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1 Volume-7, Issue-6, November-December 2017 International Journal of Engineering and Management Research Page Number: Modification of Contemporaneous and Causality Model of Trading Measured by Depth,, Trading Volume and Bid-Ask Spread (Study Case: Jakarta Islamic Index) A. Agung 1, E.H. Nugrahani 2, D. C. Lesmana 3 1,2,3 Department of Mathematics, Bogor Agricultural University, INDONESIA ABSTRACT Capital market is a market where securities such as shares and bonds are issued to raise medium to long-term financing, and where the securities are traded. The information trading stocks obtained is required by capital market actors in decision making. This paper empirically examines the relationship among several financial variables, i.e. depth, stock return, trading volume and bid-ask spread for 28 stocks in Jakarta Islamic Index (JII). Furthermore, the contemporaneous relationship and causal relationship between the financial variables are presented. The Granger causality test result show that the information contents of depth, trading volume and bid-ask spread are useful for predicting stock return in JII. Keywords-- bid-ask spread, depth, volatility, stock return, trading volume I. INTRODUCTION Capital market is a market where buyers and sellers engage in trade of financial securities like bonds and stocks. A stock is a general term used to describe the ownership certificates of any company. price movements are related to uncertainty factors, therefore, investors should first consider it well before investing so that the risk is not too large. Investor can avoid the uncertainty factor in seeing the movement of stock by observing the through supporting variables such as return, volatility return, trading volume and bid-ask spread. Indonesia as a predominantly Moslem country is a huge market for the development of Islamic financial industry. In sharia principles, equity participation is made to companies that do not violate sharia principles. Indonesian Exchange (IDX) summarizes the companies that issue sharia shares in the Jakarta Islamic index (JII). JII consist of 30 companies that have the most liquid stock of sharia and have large market capitalization. The relationship between volatility and trading volume has been a major problem of empirical studies since several years ago. The main inspiration of this study is the central role played by trading volume in predicting the price of financial assets through the coming of new information. There is a positive contemporaneous relationship between volatility and trading volume in financial markets. Theoretically, the existence of the positive relationship is caused by two underlying hypotheses, namely the mixture of distribution hypothesis (MDH) and the sequential information arrival hypothesis (SIAH) [8]. In previous studies a causal and trading volume became the focus of attention of researchers. Lee and Rui found positive contemporaneous evidence as well as a two-way relationship between volatility and trading volume in the American, English and Japenese markets [5]. Leon also found a contemporaneous relationship between volatility and trading volume so that it can be concluded that trading volume can predict volatility in western regional economic exchanges [6]. This paper present the modified of Paital and Sharma model by adding depth to the variables under consideration. The model is then implemented to Indonesian stocks under the Jakarta Islamic Index. II. DATA AND VARIABLES DESCRIPTION Our primary data set consists of transaction price, trading volume, bid volume, ask volume, and the closed bid-ask price using intraday intervals from 30 December 2015 to 30 November 2016 for all stocks of Jakarta Islamic Index. Table 1 provides the list of companies and their industrial type. All the data are obtained electronically from Copyright Vandana Publications. All Rights Reserved.

