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How better informed are the institutional investors?
Economics Letters 106 (2010) 234–237
Contents lists available at ScienceDirect
Economics Letters j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e t
How better informed are the institutional investors? Jinghan Cai a, Jia He b, Jibao He c,⁎ a b c
Boston College, United States Sichuan University and Chinese University of Hong Kong, China Shenzhen Stock Exchange, China
a r t i c l e
i n f o
Article history: Received 7 December 2008 Received in revised form 23 November 2009 Accepted 4 December 2009 Available online 24 December 2009
a b s t r a c t We extend the EKOP model and estimate the probability of informed trading of institutions (SPIN) and individuals (DPIN) respectively. Using a unique dataset of Chinese stock market, we confirm that institutions are better informed by documenting a significantly higher SPIN. © 2009 Elsevier B.V. All rights reserved.
Keywords: Probability of informed trading Institutional investors Individual investors JEL classification: G14 G19
1. Introduction An important topic in market microstructure is the relationship between informed and uninformed trading activities. Realizing that the market maker will lose through trading with informed investors, Kyle (1985) constructs a sophisticated model to explain how market makers carefully set bid-ask spread to offset the potential loss brought forth by informed traders. He predicts that investors attempt to camouflage their trades by spreading them over time. Admati and Pfleiderer (1988) argue that investors tend to trade when volume is high. Easley Kiefer, O'Hara and Paperman (1996) derive a method (EKOP model hereinafter) for estimating the probability of informed trade to explain the observed differences in spreads for active and infrequently traded stocks. Barclay and Warner (1993) postulate the stealth trading hypothesis, which predicts that private information
will be released through trading and informed traders tend to concentrate on medium-size trades. These papers provide both theoretical and empirical evidence that some traders are better informed than others and informed traders exploit their information advantage and choose optimal trading strategy to profit from uninformed investors. However, none of these papers can answer the question: “How better informed are the institutional investors compared with individual investors”. Employing an extended EKOP model, we can theoretically estimate the ratio of informed trades within institutional investors and individual investors. On the empirical side, we use a unique dataset from Chinese Stock market and document that about 24% of trades initiated by institutional investors are ‘informed’ judged by the extended EKOP model. In contrast to that, only about 16% of the trades initiated by individuals are informed.
2. The model The method here is an extension of EKOP model: Investors trade a single risky asset and money with a market maker over t = 1, ···, T trading days. Within any trading day, time is continuous, and it is indexed by t ∈ [0, T]. The market maker stands ready to buy or sell one unit of the asset at his posted bid and ask prices at any time. Because he is competitive and risk-neutral, these prices are the expected value of the asset conditional on his information at the time of trade.
⁎ Corresponding author. Research Institute, Shenzhen Stock Exchange, Futian District, Hongli (w) Rd, Shangbu Industry Zone, Bldg 10, Shenzhen, 518028, China. Tel.: +86 755 83254246; fax: +86 755 83203431. E-mail address: [email protected] (J. He). 0165-1765/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2009.12.008
J. Cai et al. / Economics Letters 106 (2010) 234–237
235
Prior to the beginning of any trading day, nature determines whether an information event relevant to the value of the asset will occur. Information events are independently distributed and occur with probability α. These events are good news with probability 1 − δ, or bad news with probability δ. After the end of trading on any day, and before nature moves again, the full information value of the asset is realized. Trade arises from both informed traders (those who have seen any signal) and uninformed traders. Suppose there are four kinds of traders, namely informed institutions, uninformed institutions, informed individuals and uninformed individuals. On any day, arrivals of uninformed traders are determined by independent Poisson processes. Uninformed investors arrive at rate ε for buying or selling and the fractions of trading volume for uninformed individuals and uninformed institutions are β, 1 − β respectively. All informed traders are risk-neutral and competitive. If a trader observes a good/bad signal, the profit maximizing trade is to buy/sell the stock. All of these arrival processes are assumed to be independent. The arrival rate of informed traders for this process is µ for buying or selling, with fraction of trading volume φ, 1 − φ respectively for informed individuals and institutions. The tree given in Fig. 1 describes this trading process. At the first node of the tree, nature selects whether an information event occurs. If an event occurs, nature then determines if it is good news or bad news.In this model, B1t, B2t denote the buy volume for individuals and institutions respectively, and S1t, S2t denote the sell volume for individuals and institutions respectively. Thus the likelihood function θ = (α, δ, β, φ, ε, µ) −βε
LðB1 ; B2 ; S1 ; S2 jθÞ = ð1−αÞ e "
−βε
+ αδ e
ðβεÞB1 −ð1−βÞε ðð1−βÞεÞB2 −βε ðβεÞS1 −ð1−βÞε ðð1−βÞεÞS2 e e e B1 ! B2 ! S1 ! S2 ! B
B
ðβεÞ 1 −ð1−βÞε ðð1−βÞεÞ 2 −ðμϕ + e e B1 ! B2 !
