JURNAL BISNIS DAN EKONOMI, SEPTEMBER 2004
OVERNIGHT INFORMATION AND INTRADAY TRADING
BEHAVIOR: EVIDENCE OF JSX CROSS-LISTED STOCKS
Oleh : Sukmawati Sukamulja
Fakultas Ekonomi Universitas Atma Jaya Yogyakarta
Abstract
Permintaan atas saham yang diperdagangkan dapat tetap berlangsung setelah pasar tutup, khususnya untuk saham yang ditransaksikan di luar negeri (cross-listed stocks). Tulisan ini melihat pola menit demi menit (intraday patterns) volume perdagangan dan volatilitas harga untuk saham yang ditransaksikan di Bursa Efek Jakarta (JSX) dan Bursa Efek New York (NYSE). Penelitian ini melihat pergerakan harga saham setelah bursa tutup (overnight price movements) pada pasar New York (NYSE) mempengaruhi aktivitas perdagangan lokal (JSX). Besaran pergerakan harga saham di luar bursa Efek Jakarta (Telkom dan Indosat di NYSE) berelasi secara positip dengan volatilitas harga saham lokal, dan hubungan ini semakin kuat pada saat NYSE buka dan kemudian melemah sesudahnya. Penelitian ini menyimpulkan bahwa pergerakan saham di NYSE mempengaruhi return untuk saham yang sama di Bursa Efek Jakarta tidak hanya pada pagi hari tetapi juga pada interval 30 menit pertama. Hasil ini membantu untuk menjelaskan mengapa volatilitas harga intraday tinggi pada pagi hari setelah bursa di buka dan melemah pada tengah hari.
Keywords: Cross-listed stocks, intraday volatility, overnight price movements
I. INTRODUCTION
A small number of large companies are dually listed on foreign exchanges, such as U.S. or London, and also trade when these foreign exchanges are open. Telkom and Indosat are the most Indonesian large companies that traded on New York Stock Exchange (NYSE). The different time between Jakarta Stock Exchange (JSX) and NYSE make the stock transaction can be followed by investors around the world. Twelve hours difference between Indonesia and United State (New York time) support market activity for almost 24 hours. The Indonesian stock exchange (namely JSX) has normal trading hours from 9:30 a.m. until 4:00 p.m. Indonesian Time. As Indonesian capital market, NYSE opens at 9:30 a.m. and closes at 4:00 p.m. Eastern Time (EST). Based on Indonesian Time, NYSE has normal trading hours from 9:30 p.m. until 4:00 a.m. Investors can trade continuously along day and night for dually listed stocks.
Jurnal Bisnis dan Ekonomi – Vol.11 – No.2 – September 2004
Extensive empirical evidence documents that the stock market is more active at the beginning of trading session. Measures of market activity, such as trading volume, price volatility, and number of transaction, are higher at the open and close for NYSE stocks (e.g., Jain and Joh, 1988; Foster and Viswanathan, 1993; Jang and Lee; 1993; and Chan et al., 2000). Several studies conjecture that the higher market activity at the open is due to overnight information that accumulates during the NYSE nontrading period. For example, Berry and Howe (1994) document that the number of news announcements released by Reuter’s News Service increases at 8:00 a.m. (EST)—one and a half hours before the open. Foster and Viswanathan (1993) show that informed traders who gather private information during the nontrading period trade more aggressively after the open if they suspect their information will become public soon. Brock and Kleidon (1992) and Gerety and Mulherin (1992) argue that because of the new information that arrives during the nontrading period, the portfolio that is optimal during the previous close will no longer be optimal when the market reopens. Therefore, market activity increases immediately after the open as investors rebalance their portfolios.
In light of the relation between market activity and information flow, many authors examine internationally cross-listed stocks (see among other, Barclay et al., 1990; Kleidon and Werner, 1993; Chan et al., 1994; Chole, 1994; Foster and George, 1994; and Sukamulja, 2002). Arshanapalli and Doukas (1993), using a cointegration, report an increasing degree of interdependence among world capital markets. Despite the intuitive appeal that the trading behavior of these cross-listed stocks in the morning is related to overnight information releases in their local markets, my study test this possibility. This paper examines the intraday pattern of trading volume and price volatility for stocks traded on the JSX and listed on NYSE, especially the largest companies like Telkom and Indosat. The overnight information is used to flow of these cross-listed stocks (NYSE) directly from price movements in their local market (JSX).
