Efficient Market Hypothesis : Evidence from Indonesia Stock Exchange (IDX) Siti Rahmi Utami Mahasiswa Program Doktor Trisakti International Business School – Maastricht School of Management Abstract In this research we test market efficiency in Indonesia Stock Exchange (IDX) by examining whether stock price changes is independent of past changes or not, and examining do profitability, liquidity, debt, and market value information of the firms have influence on stock returns, hence these information can be used to estimate the future stock returns. We use stock price and return as dependent variable, and financial ratios as independent variables. We have applied Ljung-Box Q-Test and regression to analyze the data, and collected the data of companies from the Indonesia Stock Exchange from the year 1994 to 2005. The sample size consists of 77 companies from all sector companies of LQ 45 Index. Ljung-Box Q-Test result has shown that the significant amount of lag are 16, therefore there is autocorrelation between current price and previous price, and we conclude that market is not efficient in weak-form. Meanwhile, regression results have shown that profitability information (ROA) has negative significant effect on stock returns, while ROE has positive significant effect on stock returns. As one of market value information, dividend has positive significant influence on stock returns. However, sales to asset (SALASS), all debt information (TLTE, LTLTE, and TLTA), liquidity information (current ratio), and price to earning ratio (PER), have insignificant effect on stock returns. Key words Efficiency : Efficient Market Hypothesis (EMH), Stock Return, Weak-Form Market 1. Introduction Market efficiency means that the market price of a security represents the market’s consensus estimate of the value of that security. An efficient financial market exists when security prices reflect all available public information about the economy, about financial markets, and about the specific company involved. The implication is that market prices of individual securities adjust very rapidly to new information (Van Horne, 1998). All investors who hold security expect to increase the return in the future. Many investors, including investment managers, believe that they can select securities that will outperform the market. Therefore, they need to use available public information to lead them in their investment decisions, as Anderson et al (2005) states that in investing the 1 investors could use financial statements as their primary decision-making tool. If the market is efficient, investor will purchase the security at least at its current market price based on all available public information. Therefore, investors who purchase the stock or any other security interpret that their information as a higher appraisal (Van Horne, 1998). In the literature, such as in Corrado and Jordan (2000), the question of whether a market is efficient is meaningful only relative to some type of information. Three general types of information are particularly interesting to define three forms of market efficiency are weak-form efficient market, semi-strong-form efficient market, and strong-form efficient market. In Indonesia Stock Exchange, we assume that the stock market is efficient in weak-form as investors can not use a time series of past stock price to discern a pattern of price changes in predicting future stock return. Therefore, we are interested to examine whether stock price changes is independent of past changes or not, and to examine the extent to which market reacted with signal on releasing public information in Indonesia Stock Exchange. To answer these questions, we test the serial correlation between previous and current stock price, and examine whether profitability, liquidity, debt, and market value information have any impact on stock returns, so that all these information can be used to estimate the future stock returns. By using Ljung-Box Q-Test and regression analysis, and collecting the data of firms in IDX over the period 1994-2005, Ljung-Box Q-Test result has shown that the significant amount of lag are 16, therefore there is autocorrelation between current price and previous price. The interpretation of the result is that the market is not efficient in weak-form, and it indicates that investor could use price from one period to predict returns in later periods and make higher profits. Regression result of testing the extent to which market reacted with signal on releasing public information, have shown that ROA, ROE, and dividend can predict the future movement of stock returns, while SALASS, TLTE, LTLTE, TLTA, current ratio, and PER, can not be used to estimate the stock returns. The rest of the paper is divided into 5 sections. Section 2 explains the theoretical framework and also covers some results from earlier studies. Section 3 discusses the hypotheses of the research. Section 4 presents the methodology used for the study. Section 5 interprets the empirical results and the statistic results. Section 6 presents the conclusions respectively. 