+ αð1−δÞe
−βε
−ð1−βÞε
ðβεÞS1 e S1 !
βεÞ
S2
ðμϕ + βεÞ 1 −ðμð1−ϕÞ + ð1−βÞεÞ ðμð1−ϕÞ + ð1−βÞεÞ e S1 ! S2 !
−ðμϕ + βεÞ
ðð1−βÞεÞS2 e S2 !
S
!
# ð1Þ
ðμϕ + βεÞB1 −ðμð1−ϕÞ + ð1−βÞεÞ ðμð1−ϕÞ + ð1−βÞεÞB2 e : B1 ! B2 !
Assume days are independent; the likelihood of observing the data D = (B1t, B2t, S1t, S2t)Tt= 1 over T days is just the product of the daily likelihoods T
LðD jθÞ = Π Lðθj B1t ; B2t ; S1t ; S2t Þ:
ð2Þ
t =1
Fig. 1. Probability tree of the trading model.
236
J. Cai et al. / Economics Letters 106 (2010) 234–237
Table 1 Descriptive statistics of daily trading. Table 1 shows the daily cross-sectional statistics of individual and institution trading, including number of participating investors, share volume, RMB Yuan volume, and number of trades per stock per day. Number of participating investors
Share volume (shares)
RMB Yuan volume
Institutional
Institutional
Institutional
Individual
Initiated by Buyer Mean Median Std 25 pct 75 pct
Seller
16.8965 9.5456 9.3153 7.7342 45.8453 8.6739 6.0546 5.2707 14.3576 10.7048
Individual
Individual
Number of trades Institutional
Individual
Buyer
Seller
Buyer
Seller
Buyer
Seller
Buyer
Seller
Buyer
Seller
Buyer
Seller
Buyer
Seller
234.18 177.55 192.83 127.56 278.44
227.23 170.13 203.15 119.58 266.28
80328 40753 151755 23722 71845
67035 34852 117336 20592 66206
455186 349237 366063 234718 549753
438098 330739 368117 218840 530835
1121633 288068 4134060 145749 839236
881721 269189 4184800 125722 706737
4007540 2529806 6994640 1491854 4601507
3955015 2359126 7672528 1419047 4408847
31.2486 16.0004 76.3941 9.7825 31.5681
19.9328 13.5297 22.2410 9.0113 22.9523
279.9366 213.2689 236.8752 150.7554 335.7453
273.8737 205.0588 255.8537 144.9405 322.4105
The (EKOP) PIN value is thus defined as:
PIN =
αμ : 2ε + αμ
ð3Þ
Fig. 2. Private information parameters. Note: ε is the arrival rate of uninformed traders; µ is the probability of an information event; volume is the daily trading volume; α is the probability of an information event; β is the proportion of uninformed individual trades to total uninformed trades; φ is the proportion of informed individual trades to total informed trades.
