1 Stock Prices and Macroeconomic Variables in Vietnam: An Empirical Analysis Nguyen Trong Hoai 1 Nguyen Thi Bao Khuyen 2 The article employs the cointegration and error correction version of Granger causality tests to investigate whether the Vietnamese stock market exhibits the publicly informational efficiency. The test results strongly suggest informational inefficiency in the Vietnamese stock market. Specifically, the results from bivariate analysis suggest that the Vietnamese stock market is not informationally efficient in both short- and long-run. In addition, the stock market seems to even divorce from the most part of the economy. Therefore, it is still possible for a “professional” trader to make abnormal returns by analyzing good or bad news contained in some macroeconomic variables. The findings re-assure that the Vietnamese stock market is not well functioning in scarce resource allocation and not attractive enough to encourage foreign investors. Since the market is not informationally efficient, especially with respect to monetary variables, it may be dangerous for policy makers to realize the role of monetary policies, especially the so-called demand stimulus packages. In terms of the investors’ point of view, fundamental analysis is still significant for their investment decisions. Thus, companies with strong equity analysts would have higher comparative advantages in this inefficient market. Furthermore, instead of becoming more efficient over time, as one might expect, the Vietnamese stock market appears to have become increasingly divorced from reality. This also reveals that the last financial crisis has serious impact on the Vietnamese stock market. 1. Introduction Efficient market hypothesis has been at the center of debates in financial literature for several years. The term efficiency is used to describe a market in which all relevant information is immediately impounded into the price of financial assets. If the capital market is sufficiently efficient, investors cannot expect to achieve superior profits from their investment strategies. As a result, capital asset pricing models could be useful for various investment decisions. In the economic perspective, the efficient market is even more important because it implies that the stock market is well functioning in scarce resource allocation. However, this is not always the case, especially in the emerging stock markets. 1 Professor of economics, University of Economics, Ho Chi Minh City, Vietnam; Dean, Faculty of Development Economics, University of Economics, Ho Chi Minh City, Vietnam. 2 Senior Analyst, VietFund Management Company, Vietnam. 2 Islam and Khaled (2005) calls developing countries as ‘capital starved economies’, so efficient allocation of scarce resources and encouragement of private foreign investment are both of vital importance. They also stated that the success of an increasing privatization of these economies will depend crucially on the presence of an active and efficient stock market. Indeed, rational investors expectedly drive their investments into the most profitable projects, given acceptable risks. The efficient market can address the ‘mixed feelings’ problem, which investors are always skeptical about the intrinsic value of any stock under consideration. This may lead their decisions based on others. In other words, this phenomenon is commonly considered as herding behavior. For foreign investors, inefficient markets are usually equivalent to high risky markets when making their investments abroad. Hence, they tend to apply higher hurdle rates, which in turn underestimate investment opportunities in developing countries. Eventually, it’s hard for any developing country with inefficient/weak stock market to attract foreign portfolio investment flows. The efficient market hypothesis is theoretically viewed in three common forms, depending on the kind of available information embodied. These are commonly classified into weak-form, semi strong-form, and strong-form efficiency. The weak form is the lowest form of efficiency that defines a market as being efficient if current prices fully reflect all information contained in past prices only. The semi-strong form efficiency suggests that the current price fully incorporates all publicly available information. Semi-strong efficiency requires the existence of market analysts who are not only financial economists able to comprehend implications of vast financial information, but also macroeconomists, experts adept in understanding processes in product and input markets (Ross et al., 2006). The strong form efficiency states that the current price fully incorporates all existing information, both public and private (also called inside information). The main difference between the semi-strong and strong efficiency hypotheses is that, in the latter, nobody should be able to systematically generate profits even if trading on information not publicly known at the time. The rationale for strong-form market efficiency is that the stock market anticipates, in an unbiased manner, future developments and therefore the stock prices may have incorporated all relevant information and evaluated in a much more objective and informative way than the insiders. According