2 Table 1 List of constituents of Jakarta Islamic Index (JII) Name Industry Code AALI Astra Agro Lestari Plantation ADRO Adro Energy Mining AKRA AKR Corporindo Chemistry ASII Astra International Automobile ASRI Alam Sutera Reality Real Estate BSDE Bumi Serpong Damai Real Estate ICPB Indofood CBP Sukses Makmur Food and Drink INCO Vale Indonesia Metals and Minerals INDF Indofood Sukses Makmur Food and Drink INTP Indocement Tunggal Prakarsa Cement JSMR Jasa Marga (Persero) Transportation KLBF Kalbe farma Pharmaceuticals LPKR Lippo Karawaci Real Estate LPPF Matahari Department Store Consumer goods LSIP PP London Sumatra Indonesia Plantation MIKA Mitra Keluarga Karyasehat Health PGAS Perusahaan Gas Negara (Persero) Energy PTPP PP (Persero) Construction and PWON Pakuwon Jati Real Estate SILO Siloam International Hospitals Pharmaceuticals SMGR Semen Indonesia (Pesero) Cement SMRA Summaraccon Agung Real Estate SSMS Sawit Sumbermas Sarana Perkebunan TLKM Telekomunikasi Indonesia (Persero) Telecommunication service UNTR United Tractors Steel UNVR Unilever Indoneisa Consumer goods WIKA Wijaya Karya (Persero) Construction and WSKT Waskita Karya (Persero) Construction and Depth, volatility, trading volume and bid-ask spread are considered relevant for this study. The percentage return of the stock is defined as ( ), where is the logarithmic percentage return at time and represent closed price at time. Following [6], [7] and [10], squared return is used to measure the price volatility. Furthermore, following [1], [3] and [11], the proportional intraday bid-ask spread at time is calculated as: * +. Next, the trading volume is the total number of shares traded at each day. Following [2] and [9] trading volume is calculated as follow: ( ) where is highest request price volume at time, is volume of the lowest offering price at time, and is the number of trading days. III. THE MODIFIED CONTEMPORANEOUS AND CAUSAL MODELS Contemporaneous Relationship In this study, the proposed modified model based on Paital and Sharma is formulated by adding the depth variable to obtain modified equation, (1) (2) where,,, and are return volatility, depth, trading volume and bid-ask spread, respectively, at time. The estimated parameter in equation (1) measures the contemporaneous relationship between depth and return volatility. A statistically significant value of would indicate a contemporaneous relationship between depth and return volatility. Similarly measures the contemporaneous relationship between depth and trading volume and measures the contemporaneous relationship between depth and bid-ask spread in equation (1). In equation (2), the interpretation of the parameters are similar. Causal Relationship Our study covers not only the contemporaneous relationship but also the causal relationship. Based on sequential information arrival hypothesis (SIAH) we test whether depth, trading volume, bid-ask spread are useful for predicting stock return volatility. Based on Paital and Sharma, the modified model causal relationship as follow: (3) (4) where and are intercepts, and and are parameters and denotes the lag length. We formulate the linear Granger causality restriction as follows: if some of in equation (3) values are statistically not zero, then return volatility is said to Granger cause depth. Similarly for and. The optimum lag length is selected based on Schwarz information criterion (SC). The study uses logarithmic value of volume instead of raw volume to improve the normality properties of the series. Finally, depth is the stock sheet volume at the lowest selling order price and the highest purchase order price. Following [4] depth is calculated as follow : 142 Copyright Vandana Publications. All Rights Reserved. IV. IMPLEMENTATION Cross-Correlation Analysis As the first step, to investigate the relationship between depth, volatility, volume and spread, we