J. Cai et al. / Economics Letters 106 (2010) 234–237
237
Table 2 PIN, SPIN and DPIN. This table shows the PIN values, the SPIN values and DPIN values. The first column indicates the estimated PIN, SPIN and DPIN in the whole sample period, while the rest of the columns contain the estimates of PINs, SPINs and DPINs in each quarter of the sample period. Paired t-tests and Wilcoxon sign-rank tests results are also shown in the table. Sample period
Pooled
2005Q1
2005Q2
2005Q3
2005Q4
2006Q1
2006Q2
2006Q3
2006Q4
2007Q1
Observations
376
270
262
339
224
276
335
327
325
362
PIN SPIN DPIN PIN = SPIN
0.1653 0.2427 0.1601 16.91*** 12.93*** − 11.27*** − 12.63*** − 16.85*** − 12.94***
0.1402 0.1692 0.1351 3.49*** 1.98** − 4.22*** − 1.88* − 3.68*** − 2.09**
0.1373 0.1928 0.1312 6.32*** 4.95*** − 4.74*** − 4.98*** − 6.40** − 5.00***
0.1243 0.1498 0.1200 4.03*** 2.27** − 5.54*** − 2.50** − 4.34*** − 2.39**
0.1410 0.1915 0.1324 5.08*** 3.84*** − 5.48*** − 4.28*** − 5.34*** − 3.94***
0.1198 0.1971 0.1109 8.61*** 7.18*** − 8.40*** − 7.52*** − 8.82*** − 7.30***
0.1331 0.1957 0.1286 8.71*** 7.27*** − 6.76*** − 7.27*** − 8.72*** − 7.28***
0.1335 0.1843 0.1296 6.71*** 4.65*** − 6.05*** − 4.44*** − 6.79*** − 4.68***
0.1222 0.1998 0.1152 11.16*** 9.31*** − 7.80*** − 9.15*** − 11.15*** − 9.33***
0.1150 0.1672 0.1092 14.99*** 12.79*** − 10.11*** − 12.71*** − 14.74*** − 12.79***
PIN = DPIN SPIN = DPIN
Paired t Sign rank Paired t Sign rank Paired t Sign rank
Note: *, ** and *** indicate significance level of 10%, 5% and 1% respectively.
The PIN for institutional trades (SPIN hereinafter) is defined as: SPIN =
αð1−ϕÞμ : 2εð1−βÞ + αð1−ϕÞμ
ð4Þ
And the PIN for individual trades (DPIN hereinafter) is defined as: DPIN =
αϕμ : 2εβ + αϕμ
3. Data and empirical results 3.1. The data In this paper we use all the main board stocks traded on the Shenzhen Stock Exchange, China, during the time period Jan 2005 to Apr. 2007. For each stock, we have the detailed records of each trade during the sample period, including each investor's identification type (individual or institution), and thus we are able to precisely identify whether a trade is initiated by the buyer or the seller. According to the identity of initiator, we can assign each trade into one of the four categories: institutional buy, institutional sell, individual buy, and individual sell. Table 1 shows the daily cross-sectional statistics of individual and institution trading per stock, including number of participating investors, share volume, RMB Yuan volume, and number of trades.
3.2. Parameter estimation We maximize the likelihood function given in Eq. (1) with respect to the parameter space θ = {ε, µ, δ, α, β, φ}. We allow the parameters to vary across stock and time, so we can have a separate likelihood function for each stock in each period. In Fig. 2, we plot a time series1 of the parameter estimates using the updated EKOP model as indicated in Eq. (1), as well as the PIN, the SPIN and the DPIN defined by Eqs. (3)–(5) respectively. The parameters ε and µ are not stationary. These parameters are related to the trading frequency. So we can observe that they are trending upwards as the number of transactions increases over the seasons. Correspondingly, the trading volume increases, too. On the other hand, the α, β and φ, as well as the PIN, SPIN and DPIN are stationary over time. Table 2 shows the PIN, SPIN and DPIN during the full sample period and in each sub-sample. Consistent with the last figure of Fig. 2, we can see that in the whole sample period, the aggregate PIN value is 1 We separate the whole sample period into 9 sub-samples from the first quarter of 2005 to the first quarter of 2007 (actually for 2007 we use four months' data from Jan. to Apr.).