Since most information generated during the NYSE nontrading period about these foreign stocks is reflected in JSX, foreign cross-listed stock price movement (price movement of Telkom and Indosat) is good proxy for overnight information. If market activity at the open is related to overnight information, this research expects to find a positive relation between the level of market activity in the morning and the magnitude of local stock price movement. Furthermore, as information about these foreign stocks is more likely to arrive during the NYSE overnight period (during 4:00 p.m. until 9:30 a.m. EST) than during the trading day period (9:30 a.m. through 4:00 p.m. EST), market activity is greater in the morning than the mid-day. Once we control for the effect of overnight information (NYSE stock price movement), intraday variations in market activity (JSX stock price movement) will be reduced.
This study uses overnight information from the NYSE price movement rather than from the JSX opening returns. Although NYSE price movement and JSX opening returns are closely related, they are not perfectly correlated, as the price in one market could move because of the trading activity there (Sukamulja, 2002). This paper examines how NYSE price movements (Telkom and Indosat price movement in NYSE) affect the trading activity of the JSX stocks (Telkom and Indosat price movement in JSX). Unlike Berry and Howe (1994) who use the number of news articles released during the nontrading period, or other researchers who use close-to-open return volatility, this study infers the overnight information flow of these cross-listed stocks directly from price movements in their foreign market (NYSE). Unlike previous studies, this paper also infers overnight information from the foreign price (NYSE) movement rather than from the JSX opening returns. The reason is although the NYSE movement and the JSX opening returns are closely related, they are not perfectly correlated (Sukamulja, 2002), as the price in one market could move because of the trading activity there. Furthermore, local trading sessions for JSX stocks are closed before the NYSE opens. Therefore, this research examines how NYSE price movements, which are public information to Indonesian investors, affect the trading activity of cross-listed stocks on JSX.
This study finds that overnight price movements in NYSE affect not only opening returns of Telkom and Indosat, but also returns during the first 30 minutes on the JSX. Also, the magnitude of NYSE price movements is positively related to the price movement of local stocks in the morning. The relation is stronger around the open and weaker afterward. This diminishing effect of overnight information on intraday price movements helps explain why price volatility is higher at the open and lower at midday. I also find the trading volume of Telkom and Indosat stocks on NYSE is strongly correlated with JSX opening price movement and weakly correlated with JSX midday price movement. I interpret this result as evidence that the trading activity of Telkom and Indosat on the JSX is related more to liquidity trading of Indonesian investors and less to NYSE information.
The paper proceeds as follows. Section II discusses the relation between overnight information and intraday market activity. Section III describes methodologies and the data. Section IV presents summary statistics and empirical results. Section V is conclusion.
II. OVERNIGHT INFORMATION AND INTRADAY MARKET ACTIVITY
A number of papers explore the role of information flow and other microstructure variables as determinants of intraday return volatility. Trading noise, public information and private information have all been identified as potentially important determinants of the volatility of stock returns.
Wood et al. (1985), Harris (1986), and Lockwood and Linn (1990) examine intraday stock returns and find that price volatility is higher near the open and close of the trading session. Jain and Joh (1999), Foster and Viswanathan (1993), and Jang and Lee (1993) find that trading volume and number of transaction are also higher at the open. Amihud and Mendelson (1991) uses the fact that the Tokyo Stock Exchange has two trading periods to argue that higher opening volatility is mostly the result of the incorporation with overnight information. They find that the two-hour midday break in Japan has a higher return volatility than the overnight break and argue that this difference could result from the greater intensity of information arrival during the day compared to the overnight period. Contrarily, Lam and Tong (1999) find opposite result in Hong Kong market where the overnight variance is more than double the midday break variance.
Several explanations may account for this trading behavior. First, much public information accumulates overnight and is not reflected in prices during the capital market nontrading period. Once the capital market opens, overnight information is quickly incorporated into prices, resulting in a large price movement at the open. Berry and Howe (1994) and Mitchell and Mulherin (1994) examine the effect of public information on market activity. Using the number of news announcements released by Reuter’s News Service as a measure of public information flow, Berry and Howe (1994) document that information flow substantially increases at 8:00 a.m. EST.
