Transmission of Monetary Policy to the Stock

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Transmission of Monetary Policy to the Stock Exchange: Further Evidence from Singapore Loh Mun Seong Faculty of Management and Economics, University Malaysia Terengganu Malaysia

Abstract: The aim of this study is to investigates the further evidence of monetary policy affected to the Singapore stock exchange during January 1991 to September 2013. This study employs the Engle-Granger Cointegration, Engle-Granger two step Error Correction Model (1987) and Pairwise Granger Causality. The empirical evidences reveal there are short run and long run linkages between monetary policy instruments and Singapore stock exchange. Empirical results also reveal one of the main factor affected to the developed country like Singapore stock exchange (Straits Times Index) is monetary policy because of Pairwise Granger causal relation show monetary policy instruments causal to the stock exchange. Keywords: Stock Market, Monetary Policy, Causality

1. Introduction Tobin's q theory explain the transmissions of monetary policy to the economy through its effects on the valuation of equities (stock) market (Tobin, 1969). But how might monetary policy affect stock prices in the Tobin's q theory? When monetary policy is expansionary, the public finds that it has more money than it wants and so gets rid of it through spending. One place the public spends money is in the stock market, increasing the demand for stocks and consequently raising their prices (Tobin, 1969). Tobin (1969) defines q as the market value of firms divided by the replacement cost of capital. If q is high, the market value of firms is high relative to the replacement cost of capital, and new plant and equipment capital is more cheapest relative to the market value. Companies can then issue stock and get a high price for it relative to the cost of the facilities and equipment that are buying investment spending will rise, because firm can buy a lot of new investment goods with only a small issue of stock. Money growth also affects interest rates and prices and those in turn will influence to the stock prices. Assuming that money demand remains constant, increase in money supply raises interest rates thereby increasing the opportunity cost of holding cash as well as stocks. Lured by higher interest earnings, people are likely to convert all their cash and stock holdings to interest-bearing deposits and securities with obvious implications for the stock prices. Many experts however believe that positive effects will outweigh the negative effects and stock prices will eventually rise due to growth of money supply (Mukherjee and Naka, 1995).

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Figure 1: Straits Times Index, M1, M2, M3, Fixed Deposit Rate and Lending Rate for Singapore

Straits Times Index (Index) M1 (S$ Mil) 600 8,000

400 6,000

200 4,000

0 2,000

-200 0

-400 -2,000

-600 -4,000 92 94 96 98 00 02 04 06 08 10 12 92 94 96 98 00 02 04 06 08 10 12

M2 (S$ Mil) M3 (S$ Mil) 25,000 10,000

20,000 7,500

15,000 5,000

10,000 2,500

5,000 0

0 -2,500

-5,000 -5,000 92 94 96 98 00 02 04 06 08 10 12 92 94 96 98 00 02 04 06 08 10 12

Deposit Rate (%) Lending Rate (%) 1.0 1.0

0.5 0.5

0.0 0.0 -0.5

-0.5 -1.0

-1.5 -1.0 92 94 96 98 00 02 04 06 08 10 12 92 94 96 98 00 02 04 06 08 10 12

Source: Monetary Authority of Singapore, September 2013 Figure 2: Seasonal Moving Average for Straits Times Index, M1, M2, M3, Fixed Deposit Rate and Lending Rate

Straits Times Index M1 (S$ Mil)

4,000 160,000

3,600

3,200 120,000

2,800

2,400 80,000

2,000

1,600 40,000

1,200

800 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

M2 (S$ Mil) M3 (S$ Mil)

500,000 600,000

500,000 400,000

400,000 300,000 300,000 200,000 200,000

100,000 100,000

0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Deposit Rate (%) Lending Rate (%)

6 8.0

5 7.6

7.2 4 6.8 3 6.4 2 6.0

1 5.6

0 5.2 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Means by Season

Source: Monetary Authority of Singapore, September 2013

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Above figure showing fluctuation and the seasonal moving average for straits times index, m1, m2, m3, fixed deposit rate and lending rate from January 1991 to September 2013. Figure 1 and figure 2 show the movements of the stock market. The stock market in Singapore were badly hit by the crisis. The stock market opened January 1997 with the Straits Times Index at 2,055.44. The ST Index dropped drastically to a 10-year low of 856.43 in September 1998, a decline of some 60 percent over a fourteen-month period. The more rapid slowdown in M1 and M3 during the current economic downturn reflected mainly the larger gross domestic product (GDP) contraction as well as structural changes in the economy in which a more developed equity market exerted relatively more significant negative wealth effect on the economy. In recent time, Keynesian economists argue negatively relationship between stock prices and money supply. The Keynesian economists further argue that change in the money supply will affect the stock prices only if the change in the money supply alters expectations about future monetary policy. According to them, a positive money supply shock will lead people to anticipate tightening monetary policy in the future. Whereas real activity theorists argue two variables relationship is positive (Sellin, 2001). The real activity economists believe that change in money supply, assuming accommodating monetary policy, provides information on money demand. In other words, they argue that increase in money supply means that money demand is increasing in anticipation of increase in economic activity. Hence, the real activity theorists argue that there is a positive relationship between money supply and stock prices (Sellin, 2001).