ð5Þ
16.53%, meaning that about 16.5% of the total trades are informed while others can be seen as uninformed. However, for all the trades initiated by institutional investors, about 24.27% of them are informed, which is well above the average ratio of informed individuals (16.01%). The gap between SPIN and PIN is 7.74%, and that between PIN and DPIN is only 0.52%, however, both of them are significant at 1% level (for both means and medians). This pattern holds in each sub-sample, too. 4. Conclusions This paper extends EKOP model for estimating the SPIN and DPIN respectively. Using a unique dataset of Chinese Stock Market, we document that 24.27% of the trades initiated by institutional investors can actually be considered as informed, which is significantly higher than those initiated by individuals (16.01%). The result is consistent with the street lore that institutional investors are informed (e.g., Chakravarty, 2001), and the fact that institutional investors can gain much more profits than individuals (e.g., Barber et al., 2009). Acknowledgments Jibao He thanks NSFC project (70602019) for financial supports. All errors remain ours. References Admati, A., Pfleiderer, P., 1988. A theory of intraday trading patterns: Volume and price variability. Review of Financial Studies 1, 3–40. Barber, B., Lee, Y., Liu, Y., Odean, T., 2009. Just how much do individual investors lose by trading? Review of Financial Studies 22, 609–632. Barclay, M.J., Warner, J., 1993. Stealth trading and volatility: which trades move price? Journal of Financial Economics 34, 281–305. Chakravarty, S., 2001. Stealth trading: Which trader's trades move stock prices? Journal of Financial Economics 61, 289–307. Easley, D., Kiefer, N., O'Hara, M., Paperman, 1996. Liquidity, Information and Less Frequently Traded Stocks. Journal of Finance 51, 1405–1436. Kyle, A., 1985. Continuous auctions and insider trading. Econometrica 53, 1315–1335.
Contents lists available at ScienceDirect
Economics Letters j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e t
How better informed are the institutional investors? Jinghan Cai a, Jia He b, Jibao He c,⁎ a b c
Boston College, United States Sichuan University and Chinese University of Hong Kong, China Shenzhen Stock Exchange, China
a r t i c l e
i n f o
Article history: Received 7 December 2008 Received in revised form 23 November 2009 Accepted 4 December 2009 Available online 24 December 2009
a b s t r a c t We extend the EKOP model and estimate the probability of informed trading of institutions (SPIN) and individuals (DPIN) respectively. Using a unique dataset of Chinese stock market, we confirm that institutions are better informed by documenting a significantly higher SPIN. © 2009 Elsevier B.V. All rights reserved.
Keywords: Probability of informed trading Institutional investors Individual investors JEL classification: G14 G19
1. Introduction An important topic in market microstructure is the relationship between informed and uninformed trading activities. Realizing that the market maker will lose through trading with informed investors, Kyle (1985) constructs a sophisticated model to explain how market makers carefully set bid-ask spread to offset the potential loss brought forth by informed traders. He predicts that investors attempt to camouflage their trades by spreading them over time. Admati and Pfleiderer (1988) argue that investors tend to trade when volume is high. Easley Kiefer, O'Hara and Paperman (1996) derive a method (EKOP model hereinafter) for estimating the probability of informed trade to explain the observed differences in spreads for active and infrequently traded stocks. Barclay and Warner (1993) postulate the stealth trading hypothesis, which predicts that private information
will be released through trading and informed traders tend to concentrate on medium-size trades. These papers provide both theoretical and empirical evidence that some traders are better informed than others and informed traders exploit their information advantage and choose optimal trading strategy to profit from uninformed investors. However, none of these papers can answer the question: “How better informed are the institutional investors compared with individual investors”. Employing an extended EKOP model, we can theoretically estimate the ratio of informed trades within institutional investors and individual investors. On the empirical side, we use a unique dataset from Chinese Stock market and document that about 24% of trades initiated by institutional investors are ‘informed’ judged by the extended EKOP model. In contrast to that, only about 16% of the trades initiated by individuals are informed.