Second, informed traders gather private information during the nontrading period and may act strategically when trading with liquidity traders. In Foster and Viswanathan (1990) model, the informed trader receives private information at the beginning of the week. Since a portion of the private information is made public each day, the information becomes less valuable through time. The informed trader, knowing a public signal is forthcoming, trades more aggressively so that more information is reflected through trading. A similar logic can be applied to intraday trading. If informed traders receive private information overnight and suspect the information may be leaked in the day, they will trade immediately after the open. This happen because the larger price changes during the trading day have been interpreted as evidence of either trading on private information or trading induced by noise.
Third, volume at the close and open reflects trades made to rebalance portfolios before and after the overnight trading halt. Brock and Kleidon (1992) argue that because of overnight information, portfolios that are optimal during the previous close will no longer be optimal when the market reopens. Furthermore, portfolios that are optimal at the close can differ from portfolios that are optimal during the continuous trading period. This inelastic demand to trade induces a surge in trading activity at the open and close.
Fourth, open-to-open return variances are greater than close-to-close return variances (see Amihud and Mendelson, 1987; and Stoll and Whaley, 1990). This implies that opening prices contain larger pricing errors than closing prices.However, subsequent studies (e.g., Amihud and Mendelson, 1991; Choe and Shin, 1993; and Masulis and Ng, 1995) find similar evidence for stocks traded on other exchanges that have different trading mechanisms. These studies suggest that higher transitory volatility at the open is in fact due to the overnight trading halt. Without trading venues, the overnight trading halt disturbs the process of price formation until the open (see among other, Grundy and McNichols, 1989; Dow and Gorton, 1993; and Leach and Madhavan, 1993). Gerety and Mulherin (1994) find that transitory volatility declines during the trading day both for the Dow Jones 65 Composite prices index and for individual firms in the Dow Jones 30 index.
III. METHODOLOGY AND DATA
One reason for increased market activity at the open is that overnight information accumulates during the stock exchange nontrading (overnight or close-to-open) period. This is true even when the overnight information becomes public, since investors experience uncertainty in interpreting the information. Furthermore, as several researchers (Grundy and McNichols, 1989; Dow and Gorton, 1993; and Leach and Madhavan, 1993) argue, multiple rounds of trading can produce prices that are less noisy and reveal more information than a single round of trading. Therefore, overnight information affects market activity at the open, but the effect diminishes during the day. The diminishing effect of overnight information might explain why the market activity surges at the open and declines afterward. Theobolt and Price (1984) argue that the nontrading of securities within an index tend to reduce the observed daily seasonalities for that index.
Based on Amihud and Mendelson, 1987 and Stoll and Whaley, 1990, this relation can be illustrated by a simple regression can be used as a model.
RETi,t = a + b RETi,t-1 + ei,t ……………………………………………….. (1)
Where:
RETi,t = denotes intraday market activity (either trading volume or price movement) for interval i at day t, opening price during 30 minutes after JSX open for Telkom and Indosat stocks.
RETi,t-1 = denotes overnight information, trading activity on NYSE
ei,t = error terms
The regression model assumes that variations in market activity are solely caused by overnight information. This can be justified, especially for cross-listed stocks that have much information released in local market (JSX) from overnight foreign market (NYSE). If other variables contribute to intraday variations in market activity, the a intercept will not be the same even after controlling for RETi,t-1
This research obtains intraday data from the JSX database for Telkom and Indosat that are traded in NYSE and JSX. The trade records contain the time to the nearest second, date, ticker symbol, price, and number of shares traded; the quotation records contain the time, date, ticker symbol, bid and ask. The prices are in terms of Rupiah. I translated Telkom and Indosat of NYSE into the Rupiah and used in American Depository Receipt (ADR) bundling. One ADR is 20 shares of Telkom and 10 shares of Indosat. The sample period is one year opening prices and closing prices, from January 2, 2001 to December 27 2001 for the NYSE data and opening prices through 30 minute after JSX open from January 3, 2001 until December 28, 2001 for the JSX data. Exchange rate Rupiah to Dollar is used from January 3, 2001 to December 28, 2001
It is necessary to use stocks that have at least 20 days and more 10 quotes a day to show the liquidity of the stocks. It was why I choose Telkom and Indosat. Each day, the transactions are matched for NYSE stocks with daily stock prices in JSX.
