DOES UNCERTAINTY IN MONETARY GROWTH AFFECT THE EQUITY MARKET: THE CASE OF THAILAND

Jurnal EKONOM, Vol. 13, No. 4 Bulan Tahun

DOES UNCERTAINTY IN MONETARY GROWTH AFFECT THE EQUITY MARKET: THE CASE OF THAILAND 1

Bany Ariffin, 2Wahyu Ario Pratomo, 3Agus Harjito 1

Lecturer at Universiti Malaya, Malaysia Lecturer at Faculty of Economics, Universitas Sumatera Utara 3 Lecturer at Faculty of Economics, Universitas Islam Indonesia, Yogyakarta 2

Abstract: Banyak kajian tentang hubungan pertumbuhan jumlah uang beredar dan harga surat berharga, namun hanya sedikit yang memperhatikan dampak dari ketidakpastian pada pertumbuhan uang terhadap harga surat berharga. Elemen dari tidakpastian pada pertumbuhan uang menurut para pakar adalah meningkatnya kehati-hatian dari asset finansial yang beresiko dan ini berdampak kepada harga surat berharga. Dengan menggunakan teknik ekonometrik, kajian ini dibuat untuk mengetahui hubungan antara kedua variable di negara yang memiliki pasar modal yang sedang berkembang, yaitu Thailand. Berbeda dengan hipotesis, adanya ketidakpastian pada pertumbuhan uang tidak memberikan dampak terhadap harga surat berharga. Namun ketidakpastian tersebut memberikan dampak hubungan dinamis yang signifikan dalam jangka panjang. Keywords : Ketidakpastian dalam pertumbuhan moneter dan ekuitas INTRODUCTION Over the years empirical studies that examine the relationship between money supply and stock prices are abundant and with particular references to advanced market. As a matter of fact, documented evidence suggests that the issue has been investigated as early as the sixties (Brunner 1961, Friedman 1961, Friedman and Schwartz 1963 and Sprinkel 1964). From seventies era through out the nineties, the studies continue to be expanded in the covering various markets worldwide i. Based on the existing theoretical frameworks, the propose relationship between the two variables is a unidirectionally causation relationship running from money supply to stock prices (Rozeff 1974). However despite the extensiveness of the studies on these two variables, empirical findings fail to reach a conclusive agreement ii. Hence, the issue continues to receive attention from both finance scholars and researchers. Theoretically, the value of a common stock is determined by the present value of the discounted expected cash inflows to be received by investors who owned the stock in this case from dividend payments iii. Therefore we may suggest that the ability of a company to pay dividends will influence

the price of its stock. Monetary economics framework has provided an argument that links money supply with stock prices iv. Thorbecke (1995) for instance, argue that monetary policy has real and quantitatively important effects on the economy. He then prove in his model that expansionary monetary policy will exerts positive real effects by increasing future cash flows or by decreasing the discount factors at which those cash flows are capitalized". Since the value of a stock is a function of its cash flows and the discounting factors, the model may indicate that positive monetary shocks shall increase industry stock returns. The focus of this study is not on the impact of money supply on stock prices per say, but instead the impact of money supply uncertainty on stock prices, which in our opinion is still unclear. According to Friedman (1983 & 1984) because money supply has a real effect on the economy, therefore monetary growth variability should increases the degree of perceived uncertainty in the market. Given this argument and the fact that financial asset ( i.e. common stock) prices are dependence on investor expectations (Rozeff 1974), an argument was put forward by Boyle (1990) who argues that changes in uncertainty regarding money 157

Bany Ariffin, Wahyu Ario Pratomo, Agus Harjito: Does Uncertainty in Monetary Growth Affect

stock will affect prices of common stock. Theoretically, he claims that monetary uncertainty alter the equity risk premium to reflect the additional expected return investors require for bearing the risk of holding stocks during period economy uncertainty. v Given the stock valuation model presented in (see note 3), increase in risk premium will result in decline in stock price. Thus, monetary uncertainty is implied to have an adverse relationship with stock prices. Since we believe this proposition has never been tested in developing market, this paper intends to ascertain the relationship between monetary uncertainty and stock prices, with a special reference to the Thai stock market. Hence, the objectives of this paper are twofold - first to test for the existence of a relationship between the uncertainty associated with the past variability (uncertainty) of the money growth and the stock prices. Secondly, to determine if there presence a long-run dynamics relationship between uncertainty in monetary aggregate and stock prices. The next section of the paper will discuss the background of the study followed by the presentation of data, methodology and findings. A brief discussion of the findings and its conclusion will end the paper. Motive of the Study In the context of Thai financial market, Rungsun (1997) has performed one of the comprehensive studies to determine the relationship between money supply and stock prices. They employ the Vector Autoregression (VAR) methodology in order to identify the relationship between money supply and stock prices while controlling for two others variables namely income and interest rate. The result of the Granger causality test shows the presence of a significant uni-directionaI relationship running from money supply to stock prices. Using the impulse response function (IRF) it was shown that stock prices response positively following monetary expansion and the impact peaks after seven month from the initial shock. Motivated with the belief that stock prices reflect real economic performance (Roseff 1974), the authors conclude that their finding is consistent with

