designated the buying weeks, and those with the largest negative NIF the selling weeks. Each quintile of buy or sell weeks has 100 observations. One day, one week and one month returns are computed following the heavy buy or sell trading day. The mean NIF for heavy buying and selling days are reported in the table. The t-statistic tests the differences in mean between current and post trading returns and are reported in italic. NIF 1 day 1 week 1 month Sell Buy Sell Buy Sell Buy Sell Buy Online -0.037 0.054 0.004 0.001 -0.003 -0.001 -0.013 -0.009 5.939 *** 7.644 *** 4.989 *** 3.805 *** 3.608 *** 1.051 Foreigners -0.260 0.306 0.000 0.003 0.007 0.000 0.007 0.010 3.872 *** 3.610 *** 3.915 *** 2.618 *** 1.985 * 0.185 Institutions -0.214 0.192 -0.002 0.002 -0.004 0.007 -0.005 -0.004 0.033 0.229 0.452 0.996 0.304 0.724 Securities -0.395 0.330 0.001 -0.003 0.009 -0.009 0.011 0.002 2.680 *** 3.119 *** 2.909 *** 3.314 *** 1.791 * 0.578 *, **, *** denotes significance at 10, 5 and 1 per cent level
  • Table 8: Market Timing by Different Investors Purchases − Sales This table shows daily and weekly relation between NIF (defined as ) for each investor type and lead/lag market returns. The functional Purchases + Sales 5 specification is NIF = α + ∑ β i Ret t + i + ε .. Ret is the daily return of KOSPI index for panel A and weekly returns for panel B. Daily and weekly number of −5 observations are 520 and 114 respectively. The figures are Newey-West heteroskedasticity and autocorrelation adjusted. Panel A: Daily Online Foreigners Institutions Securities Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Intercept 0.007 5.663 *** Intercept 0.010 1.044 Intercept -0.005 -0.594 Intercept -0.027 -2.330 ** Rett-5 -0.005 -0.105 Rett-5 0.626 1.696 * Rett-5 -0.349 -1.157 Rett-5 -0.479 -0.848 Rett-4 -0.062 -1.251 Rett-4 0.395 1.165 Rett-4 -0.135 -0.469 Rett-4 0.261 0.599 Rett-3 -0.052 -0.871 Rett-3 0.045 0.148 Rett-3 -0.153 -0.513 Rett-3 1.003 1.758 * Rett-2 -0.176 -3.113 *** Rett-2 0.648 2.153 ** Rett-2 0.306 1.135 Rett-2 2.058 3.753 *** Rett-1 -0.197 -4.691 *** Rett-1 3.792 8.403 *** Rett-1 -1.039 -3.032 *** Rett-1 -1.521 -2.344 ** Ret -0.923 -8.418 *** Ret 3.380 9.767 *** Ret 0.917 2.200 ** Ret 2.395 2.271 ** Rett+1 -0.059 -1.130 Rett+1 0.333 0.983 Rett+1 0.378 1.250 Rett+1 -0.645 -1.128 Rett+2 -0.018 -0.354 Rett+2 -0.246 -0.739 Rett+2 0.925 2.998 *** Rett+2 -0.096 -0.196 Rett+3 0.046 0.980 Rett+3 0.027 0.081 Rett+3 -0.133 -0.451 Rett+3 -0.816 -1.547 Rett+4 0.041 0.870 Rett+4 -0.105 -0.297 Rett+4 -0.045 -0.136 Rett+4 -0.748 -1.472 Rett+5 -0.038 -0.799 Rett+5 0.113 0.380 Rett+5 0.188 0.631 Rett+5 0.175 0.344 2 2 2 2 R 0.399 R 0.300 R 0.065 R 0.097 53