2. Literature Review The term "efficient market" is introduced for the first time by Eugene Fama (1965), in his paper titled “Random Walks in Stock Market Prices”. He defined an efficient market as : “a market where there are large numbers of rational profit maximizers actively competing, with each trying to predict future market values of individual securities, and where important current information is almost freely available to all participants”. It implies that investors trying to use information to predict future market values of securities. 2 Corrado and Jordan (2000) noted three general types of information are particularly interesting to define three forms of market efficiency are the following : (1) A weak-form efficient market is one in which the information reflected in past prices and volume figures is of no value in beating the market. If past prices and volume are of no use, then technical analysis is of no use whatsoever. (2) In a semi-strong-form efficient market publicly available information of any and all kinds is of no use in beating the market. If a market is semi-strong-form efficient, then the fundamental analysis techniques are use less. Past prices and volume data are also publicly available information, so if a market is semi-strong-form efficient, it is also weak-form efficient. (3) Finally, in a strong-form efficient market no information of any kind, public or private, is useful in beating the market. If a market is strong-form efficient, it is clear that nonpublic inside information of many types would enable us to earn essentially unlimited returns. According to Fama (1965), weak-form market efficiency means that the unanticipated return is not correlated with previous unanticipated returns. Semi strongform market efficiency means it is not correlated with any publicly available information. Strong-form market efficiency, indicates the unanticipated return is not correlated with any information, be it publicly available or insider. Based on the view of Spiegel and Stanton (2000), market efficiency refers to the extent that market efficiency prices reflect all available information. In their article, three primary forms of market efficiency are (1) Weak-form efficiency exists if market prices incorporate all past price information. (2) Semi-strong form efficiency exists if market prices incorporate all publicly available information. (3) Strong-form efficiency exists if market prices incorporate all information (both public and private). Many researchers have tested the weak-form market efficiency hypothesis. Rindisbacher (2002) presented the evidence that in “weak-form”, US market that contrarian strategies achieve abnormal returns in the long run, while US market that momentum strategies achieve abnormal returns in the short run. Brock, Lakonishok, and LeBaron (1992) find that relatively simple technical trading rules would have been successful in predicting changes in the Dow Jones Industrial Average. Rosenberg and Rudd (1982) found that the first order serial correlation of daily return residual from the market model is small but significantly negative. Gibbons and Hess (1981) reported “the Monday Effect” of stock prices tended to go down on Mondays. This finding was clearly inconsistent with the weak-form market efficiency. They also noticed that the Monday Effect seemed to decrease over time. Meanwhile, many researchers tested the semi-strong-form market efficiency hypothesis. The study of Rindisbacher (2002) presented the evidence that in semi-strongform, public information is reflected in the stock prices. The evidence is that markets react quickly. Survey of market efficiency of Fama (1991) has focused on testing informational efficiency. He reports a stronger evidence a predictability in returns based both on lagged values of returns and publicly available information. The study of Roncati (2005) which has the objective to determine whether a group of investors, is able to forecast the future security price, using all information it wishes to employ, to increase returns on the portfolio and consequently to beat the market. The sample consists of the returns on the portfolios of 80 actively managed Swiss equity funds and of 14 passively managed Swiss equity funds for which monthly 3 performance (or rate of return) information and detailed information were available at Reuters and Lipper for the period 2001-2005. The result of his work