Credit growth, macroeconomic factors and stock performance the case of hose 2002 2010

ABSTRACT The paper analyzes the dynamic interactions among credit growth, some fundamental economic factors exchange rate, inflation, industrial production, interest rate, gold price and

VIETNAM- NETHERLANDS PROGRAM FOR M.A IN DEVELOPMENT ECONOMICS

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FACTORS AND STOCK PERFORMANCE:

THE CASE OF HOSE 2002-2010

A thesis submitted in partial fulfillment of the requirements for the degree of

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

NGUYEN THI NGOC HAN

Academic supervisors

Dr PRAM HOANG VAN

Dr NGUYEN TRONG HOAI

HO CHI MINH CITY, MARCH 2011

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TABLE OF CONTENT

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ABSTRACT

The paper analyzes the dynamic interactions among credit growth, some fundamental economic factors (exchange rate, inflation, industrial production, interest rate, gold price) and the performance of Vietnam Ho Chi Minh Stock Exchange using time series econometrics of cointegration and causality tests In the analysis, we explore further with V AR-based variance decomposition and impulse response functions to capture the direct and indirect effects of innovations in one variable and other ones in the same model The interesting results come out with negative reaction of stock price to credit growth in first 11 months of study period before any reverse trend occurs And then it will still remain the sign in longer term However, it seems

macro-no significant evidence to prove the positive short-run impact of credit rate on stock price increase So Interest subsidy policy after global crisis (2008) is not the major reason to rescue equity market In addition, the variation of key variables including interest rate, inflation and exchange rate has significant impacts on stock volatility in long run One important policy implication is that authorities should be cautious in implementing monetary policies exposed to inflation risk as it has a consistent adverse influence on stock change in both short and long term

Keywords: Credit growth, Macro-economics, Stock performance and

Impulse Response Function

CHAPTER 1: INTRODUCTION

1.1 Research context:

Ho Chi Minh Stock Exchange (HOSE), formerly HOSTC, is the older of the two stock exchanges in Vietnam Established on 20 July of 2000, it started operation on 28 July in the same year The trading system of all stocks listed in HOSE is under the control of State Securities Commission (SSC) HOSE runs as a state-owned one member limited liability company with one-billion VND of chartered capital At the very beginning, there were only two listed firms, namely REE and SACOM traded two days per week From 1 March 2002, market traded daily with two order-matching sessions Till 31 December 2007, 138 stocks were listed and traded five days per week through a fully-computerized trading system, Automatic Order-Matching and Put- Through Trading system In general, the total capitalization in HOSE accounts for over 40% GOP During operating time, Vn-Index had a sharp fluctuation peak to 1137 in February of2007 and then turned down sharply, which shocked almost investors and policy authorities According

to some former studies, the root cause is originated from market participant's over-expectation

on booming price

Despite HOSE's certain achievements over year, it is still fragile due to its own high risk, big price volatility and poor trading system As one of Asian emerging markets, HOSE has experienced ups and downs because of the significant influences from external and internal factors In reality, many controversial problems signal the market inefficiency and instability in terms of information asymmetry, a weak legal framework, the lack of transparency in financial reporting, too much Government intervention in trading transactions and herding investor

behaviors Indeed, these weaknesses are the challenges of Vietnam economy towards financial liberalization process Thus, HOSE in particular or Vietnam stock exchange in general hopefully will develop into a strongly efficient capital-raising channel in the near future

In the development period, the required tasks for policy makers from now on is to do more qualified researches on Vietnam stock market and prevailing problems for timely adjustment Observing the economic changes since 2002, the rapid domestic credit has grown by nearly 10 times, from about 2 hundred thousand up to more than 2 million billion dong Simultaneously, stock market boomed aggressively in 2007 when VN Index created history

further estimation Then its empirical result below can explain the relationship between domestic credit growth and stock volatility, especially in the period right after the peak in 2007 until the broadening monetary policy in 2009

!