  • Panel B: Weekly Online Foreigners Institutions Securities Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic C 0.006 4.397 *** C 0.018 1.306 C -0.007 -0.698 C -0.020 -1.612 Rett-5 -0.011 -0.342 Rett-5 -0.044 -0.173 Rett-5 0.100 0.450 Rett-5 0.247 0.589 Rett-4 0.066 1.915 Rett-4 -0.382 -1.598 Rett-4 0.208 0.983 Rett-4 0.153 0.502 Rett-3 0.014 0.582 Rett-3 -0.036 -0.158 Rett-3 0.163 0.801 Rett-3 0.198 0.585 Rett-2 -0.007 -0.293 Rett-2 0.078 0.311 Rett-2 -0.047 -0.260 Rett-2 0.025 0.109 Rett-1 -0.023 -0.767 Rett-1 0.415 1.659 Rett-1 -0.105 -0.465 Rett-1 -0.110 -0.334 Ret -0.308 -6.698 *** Ret 1.628 5.524 *** Ret 0.248 1.232 Ret 0.596 1.260 Rett+1 -0.005 -0.216 Rett+1 -0.002 -0.008 Rett+1 0.171 0.789 Rett+1 -0.174 -0.661 Rett+2 0.037 1.724 Rett+2 -0.146 -0.753 Rett+2 -0.056 -0.360 Rett+2 -0.465 -1.716 * Rett+3 -0.022 -0.813 Rett+3 0.216 1.074 Rett+3 0.098 0.497 Rett+3 0.497 1.670 Rett+4 -0.009 -0.293 Rett+4 0.001 0.003 Rett+4 -0.227 -1.050 Rett+4 0.629 2.131 ** Rett+5 -0.016 -0.467 Rett+5 0.426 1.885 * Rett+5 0.015 0.056 Rett+5 -0.425 -1.323 2 2 2 2 R 0.541 R 0.363 R 0.054 R 0.176 54
  • Fig 1: Comparative Size of the Online Trading Value Compared to Total Value Traded in both Korean Stock Exchange and KOSDAQ. (Source: KSDA) Total Stock Value (KSE and KOSDAQ) 400000000 350000000 300000000 Trading Value (mil. Won) 250000000 Total Value 200000000 Non-online 150000000 100000000 50000000 0 1998 May 1999 May 2000 May 2001 May 2002 May 1998 Jan 1998 Sep 1999 Jan 1999 Sep 2000 Jan 2000 Sep 2001 Jan 2001 Sep 2002 Jan 2002 Sep 1998 Nov 1999 Nov 2000 Nov 2001 Nov 2002 Nov 1998 Mar 1999 Mar 2000 Mar 2001 Mar 2002 Mar 1998 Jul 1999 Jul 2000 Jul 2001 Jul 2002 Jul Date 55
  • cumulative Performance -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 20010102 20010119 20010212 20010302 20010321 20010410 20010427 20010517 20010605 Fig 2 : Daily Cumulative Performance 20010625 20010712 20010801 20010821 20010907 Foreigners 20010926 20011018 20011106 20011123 20011212 20020103 20020122 20020208 Dates 56 20020305 20020322 20020411 20020430 20020520 20020607 Institutions 20020627 20020718 20020806 20020826 Securities 20020912 20021002 20021022 20021108 20021127 Online 20021216 20030108 20030127 20030214
  • Cumulative Performance -0.1 0 0.1 0.2 0.3 0.4 -0.05 0.05 0.15 0.25 0.35 01-Jan-01 04-Jan-01 02-Feb-01 05-Feb-01 03-mar-01 01-Apr-01 04-Apr-01 03-May-01 02-Jun-01 Fig 3: Weekly Cumulative Performance 01-Jul-01 04-Jul-01 03-Aug-01 01-Sep-01 04-Sep-01 Foreigners 03-Oct-01 01-Nov-01 04-Nov-01 03-Dec-01 02-Jan-02 05-Jan-02 Dates 03-Feb-02 Securities 57 02-Mar-02 01-Apr-02 04-Apr-02 03-May-02 01-Jun-02 04-Jun-02 03-Jul-02 Institutions Online 01-Aug-02 04-Aug-02 03-Sep-02 02-Oct-02 05-Oct-02 03-Nov-02 02-Dec-02 01-Jan-03 04-Jan-03 02-Feb-03