is that on average the active funds managers are not able to predict the security prices well enough to outperform the market and consequently the passive funds and consequently the market seems to be efficient. Researchers who have tested EMH in the strong-form are the following. Penman (1982) found that insiders can achieve high return by buying shares before the announcement and selling their shares after the announcement. Rindisbacher (2002) found that corporate insiders earn positive abnormal returns, and prices do not seem to reflect all information. Morse (1980) found a grater trading than normal a day before public announcement. Keown and Pinkerton (1981) observed high abnormal return and trading volume prior to merger announcement. Abdel-Khalik and Ajinkya (1982) discover high return a week before analyst earning announcement. Some of study results are inconsistent with EMH, such as the study result of Ozmen (1997). He finds that future prices are predictable by using past price movements, and because of this ISE is not weak-form efficient. It is possible for professional investors who carefully observed this trend to make extraordinarily high profit. Sullivan, Timmermann, and White (1999) find that the apparent historical ability of technical trading rules to generate excess returns. Fama and French (1992) found that after controlling for firm size and the variance of portfolio returns, stocks with low priceearnings ratios outperform the market. Several previous research review the issue of whether financial ratios have the influence on stock return or not. For instance, in the study of Martel and Padrón (2006), the methodology of event study has been used for testing empirically if the share price in the Spanish Stock Market reacts to the straight debt issue by companies that have quoted in the Stock Exchange of Madrid over the period 1989-1998. The obtained results show that the Spanish Stock Market reacts positively and significantly to debt issue announcements. The market response to the debt issue announcements has been positive and significant for the companies with a policy of low dividends. On the other hand, this relation has been positive but non-significant for companies with high dividend payouts. Hadi (2006) examines the market reaction to accounting numbers release by using stock returns as the dependent variable and dividends, net income on sale, return of equity, return on asset, debt ratio, interest coverage, current ratio, and price/earning ratio as independent variables. He used ordinary least square method (OLS) to solve the regression equations, and all analysis is performed at pool 2000-2003. His study result shows that dividend, net income on sale and ROA have impact on security returns, current ratio can be used an estimate for future return, debt ratio coefficient is negative which carry negative information to the market, and interest coverage can be used to estimate for future returns. An empirical study conducted by Karan (1996) using the Istanbul Stock Exchange data, showed that there is PER effect on stock return. Bhana (2002) observes a sample of 100 companies listed in Johannesburg Stock Exchange (JSE), announcing special dividends over the period 1975 – 1994. The result shows that share price reactions are negatively related to dividend declaration frequency. Woolridge (1983) has argued that one cannot infer that dividend increases convey positive information about the firm by examining share prices alone. 4 According to Beechey, Gruen, and Vickery (2000), in an efficient market, publicly available information should already be reflected in the asset price. In the stock market, such as, public information on price-earnings ratios, cash flows or other measures of value should not have implications for future share returns (unless these variables are revealing information about the riskiness of the asset). Abeyratana et al. (1993) found a significant abnormal return in relationship with firms announcing cash dividends. Hadi (2005) found evidences from Kuwait that market reactions of the release dividedness information. 3. Hypothesis Based on the efficient market hypothesis and previous research findings reviewed above, we hypothesize that : H1 : There is a weak-form of market efficiency in IDX, as weak-form of market efficiency hypothesis asserts that the current price fully incorporates information contained in the past history of prices only. H2 : Profitability information, liquidity information, debt information, and market value information have influence on stock return. 