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the long-run effect of money on the real sector. Kessara (2001) conducts similar study for the Thai market and found an almost identical result. Using the cointegration and error-correction model (ECM), He discovers that the two variables stock prices and money, are nonstationary in their level form but are co-integrated in the long-run with the presence of error correction representation. More significantly, he finds from the error -correction model that money supply, represented by M3, Granger cause stock prices but not otherwise. He concludes that his finding is inconsistent with the efficient market hypothesis (EMH) since market participants will be able to predict stock prices in the market using information on broad money supply, M3, as a trading rule to earn excess returns. The empirical results that suggest the importance of monetary policy in influencing stock prices in Thailand is not surprising since the government has long been known to pursue monetary policy in attaining economic goals, one of which is price stability in the country. The Thai central bank is entrusted with the responsibility of formulating and implementing the country's monetary policy. One of the earlier strategies has been targeting the monetary aggregate, which saw the emphasis given on Ml and eventually M3 to ensure sufficient liquidity in the system to meet the demand of the economy vi. The success of the monetary targeting strategy is evident in its ability to spur economic growth until mid- 1990’s. The Thai’s economy continues to record unprecedented growth rates from 1988 until then. However, the large capital influx into the country since early 1990's has caused considerable instability in the relationship between monetary aggregates and nominal GDP (Central Bank of Thai 2001). Output growth is seen to cause monetary growth and not vice versa, forcing the central bank- to shift its strategy from monetary aggregate targeting to interest rate targeting. Nevertheless, the central bank still monitors the monetary aggregates very closely despite suggestions that they become unreliable indicators of economic activity. During this period, monetary velocities - the ratios of nominal GDP to various monetary

Jurnal Ekonom, Vol. 13, No. 4 Oktober 2010

aggregates, are reported to frequently-depart from the historical patters. This departure from the historical pattern implies variation in money growth (Central Bank of Thai 2001). Following the Thorbecke (1995) argument of monetary policy real and quantitative effect on the economy, we suspect that this phenomena -instability of monetary aggregates over those periods should have an affect on the level of economic activity. In addition, since stock prices reflect real economic performance, the impact of the monetary uncertainty should transpire in the common stock prices. Considering this development, it is deemed appropriate to pursue empirical evidence to establish the relationship between monetary uncertainty and stock price behaviors. With this in mind, this paper is designed to test two hypotheses, namely (i) monetary uncertainty is negatively related to stock prices vii, and (ii) stock price uncertainty uncertainty to monetary uncertainty. The major contribution of this paper is in its attempt to clarify the issue of monetary uncertainty and its effect on the Thai stock market. The findings will be of use for market participants and regulators alike. METHODOLOGY AND FINDINGS This study employs monthly data running from 1989:01 to 2001:03. Using M1 viii to represent monetary aggregate and SET index ixas a proxy of stock prices, the month to month (i.e. January-to January ) rate of change is computed to generate new series that represent changes in M1 and SET index. The monetary variability is measured by calculating the standard deviation of changes in the M1 series over one year period x. To measure the past history of monetary instability, following Boyle (1990) a series of one-year moving average of the standard deviation is constructed. Similar process is also performed on the SET index series to accomplish the second objective of the study. Overall, three series are generated – namely moving average of standard deviation of monetary growth, stock price growth and moving average of standard deviation for stock price growth. In order to examine whether there is a long run equilibrium relationship among