2. The model The method here is an extension of EKOP model: Investors trade a single risky asset and money with a market maker over t = 1, ···, T trading days. Within any trading day, time is continuous, and it is indexed by t ∈ [0, T]. The market maker stands ready to buy or sell one unit of the asset at his posted bid and ask prices at any time. Because he is competitive and risk-neutral, these prices are the expected value of the asset conditional on his information at the time of trade.
⁎ Corresponding author. Research Institute, Shenzhen Stock Exchange, Futian District, Hongli (w) Rd, Shangbu Industry Zone, Bldg 10, Shenzhen, 518028, China. Tel.: +86 755 83254246; fax: +86 755 83203431. E-mail address: [email protected] (J. He). 0165-1765/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2009.12.008
J. Cai et al. / Economics Letters 106 (2010) 234–237
235
Prior to the beginning of any trading day, nature determines whether an information event relevant to the value of the asset will occur. Information events are independently distributed and occur with probability α. These events are good news with probability 1 − δ, or bad news with probability δ. After the end of trading on any day, and before nature moves again, the full information value of the asset is realized. Trade arises from both informed traders (those who have seen any signal) and uninformed traders. Suppose there are four kinds of traders, namely informed institutions, uninformed institutions, informed individuals and uninformed individuals. On any day, arrivals of uninformed traders are determined by independent Poisson processes. Uninformed investors arrive at rate ε for buying or selling and the fractions of trading volume for uninformed individuals and uninformed institutions are β, 1 − β respectively. All informed traders are risk-neutral and competitive. If a trader observes a good/bad signal, the profit maximizing trade is to buy/sell the stock. All of these arrival processes are assumed to be independent. The arrival rate of informed traders for this process is µ for buying or selling, with fraction of trading volume φ, 1 − φ respectively for informed individuals and institutions. The tree given in Fig. 1 describes this trading process. At the first node of the tree, nature selects whether an information event occurs. If an event occurs, nature then determines if it is good news or bad news.In this model, B1t, B2t denote the buy volume for individuals and institutions respectively, and S1t, S2t denote the sell volume for individuals and institutions respectively. Thus the likelihood function θ = (α, δ, β, φ, ε, µ) −βε
LðB1 ; B2 ; S1 ; S2 jθÞ = ð1−αÞ e "
−βε
+ αδ e
ðβεÞB1 −ð1−βÞε ðð1−βÞεÞB2 −βε ðβεÞS1 −ð1−βÞε ðð1−βÞεÞS2 e e e B1 ! B2 ! S1 ! S2 ! B
B
ðβεÞ 1 −ð1−βÞε ðð1−βÞεÞ 2 −ðμϕ + e e B1 ! B2 !
+ αð1−δÞe
−βε
−ð1−βÞε
ðβεÞS1 e S1 !
βεÞ
S2
ðμϕ + βεÞ 1 −ðμð1−ϕÞ + ð1−βÞεÞ ðμð1−ϕÞ + ð1−βÞεÞ e S1 ! S2 !
−ðμϕ + βεÞ
ðð1−βÞεÞS2 e S2 !
S
!
# ð1Þ
ðμϕ + βεÞB1 −ðμð1−ϕÞ + ð1−βÞεÞ ðμð1−ϕÞ + ð1−βÞεÞB2 e : B1 ! B2 !
Assume days are independent; the likelihood of observing the data D = (B1t, B2t, S1t, S2t)Tt= 1 over T days is just the product of the daily likelihoods T
LðD jθÞ = Π Lðθj B1t ; B2t ; S1t ; S2t Þ:
ð2Þ
t =1
Fig. 1. Probability tree of the trading model.