4. Research Methodology 4.1 Hypotheses Testing and Measurement of Variables To test the hypothesis 1, we examine the serial correlation between the current stock price and the stock price over a previous period, following research methodology used by Rosenberg and Rudd (1982). Meanwhile, for testing the hypothesis 2, we examine the effect of profitability, liquidity, debt, and market-value information on stock returns, by applying the methodology of research used by Hadi (2006), but with appropriate modification. The selection of variables and definitions are following Van Horne (1998). The regression model of hypothesis 2 and definition of variables used in the research are as follow : STRET = a + b1*ROA + b2*ROE + b3*SALASS + b4*CURRAT+ b5*TLTE + b6*LTLTE + b7*TLTA + b8*PER + b9*DIV + ε Where : a. Profitability Information Stock Return or STRET is return for stockholders. Return on assets, or the ROA is measured as net profits after taxes to total assets. Return on Equity or ROE is measured as net profits after taxes minus preferred stock dividend divided by shareholders’ equity. This ratio tells us the earning power on shareholders’ book investment. Asset turnover 5 ratio is measured as total sales to total assets (SALASS). This ratio tells us the relative efficiency with which the firm utilizes its resources in order to generate output. b. Liquidity Information Liquidity ratio is used to judge a firm’s ability to meet short-term obligations. From them, much insight can be obtained into the present cash solvency of a company and its ability to remain solvent in the event of adversities. One of the most general and most frequently used of liquidity ratios is the current ratio (CURRAT) as measured by current assets to current liabilities. c. Debt Information The debt-to-equity ratio is computed by simply dividing the total debt of the firm (including current liabilities) by its shareholders’ equity (TLTE). The long-term debt-toequity ratio (LTLTE) is computed by simply dividing the long-term debt of the firm by its shareholders’ equity. Total debt to assets ratio (TLTA) is computed by dividing total liabilities by its total Assets. Debt information is used to measure the relative obligations of a company. d. Market-Value Information The price to earnings ratio (PER) of a company is simply share price divided to earnings per share. This ratio is described as one measure of relative value. The higher this ratio, the more the value of the stock that is being ascribed to future earnings as opposed to present earnings. The dividend (DIV) for a stock relates the annual dividend per share. 4.2. Data Analysis A. Ljung-Box Q-Test Following the methodology in the study of Rosenberg and Rudd (1982), we use serial correlation test to measure the association between two elements of time series separated by a constant number of time periods. The objective of this test is to examine the autocorrelation between the current stock price and the stock price over previous period. Hence, we employ Ljung-Box Q-Test to detect serial correlation of the data 16th lag (Ghozali, 2002). B. Regression Analysis We employ regression technique and equation to measure the degree of relationship between two or more variables in two different but related ways. The model of the relationship has hypothesized, and the objective of the analysis is to test whether or not profit information, liquidity information, debt information, and market value information, have influence on stock returns. 6 4.3. Data Description We have collected the data of companies from the Indonesia Stock Exchange 1(IDX), from the year 1994 to 2005. The sample size consists of 77 companies and includes all sector companies of LQ 45 Index as sample. The Index is one of Indonesia’s Stock Exchange Index, which consists of 45 firms from all sectors. 5. Hypotheses Test Results 5.1. Ljung-Box Q-Test Result For testing hypothesis 1, we examine whether IDX is efficient in weak-form market efficiency or not. Therefore, we test the serial correlation between current price and previous price of the stocks, by employing the Ljung-Box Q-Test for analyzing serial correlation between variables. The result of the test is presented in table 1 as follows. Table 1 : Ljung-Box Q-Test Result Autocorre lations Series : Current _price Lag 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Autocorrel ation .275 .231 .193 .088 .048 .045 .006 -.029 -.051 -.050 -.046 -.021 -.012 -.013 -.013 .037 a St d.Error .049 .049 .049 .049 .049 .049 .048 .048 .048 .048 .048 .048 .048 .048 .048 .048 Box-Ljung Stat istic b Value df Sig. 31.753 1 .000 54.150 2 .000 69.876 3 .000 73.142 4 .000 74.112 5 .000 74.988 6 .000 75.002 7 .000 75.351 8 .000 76.483 9 .000 77.536 10 .000 78.450 11 .000 78.637 12 .000 78.695 13 .000 78.772 14 .000 78.847 15 .000 79.428 16 .000 a. The underlying proc ess ass umed is independence (white noise). b. Based on t he asym ptotic c hi-square approx imation. 