to Ross et al. (2006), a very strong assumption of this form is that inside information cost is always zero. However, this assumption hardly exists in reality, so the strong form efficiency is not very likely to hold. Despite its impressive growth, the Vietnamese stock market is really struggling with various typical weaknesses of an emerging market (Truong, 2006). As a result, trading behavior in the Vietnamese stock market may be much different from that in developed/newly emerging stock markets. Investors, especially those who have just experienced the economic downturn, may base their actions on the decisions of others who are well informed about market developments by following the market consensus (Nguyen, 2009). Thus, the 3 question is whether the Vietnamese stock market is informationally efficient? And whether the market pattern is seriously affected by the financial crisis? The mixed evidence from the study of Truong (2006) in the 2002-2004 period may imply that the Vietnamese stock market is, to which extent, characterized by the weak-form efficiency. However, this lowest form of efficiency cannot assure the Vietnamese stock market is well functioning in scarce resource allocation and attractive enough to encourage foreign investors (Nguyen, 2006). Both investors and policy makers mostly concern if the current market prices reflect all publicly available information, such as information on inflation, economic growth, money supply, exchange rates, interest rates, annual earnings, stock splits, etc. Ibrahim (1999) states that the significant lagged effects of macroeconomic variables on stock prices indicate informational inefficiency of the stock market. If this is the case, individual investors can earn abnormal profits by exploiting past macroeconomic information. As a result, this exploitable opportunity would seriously distort the market’s ability to efficiently allocate scarce resources. The reverse effects of stock prices on macroeconomic variables imply that stock market movements anticipate future economic conditions. Accordingly, they may be employed as a leading indicator in helping formulating current economic stabilization policies. This article will investigate these dynamic interactions for the case of the Vietnamese stock market. The organization of the paper is as follows. The next section briefly reviews the literature of semi-strong market efficiency. Then, Section 3 outlines the analytical framework. Section 4 describes the data and examines their temporal properties using integration and cointegration tests. Section 5 presents the results of bivariate causality tests. Finally, our concluding remarks are contained in Section 6. 2. Literature Review Since its introduction into the financial economics literature over almost 50 years ago, the efficient markets hypothesis has been examined extensively in numerous documents. Most previous studies of semi-strong form efficiency have been based on the analysis of the causal relationship between macroeconomic variables and stock prices. The types of relationships between stock market returns and macroeconomic variables can be varied. As Mahdavi and Sohrabian (1991) report, there was an asymmetric causal relationship between those variables when they explored the relationship between changes in stock prices and GDP growth in the U.S markets using the standard version of Granger causality tests. The stock market growth rate caused GDP growth rate, yet no reverse causation was found. Chen (1991), however, finds that the current and future economic growth could be revealed by several domestic variables, such as the market dividend-price ratio, short-run 4 interest rates, production growth rate, term premium, and the default premium. In addition, Rousseau and Wachtel (2000) reveal that equity markets have been key institutions in promoting real economic activity in 47 countries. However, it is worth noting that this finding may be different in countries with less financially developed markets (Minier, 2003). Mauro (2003) suggests that the developments in stock prices should be taken into account in forecasting output. However, the relationship between stock returns and economic growth has not been stable over time (Stock and Watson 1990). Cheng (1995) argues that a number of systematic economic factors significantly influenced the U.K stock returns. This result seems to contradict with that of Poon and Taylor (1991) who also observe the interrelationship between macroeconomic factors and stock prices in the United Kingdom. The relationship between stock prices and economic activity is not only limited to the relationship between stock prices and economic growth, but also extends to other economic factors (Fama, 1981). Abdullah and Hayworth (1993) argue that stock returns are positively related to inflation and growth of the domestic money supply in the United States, but negatively related to domestic interest rates. By the same manner, Beenstock and Chan (1988) find that interest rates, input costs, money supply, and inflation are the significant risk factors of the London stock market. For the Pacific region, in Australia, there was a unidirectional relationship (in negative fashion) between inflation and the nominal stock