Figure 3: The Slop of LM curve to the Stock Market

Another debate regarding money supply and stock prices is that stock prices are believed to react differently to the anticipated and unanticipated component of the money supply. Figure 3(a), a very steep LM curve leads to a movement from E to E', with a large increase in interest rates, small increase in output, and therefore an decrease in stock price. Figure 3(b), a very flat LM curve leads to a movement from E to E', with a small increase in interest rates, large increase in output, and therefore an increase in stock price. If investors fully anticipated an expansionary monetary policy then the stock market will not react, but when they are unexpected, the stock price will increase (Olivier Blanchard, 2011). Likewise, Sorensen (1982) pointed out unanticipated changes in the money supply have a larger impact on the stock market than anticipated changes, supporting the efficient market hypothesis. Gan (2006) argued stock market consistently determined by the interest rate and money supply. Similarly Sharri et al. (2010) finding denote monetary aggregates (M2) impact to the stock prices. Yeap (2004) and Nawaz and Fazal (2007) research demonstrated exist dynamic interaction between monetary policy and stock market. However, Yacob (2001) and Sourial (2002) demonstrated monetary aggregate didn't have a significant impact on the stock market performance. COPY RIGHT © 2013 Institute of Interdisciplinary Business Research 386

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2. Methodology In this study, I used monthly time series data ranging from January 1991 to September 2013. The basic linear function used in this study is as stated in equation (1),

= ) (1) Where proxy Straits Times Index, , , proxy money supply (measured in S$ million), proxy fixed deposit rate (measured in percentage) and proxy lending rate (measured in percentage). All data were obtained from Monetary Authority of Singapore. Engle-Granger procedure estimating the cointegrating regression by Ordinary Least Square (OLS), obtain their residuals and applying unit roots test for the residual (Engle & Granger, 1987). Basesd on the definition given by Engle and Granger (1987), cointegration necessitates that the variables be integrated of the same order. Therefore, each variable has to be pre-test by using Augmented Dickey Fuller (1981) and Philip and Perron (1988) unit root to identify the degree of integration I(d). To obtain the residual, the following cointegrating regressions are performed in the equation (2): = + + + + + + (2) and the ADF test is as follows equation (3):

= + + (3) where include sequence and with the null hypothesis of : =0 (no cointegration). The value of optimal lag length p is selected by the smallest Akaike information criterion (AIC). Since the residual series is calculated from a cointegrating equation, an intercept of time trend is omitted from the equation (Enders, 1995). If the variables cointegrated, the second step of the EG procedure involves specifying an error correction model (ECM) for each equation in the system. Engle-Granger (1987) two-step error correction model procedure was adopted for the estimation of the models. The models are specified as below equation (4), (5), (6), (7), (8): = + + + (4) = + + + (5) = + + + (6) = + + + (7) = + + + (8)

Where ∆ denotes the first difference operation on the respective variables, , , , , are the coefficients showing the short run equilibrium relationships connecting the independent and dependent variables , , , are the coefficient showing the long run relationships connecting the explanatory variables and dependent variable. is the residual obtained from the linear regression of the I(1) variables and lagged by one as a requirement of the granger representation theorem. is the disturbance term for the model. 3. Empirical Evidence The results of the ADF (Augmented Dickey Filler) and PP (Philip Peron) tests for the unit root test with trend and without trend are presented at Table 1. The results of ADF and PP tests indicate that we cannot reject hypothesis in level. Hence, all of the variables are stationary in the first difference, so, we can reject the unit root hypothesis based on Mackinnon's critical at 1%. dmax = 1 indicating series are stationary at I(1).