1 www.idx.co.id 7 Ljung-Box Q-Test is one method that can be used to detect autocorrelation. Autocorrelation arises if significant amount of lag is more than two, whereas if significant amount of lag is less than two, then there is no autocorrelation between variables (Ghozali, 2002). Table 1 shows that sixteen lag is significant. Therefore, we conclude that there is autocorrelation between current price and previous price. The interpretation of the result is that the market is not efficient in weak-form, and it indicates that investor could use price from one period to predict returns in later periods and make higher profits. 5.2. Regression Results The objective of testing hypothesis 2 is to examine whether profitability, liquidity, debt, and market value information have significant effect on stock return. A. Regression Assumptions Before analyzing regression coefficients of variables, we must first make several assumptions about the population of the research. They represent an idealization of reality, and as such, they are never likely to be entirely satisfied for the population in any real study (Van Horne, 1998). A good regression model should not has the following assumptions. 1. Multicollinearity Multicollinearity implies that for some set of explanatory variables, there is an exact linear relationship in the population between the means of the response variable and the values of the explanatory variables (Van Horne, 1998). The goal of the multicollinearity test is to analyze whether there is correlation between independent variables. Multicollinearity in the regression model can be detected such as by testing the R2 value and/or analyzing the correlation matrix (Ghozali, 2002). Table 2 : Model Summary Model Summ aryb Model 1 R R Square .439a .193 Adjust ed R Square .149 St d. Error of the Es timate 10414. 00632 DurbinW atson 2.482 a. Predic tors: (Constant), DIV, SALASS, PER, TLTE, ROE, CURRAT, LTLTE, TLTA, ROA b. Dependent Variable: STRET 8 Table 2 shows the value of R2 that has generated by an empirical regression model estimation. It values is 0.193 (less than 0.90). We also see from table 2 that the Adjusted R-squared value of profitability information, liquidity information, debt information, and market value information as predictors for stock return is 0.149. These provide one evidence that only 14.9% of the movement of the stock return could be explained by the existence of these information. Therefore there is no multicolinearity in the regression model. Table 3 : Correlations Correlations ROA ROA ROE SALASS CURRAT TLTE LTLTE TLTA PER DIV Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N 1 641 .317** .000 640 -.321** .000 623 .185** .000 518 .048 .226 641 .101* .010 641 .027 .490 641 -.091* .046 483 .436** .000 290 ROE SALASS CURRAT .317** -.321** .185** .000 .000 .000 640 623 518 1 -.137** .020 .001 .652 643 622 520 -.137** 1 -.320** .001 .000 622 623 509 .020 -.320** 1 .652 .000 520 509 521 .237** -.006 -.008 .000 .872 .849 643 623 521 .254** -.010 .008 .000 .810 .851 643 623 521 .148** -.054 -.103* .000 .177 .019 640 623 518 -.069 -.027 .001 .128 .560 .984 483 468 390 .341** -.044 .186** .000 .462 .004 290 282 244 TLTE .048 .226 641 .237** .000 643 -.006 .872 623 -.008 .849 521 1 644 .951** .000 644 .097* .014 641 -.017 .716 484 -.115 .051 290 LTLTE .101* .010 641 .254** .000 643 -.010 .810 623 .008 .851 521 .951** .000 644 1 644 .076 .054 641 -.016 .721 484 -.175** .003 290 TLTA .027 .490 641 .148** .000 640 -.054 .177 623 -.103* .019 518 .097* .014 641 .076 .054 641 1 641 -.061 .181 483 -.214** .000 290 PER -.091* .046 483 -.069 .128 483 -.027 .560 468 .001 .984 390 -.017 .716 484 -.016 .721 484 -.061 .181 483 1 505 -.120 .079 217 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). The correlations matrix above shows that there is only one quite high correlation value (more than 0.90), that is the correlation between independent variables of TLTE and LTLTE. Meanwhile, the other correlation values are less than 0.90. This is an indication that multicollinearity is not exist in the regression model. 9 DIV .436** .000 290 .341** .000 290 -.044 .462 282 .186** .004 244 -.115 .051 290 -.175** .003 290 -.214** .000 290 -.120 .079 217 1 290 2. Autocorrelation Autocorrelation requires probabilistic independence of the errors. This assumption means that information on some of the errors provides no information on other errors. For time series data this assumption is often violated. This is because of a property called autocorrelation (Van Horne, 1998). Test of autocorrelation aims to examine whether in a linear regression model has correlation between gadfly errors in the period t with an error in the period t-1 (before). One of the method that can be used to detect autocorrelation is the Durbin Watson (DW). Table 2 shows that the DW value of 2.482 which means that there is no autocorrelation in regression model. 