returns during the 1965-1979 period, with price levels leading the equity index (Saunders and Tress 1981). Other researchers (Leonard and Solt, 1987; Giovanini and Jorion, 1987; Kaul and Seyhun 1990; Randal and Suk, 1999) also support a significant relationship between inflation or expected inflation and stock market prices. In terms of the relationship between stock market returns and exchange rate, Johnson and Soenen (1998) state depreciation may cause the cost of imports to increase, leading to domestic price level increases, which would expectedly have a negative impact on stock prices. Morley and Pentecost (2000) also confirm that stock markets and exchange rates are linked, and note that this connection is through a common cyclical pattern rather than a common trend. For the Asia-Pacific region, Hamao (1988) found a significant relationship between the Japanese stock returns and several factors, such as the changes in expected inflation and the term structure of interest rates. Ibrahim (1999) also observed the Malaysian exchange rate by using bivariate and multivariate cointegration as well as Granger causality tests and found cointegration when the M2 measure of money supply and reserves are included, but no long-run relationship between the exchange rate and stock prices was found using bivariate models. These suggest that in the short run the exchange rate might play 5 a significant role in the domestic economy, and that the Malaysian stock exchange is informationally inefficient. However, in some cases, macroeconomic factors cannot be reliable indicators for stock market prices movement in the Asian markets because of the inability of stock markets to fully capture information about the change in macroeconomic fundamentals (as is cited in Wongbangpo and Sharma, 2002). Ibrahim (1999) investigates the dynamic interactions between seven macroeconomic variables (the industrial production index, consumer prices, M1, M2, credit aggregates, foreign reserves and exchange rates) and the stock prices for an emerging market, Malaysia, using Granger causality tests and error correction mechanism tests. This analysis is conducted using monthly data series for the period from January 1977 to June 1996. To smooth possible volatility, all data series are expressed in logarithmic forms. Generally, the results suggest informational inefficiency in the Malaysian market. Using the bivariate causality tests, Ibrahim (1999) suggests three important points. First, the results largely indicate that the lagged changes in macroeconomic variables have no significant predictive ability for the movements in stock prices. Second, the stock market movements could help anticipate variations in the industrial production, the M1 money supply, and the exchange rate. From this finding, he says that the causal link from stock prices to the M1 money supply may reflect the importance of the stock market on the M1 money demand. Third, there exists the cointegration between the stock prices and three macroeconomic variables – consumer prices, credit aggregates and official reserves. The results suggest that deviations from the equilibrium path are adjusted by about 5%–8% the next month through the movements in stock prices. Thus, the adjustment toward the long-run relationship is extremely low in Malaysian stock market. Hanousek and Filer (2000) examine the possibility that newly-emerging equity markets in Central Europe exhibit semi-strong form efficiency such that no relationship exists between lagged values of changes in macroeconomic variables (M1, M2, exports, imports, trade balance, foreign capital inflow, budget deficit, government debt, CPI, PPI, exchange rate, and industrial production) and changes in equity prices using Granger causality tests. They find that while there are connections between real economy and equity market returns in Poland and Hungary, these links occur with lags, suggesting the possibility of profitable trading strategies based on public information and rejecting semi-strong efficiency hypothesis. For Czech Republic and Slovakia, the situation is more complex. In the early years of their existence, these markets may have possessed elements of semi-strong efficiency, with both lagged and contemporaneous relationships between real variables and equity markets. However, these links have disappeared over time. In other words, these stock markets appear to have become increasingly divorced from reality. In the same manner, Azad (2009) 6 conducts a cross-market test including China, Japan, and South Korea and concludes that while Chinese stock market is inefficient, Japanese and South Korean stock markets are semi-strongly efficient. These empirical studies, along with other studies about the U.S markets, suggest that developed equity and newly-emerging stock markets exhibit semi-strong form efficiency, while this is not a case in developing stock markets. Atmadja (2005) examines the existence of Granger causality among stock prices indices and macroeconomic variables in five ASEAN countries, Indonesia; Malaysia; the Philippines; Singapore; and Thailand with particular attention to the 1997 Asian financial crisis and period onwards. Using monthly time series data of the countries, a Granger-causality