Let's discuss more about Vietnam economic background and why credit growth accelerated the period prior to 2008 A relevant update from World Bank (2007) gave some explanation related to the so-called "Impossible trinity" of simultaneously maintaining nearly fixed foreign exchange, independent monetary policy and an open capital account Under this implementation, the incident following increasing capital inflows was foreign exchange depreciation or domestic currency appreciation that kept appealing more investors The economy was put in a challenge of excess liquidity Due to its negative impact on Vietnam competitiveness of export and growth slow-down, the Government intervened by purchasing foreign exchange and selling securities Then it moved foreign exchange market much flexibly However, the foreign reserve accumulation and VND appreciation forced SBV to choose monetary and credit expansion in term of sterilization This also hid potential exposure to inflation and then unpredictable capital outflows, most seriously a crisis (2008) when the stock value returned its real value So it raises the interest in finding the real impact of credit growth on stock performance scientifically over the period which will be presented in the following parts

1.2 The scientific challenge:

The research will discover whether credit shocks have significantly affected HOSE performance Particularly, the credit growth under interest support program in 2009 aimed at economic stimulation after global crisis in 2008 And thesis also generalizes how lagged length between credit growth and stock price reaction would be Further estimation will uncover which

of key macroeconomic and trend variables has remarkably influenced on stock price in both short and long run since 2002 Based on the empirical result, stock investors, academic economists and authorities can refer to the findings for their own decision-making However,

some existing limitation of data and quantitative tools to interpret it in economic meaning are necessary for further research then

1.3 Goal and objectives of the research:

The overall goal of the project is to provide scientific results as trustworthy references for stock investors And then it gives some appropriate policy recommendations related to credit applicable for the development of Ho Chi Minh Stock Exchange and Vietnam economy as a whole

To meet the goal, the first specific objective of the research is to identify the cointegration and causality of domestic credit growth to HOSE's performance Next is to analyze the lag length between the prominent change in some monetary policies and its impact on stock price market over 2002-20 I 0 Especially and specifically, how Decision 131-2009-QD-TTg on Interest Rate Support for Organizations to expand their Production and Business affected stock price will be discussed in the paper By application of V AR and monetary transmission mechanism (MTM), the research functions forecasting the stock volatility

The second objective is to explain more about interactions among credit growth, other key macroeconomic indicators and HOSE index The testing will identify how significant and which relationships, negative or positive, each independent macro-variables impact on the dependent stock price

The last is to gtve some recommendation for both stock exchange managers and government policy makers

1.4 Research Questions:

In order to achieve the above objectives, let's try to answer the following questions:

1 Is there any relationship between credit growth rate and stock price index in term as well as short-term?

long-2 Among potential substitute investment channels via foreign exchange, gold, money and overseas stock markets, does domestic interest rate have the immediate effect on the stock price variation?

3 Associated with credit, are all selective macroeconomic factors necessary for forecasting HOSE price change in the long-run? If yes, what is the sign of individual relationship between stock price and the others?

1.5 Structure of the thesis:

The thesis will follow introduction section with four other chapters Chapter 2 reviews the applicable theories of stock price determination as well as empirical studies about the relationship between security index and macro-economic indicators Chapter 3 describes research methodology including data collection, variables of interest, econometric model together with empirical procedures Chapter 4 analyses the research results according to methods recommended previously It answers the thesis hypotheses whether the causality of Credit growth to stock price change exists, which market the main substitute for stock investment channel is and whether other macro-variables have significant impacts on stock variations And chapter 5 closes the study with a conclusion, policy implication and opens with its limitation for further studies

CHAPTER 2: LITERATURE REVIEW FOR STOCK'S RELATIONS WITH

MACRO-ECONOMIC FACTORS

This section will provide general concepts and previous valued researches based on which the study can construct It includes four main parts: key concepts, theoretical review, empirical review and conceptual framework

2.1 Key concepts:

2.1.1 Credit channel

Credit channel as suggested by Mishkin (1995) operates through two components - the balance sheet channel and the bank lending one The concept explains that increasing money supply increases total credit that banks can supply to country economy And then through the bank financing channel, it will in tum boost aggregate demand and output, eventually push up stock price In line with the balance sheet channel, Bemanke and Gertler (1995) concerned the external finance premium, which they defined as the bridge between the externally-raised cost of funds and the opportunity cost of internal funds Yet, this seems not significant in the case of Vietnam because most credit has recently been granted to big state-owned enterprises regardless ofthe consideration of their financial position Briefly, domestic credit growth via banking loans

is the channel the State Bank uses to inject liquidity to the whole market

2.1.2 Macro-economics

Macroeconomics is the study of the performance, structure and behavior of the entire economy as a whole It is different from microeconomics which focuses more on individuals and

how they make economic decisions Actually, macro-economy is so complicated because there are many factors influence on it We usually analyze macro-economy by primarily looking at national output (GDP), unemployment and inflation Besides, there are consumption, interest rate, foreign exchange, international trade and international finance which are modeled to explain economic relationships The study herein will employ some of the mentioned indicators to see how they affect stock market