3. Heteroscedasticity This assumption concerns variation around the population regression line. Specifically, it states that the variation of the Y’s about the regression line is the same, regardless of the values of the X’s (Van Horne, 1998). Test of heteroscedasticity aims to interpret whether the regression model has the differences residual variance from one observation to another observation (Ghozali, 2002). If the residual variance from one observation to another observation is the same, it is called homoscedasticity. Figure 1 : Scatterplot Scatterplot Regression Studentized Residual Dependent Variable: STRET 5 0 -5 -10 -4 -2 0 2 4 6 8 Regression Standardized Predicted Value 10 The graphic of scatterplot (figure 1) shows that the dots have not established a specific pattern. Some of the dots located adjacent but some other dots spread above and below the numbers of 0 at the axis Y. Thus, that the data in the graphic exhibit homoscedasticity. 4. Normally Distributed The assumption states that the errors are normally distributed. We can check this by forming a histogram of the residuals. If the assumption holds, then the histogram should be approximately symmetric and bell-shaped. But if there is an obvious skewness, too many residuals more than, say, two standard deviations from the mean, or some other non-normal property, then this indicates a violation of the assumption (Van Horne, 1998). Figure 2 : Histogram Histogram Dependent Variable: STRET 100 Frequency 80 60 40 20 Mean = -4.99E-17 Std. Dev. = 0.974 N = 177 0 -10 -5 0 5 10 Regression Standardized Residual 11 Figure 3 : Normal P-P Plot Normal P-P Plot of Regression Standardized Residual Dependent Variable: STRET 1.0 Expected Cum Prob 0.8 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0 Observed Cum Prob From the graphics of histogram (figure 2) and normal P-P plot (figure 3), we concluded that the histogram gives the normally pattern of distribution. Meanwhile, the graphic of normal P-P plot shows that the dots spread around the diagonal line, and the spreading follows the diagonal line. Both of graphics show that the data meets reasonable assumption of normality. Based on the results of assumptions of population described above, the regression model does not has the assumptions of heteroscedasticity, multicollinearity, autocorrelation, and the data is normally distributed. Thus, our regression model is appropriate to use for testing the hypothesis 2. B. Regression Coefficients Table 4 presents the regression results to analyze the influence of profitability, liquidity, debt, and market value information on stock returns. 12 Table 4 : Regression Coefficients Coefficientsa Model 1 (Constant) ROA ROE SALASS CURRAT TLTE LTLTE TLTA PER DIV Unstandardized Coeffic ients B Std. Error -983.524 3527.500 -706.828 199.253 219.257 98.192 279.811 1933.777 .925 9.582 -23.667 1000.427 -1481.314 1715.732 2563.545 7145.386 44.006 56.414 18.146 3.255 Standardiz ed Coeffic ients Beta -.726 .423 .013 .010 -.003 -.093 .043 .056 .448 t -.279 -3.547 2.233 .145 .097 -.024 -.863 .359 .780 5.574 Sig. .781 .001 .027 .885 .923 .981 .389 .720 .436 .000 Collinearity Statistics Tolerance VIF .115 .135 .605 .487 .347 .415 .332 .946 .747 8.667 7.416 1.652 2.055 2.885 2.410 3.016 1.058 1.338 a. Dependent Variable: STRET Table 4 shows the following results : Profitability information on stock return ROA as measured by the return on assets, has negative significant regression coefficient on stock return, with 0.001 level of significance and -3.547 t-value. This result suggests that firms with high ROA would tend to create low return of stock to their stockholders. ROE has positive significant regression coefficient on stock return, with 0.027 significance level and 2.233 t-value. This result implies that ROE can be used for estimating the future stock returns. SALASS has insignificant positive regression coefficient on stock return, with 0.885 level of significance and 0.145 t-value. This result describes that SALASS ratio can not be used to predict the future stock returns. Liquidity information on stock return Current ratio has insignificant positive influence on stock returns with 0.923 level of significance and 0.097 t-value. This result implies that liquidity information do not affect stock returns, hence it can not be used to predict the future returns. Debt information on stock return TLTE and LTLTE have negative but not significant effects on stock returns. However, TLTA has positive but not significant influence on stock returns. These results suggest that debt information can not be applied for estimating the future stock returns. 