Since the data for Telkom and Indosat stocks are closing prices, we can construct only local close-to-close returns, which reflect the price reaction to both overnight information released in the JSX trading session at day t and to information generated during the NYSE trading session at day t-1. Because 12 hours different between New York time and Jakarta time, I assume the JSX trading session is closed before the NYSE opens the day after. Since JSX and NYSE trading session do not overlap, information is reflected in the two markets at different times. Information released during the NYSE trading session is first incorporated into prices in the NYSE market and then into prices in the JSX market; the reverse is true for information released during the JSX trading session. In general, most of the information about foreign stocks (e.g., firm-specific and country-specific information) is released in JSX market. However, since NYSE news has global effects,information released in the NYSE market also affects local stocks (JSX). As a result, local close-to-close returns reflect not only overnight information released in the home market at day t, but also information already incorporated into foreign stock prices in the JSX market at day t-1.
Thus, local close-to-close returns at day t consist of price adjustments to: (1) NYSE information at day t-1, captured by RETi,t-1 and (2) overnight information released in the JSX at day t. Therefore, b coefficient can be estimated by including RETi,t as an explanatory variable, which is expected to have positive coefficients. The above relation is similar even when JSX and NYSE trading session overlap. The only difference is that since some of the NYSE information at day t-1 is already reflected in JSX market returns RETi,t is measured from the close of the JSX market to the close of the NYSE market.
This study estimates regression coefficient subject to the constrains implied by Eq. (1) and the steps as follow:
- Convert daily opening price and closing price (t-1) for PT Telekomunikasi Indonesia/TELKOM (TLKM) and PT Indonesian Satellite Corporation/ INDOSAT (ISAT) of NYSE as ADR (US$) to Rupiah based on currency exchange rate. Change the ADR to stock share based on 20 shares of Telkom and 10 shares of Indosat.
X = (P x K): ADR …………………………………………………… (2)
Where
X = Daily opening price and closing price (t-1) of Telkom and Indosat in term of Rupiah and share.
P = Daily opening price and closing price (t-1) of Telkom and Indosat in term of ADR (US$).
K = Exchange rate when JSX open (t1).
ADR = American Depository Receipt (1 ADR is 20 shares of Telkom and 1 ADR is10 shares of Indosat).
-
In line with previous studies by Stoll and Whaley (1990), Jones et al. (1994), and Huang and Masulis (1999), this research measures the return and price volatility based on the absolute term. Daily returns of JSX are counted by daily opening price t1 through the last transaction of daily price during 30 minute after JSX open.
Pi1 - Pi0
Ri = ....................................................................................... (3)
Pi0
Where
Ri = Daily return of JSX from daily opening price t1 through the last transaction of daily price during 30 minute after JSX open for stock i
Pi1 = The last daily price transaction t1 during 30 minute after JSX open for stock i
Pi0 = Daily opening price t1 for stock i
-
Daily return of NYSE is counted by opening price through daily closing price t-1 that already converted to Rupiah and shares.
Pi1 - Pi0
Ri = ................................................................................... (4)
Pi0
Where
Ri = Daily return of opening price through daily closing price of NYSE, t-1, for stock i.
Pi1 = Daily closing price of NYSE t-1 for stock i.
Pi0 = Daily opening price of NYSE t-1 for stock i
-
Then, regress the daily return of daily opening price t1 through the last daily transaction, t1, during 30 minute after JSX open as dependent variable (Y) and the daily return of opening prices to daily closing prices of NYSE t-1 for each stock that already converted to Rupiah and shares.
As a note that although the error terms in regression equations may be correlated, there is no efficiency gain from using seemly unrelated regression methodology since the explanatory variable is the same for each regression (Chan et al., 2000).
IV. EMPIRICAL RESULT
The NYSE trading session (during 9:30 a.m. until 4:00 p.m. EST) is partitioned into 14 time interval: overnight period, open-to-10:00 a.m. period, and twelve successive 30-minute intervals. Based on Indonesian Time, NYSE has normal trading hours from 9:30 p.m. until 4:00 a.m. Investors can trade continuously along day and night for dually listed stocks. The overnight return is based on the opening transaction price of that day and the midpoint of the closing bid-ask quote of the previous day. The return for the open-to-10:00 a.m. period is computed from the opening price to the midpoint of the last bid-ask quote of the period. The return for other 30-minute intervals is computed from the midpoint of the last bid-ask quote of the interval. As NYSE, the JSX trading session (9:30 a.m. until 4:00 p.m.) is partitioned into 14 time interval too.