236
J. Cai et al. / Economics Letters 106 (2010) 234–237
Table 1 Descriptive statistics of daily trading. Table 1 shows the daily cross-sectional statistics of individual and institution trading, including number of participating investors, share volume, RMB Yuan volume, and number of trades per stock per day. Number of participating investors
Share volume (shares)
RMB Yuan volume
Institutional
Institutional
Institutional
Individual
Initiated by Buyer Mean Median Std 25 pct 75 pct
Seller
16.8965 9.5456 9.3153 7.7342 45.8453 8.6739 6.0546 5.2707 14.3576 10.7048
Individual
Individual
Number of trades Institutional
Individual
Buyer
Seller
Buyer
Seller
Buyer
Seller
Buyer
Seller
Buyer
Seller
Buyer
Seller
Buyer
Seller
234.18 177.55 192.83 127.56 278.44
227.23 170.13 203.15 119.58 266.28
80328 40753 151755 23722 71845
67035 34852 117336 20592 66206
455186 349237 366063 234718 549753
438098 330739 368117 218840 530835
1121633 288068 4134060 145749 839236
881721 269189 4184800 125722 706737
4007540 2529806 6994640 1491854 4601507
3955015 2359126 7672528 1419047 4408847
31.2486 16.0004 76.3941 9.7825 31.5681
19.9328 13.5297 22.2410 9.0113 22.9523
279.9366 213.2689 236.8752 150.7554 335.7453
273.8737 205.0588 255.8537 144.9405 322.4105
The (EKOP) PIN value is thus defined as:
PIN =
αμ : 2ε + αμ
ð3Þ
Fig. 2. Private information parameters. Note: ε is the arrival rate of uninformed traders; µ is the probability of an information event; volume is the daily trading volume; α is the probability of an information event; β is the proportion of uninformed individual trades to total uninformed trades; φ is the proportion of informed individual trades to total informed trades.
J. Cai et al. / Economics Letters 106 (2010) 234–237
237
Table 2 PIN, SPIN and DPIN. This table shows the PIN values, the SPIN values and DPIN values. The first column indicates the estimated PIN, SPIN and DPIN in the whole sample period, while the rest of the columns contain the estimates of PINs, SPINs and DPINs in each quarter of the sample period. Paired t-tests and Wilcoxon sign-rank tests results are also shown in the table. Sample period
Pooled
2005Q1
2005Q2
2005Q3
2005Q4
2006Q1
2006Q2
2006Q3
2006Q4
2007Q1
Observations
376
270
262
339
224
276
335
327
325
362
PIN SPIN DPIN PIN = SPIN
0.1653 0.2427 0.1601 16.91*** 12.93*** − 11.27*** − 12.63*** − 16.85*** − 12.94***
0.1402 0.1692 0.1351 3.49*** 1.98** − 4.22*** − 1.88* − 3.68*** − 2.09**
0.1373 0.1928 0.1312 6.32*** 4.95*** − 4.74*** − 4.98*** − 6.40** − 5.00***
0.1243 0.1498 0.1200 4.03*** 2.27** − 5.54*** − 2.50** − 4.34*** − 2.39**
0.1410 0.1915 0.1324 5.08*** 3.84*** − 5.48*** − 4.28*** − 5.34*** − 3.94***
0.1198 0.1971 0.1109 8.61*** 7.18*** − 8.40*** − 7.52*** − 8.82*** − 7.30***
0.1331 0.1957 0.1286 8.71*** 7.27*** − 6.76*** − 7.27*** − 8.72*** − 7.28***
0.1335 0.1843 0.1296 6.71*** 4.65*** − 6.05*** − 4.44*** − 6.79*** − 4.68***
0.1222 0.1998 0.1152 11.16*** 9.31*** − 7.80*** − 9.15*** − 11.15*** − 9.33***
0.1150 0.1672 0.1092 14.99*** 12.79*** − 10.11*** − 12.71*** − 14.74*** − 12.79***
PIN = DPIN SPIN = DPIN
Paired t Sign rank Paired t Sign rank Paired t Sign rank
Note: *, ** and *** indicate significance level of 10%, 5% and 1% respectively.