13 Market value information on stock return PER as measured by price to earning ratio, has positive but not significant regression coefficient on stock return, with 0.436 level of significance and 0.780 t-value. This result suggests that firms with high PER would tend to create high return of stock even though insignificantly. Dividend as defined dividend per share for shareholders, has positive significant regression coefficient on stock return, with 0.000 level of significance and 5.574 t-value. This result describes that firms with high dividend payment would tend to create high return of stock for their stockholders. Therefore, dividend can be applied to predict the future stock returns. 6. Conclusion After obtaining the Ljung-Box Q-Test and regression results for examining whether Indonesia Stock Market is efficient in weak-form, by using the data of LQ 45 Index within the period 1994-2005, we can draw the conclusion. Firstly, the significant amount of lag are 16, therefore there is autocorrelation between current price and previous price. It implies that market is not efficient in weak-form as one could use price from one period to predict returns in later periods and earn extraordinarily high return by using pattern of price change. Secondly, regression result of testing the extent to which market reacted with signal on releasing public information, have shown that profitability information (ROA) has negative significant effect on stock returns, while ROE has positive significant effect on stock returns. Meanwhile, as one of market value information, dividend has positive significant influence on stock returns. However, profitability information (SALASS), debt information (TLTE, LTLTE, and TLTA), liquidity information (CURRAT), and market value information (PER), have not significant effect on stock returns. These results suggest that debt information and liquidity information can not be used to estimate the stock returns, while ROA, ROE, and dividend can predict the future movement of stock returns. Based on these results, there is an indication that Indonesia Stock Exchange is not efficient in weak-form. Our results are inconsistent with Efficient Market Hypothesis, as it states that, it is not possible to earn extraordinarily high return in any form efficient market by using pattern of price change, and also to predict future stock returns. Efficient market prevents investors to use information because prices have already adjusted to take that information into account. The efficient markets hypothesis also suggests that profiting from predicting price movements is very difficult and unlikely as security prices adjust before an investor has time to trade on and profit from a new information. Therefore, there is no reason to believe that prices are too high or too low. 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Ljung-Box Q-Test Results Model De scription Model Name Series Name MOD_1 Previous_price 1 2 Current_price Transformation Non-S easonal Differenc ing None 0 Seasonal Differenc ing Length of S eas onal Period 0 No periodic ity Maximum Number of Lags 16 Proces s A ssumed for Calc ulating the S tandard Errors of the A utoc orrelations Display and Plot a Independence(whit e noise) All lags Applying the model specific ations from MOD_1 a. Not applicable for c alculating the st andard errors of the partial autocorrelations. Ca se Processi ng Sum mary Series Length Number of Miss ing Values Us er-Missing Sy stem-Missing Number of Valid Values Number of Computable Firs t Lags Previous_ price 423 0 a Current_price 423 0 a 6 6 417 417 416 416 a. Some of the missing values are imbedded within the series. 17 Previous Stock Price Autocorre lations Series : Previous_price Lag 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Autocorrel ation .402 .215 .217 .112 .041 .015 -.004 -.038 -.037 -.044 -.048 -.032 -.021 -.020 -.005 -.005 Box-Ljung Stat istic b Value df Sig. 67.807 1 .000 87.211 2 .000 107.107 3 .000 112.457 4 .000 113.181 5 .000 113.275 6 .000 113.281 7 .000 113.890 8 .000 114.467 9 .000 115.313 10 .000 116.315 11 .000 116.751 12 .000 116.947 13 .000 117.113 14 .000 117.124 15 .000 117.136 16 .000 a St d.Error .049 .049 .049 .049 .049 .049 .048 .048 .048 .048 .048 .048 .048 .048 .048 .048 a. The underlying proc ess ass umed is independence (white noise). b. Based on t he asym ptotic c hi-square approx imation. Previous_price Coefficient 1.0 Upper Confidence Limit Lower Confidence Limit ACF 0.5 0.0 -0.5 -1.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Lag Number 18 Partial Autocorrelations Series : Previous_price Lag 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Partial Autocorrel ation .402 .063 .133 -.029 -.028 -.023 -.011 -.034 -.006 -.022 -.012 .004 .003 -.003 .009 -.007 Std.Error .