Since the data for Telkom and Indosat stocks are closing prices, we can construct only local close-to-close returns, which reflect the price reaction to both overnight information released in the JSX trading session at day t and to information generated during the NYSE trading session at day t-1. Because 12 hours different between New York time and Jakarta time, this study assumes the JSX trading session is closed before the NYSE opens the day after. Figure 1 demonstrates the relation between NYSE and JSX trading session. Based on Figure 1 on NYSE trading session, while the NYSE is getting open-to-close return the JSX gets close-to-close return. The opposite happens on the JSX trading session.
The data set used in this research consists of 30 minute interval values of Telkom and Indosat for NYSE and JSX data. Using each interval’s last trade value, the return is calculated as formula (3) and formula (4). For NYSE data, formula (2) is used to convert Dollar to Rupiah before making calculation.
Figure 1.Trading Session on Cross-Listing Stock in the JSX and the NYSE Market
Theobold and Price (1984) argue that the nontrading within an index tend to reduce the observed daily seasonalities for that index. There is no stock within the JSX that is not traded during the day. And, the observation indicates that all stocks are traded in each 30 minute-interval of the trading day. Since the data for Telkom and Indosat stocks are closing prices, we can construct only local close-to-close returns, which reflect the price reaction to both overnight information released in the JSX trading session at day t and to information generated during the NYSE trading session at day t-1. Thus, local close-close returns at day t consist of price adjustments to: (1) NYSE information at day t-1, captured by RETi,t-1 and (2) overnight information released in the JSX at day t. Therefore, b, coefficient can be estimated by including RETi,t as an explanatory variable, which is expected to have positive coefficients.
Table 1 shows descriptive statistics results of each market during the sample period. For the whole sample period and for both stocks, the NYSE has higher level of mean and standard deviation than the JSX. The daily opening price through daily closing price t-1 of NYSE is bigger than the daily opening price through the last daily price transaction t1 during 30 minute after JSX open. In other words, the NYSE dominates the trading time.
Table 1.
Descriptive Statistics
| Company | Mean | Standard Deviation | N |
| Telkom |
|
| 233 |
| RETi,t-1 (NYSE) | 0.0193 | 0.0253631 |
|
| RETi,t (JSX) | 0.00943 | 0.00924714 |
|
| Indosat |
|
| 229 |
| RETi,t-1 (NYSE) | 0.0129 | 0.0173161 |
|
| RETi,t (JSX) | 0.00772 | 0.007572665 |
|
The NYSE has higher level of mean and standard deviation than the JSX. The daily opening price through daily closing price t-1 of NYSE is bigger than the daily opening price through the last daily price transaction t1 during 30 minute after JSX open. In other words, the NYSE dominates the trading time.
Table 2 reports regression results. Since the t-statistics are significant for both Telkom and Indosat, the daily return of NYSE has positive impact to daily return of JSX. During 30 minute after JSX open. Telkom has higher closed-to-open return than Indosat (with the higher t-statistic). This indicates that the NYSE market information is incorporated into opening prices of JSX. Since NYSE market is already closed before the JSX opens, this suggests that not all of the NYSE information is incorporated into JSX opening prices.
Table 2
Regression of Intraday Return for Telkom and Indosat Traded on the NYSE
Panel A: Telkom
| Model |
| Unstandardized Coefficients | Standardized Coefficients | t | Sig. | Collinearity Statistics | ||
| B | Std. Error | Beta |
|
| Tolerance | VIF | ||
| 1 | (Constant) | 7.386E-03 | .001 |
| 9.115 | .000 |
|
|
|
| NYSE | .106 | .029 | .236 | 3.688* | .000 | 1.000 | 1.000 |
Dependent Variable: JSX
* Significant at 1% and RETi,t denotes intraday market activity (either trading volume or price movement) for interval i at day t, opening price during 30 minutes after JSX open for Telkom and Indosat stocks. RETi,t-1 denotes overnight information, trading activity on NYSE. Overnight information from the NYSE has positive impact to the JSX market activity.