2.1.3 Stock performance

Stock performance is a measure of the returns on shares over a period of time There are several measures of stock performance and each includes its own characteristics and benefits during an analysis of returns In order to understand stock performance of HOSE in Vietnam, the thesis will employ stock index (VN-Index) as its proxy

2.2 Theoretical literature:

2.2.1 Arbitrage Pricing Theory (APT)

This research is drawn from Arbitrage Pricing Theory (APT) Developed by Stephen Ross (1976), the theory asserts that an asset's expected return is a linear function of unanticipated change in a small number of macroeconomic factors The sensitivity of the asset's return to the individual elements is determined empirically by way of statistical technique The APT does not identify the specific factors that would affect asset returns Yet four factors are frequently evaluated in most application of the APT: (1) inflation, (2) industrial production, (3) risk premium and (4) the term structure of interest rate Different from Capital Asset Pricing Model's risk measure by asset "beta" against market index, the four forces are primary

influences on stock market Based on the traditional discounted cash flow valuation, the unanticipated variations of inflation rate and industrial production are related to the real value of future cash flow The other two variables seem intuitively to be more linked to the risk-adjusted discount rate Risk premium measures investor attitudes toward risk perception about general level of economic uncertainty while the term structure of interest rate influences on discount rate for multiply year cash flows However, the study did not show the evidence to deny the rest of unsystematic variables affecting asset returns

2.2.2 Discounted Cash Flow (DCF)

These four above factors find based on a common financial theory, namely, Discounted Cash Flow valuation (DCF) which was identified by Chen Ross & Roll (1986) It is one of the foundations to estimate the true value of common stock investment Since the study aims to identify key determinants of stock's true value rather fundamental analysis, Dividend Discount Model (DDM) is suitable for review in this section The model assumes that the time money value of common stock is the present value of all future dividends In the following equation [2.1 ], interest rate and inflation affect discount rate and future cash flows while industrial production reflects the health of business performance via dividend payment to shareholders

Where Vj: Value of common stockj

Dt: Dividend accrued from period t

Ke: Cost of Equity

Assume that the dividend growth rate holds constant (g%), the time value of stock is written in form ofthe following function

DI

Vj = - - [2.2]

ke-g

g : Constant growth rate

Based on the above function, there are 2 fundamental determinants, cost of equity and expected growth rate, to examine the intrinsic value of a stock They are independent to each other pursuant to Keilly and Brown ( 1997) Growth rate depends on retention rate (b) and return on equity investment (ROE) via the function: g = b * ROE while ke stands for risk factors Total risk is a combination between systematic risk (market risk) and unsystematic risk (firm-specific financial risk) Systematic risk is the variability of return on stocks or portfolio associated with changes in return on the market as a whole whereas unsystematic one is also the return variation

investment stock portfolio In regard to market risk, an aversion investor can look at macroeconomic and political conditions for their investment decisions Damodaran (2002)

about economy and political stability On the other hand, disregarding reinvestment rate b determined by individual firm's dividend policy, ROE in the equation of growth rate should be

•

ROE=ROC+D/E.[ROC-r(l-t)] [2.3]

Where ROC: Return on capital investment

D/E: Capital structure (Debt over Equity)

r: Interest expense on debt

t: Tax rate on ordinary income

Therefore, expected growth rate of a stock will eventually base on both monetary and fiscal policy changes since r is related to monetary policy and t refers to fiscal one From all above- mentioned equations, we can conclude that the change in stock price would be partially determined by macro-economic variables related to interest rate, inflation, industrial production, exchange rate, money supply, credit aggregate, taxation and government budget The research will employ some similar fundamental indicators based on this theory

2.2.3 Inflation and Stock Market

Beside APT and DCF, two alternative studies about why inflation affects stock price were discussed in Feldstein ( 1980) and Modigliani -Cohn ( 1979)