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Table 1: Unit Root Test ADF PP dmax

tc tt zc zt STI -1.8721 -2.7388 -1.8241 -2.5254 ∆STI -8.7450* -8.7317* -15.6078* -15.5796* 1 M1 6.1253 2.2516 7.0152 2.1625 ∆M1 -6.5969* -8.3173* -17.1958* -19.6660* 1 M2 4.0875 0.1338 4.7118 0.2012 ∆M2 -7.1511* -8.1834* -14.1892* -15.3483* 1 M3 4.7579 1.0231 5.7830 1.1840 ∆M3 -6.1878* -7.3203* -13.6824* -15.2225* 1 DR -1.7593 -2.8538 -1.5695 -2.1342 ∆DR -7.4209* -7.4312* -9.3061* -9.3097* 1 LR -3.1768 -3.5088 -2.7693 -2.8678 ∆LR -7.4466* -7.4705* -9.2814* -9.2936* 1 Notes: * denote 1% level of significant. ADFtc and PPzc without trend and ADFtt and PPzt with trend. H0=The Series has a unit root. Lag length=2 is based on AIC lag length selection. Engle-Granger Cointegration showed that all variables were cointegrated at 1% significant level. This meaning that there are long run equilibrium relationships between all monetary policy instruments and Singapore stock market. Table 2 illustrates Engle-Granger Error Correction Model (ECM). The result exhibit M2 and M3 are positively significance in the short run but negatively significance in the long run with the Singapore stock market at 5% significant level. Finding alike Wong and Sharma (2002) reveal there were strongly significant impact of money supply towards to the stock prices.

LR are negatively significant in the short run and long run relationships with the Singapore stock market at 5% significant level. DR are negatively significant in the short run at 10% level of significant but in the long run relationship significance level at 5% with the Singapore stock market. In addition, Maysami and Koh (2000) also found that long and short term interest rates, money supply and Singapore stock market have cointegrating relations. Mahmudul and Gazi (2009) stated that stock exchange and interest rate are two crucial factors of economic growth of a country. The impacts of interest rate on stock exchange provide important implications for monetary policy, risk management practices, financial securities valuation and government policy towards financial markets. Besides that, finding reveal M1 insignificant in the short run but negatively significant in long run relationship with the Singapore stock market at 5% level of significant. Similarly estimated result of Husain and Mahmood (1999) study indicated there have a long-run relationships between stock prices and money supply for M1. Similarly with Masih et. al (1996) for instance find that being the most exogenous of all, money supply particularly M1 appears to have played the leading role of a policy variable. The long run coefficient was significant implying that stable monetary policy variables in the long run would help to improve the performance of the Singapore stock market. Likewise, Yeap (2004) further showed that the relationships between monetary policy and stock market in Malaysia have are significant in a long run dynamics.

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Table 2: Engle-Granger Error Correction Model Independent Dependent Variables Variable STI Short-Run Long-Run Coefficient t-Statistic Coefficient t-Statistic M1 0.0019 0.2913 -0.0474 -2.3178** M2 0.0069 2.4006** -0.0495 -2.4480** M3 0.0071 2.1201** -0.0528 -2.5862** DR -105.8196 -1.8820*** -0.0407 -1.9824** LR -139.1722 -2.1096** -0.0436 -2.1457** Note : ** and *** denotes 5% and 10% significance level. The long run equilibrium coefficients had a expected a priori negative sign implying that the model where appropriately specified (Abraham, 2012). This implies that 4.74%, 4.95%, 5.28%, 4.07% and 4.36% of the short run distortions affecting the performance of Singapore stock market could be corrected in the long run. Table 3: Pairwise Granger's Causality Test Independent Dependent Variables Variable STI M1 M2 M3 DR LR STI 4.0912 0.6362 2.1110 1.2677 0.1182 (0.0178)** (0.5301) (0.1231) (0.2832) (0.8886) M1 1.7409 0.6815 1.1734 0.4236 0.8087 (0.1774) (0.5067) (0.3109) (0.6551) (0.4465) M2 2.5517 5.7764 0.5929 1.3856 1.3873 (0.0799)*** (0.0035)* (0.5534) (0.2520) (0.2515) M3 3.1732 6.3143 0.6798 1.1416 0.8196 (0.0435)** (0.0021)* (0.5076) (0.3209) (0.4417) DR 2.9759 2.1761 2.1909 0.0592 5.2792 (0.0527)*** (0.1155) (0.1138) (0.9425) (0.0056)* LR 2.4224 1.6244 1.0628 0.0138 9.1173 (0.0907)*** (0.1990) (0.3469) (0.9863) (0.0001)* Granger STI M1 Causality M2 STI M2 M1 M3 STI M3 M1 DR STI DR LR LR STI LR DR Notes: *, ** and *** indicate 1%, 5% and 10% significance level ( ) Numbers in parentheses denotes p-value Table 3 illustrated the result of Pairwise Granger Causality. Pairwise Granger causal relation show unidirectional causal between monetary policy instruments to Singapore stock market (M2, M3, DR and LR causal relation to STI). This finding is sim