test based on the vector autoregressive (VAR) analytical framework was employed to empirically reveal the causality among the variables. This research finds that there were few Granger causalities found between the country’s stock price index and macroeconomic variables. This indicates that the linkages between domestic stock price movements and macroeconomic factors were very weak. Due to that, the ASEAN stock markets were relatively unable to efficiently capture changes in economic fundamentals during the observation period in most of the countries in accordance to the literature in emerging stock markets, and that the influence of specific macroeconomic factors on the domestic economies differ across countries. This also implies that the stock markets do not seem to have played a significant role in most countries’ economies, and macroeconomic variables are unlikely to be appropriate indicators to predict not only the future behavior of other macroeconomic variables, but also that of the stock market price indices. While causality analysis is widely discovered in both developed and newly emerging markets, only a few studies have been conducted for emerging markets such as Vietnam. A recent notable study for the Vietnamese stock market is that of Nguyen (2006). Applying the Engle-Granger cointegration tests, she find no evidence for cointegration between the VN-Index and macroeconomic variables such as industrial output, inflation rates, money supply, exchange rates, imports and exports using data that span from July 2000 to June 2006. Based on these findings, she concludes that the Vietnamese stock market is informationally inefficient with respect to all selected macroeconomic variables. In order to contribute to this line of literature for emerging markets, this paper would like to extend existing studies on the informational efficiency of the Vietnamese stock market on the following ways. First, we examine the market efficient hypothesis using a wider range of macroeconomic variables 3 . In particular, we use twelve macroeconomic variables, namely, consumer price, industrial production, imports, exports, exchange rates, M1 money supply, M2 money supply, lending rates, deposit rates, domestic credit, foreign reserves, and 3 Because the time span is now longer than that of the previous study. 7 money reserves. Second, we make every effort to compare the informational efficiency situation of the Vietnamese stock market with that of other emerging markets. Third, we will take the impact of the last financial crisis into account so as to check the robustness of market efficiency exhibition. 3. Analytical Framework The analytical framework of this paper is to employ the Granger causality tests. In doing so, we will first examines whether the variables of concern are non- stationary and cointegrated. As widely accepted, if all variables under consideration are integrated of order 1, I(1), and they are not cointegrated, we must apply the standard version of Granger causality test using the first differences of the variables. If this is the case, we are just able to test whether the stock market exhibits the short-run efficiency. By contrast, if the variables under consideration are not only I(1), but also cointegrated, then we should employ the cointegration and error correction (ECM) version of Granger causality tests. According to Ihrahim (1999), the ECM conveniently combines the short-run dynamics and long-run equilibrium adjustments of the variables. In the efficient market hypothesis literature, this allows us analyse whether the stock market exhibits both short-run and long-run efficiency. 3.1 Unit-Root Tests In order to establish the order of integration of the variables concerned, this study employs both augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests. Generally, a variable is said to be integrated of order d, written by I(d), if it turn out to be stationary after differencing d times. The variable is integrated of order greater than or equal to 1 is non-stationary. According to Asteriou (2007), most macroeconomic variables are cointegrated of order 1. In testing for the existence of a unit root of the time series Y t (H 0 : δ = 0), we can select one out of the following three possible forms of the ADF test: ∑ = −− +∆β+δ=∆ p 1i titi1tt uYYY (1) ∑ = −− +∆β+δ+α=∆ p 1i titi1t0t uYYY (2) ∑ = −− +∆β+δ+α+α=∆ p 1i titi1t10t uYYTY (3) The difference between the three regressions concerns the presence of the deterministic elements α 0 and α 1 T. For choosing the best one among the three equations, we will first plot the data (of each variable) and observe the graph 8 because it can, to which extent, indicate the presence or not of deterministic regressors. If the test equation is characterised by the serial correlation, the PP test, conducted in a similar manner of ADF tests, becomes the most appropriate alternative. 