Feldstein's "Inflation and the Stock Market" indicated the inverse relation between higher rate of inflation and sustainable reduction in the ratio of share prices to pretax earnings But what was the cause to the failure of price increase during a decade (1980s) of steady inflation in US? This adverse effect resulted from the features of State tax laws, particularly historic cost depreciation and the taxation on corporate-source income Further, the study

analyzed the difference between the effect of high constant inflation rate and its increase expected for future If the steady-state of inflation rate is higher, stock prices would go up at faster rate And an increase in expected future of inflation rate would lead to a concurrent fall in the ratio of share price to current earnings (PE) Then the price rises at higher rate of inflation but

PE ratio will be permanently lower as inflation pushes up the effective tax rate on corporate revenue Despite Feldstein's affirmation on the inverse influence of inflation on stock price via

US tax regime, he did not deny the anticipated factors - the slowdown in productivity growth, booming cost of energy and increasing international competition - decreasing pretax profitability Sharing the same view of the negative relation between inflation and stock price, Modigliani-Cohn approached it in different ways The reason why the ratio of market value to profits declined in the late 1960s is two errors in evaluating common stocks The first is that investors capitalize equity earning at a rate which is equal the nominal interest rate not economically correct real rate The second is that investors fail to allow for the gain to shareholders accruing from depreciation in the real value of nominal corporate liability In other words, the inflation-induced errors are exposed to a permanently depressing effect on reported earnings even to the point of turning real profit into growing losses In short, the rate of inflation

is an indispensable proxy to measure the uncertainty of an economy in general or stock market in particular

2.2.4 Monetary Transmission Mechanism (MTM):

One more applicable theory within the analysis is Monetary Transmission Mechanism (MTM) It describes how a monetary policy change affects some important macroeconomic variables The specific channels of MTM operate through the effects of monetary policy on interest rates, exchange rates, equity and real estate prices, bank lending and corporate balance

sheet According to Mishkin (2006), the increase in money supply may lead the rise of price level and potential growing real output in the short-run, which can occur through four typical channels like interest rate, credit, exchange rate and asset price channel In case of Vietnam, the credit channel explains much of the way in which credit as money supply affects real output It is more important than interest rate channel resulted from a study of Le Viet Hung, an expert from Foreign Exchange Department (2008)

2.2.5 Efficient Market Hypothesis (EMH):

Lastly, one of the most influential theories of asset pncmg IS Efficient Market Hypothesis (EMH) developed by Fama (1970) It asserts that financial markets are informatively efficient when market prices incorporate and reflect all relevant available information There are three forms of market efficiency If all available information is related to past prices, it is classified into "weak form" And if it refers to all public information, the market

is defined as "semi-strong form" efficiency Otherwise it would be "strong-form" when share prices adjust to all public and private information In the last form, investors cannot consistently earn excess returns over a long period of time The theory has a lot of disputes and controversy, especially from technical analysts as they base their expectation on past prices, earnings track records and other indicators to predict the market trends In reality, it is very hard to become a strong-form efficient market due to asymmetric problems

According to the above-mentioned theoretical review, there remain two signs of the dynamic interactions running from the key macro-economic factors to stock price The negative sign refers to the causality of interest rate, inflation and exchange rate to stock index And the positive exhibits its correlation with money supply and industrial production Different from the

previous analytical evidence, the thesis is developed to find out some specific relationships among key macro elements and the proxy variable of stock performance in the case of Ho Chi Minh Stock Exchange First of all is whether a consecutive credit growth, main independent variable, over studying period has any impact on VN-Index immediately or within 12 months The research interest is raised from whether the same upward trend of data series (Figure 1) over years presents a cointegration in long term and a causal relation in short term Second, as observed Vietnam investment market from 2002 up to now, almost individual investors have changed the strategies toward money market when there are negative risk signals from stock market Banking deposit is one of safe substitutes That is the reason why the research wants to answer if money market is the most active investment alternative for equity in short-run One more discussion is the author's ambition to understand whether 4 selective variables of interest, credit growth, foreign exchange, interest rate and inflation, have forecasting abilities to stock volatility in long-run

Ibrahim and Yusoff (200 I) from Malaysia employed mainly VAR, Johansen-Juselius integration, Monetary Transmission Mechanism and AS-AD model to analyze the relationship between Kuala Lumpur Composite Index (KLCI) with selective macro-indicator including M2 monetary aggregate as money supply, real industrial production to capture real economic activities, CPJ and exchange rate (Ringgit/USD) expressed in natural logarithms All the data were collected in the period of Jan 1977-Aug 1998 As a result, they concluded that more money supply leads to more positive effects on stock price in short-run and vice versa in long-run