3 computed the cross-correlation coefficients for all stocks. The correlation coefficients are reported in Table 2. We found positive correlation between return volatility and depth except LPKR, PTPP, SILO, SMRA, TLKM, and WSKT. We also found positive correlation between return volatility and volume. Finally we found positive correlation between return volatility and spread except AALI, ADRO, ASII, BSDE, ICBP, INDF, LPPF, PWON, SMGR, SMRA, UNVR and WIKA. This correlation gives an indication for contemporaneous relationship. Table 2 Cross Correlation Coeficients No Depth Volume Spread 1 AALI ADRO AKRA ASII ASRI BSDE ICBP INCO INDF INTP JSMR KLBF LPKR LPPF LSIP MIKA PGAS PTPP PWON SILO SMGR SMRA SSMS TLKM UNTR UNVR WIKA WSKT Contemporaneous Relationship among with Depth, Trading Volume and Bid-Ask Spread The results of the OLS regression using equation 1 to explain the contemporaneous relationship with dependent variable depth is reported in Table 3. Parameter, which measures the contemporaneous relationship between volatility and depth, is statistically significant for 10 stocks (i.e. ADRO, ASRI, BSDE, ICBP, INDF, INTP, KLBF, LPPF, MIKA and PWON) out of 28 stocks. Parameter, which measures the contemporaneous relationship between volatility and volume, is statistically significant for all the 24 stocks except for MIKA, SILO, SSMS and TLKM. Table 3 Contemporaneous relationship among volatility with depth and volume No Saham R-Squared 1 AALI * * ADRO * * * AKRA * * ASII * * ASRI * * * BSDE * * * ICBP * * * INCO * * INDF * * * INTP * * * JSMR * * KLBF * * * LPKR * * LPPF * * * LSIP * * MIKA * PGAS * * PTPP * * PWON * * * SILO SMGR * * SMRA * * SSMS TLKM UNTR * * UNVR * * WIKA * * WSKT * * Notes : *Significant at 10% level. Contemporaneous Relationship among with Depth, Trading Volume and Bid-Ask Spread The results of the OLS regression using equation 2 to explain the contemporaneous relationship dependent variable volatility is reported in Table 4. Parameter, which measures the contemporaneous relationship between volatility and depth, is statistically significant for 9 stocks (i.e. ICBP, JSMR, KLBF, LPKR, LPPF, MIKA, PGAS, SMRA and WSKT) out of 28 stocks. Parameter, which measures the contemporaneous relationship between volatility and volume, is statistically significant for 23 stocks. Finally, parameter, which measures the spread, is statistically significant for all stocks. Table 4 Contemporaneous relationship among volatility with depth, volume and spread Saham R-Squared AALI * * ADRO * * AKRA * * ASII * * ASRI * * * BSDE * -0, , * * ICBP * 0, * 0, * * INCO * 0, , * * INDF * 0, , * * INTP * 0, , * * JSMR * -0, * 0, * * KLBF * 0, * 0, * * LPKR * -0, * 0, * * LPPF * 0, * 0, * * LSIP * -0, , * * MIKA , * 0, * PGAS * -0, * 0, * * PTPP * -0, , * * PWON * 0, , * * SILO , , * SMGR , , * * SMRA , * 0, * * SSMS , , * * TLKM , , * * UNTR , , * * UNVR , , * * WIKA * 0, , * * WSKT * 0, * 0, * * Notes : *Significant at 10% level. Causal Relationship with Dependent Variable Depth 3 to explain the causal relationship between depth and volatility are reported in Table 5. The null hypothesis that depth does not granger cause volatility ( ) is rejected for 9 stocks ( i.e. AALI, BSDE, ICBP, MIKA, 143 Copyright Vandana Publications. All Rights Reserved.

4 PGAS, PTPP, SMGR, UNVR and WSKT). On the other hand, the null hypothesis that past volatility does not granger cause depth ( ) is rejected for 2 stocks out of 28. We also found two-way causal between depth and volatility ( ) at SMRA. This finding implies that in the presence of current and past depth, volatility adds some significant predictive power for future depth. 3 too explain the causal relationship between depth and volume are reported in Table 5. The test results show that the null hypothesis that lagged depth does not granger cause volume ( ) is rejected for 3 stocks ( i.e. INCO, INDF and SSMS), whereas, the null hypothesis that lagged volume does not granger cause depth ( ) is rejected for 7 stocks ( i.e. ASRI, JSMR, PGAS, PTPP, SMGR, SMRA and UNVR). We also found two-way causal between depth and volume ( ) at ICBP and LSIP. Finally, the Granger causality test results using equation 3 explain the causal relationship between depth and bid-ask spread are reported in Table 5. The test results show that the null hypothesis that lagged depth does not granger cause bid-ask spread ( is rejected for 4 stocks ( i.e. ADRO, INCO, INDF and SSMS), whereas, the null hypothesis that lagged depth does not granger cause volume ( is rejected for 7 stocks ( i.e. ASRI, JSMR, PGAS, PTPP, SMGR, SMRA and UNVR). We not found two-way causal between depth and spread (. Table 5 Causal Relationship with Dependent Variable Depth AALI ADRO AKRA ASII ASRI BSDE ICBP INCO INDF INTP JSMR KLBF LPKR LPPF LSIP MIKA PGAS PTPP PWON SILO SMGR SMRA SSMS TLKM UNTR UNVR WIKA WSKT Notes: * = there is causal relationship Causal Relationship with Dependent Variable 4 to explain the causal relationship between volatility and depth ( ) are reported in Table 6. The test results show that the null hypothesis that lagged volatility does not granger cause depth in rejected in case of 1 stocks (i.e. MIKA), whereas, the null hypothesis that lagged depth does not granger cause volatility ( ) is rejected in case of 1 stocks (i.e. SMRA). We not found two-way causal between volatility and depth ( ). 