3.2 Cointegration Tests One the order of integration of each variable is established, we then evaluate whether the variables under consideration is cointegrated. According to Ibrahim (1999), the cointegration of the time series such as the stock prices (P t ) and a macroeconomic variable (M t ) suggests the existence of a long-run relationship that constrains their movements. That is, although the variables are individually nonstationary, they cannot drift farther away form each other arbitrarily. According to Asteriou and Hall (2007), if two variables are non-stationary, then we could normally expect that they would combine to produce another non- stationary process. However, in the special case that two variables, say P and M, are really related, we would expect the two stochastic trends could be very similar to each other and the combination of them could eliminate the nonstationarity. In this special case, we say that the variables are cointegrated. If the variables do not cointegrate, we usually face the problems of spurious regression and econometric work becomes almost meaningless (Granger and Newbold, 1974). Inversely, if the stochastic trends do cancel to each other, then we have cointegration. To test for cointegration, this paper employs the Engle and Granger (1987) approach 4 . This can be briefly summarised as follows: Step 1: Test the variables for their order of integration. By definition, cointegration necessitates that the variables be integrated of the same order. Thus the first step is to test each variable to determine its order of integration. This study will apply the ADF tests. There are three possible cases: a) If both variables are stationary (I(0)), it is not necessary to proceed since standard time series methods (OLS) can be correctly applied. b) If the variables are integrated of different orders, it is possible to conclude that they are not cointegrated. c) If both variables are integrated of the same order, then we proceed with step two. Step 2: Estimate the long-run (possible cointegrating) relationship. 4 The Johansen cointegration approach is also widely used in empirical studies. 9 If the results of step 1 indicate that both P t and M t are integrated of the same order (usually in economics I(1)), the next step is to estimate the long-run equilibrium relationship of the form: tt21t u ˆ M ˆ ˆ P +β+β= (4) and obtain the residuals of this equation. If there is no cointegration, the results obtained will be spurious. In this case, we will apply the standard version of Granger causality tests for investigating short-run efficicieny. However, if the variables are cointegrated, then OLS regression yields “super-consistent” estimators for the cointegrating parameter 2 ˆ β . In this case, we will apply the ECM version in order to analyse both short-run and long-run efficiency. Step 3: Check for (cointegration) the order of integration of the residuals. In order to determine if the variables are actually cointegrated, denote the estimated residual sequence from the equation by t u ˆ . Thus, t u ˆ is the series of the estimated residuals of the long-run relationship. If these deviations from long-run equilibrium are found to be stationary, the P t and M t are cointegrated. Because t u ˆ is a residual, we do not include a constant nor a time trend in the ADF equation. 3.3 Standard Version of Granger Causality Tests Engle and Granger (1987) developed a relatively simple test that defined causality as follows: a (stationary) variable ∆P t is said to Granger-cause (stationary) variable ∆M t , if ∆P t can be predicted with greater accuracy by using past values of the ∆M t variable rather than not using such past values, all other terms remaining unchanged. The standard version of Granger causality test for the two stationary variables ∆P t and ∆M t , involves as a first step the estimation of the following VAR model: pt s 1j jtj r 1i iti1t MPaP ε+∆β+∆α+=∆ ∑∑ = − = − (5) mxt q 1j jtj p 1i iti2t PMaM ε+∆δ+∆θ+=∆ ∑∑ = − = − (6) where it is assumed that both ε pt and ε mt are uncorrelated white-noise error terms. In this model, we can have the following different cases: 10 Case 1: The lagged ∆M terms in (5) may be statistically different from zero as a group, and the lagged ∆P terms in (6) not statistically different from zero. In this case, we have that M t causes P t . Case 2: The lagged ∆P terms in (6) may be statistically different from zero as a group, and the lagged ∆M terms in (5) not statistically different from zero. In this case, we have that P t causes M t . Case 3: Both sets of ∆P and ∆M terms are statistically different from zero as a group in (5) and (6), so that we have bi-directional causality. Case 4: Both sets of ∆P and ∆M terms are not statistically different from zero in (5) and (6), so that P t is independent of M t . According to Hanousek and Filer (2000), a market is semi-strongly efficient, if two following conditions must hold. First, a contemporaneous relationship must exist between stock prices (∆P t ) and macroeconomic variable (∆M t ). Second, lagged values of ∆M must not be enabling a potential investor to predict current returns (∆P t ) in the market (Case 2 or Case 4). Both of these relationships are important. Although the first is often ignored in empirical research, if it fails to hold, then the fact that the second does is not proof of efficiency. It may simply be that the variable under examination is irrelevant in determining prices in the equity market. These relationships can be expressed as follows: t r 1i iti0t PP ε+∆α+α=∆ ∑ = − (7) tt0 r 1i iti0t MPP ε+∆β+∆α+α=∆ ∑ = − (8) and ∑∑ = − = − ε+∆β+∆β+∆α+α=∆ s 1j tjtjt0 r 1i iti0t MMPP (9) where r and s are the appropriate lag lengths (which are usually determined by AIC criterion). The conventional test of Granger causality is whether or not equation (9) better explains movements in the dependent variable than equation (8). In addition, to ensure that a failure of lagged macroeconomic factors to have a significant effect on stock market returns results from the market efficiently processing information rather than from that information not being an important determinant of prices, we also examine whether equation (8) better predicts [...]