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Conversely, changes in stock price also drive the increase in demand for real money, interest rate and subsequently the value of domestic currency Furthermore, domestic currency depreciation presented by exchange rate appreciation is both contractionary and inflationary Therefore, Malaysian authorities should pay attention to stabilizing their exchange rate and monetary policies due to its adverse repercussion on financial market

CooperMaysami, Howe and Hamzah (2004) tested the interactions between chosen factors including interest rate, industrial production, price level, money supply, exchange rate and Singapore Exchange Sector Indices of Finance, Property and Hotel by applying Johansen's VECM model ( 1990) in multivariate context, a full information maximum likelihood estimation model Based on intuitive financial theory (Chen et al 1986 & Fama 1981 ), the model allows for testing a whole system of equation in one step and avoids carrying over errors from the first into the second step And the conclusion drawn from its empirical study is that Singapore stock market and SES Ali-S Equities Property Index formed significant relationships with all macroeconomic variables identified while the other two did with only selected ones Specially, the financial sector is significant affected by inflation rate, exchange rate and both long and short-term interest rate whereas its relation to money supply is weaker in comparison with Singapore's stock market as a whole From this point, the authors came up with a statement that

"stock picking" could lead to superior earning capability

Another research is from the case of Norwegian stock market Anderson and Lauvsnes (2007) did a research on co-integration between stock price index and domestic credit instead of money supply used by other previous studies for forecasting purpose Various methods and comparisons were employed in their study First, they compared forecasts from V AR models with and without imposing co-integration restrictions with simple uni-variate models in different

forecasting horizons Then it stepped up with comparing Mean Square Errors MSE and Mean Absolute Errors MAE from 4 models: VAR log-differences, VAR - VECM, Random walk, ARIMA(l, 1,1) in Box-JENKINs analysis According to many problems happening since 1980s, they particularly found financial turbulence with booms in stock markets, a strong overall growth

in credit and asset prices (property & stock), concerns about sustainability of domestic private

credit via excessive borrowing causing to increases in cost (e.g interest rate) and reduction of income as the result In next step, these scholars tried answering relevant hypothesis: How to improve stock index and credit growth forecasts? What are the fragile endogenous relationships

in economy (Minsky1987)? Does previously detected co-integration between these variables contribute to forecast accuracy? Additionally, they addressed comparison between the out-of- sample forecast and previous in-sample results They scientifically figured out the results that Stock index is better predicted when co-integration is imposed Credit variable is better predicted with multivariate models than the uni-variate ones

In the case of Thailand, many scholars from different approaches identified macro-economic elements affecting their stock market performance In 2007, Tantatape and Komain used co- integration model to test the long-run relationship and ECM to determine short-run deviation from long-run equilibrium The study collected monthly data of Money supply, FX, IPI and Oil price, Inflation rate logarithm (1992-2003) In addition, Mahmood and Dinniah (2007) expanded their study of 6 Asia Pacific countries consisting of Malaysia, Korea, Thailand, Hong Kong, Japan and Australia to make a comparison at regional level With the similar methodology (ECM) and observation periods but less independent variables (exchange rate, inflation rate and industrial output), the results came up with the co-integration between stock price and exchange rate in Hong Kong market and industrial output in Thailand during 1993-2002 And

macroeconomic indicators in other countries seemed to produce a negligible impact on stock return From different approaches, Nguyen Dinh Tho (2010) employed Arbitrage pricing theory (APT) to explore the behaviors of Thailand stock price against the changes of macroeconomic factors Moreover, the research discovered its relationship with exchange rate, IP growth rate, inflation rate, current account balance, international-domestic interest rate difference and the changes in domestic interest rate He collected monthly dataset before Asia Financial Crisis (from Jan 1987 to Dec 1998) to compute all with basic test of correlation coefficient to avoid multi-colinearity, auto-correlation, ADF unit root test and finally OLS The reliable result was thanks to various tests on flexible combination of endogenous and exogenous variables The study ended with "Industrial production and exchange rate systematically affect stock returns while the returns on value weighted SET index, used as a proxy of market portfolio, fails to show its significance"