4 too explain the causal relationship between volatility and volume are reported in Table 6. The test results show that the null hypothesis that lagged volatility does not granger cause volume ( ) is rejected of 3 stocks ( i.e. INCO, PGAS and PTPP), whereas, the null hypothesis that lagged volume does not granger cause volatility ( ) is rejected for 3 stocks ( i.e. AALI, INDF and WSKT). We not found two-way causal between volatility and depth ( ). Finally, the Granger causality test results using equation 4 to explain the causal relationship between volatility and spread are reported in Table 6. The test results show that the null hypothesis that lagged volatility does not granger cause spread is rejected for 2 stocks ( ) ( i.e. PTPP and MIKA), whereas, the null hypothesis that lagged spread does not granger cause volatility ( ) is rejected for 6 stocks ( i.e. ADRO, JSMR, PWON, SMRA, SSMS and UNTR). We not found two-way causal between volatility and spread. Table 6 Causal Relationship with Dependent Variable AALI ADRO AKRA ASII ASRI BSDE ICBP INCO INDF INTP JSMR KLBF LPKR LPPF LSIP MIKA PGAS PTPP PWON SILO Copyright Vandana Publications. All Rights Reserved.

5 SMGR SMRA SSMS TLKM UNTR UNVR WIKA WSKT Notes: * = there is causal relationship V. CONCLUSION The findings provide evidence of a volume as well as volatility and spread. However, in case of depth and volatility the explanatory power of this contemporaneous relation is weak. This indicate that, the Indonesian market is informationally inefficient. One way and two-way causal relationships on the depth are more influential than the causal relationships of return volatility. Our study not only focused on the contemporaneous relationship but also investigated the causal relationship. We investigated the information content of depth, volume and spread for future volatility by means of Granger causality test and found fo majority of the cases, depth, volume and spread caused volatility. [8] Paital RR, Sharma NK Bid-ask spreads, trading volume and return volatility. Eurasian Journal of Economics and Finance. 4(1): [9] Tian GG, Guo M Intraday and intraday volatility: Additional evidence from the Shanghai stock exchange. Review of Quantitative Finance & Accounting. 28: [10] Tripathy N The relation between price changes and trading volume: A study in Indian stock market. Interdisciplinary Journal of Research in Business. 1(7): [11] Wei SJ Anticipations of foreign exchange volatility and bid-ask spreads. Board of Governors of the Federal Reserve System International Finance Discussion Papers. 409:1-45. REFERENCES [1] Abhyankar A, Ghosh D, Levin E, Limmack RJ Bid-ask spreads, trading volume and volatility: intra-day evidence from the London stock exchange. Journal of Business Finance and Accounting. 24(3-4): [2] Al-Jafari MK, Tliti A An empirical investigation of the relationship between stock return and trading volume: Evidence from Jordanian banking sector. Journal of Applied Finance & Banking. 3(3): [3] Hussain SM The intraday behavior of bid-ask spreads, trading volume and return volatility: Evidence from XDAS30. International Journal of Economics and Finance. 3(1): [4] Komalasari PP, Baridwan Z Asimetri Informasi dan Cost Of Equity Capital. Jurnal Riset Akutansi Indonesia. 5(2): [5] Lee BS, Rui OM The dynamic relationship between stock returns and trading volume:domestic and cross country evidence. Journal of Banking and Finance. 26: [6] Leon N An empirical study of the relation between stock return volatility and trading volume in the BRVM. African Journal of Business Management. 1(7): [7] Medeiros O, Doornik BV The empirical relationship between stock returns, return volatility and trading volume in the Brazilian stock Market. Brazilian Business Review. 5(1): Copyright Vandana Publications. All Rights Reserved.