RETi,t = 0.007386 + 0.106 RETi,t-1
(3.688)
Panel B: Indosat
| Model |
| Unstandardized Coefficients | Standardized Coefficients | t | Sig. | Collinearity Statistics | ||
| B | Std. Error | Beta |
|
| Tolerance | VIF | ||
| 1 | (Constant) | 6.436E-03 | .001 |
| 10.529 | .000 |
|
|
|
| NYSE | .100 | .029 | .227 | 3.507* | .001 | 1.000 | 1.000 |
Dependent Variable: JSX
* Significant at 1%
Overnight information released in the JSX has positive significantly affect to the JSX market return. During 30 minute after JSX open. Telkom has higher closed-to-open return than Indosat (with the higher t-statistic). This indicates that the NYSE market information is incorporated into opening prices of JSX.
RETi,t = 0,006436 + 0,100 RETi,t-1
(3,507)
The regression intercepts (a) decline monotonically during the morning for each market. Indosat decline more than Telkom. â coefficient also decline monotonically during the day (Table 3). This study tests whether the a coefficients are the same and reject this for both groups of stocks. The result supports the hypothesis that the reaction of intraday price movement to overnight information is higher at the open and decline during the day. Overall, the evidence confirms previous studies that find the intraday price movement for local stock traded on the NYSE is higher at the open and declines during midday. This explains why price movement is higher during the early morning. After the effect of overnight information is controlled, intraday variations in movement are less pronounced. However, the effect of overnight information on trading volume does not decline during the day, therefore, intraday variations in volume remain unexpected.
Table 3
Regression of Intraday Price Volatility of Telkom and Indosat on NYSE and JSX
Panel A. Intercept a
| Interval | NYSE | JSX | ||
| Telkom | Indosat | Telkom | Indosat | |
| Close-10:00 a.m. | 0.04692 | 0.01072 | 0.04224 | 0.03132 |
| 10:00-10:30 a.m. | 0.03669 | 0.00839 | 0.01488 | 0.01290 |
| 10:30-11:00 a.m. | 0.02869 | 0.00656 | 0.01164 | 0.01014 |
| 11:00-11:30 a.m. | 0.02243 | 0.00513 | 0.01044 | 0.00924 |
| 11:30-12:00 p.m. | 0.00175 | 0.00401 | 0.00870 | 0.00816 |
| 12:00-12:30 p.m. | 0.00137 | 0.00245 | 0.00798 | 0.00804 |
Panel A. Intercept â
| Interval | NYSE | JSX | ||
| Telkom | Indosat | Telkom | Indosat | |
| Close-10:00 a.m. | 0.31251 | 0.21641 | 0.08736 | 0.03132 |
| 10:00-10:30 a.m. | 0.14220 | 0.13232 | 0.01560 | 0.01290 |
| 10:30-11:00 a.m. | 0.14766 | 0.13637 | 0.01231 | 0.01014 |
| 11:00-11:30 a.m. | 0.17916 | 0.16221 | 0.00999 | 0.00924 |
| 11:30-12:00 p.m. | 0.05934 | 0.02782 | 0.00599 | 0.00816 |
| 12:00-12:30 p.m. | -0.03426 | -0.02454 | -0.00798 | -0.00024 |
The regression analysis for intraday return of every interval is |RETi,t | = a + b |RETi,t-1 | + ei,t . The regression intercepts (a) decline monotonically during the morning for each market. Indosat decline more than Telkom. â coefficient also decline monotonically during the day. The result supports the hypothesis that the reaction of intraday price movement to overnight information is higher at the open and decline during the day. Overall, the evidence confirms previous studies that find the intraday price movement for local stock traded on the NYSE is higher at the open and declines during midday.
When the JSX opens, Indonesian investors react to overnight information, causing increases in both trading volume and price volatility. This is true even when the overnight information is public at the open, since investors experience uncertainty in interpreting the information. However, as trading proceeds, prices become less noisy, so that trading volume and price volatility decline. The residual reaction of overnight information shows on Table 4.