Regarding a limited number of empirical studies in Vietnam, Loc, Lanjouw and Lensink (2008) conducted auto-correlation together with variance-ratio tests to discover whether Vietnam stock market holds weak-form efficiency It also answers the question if there is a possible bias

of the results caused by thin-trading that characterizes the market The employed data was collected on weekly basis (2000M7D28-2004M12D31) The findings scientifically reported that Stock Trading Center (STC) is not weak-form efficient in thin-trading period, even in case, its corrections of thin-trading are made In the same year, Long (2008) used daily data of closing stock price and return rate of index (%) in 2000-2007 period to examine stock return volatility against regime changes By testing in GARCH model, his predominant outcome proved that Financial Liberalization has a negative influence on stock return variation However, it is not easy to separate the influence of Financial Liberalization on stock performance from that of the

20

attractiveness to encourage almost foreign and domestic investors

Learning and developing from such above literatures, the research exploits further the performance of Vietnam stock market via HOSE price index Moreover, the other differentiation

is the selection of main independent variable, using credit growth instead of money supply in a previous studies of Malaysia and Vietnam in order to explain their relationships if any Besides, the study keeps employ the similar fundamental economic factors including exchange rate, inflation rate, interest rate, domestic deposit, industrial production to Asia Pacific researches Another is the input of compelling variables related to HOSE stock's co-integrations with US stock index (Dow Jones) - the proxy for a developed market, China stock index (SSE) - the proxy for an emerging market and gold price All the up-to-date data (2002-2010) will be collected for the quantitative estimation of stock performance via the below conceptual

Impulse response functions, to test the target interactions between stock price and other

economic factors

Let's discuss three main hypotheses that the paper will econometrically explain in following parts The first is to satisfy the author's question whether the positive interaction between credit growth and stock price has existed as the scientific results of Norway and Vietnam's former studies In the recent context of Vietnam, Government has applied monetary policy to stimulate the whole economy after Global crisis Particularly, this is Government Stimulus Package of 1 billion US dollars starting from 2009 So wonder if the enforcement during that time took effect somehow toward stock performance and the entire economy If yes,

it should be employed afterwards The major query is translated into the below hypothesis

1 Credit growth has a significantly positive effect on the HOSE index within 12 months The hypothesis will be tested using the following equation:

VN-Index = f(Credit growth rate t, t-1, t-2, t-n) in logarithm

The second developed from the controversy whether money market is a main investment alternative when stock market is not attractive in short-term As supervised over time, almost local investors at HOSE often shift their investment to some alternatives such as bank deposit to earn interest or avoid VND depreciation, gold or foreign exchange trade to obtain margin and speculation in real estate with a big capital The thesis will test on these channels except real estate because of data limit and the big volume of capital And below is described in term of hypothesis

2 In Vietnam, money market is the most active investment substitution for stock exchange compared to other studies markets in short-run

Finally, the studies will go further to explore the individual relationship of some selective macro indicators with stock index It can be informative and useful for policy makers, stock investors together with economic scholars then Whether the selected macro factors have predictable abilities for stock volatility is what the next sections will answer

3 The credit growth rate is a more significant determinant of the stock performance than the other macroeconomic factors of interest

Industrial Production) in logarithm

In summary, these three hypotheses above will be further analyzed by using either economic theories or quantitative techniques throughout the thesis And the following framework initially presents the overview of all inclusive variables, resources as well as econometric tools

VAR MODEL:

UNIT ROOT TEST (ADF)- STATIONARY

CO-INTEGRATION (EG & JJ)

ESTIMATED STOCK MARKET PERFORMANCE:

REFERENCE FOR POLICY

DEPOSIT

EXCHANGE RATE

INFLATION RATE

CREDIT GROWTH RATE

INTEREST RATE

INDUSTRIAL PRODUCTION

GOLD PRICE

MONTHLY DATA: -IMF

-GSO -Reuters -Bloomberg

CHAPTER 3: RESEARCH METHODOLOGY

To investigate the relationship between stock performance and credit growth for the case