The PIN for institutional trades (SPIN hereinafter) is defined as: SPIN =
αð1−ϕÞμ : 2εð1−βÞ + αð1−ϕÞμ
ð4Þ
And the PIN for individual trades (DPIN hereinafter) is defined as: DPIN =
αϕμ : 2εβ + αϕμ
3. Data and empirical results 3.1. The data In this paper we use all the main board stocks traded on the Shenzhen Stock Exchange, China, during the time period Jan 2005 to Apr. 2007. For each stock, we have the detailed records of each trade during the sample period, including each investor's identification type (individual or institution), and thus we are able to precisely identify whether a trade is initiated by the buyer or the seller. According to the identity of initiator, we can assign each trade into one of the four categories: institutional buy, institutional sell, individual buy, and individual sell. Table 1 shows the daily cross-sectional statistics of individual and institution trading per stock, including number of participating investors, share volume, RMB Yuan volume, and number of trades.
3.2. Parameter estimation We maximize the likelihood function given in Eq. (1) with respect to the parameter space θ = {ε, µ, δ, α, β, φ}. We allow the parameters to vary across stock and time, so we can have a separate likelihood function for each stock in each period. In Fig. 2, we plot a time series1 of the parameter estimates using the updated EKOP model as indicated in Eq. (1), as well as the PIN, the SPIN and the DPIN defined by Eqs. (3)–(5) respectively. The parameters ε and µ are not stationary. These parameters are related to the trading frequency. So we can observe that they are trending upwards as the number of transactions increases over the seasons. Correspondingly, the trading volume increases, too. On the other hand, the α, β and φ, as well as the PIN, SPIN and DPIN are stationary over time. Table 2 shows the PIN, SPIN and DPIN during the full sample period and in each sub-sample. Consistent with the last figure of Fig. 2, we can see that in the whole sample period, the aggregate PIN value is 1 We separate the whole sample period into 9 sub-samples from the first quarter of 2005 to the first quarter of 2007 (actually for 2007 we use four months' data from Jan. to Apr.).
ð5Þ
16.53%, meaning that about 16.5% of the total trades are informed while others can be seen as uninformed. However, for all the trades initiated by institutional investors, about 24.27% of them are informed, which is well above the average ratio of informed individuals (16.01%). The gap between SPIN and PIN is 7.74%, and that between PIN and DPIN is only 0.52%, however, both of them are significant at 1% level (for both means and medians). This pattern holds in each sub-sample, too. 4. Conclusions This paper extends EKOP model for estimating the SPIN and DPIN respectively. Using a unique dataset of Chinese Stock Market, we document that 24.27% of the trades initiated by institutional investors can actually be considered as informed, which is significantly higher than those initiated by individuals (16.01%). The result is consistent with the street lore that institutional investors are informed (e.g., Chakravarty, 2001), and the fact that institutional investors can gain much more profits than individuals (e.g., Barber et al., 2009). Acknowledgments Jibao He thanks NSFC project (70602019) for financial supports. All errors remain ours. References Admati, A., Pfleiderer, P., 1988. A theory of intraday trading patterns: Volume and price variability. Review of Financial Studies 1, 3–40. Barber, B., Lee, Y., Liu, Y., Odean, T., 2009. Just how much do individual investors lose by trading? Review of Financial Studies 22, 609–632. Barclay, M.J., Warner, J., 1993. Stealth trading and volatility: which trades move price? Journal of Financial Economics 34, 281–305. Chakravarty, S., 2001. Stealth trading: Which trader's trades move stock prices? Journal of Financial Economics 61, 289–307. Easley, D., Kiefer, N., O'Hara, M., Paperman, 1996. Liquidity, Information and Less Frequently Traded Stocks. Journal of Finance 51, 1405–1436. Kyle, A., 1985. Continuous auctions and insider trading. Econometrica 53, 1315–1335.