Table 4
Residual Reaction of Overnight Information
Panel A. Residuals Statistics of Telkom
|
| Minimum | Maximum | Mean | Std. Deviation | N |
| Predicted Value | .0073861 | .0265755 | .0094344 | 2.180574E-03 | 233 |
| Std. Predicted Value | -.939 | 7.861 | .000 | 1.000 | 233 |
| Standard Error of Predicted Value | .0005900 | .0046851 | .0007510 | 3.642682E-04 | 233 |
| Adjusted Predicted Value | .0071263 | .0294355 | .0094455 | 2.288654E-03 | 233 |
| Residual | -.0139798 | .0366587 | -1.0348780E-18 | 8.986361E-03 | 233 |
| Std. Residual | -1.552 | 4.071 | .000 | .998 | 233 |
| Stud. Residual | -1.570 | 4.086 | -.001 | 1.002 | 233 |
| Deleted Residual | -.0143092 | .0369312 | -1.1126323E-05 | 9.069053E-03 | 233 |
| Stud. Deleted Residual | -1.576 | 4.233 | .003 | 1.012 | 233 |
| Mahal. Distance | .000 | 61.792 | .996 | 4.555 | 233 |
| Cook's Distance | .000 | .186 | .005 | .015 | 233 |
| Centered Leverage Value | .000 | .266 | .004 | .020 | 233 |
Dependent Variable: JSX
Panel B. Residuals Statistics of Indosat
|
| Minimum | Maximum | Mean | Std. Deviation | N |
| Predicted Value | .0064359 | .0254499 | .0077246 | 1.716881E-03 | 229 |
| Std. Predicted Value | -.751 | 10.324 | .000 | 1.000 | 229 |
| Standard Error of Predicted Value | .0004885 | .0050775 | .0005989 | 3.449059E-04 | 229 |
| Adjusted Predicted Value | .0063667 | .0186877 | .0077015 | 1.479046E-03 | 229 |
| Residual | -.0136441 | .0350159 | -3.1475005E-18 | 7.375472E-03 | 229 |
| Std. Residual | -1.846 | 4.737 | .000 | .998 | 229 |
| Stud. Residual | -1.900 | 4.748 | .001 | 1.003 | 229 |
| Deleted Residual | -.0144612 | .0351784 | 2.310060E-05 | 7.472088E-03 | 229 |
| Stud. Deleted Residual | -1.911 | 4.992 | .004 | 1.013 | 229 |
| Mahal. Distance | .000 | 106.587 | .996 | 7.168 | 229 |
| Cook's Distance | .000 | .887 | .007 | .059 | 229 |
| Centered Leverage Value | .000 | .467 | .004 | .031 | 229 |
Dependent Variable: JSX
Indonesian investors react to overnight information, causing increases in both trading volume and price volatility. This is true even when the overnight information is public at the open, since investors experience uncertainty in interpreting the information. However, as trading proceeds, prices become less noisy, so that trading volume and price volatility decline.
V. CONCLUDING REMARK
This paper examines the intraday patterns of trading volume and price volatility for stocks traded on the JSX and listed on NYSE. This study conducts how overnight price movements in foreign market (NYSE) affect the trading activity of the local market (JSX). The magnitude of foreign price movements (Telkom and Indosat in NYSE) is positively related to price volatility of local stocks (Telkom and Indosat in JSX), and this relation is stronger at the JSX open and weaker afterward. This research finds that foreign price movements affect not only the opening returns of local stocks, but also their return in the first 30 minute interval. This result helps explain why intraday price volatility is high at the open and lower at midday.
Profit can be obtained by using a simple trading rule such as buying and selling stocks at particular time based on the intraday seasonality in stock return observed. The potential gain from the timing strategy can be significant. In Cheung’s study (1995) shows that if the sales are made in Monday morning instead of Monday close in Hong Kong Stock Market, the investor may earn an additional USD 109,200 (1,000,000*0.0021*52) per year for the fund where the cash flow of USD 1,000,000 has to be made every Monday by selling stocks and the return on Monday is 0.21 percent. If the investor can sell the stocks at the last 15 minute session of the trading day, then additional profit of USD 57,200 (1,000,000*0.0011*52) can be made annually. The gains can only be made if securities are sold at the day-end prices. Similarly, if the sales are made on Tuesday morning at 10:15 a.m. instead of Tuesday close, an additional 0.32 percent return could have been earned weekly before round-trip transaction costs. Seller should prefer selling at the openings whereas buyers should not purchase on openings and closings.
Active trading strategies based on intraday seasonalities observed in stock returns, such buying a stock and selling it at specific times in the same daycan generate higher returns. The result shows that there may be significant opportunity to make profit if traders have discretion to time their trades. On the other hand, since traders do not actually trade market indices, it is important to know whether the systematic pattern characterized across securities. Therefore, my evidence suggests that the trading activity of JSX for cross-listed stocks is affected more by liquidity trading of Indonesian investors and less by NYSE market information. Intraday seasonalities that also exist significantly in the JSX are consistent with the previous studies.
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