of Vietnam, this thesis establishes three hypotheses (i) there remains a positive short-run and negative long-run relationship between credit growth rate and VN-index; (ii) Money market rate has a Granger cause to the variation of stock price at significant level compared with other factors; (iii) In the long-run multivariate relation, credit factor accounts for high percentage effect on stock index This chapter is divided into two main sections, research methodology and data collection The first section will introduce four econometric techniques to test for the validity of three proposed hypotheses First, the unit root test is used to test for stationary of all variables Second, with the bivariate and multivariate cointegration tests, the hypothesis (i) about the long run relationship between credit growth rate and VN-index will be answered Third, the bivariate Granger causality tests are applied to check the directional relationship between variables, especially the effect of interest rate on stock change - hypothesis (ii) The bivariate Granger causality test is one of ways to exclude less significant variable(s) out of the suggested model Fourth, the short run adjustment of stock price is explored by employing both vector error correction models and Impulse response function to answer for the hypothesis (iii) Next, the second part will discuss how the thesis deals with proxy variables, data collection and data analysis

It starts with a summary of time series process through some technical tools employed in the thesis like Vector Auto-regression, Unit root test for data stationary, Cointegration test for long-term relationship, Granger test for causality, and Error correction model and Variance Decomposition - Impulse Response Function

-3.1 Econometric techniques:

According to Asteriou (2007), there are various aspects to examine time series but the two most common analysis types to fully exploit data structure are time series forecasting (uni- variate analysis) and dynamic modeling (bivariate and multi-variate analysis) Different from

other econometrics the first type does not concern much with building structural models, understanding economic issues or testing hypothesis but developing forecasting frameworks By using diversified criteria (AIC, SBC, RMSE, Correlogram and Fitted-actual value comparison),

it can explore data dynamic inter-relationship over time In contrary, the second type involves more than one variable and concerns both economic comprehension and hypothesis testing However, it can be slow to adjust to external shocks Thus, the process must capture adjustment techniques Recently, the modeling has become popular thanks to two Nobel works of Clive W.J Granger (Cointegration) and Robert F Engle (ARCH) Nowadays, it has also significantly contributed to economic policy formulation

One more basic term in almost time series assumption is "stochastic process" which means a collection of random variables ordered in time, usually denoted as Yt (Gujarati, 2003) And Stock & Watson's assumption (2007) stated if the future is like the past, the historical relationships can be used to forecast the future change Otherwise, it may be unreliable Therefore, this can be formalized by the concept of stationary in time series regression It is usually applied before further causality or cointegration estimations The next part will provide some popular testing methods of this data stationary

3.1.2 Testing for Unit Roots by Correlogram, ADF & PP:

To test the stationary of time series data, we usually apply two typical ways; one is graphical analysis (Autocorrelation or Correlogram) and the other formal tests (Dickey-Fuller and Phillips-Perron test for unit root)

Supported by Eview software, the data can be analyzed in term of a graph of autocorrelation for various time lags for a variable (Hanke, 2005) It helps the researchers answer

4 questions whether the historical data is random for forecasting the future, non-stationary, stationary or seasonal Based on selecting appropriate p (Autocorrelation coefficient) and q (Partial Correlation) in the ARIMA models, we can determine data characteristics for further estimations

1 If p between Y1 and Yt-k for any lag k are close to zero, the series is random

2 If p is significantly different from zero for first several lags and then drops toward zero when the number of lags increases, it is non-stationary

3 If p is also far from zero and suddenly dies out as lag order rises, the data is stationary

4 If p occurs at the multiples of seasonal lag, the pattern has seasonal elements

This graph-based correlogram is getting very useful for time series forecasting and other business implication However, in almost academic studies, it is essential to conduct formal statistics like t-statistics, Ljung-Box statistics and especially ADF or PP Unit root tests

ADF (Augmented Dickey Fuller) is an extensive test procedure from its simple type, DF,

as the error term is unlikely to be white noise The test includes extra lagged terms of dependent variable to eliminate autocorrelation The lag length on these extra terms can be determined by AIC or SIC Below is the general model which should be started in ADF test

After answering a set of questions in regard to the model appropriateness, if it is not satisfied, the restricted equations will be tested then

The other approach of PP was developed by generalizing the above ADF test procedure

~y; =a+ 8Yt -1 + er [3.4]

The PP creates an adjustment to t-statistics of coefficient from AR (1) regression to account for serial correlation in e1 whereas the ADF corrects this by adding lagged differenced terms in model