... analyzing good or bad news contained in some macroeconomic variables The findings re-assure that the Vietnamese stock market is not well functioning in scarce resource allocation and attractive enough to encourage foreign investors In fact, investors of all kinds in the market only concern their short-term portfolios In addition, instead of becoming more efficient over time, as one might expect, the Vietnamese... from the imports to the stock prices may contain the market participants’ expectations Fifth, only four macroeconomic variables, including exports, M2 money supply, lending rates, and domestic credit, indicate the long-run relationship with stock prices; and the stock prices just have the long-run relationship with imports This may imply that the isolation between the stock market and the economic reality... that the Vietnamese stock market is not informationally efficient in both short- and long-run The stock market seems to even divorce from the most part of the economy It is still possible for a “professional” trader to make abnormal returns by analyzing good or bad news contained in some macroeconomic variables The findings re-assure that the Vietnamese stock market is not well functioning in scarce... term reinforce our findings of cointegration between stock prices and macroeconomic variables, except for the consumer prices, the M1 money supply, and the deposit rates They are signed as expected and are significant in at least one equation Specifically, the results suggest that deviations from the equilibrium path are adjusted by about 4%–8% the next month through the movements in stock prices From... these variables need to be established to assess the informational efficiency of the stock market in the short-run These issues are dealt with in the next section 5.2 Bivariate Causality Tests This section builds on the previous integration and cointegration tests to appropriately specify a dynamic framework for assessing the interactions between the stock prices and the macroeconomic variables of interest... time, as one might expect, the Vietnamese stock market appears to have become increasingly divorced from reality This also reveals that the last financial crisis has serious impact on the Vietnamese stock market The informational inefficiency implies that the scarce resources have not been allocated into the best competing uses In an inefficient stock market where stock prices do not reflect the real... returns by analyzing good or bad news contained in these leakages Inversely, uninformed investors blindly act on others’ decisions, formulating herding behavior This finding implies that an improvement of national statistical system is significantly important We should change our traditional wisdom that only public agencies need macroeconomic variables for research and policy analysis In financial sector,... from stock price to macroeconomic variable if H02 is rejected; (3) bidirectional causality between macroeconomic variable and stock price if both H01 and H02 are rejected; and (4) no causality between macroeconomic variable and stock price if both H01 and H02 are not rejected The second column of Table 4 presents optimal lag lengths of dependent variable and independent variable in each VAR model Since... review that the probability of finding inefficiency in an asset market increase as the transactions and information cost of exploiting the inefficiency increases (Proposition 2, Section 2.3) The poor information from listed companies, along with unavailable macroeconomic news, 13 This can be easily checked by exploring the IMF-CD ROM, World Development Indicators CD-ROM, or the Vietnam GSO website 23 turns... satisfying the listing conditions to be listed; to concurrently review and continue the sale of state capital in enterprises in which the State is not required holding controlling shares (iii) to convert foreign invested enterprises into shareholding companies with their shares to be listed and traded in the securities market However, experiences from other emerging markets suggest that the sequencing . among stock prices indices and macroeconomic variables in five ASEAN countries, Indonesia; Malaysia; the Philippines; Singapore; and Thailand with particular attention to the 1997 Asian financial. of macroeconomic variables on stock prices indicate informational inefficiency of the stock market. If this is the case, individual investors can earn abnormal profits by exploiting past macroeconomic. by analyzing good or bad news contained in some macroeconomic variables. The findings re-assure that the Vietnamese stock market is not well functioning in scarce resource allocation and not