the variables, we employ the method of cointegration developed by Johansen (1991). Prior to testing for co-integration, the time series properties of the variable should be investigated. If the variables are stationery, conventional regression procedures are appropriate. However, if the variables are stationary, with time- dependent means and variances, then test of co-integration are necessary to establish long run relationship. The test for unit roots employed is Augmented Dickey-Fuller, suggested by Dickey and Fuller (1979) and Phillip and Peror (1988) respectively xi. If both variable are non-stationery and integrated of the same order, then the relationship of these variables is estimated by employing the co-integration methodology suggested by Johansen (1991). The cointegration (see Engle and Granger, 1987) is a long run relationship and it implies that deviations from equilibrium are stationery, with finite variance, even though the series themselves are non-stationery and have infinite variance. If a common trend among a set of variables that move together in the long run equilibrium exists, the granger causality tests should be constructed within a vector errorcorrection model (VECM) to avoid misspecification. The granger causality test is implemented by calculating the F-statistic based on the null hypothesis that the set of coefficient on the lagged values of independent variables are not statistically different from zero. If the null hypothesis is not rejected, then it can be concluded that the independent variable does not cause dependent variable. If the coefficient of error correction term from the co-integrating regression is significant based on the tstatistic, then both independent and dependent variables have a stable relationship in the long-run. In order to examine the dynamic properties of the system beyond the sample period we use the variance decomposition (VDCs) technique. Accordingly, the VECM may be interpreted as within sample causality test ( Masih and Masih 1996). It indicate only the Granger-exogenity or endogenity of the dependent variable within the sample period. It does not provide an indicator of the dynamic properties of the system, nor do they allow us to gauge the relative strength of 159

Bany Ariffin, Wahyu Ario Pratomo, Agus Harjito: Does Uncertainty in Monetary Growth Affect

the Granger- causal chain or degree of exogenity amongst the variable beyond the sample period ( Masih and Masih 1995). VDCs which may be termed as out-ofsample causality test, by partitioning the variance of the forecast error of a certain variable (say, money supply) into proportions attributable to the innovations ( or shocks) in each variable in the system including its own, can provide an indication of these relativities. A variable that is optimally forecast from its own lagged values will have all its forecast error variance accounted for by its own disturbances (Sims 1982). RESULTS The result from the ADF tests is presented in Table 1. They indicate that only two series are nonstationery at level form but integrated of first order, I(1). The two series are moving average of standard deviation for monetary growth (MADVM1) and moving average of standard deviation for stock price growth (MADVSTK). Since these two series are integrated of the same level, it suggests the presence of co-integration relationships and that validates the use of co-integration analysis to ascertain the long-run dynamic between the two series.

Table 1. ADF tests for the presence of unit root Variable Statistic for level Statistic for first difference MADVM1 -2.236148 -3.51462*** MADVSTK -2.051253 -5.539158*** MAGSTK -5.316498*** -6.772527 Note: MADVM1= moving average std dev. of money growth MADVSTK= moving average std. dev of stock price growth MAGSTK = stock price growth * significant at 10% ** significant at 5% *** significant at 1%

The results from co-integration analysis are presented in table 2. Based on the results presented in panel A of table 2, it is found that the two series – moving average of standard deviation for monetary growth (MADVM1) and stock price growth (MAGSTK) are not co-integrated in the long-run. This is consistent with the ADF test, which shows that these two series reach their stationery at different level. However panel B shows that the series of moving average of standard deviation for monetary growth (MADVM1) and moving average of standard deviation for stock price growth (MADVSTK) do indeed possess a long-run equilibrium between them.

Table 2. results from Johansen’s co-integration analysis Series: MADVM1 MAGSTK Likelihood 5 Percent 1 Percent Hypothesized Eigenvalue Ratio Critical Critical No. of CE(s) Value Value 0.186979 21.45345 15.41 20.04 None 0.056217 4.686597 3.76 6.65 At most 1 Note: L.R. rejects any co- integration at 5% significance level Panel A.

Panel B. Eigenvalue 0.297045 0.054681

Series: MADVM1 MADVSTK Likelihood 5 Percent 1 Percent Ratio Critical Critical Value Value 33.10431 15.41 20.04 4.554846 3.76 6.65

Note: LR test indicates 2 co-integration equations at 5% level

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Hypothesized No. of CE(s) None ** At most 1 *

Jurnal Ekonom, Vol. 13, No. 4 Oktober 2010

Table 3 presents the results of VECM. The coefficients for the error correction terms (ECM) which represent the speed of adjustment are found to be significantly different from zero. This indicates the two series ( MADVM1 and MADVSTK) simultaneously correcting for the disequilibria resulting from momentary deviation from their long-run equilibrium path, thus explaining the cointegration relationship between the series. The fact that the two series are adjusting at different rates and different significant level, that gives raise to the matter of which series is the leader and which variable is the follower. Thus establishing the causal relationship between the series. Because MADVSTK adjusting more significantly in comparison to MADVM1 based on the coefficient of the ECM term and its significant level, this may indicate that MADVSTK is the follower while MADVM1 is the leader. In other words, changes MADVM1 may lead to changes in MADVSTK. Additional evidence that provide support for this causation relationship lies in their lagged coefficient. The lagged coefficients for MADVM1 are found to be significant from lagged 1 all the way to lagged 3. On the hand the only significant coefficient for MADVSTK are in lagged 4. With evidence from the ECM coefficient and the lagged coefficient, this may indicate that the uncertainty in money growth has more relevant effect on the uncertainty in stock prices than vice versa. Table 3. VECM estimates of the adjustment coefficient Dependent variable MADVSTK MADVM1 ECM t-1 -0.042757 0.001368 (0.01611) (0.00374) (-3.69225) (1.12132) D(MADVSTK(-1))