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 Previous_price Coefficient 1.0 Upper Confidence Limit Lower Confidence Limit Partial ACF 0.5 0.0 -0.5 -1.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Lag Number 19 Current Stock Price Autocorre lations Series : Current _price Lag 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Autocorrel ation .275 .231 .193 .088 .048 .045 .006 -.029 -.051 -.050 -.046 -.021 -.012 -.013 -.013 .037 Box-Ljung Stat istic b Value df Sig. 31.753 1 .000 54.150 2 .000 69.876 3 .000 73.142 4 .000 74.112 5 .000 74.988 6 .000 75.002 7 .000 75.351 8 .000 76.483 9 .000 77.536 10 .000 78.450 11 .000 78.637 12 .000 78.695 13 .000 78.772 14 .000 78.847 15 .000 79.428 16 .000 a St d.Error .049 .049 .049 .049 .049 .049 .048 .048 .048 .048 .048 .048 .048 .048 .048 .048 a. The underlying proc ess ass umed is independence (white noise). b. Based on t he asym ptotic c hi-square approx imation. Current_price Coefficient 1.0 Upper Confidence Limit Lower Confidence Limit ACF 0.5 0.0 -0.5 -1.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Lag Number 20 Partial Autocorrelations Series : Current_price Lag 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Partial Autocorrel ation .275 .168 .105 -.017 -.022 .011 -.017 -.040 -.045 -.018 -.006 .018 .011 -.003 -.008 .048 Std.Error .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 .049 Current_price Coefficient 1.0 Upper Confidence Limit Lower Confidence Limit Partial ACF 0.5 0.0 -0.5 -1.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Lag Number 21 2. Regression Results De scriptive S tatistics STRET ROA ROE SA LAS S CURRAT TLTE LTLTE TLTA PE R DIV Mean 846.2160 8.2371 16.1564 .3710 98.8537 1.3092 .6115 .4801 14.8740 163.4476 St d. Deviation 11291.34482 11.59823 21.77084 .52175 117.44746 1.33274 .71032 .19080 14.30945 278.95534 N 177 177 177 177 177 177 177 177 177 177 Note: Stock return (STRET) is return for shareholders. Return on equity (ROE) is measured as net profits after taxes minus preferred stock dividend divided by shareholders’ equity. Return on Assets (ROA) is measured as net profits after taxes to total assets. Asset turnover ratio is measured as total sales to total assets (SALASS). Current ratio (CURRAT) is measured as current assets to current liabilities. Debt-toequity ratio is computed by simply dividing the total debt of the firm (including current liabilities) by its shareholders’ equity (TLTE). Long-term debt-to-equity ratio (LTLTE) is computed by dividing the longterm debt of the firm by its shareholders’ equity. Total debt to assets ratio (TLTA) is computed by dividing total liabilities by its total assets. Price to earnings ratio (PER) of a company is simply share price divided to earnings per share. Dividend (DIV) for a stock relates the annual dividend per share. b Va riables Entere d/Re moved Model 1 Variables Entered DIV, SA LAS S, PE R, TLTE, ROE, CURRAT, LTLTE , a TLTA, ROA Variables Removed . Method Enter a. All reques ted variables ent ered. b. Dependent Variable: S TRE T ANOVAb Model 1 Regres sion Residual Total Sum of Squares 4E+009 2E+010 2E+010 df 9 167 176 Mean Square 480846800.2 108451527.7 F 4.434 Sig. .000a a. Predictors: (Constant), DIV, SALASS, PER, TLTE, ROE, CURRAT, LTLTE, TLTA, ROA b. Dependent Variable: STRET 22 Collinearity Diagnosticsa Model Dimension Eigenvalue 1 1 5.507 2 1.915 3 .930 4 .606 5 .391 6 .308 7 .152 8 .121 9 .046 10 .024 Condition Index (Constant) 1.000 .00 1.696 .00 2.433 .00 3.015 .00 3.755 .00 4.227 .01 6.020 .00 6.744 .12 10.959 .04 15.053 .82 ROA .00 .01 .00 .00 .02 .02 .00 .00 .80 .15 ROE .00 .01 .00 .00 .02 .04 .00 .00 .76 .16 SALASS .00 .03 .14 .01 .32 .02 .04 .32 .04 .07 Variance Proportions CURRAT TLTE .00 .00 .02 .01 .01 .02 .06 .02 .00 .00 .48 .00 .00 .47 .23 .25 .09 .04 .11 .18 LTLTE .00 .01 .05 .02 .01 .00 .82 .02 .05 .01 TLTA .00 .00 .00 .00 .00 .00 .01 .10 .03 .86 PER .01 .01 .03 .31 .21 .25 .02 .15 .01 .01 a. Dependent Variable: STRET Re siduals Sta tisticsa Minimum Predic ted V alue -15956.4 St d. P redic ted Value -3. 389 St andard E rror of 1112.387 Predic ted V alue Adjust ed P redicted Value -19525.6 Residual -100927 St d. Residual -9. 691 St ud. Residual -10.267 Deleted Residual -113276 St ud. Deleted Residual -16.857 Mahal. Dis tanc e 1.014 Cook's Dis tanc e .000 Centered Leverage Value .006 Maximum 34740. 98 6.835 Mean 846.2160 .000 St d. Deviat ion 4958.70516 1.000 N 8576.386 2215.074 1107.967 177 18838. 61 625.7817 52759. 02 .00000 5.066 .000 6.942 .009 99065. 41 220.43423 8.206 -.021 118.373 8.949 4.230 .034 .673 .051 4674.23250 10144. 24521 .974 1.078 12751. 69307 1.514 13.651 .332 .078 177 177 177 177 177 177 177 177 177 177 177 a. Dependent Variable: STRET 23 DIV .01 .02 .13 .29 .44 .01 .00 .02 .07 .01