1.388107 (0.10993) (13.4368)

0.033012 (0.03453) (1.34582)

D(MADVSTK(-2))

-0.486587 (0.20503) (-2.37317)

0.030333 (0.06441) (0.47105)

D(MADVSTK(-3))

-0.337784 (0.21037) (-1.60556)

-0.129220 (0.06608) (-1.95568)

D(MADVSTK(-4))

0.382757 (0.12900) (2.96676)

0.111997 (0.04053) (2.76406)

D(MADVM1(-1))

-0.794600

1.715892

(0.35437) (-2.24227)

(0.11130) (15.4172)

D(MADVM1(-2))

1.079885 (0.72587) (1.48770)

-1.138641 (0.22797) (-4.99463)

D(MADVM1(-3))

-0.671035 (0.73300) (-0.91547)

0.258231 (0.23021) (1.12172)

D(MADVM1(-4))

-0.254664 -0.014365 (0.35990) (0.11303) (-0.70760) (-0.12709) Note: Standard errors & t-statistics in parentheses

The results for the variance decomposition are found to be consistent with that of the VECM causality test. For example, in terms of own shock, MADVM1 shows its relative exogeneity with 83.649 percent of its own innovations in 20-quarter. This result tends to confirm our initial findings from the VECM formulation that the MADVM1 is more exogenous of the two series in the system developed. In 20quarter about 73.046 percent of the MADVSTK shock is explained by innovation in MADVM1 variable. This result is consistent with the earlier results provided by our VECM model, where MADVSTK may be affected by MADVM1 and not the other way around. Again these findings tend to highlight the roles played by monetary uncertainty in influencing the variability in the stock market. DISCUSSION AND CONCLUSION The paper aims at accomplishing two objectives - first, to test for the existence of a relationship between the uncertainties associated with the variability of (past values of) money growth and the (contemporaneous) stock prices. Through the analysis that has been conducted, it is found that the variability of the past values of money growth has no significant long-run relationship with stock prices, as evident by the lack of co-integration between the moving average of standard deviation for monetary growth and stock prices. This finding rejects the proposition made by Boyle (1990) who argues that changes in uncertainty regarding money stock will affect stock prices thus implying a negative relationship. But such discovery on the Thai stock market is nonetheless consistent with the concept of efficient market since past 161

Bany Ariffin, Wahyu Ario Pratomo, Agus Harjito: Does Uncertainty in Monetary Growth Affect

information does not seem to influence the contemporary stock prices. As such, it suggests that market has already considered past information of market uncertainty in determining stock prices. Secondly, the study intends to detect for the presence of long-run dynamics between uncertainty in monetary aggregate and stock prices. Using Johansen (1991) cointegration analysis, the long-run relationship between the uncertainties of the two variables is detected. This finding conforms to the suggestion that monetary policy has real and quantitatively important effects on the economy since uncertainty in the monetary aggregate, is reflected in the uncertainty in stock prices which is a proxy of economic prosperity. The extent of the relationship is further substantiated from the VECM testing. It was shown in the testing that monetary uncertainty indeed granger cause stock prices variability. In addition our variance decomposition procedure are also pointed in the same direction. This paper provides evidence that (i) monetary uncertainty has no significant relationship with the contemporaneous stock prices, and (ii) the uncertainty in monetary policy is co-integrated in the long run with the uncertainty in stock prices, at least within the context of Thailand. Given the importance of the issue of uncertainty in financial markets, this paper virtually contributes to beef up the literature on the subject particularly with reference to emerging markets like Thailand. It is hope that this effort will instigate further investigations on the topic in view of its great importance to market REFERENCES Bank Negara Malaysia. 1999, “The Central Bank and the Financial System in Malaysia: A Decade of Change 19891999,” Kuala Lumpur. Boyle, G.W. 1990, “Money Demand and the Stock Market in a General Equilibrium Model with Variable Velocity,” Journal of Political Economy 98, pp. 1039-1053. Brunner, K. 1961, “Some Major Problems in Monetary Theory,” American Economic Review Proceedings, May. 47-56.

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