3.1.3 Cointegration and Error Correction Models (ECM):

Before conducting a short-term causality between two time series, it is necessary to check their integration and cointegration initially Cointegration can prove whether the series are correlated to itself and the other in long-run or not The concept of cointegration was first introduced by Granger (1981 ), elaborated by Engle - Granger (1987) and most recently by Johansen (1995) Trended time series, macroeconomic data, can potentially create main problems in empirical econometrics due to its spurious regressions Thus, to solve it, we need to take difference the series successively at I( 1) or 1(2) until stationary is shown for regression analysis But if the estimated data are co integrated at levels 1(0), Error Correction Model (ECM) should be immediately used in evaluating their dynamic interactions The ECM results conveniently explain both the short-run dynamics and long-run equilibrium adjustment of the studied variables

In case two variables, say X and Y, are related, we would expect their co-movement and then their stochastic trends, cumulate error processes, would be similar to each other In other word, the trends can cancel to each other In econometric language, we call the characteristic a cointegration between X and Y (Asteriou, 2007)

If two variables have a long-term relationship, there will be a common trend linking them

from the following regression:

Yr = {31 + {32Xr + Ur (3.5)

And the equation of residuals:

From the previous econometric studies, we can generalize about 3 typical procedures of nonstationary series below:

version of Granger causality

conclude that there is at least one uni-directional Granger causality Then ECM enables us to estimate the direction of causation and distinguish between long-run and

3 For 2 series integrated of the different orders (or non-cointegrated), it is suggested

As the expected research result is that the differenced X and Y are cointegrated by its OLS' error term stationary at 1(0) in the case 2, we will provide the review of ECM specification as follows

~Yr = ao+a1M1 -nur-1 +&r [3.7]

~y, = ao + GJM,-n[Y, -1- {31- f32X -1] +&r [3.8]

Where

1t : the adjustment effect (error-correction coefficient) measures how much of the

equilibrium takes place each period or how much of its error is corrected Asteriou and Hall (2007) explained it in different speed of the adjustment to equilibrium below

instantaneous

In order to test either long-term or short-term causal relationship, ECM test procedure was proposed by Engle and Granger (1987) with 4 basic steps:

-Step 1: Test the time series for their order of integration

-Step 2: Estimate the long-term possible cointegrating relationship

-Step 3: Check for the integration order of the residuals

-Step 4: Examine the ECM model

3.1.4 V AR and Granger causality test:

According to Sim (1980), Vector Autoregressive Model (VAR) was developed from the idea that all variables are treated as endogenous once no distinction between endogenous and exogenous ones Suppose there are 2 time series in which Y1 is influenced by not only its past values but the past and present ones of X1• Meanwhile, X1, in tum, has the same impacts The following bivariate V AR model is given by:

white-on the independent and vice versa

According to Asteriou (2007), V AR possesses 3 advantages The first is its simplicity because of no worry about the distinction between endogenous and exogenous variables The second is its separate estimation of each equation with OLS method The last good point is its forecasting function and foundation for causality tests

weakness of non economics-based theories as restrictions on any parameters are not employed Statistical inference, yet, is used instead in order to drop insignificant coefficients and make models consistent with the underlying theory Next, VAR is argued due to the loss of degrees of freedom Thus, if the sample size is not enough large, the big number of parameters with 12 lags

for each will consume the degrees It may cause problems in study The other dispute focused on the interpretation of its obtained coefficients due to the lack of theoretical background

As above-mentioned, VAR helps test the direction of causality Granger (1969) composed a simple causality test which has been widely applied in economic analysis He assumed that Yt Granger-causes Xt provided that Xt can be predicted with more accuracy by the past values of Yt all other terms hold unchanged We can formulate the two equations below where error terms are uncorrelated white-noise

Step 1: Testing for the unit root of time series (ADF or PP tests)

Step 2: Testing for cointegration between them (EG or Johansen approach)

Step 4: Determining the optimal lag length of the 1 st_differenced variables

Firstly, supported by econometric software (Eview), we can automatically determine the

equations based on AIC or SIC criteria

If it exceeds the critical F value, we reject the null hypotheses and conclude that X1

causes Y1 or vice versa

3.1.5 VAR-based Variance Decomposition and Impulse Response Function:

The approach helps to identify how much each economic variable affects on itself and the other independent variables in term of percentage of the factor's forecast error variance in long run period And Impulse Response Function can point out the relationship signs among all employing variables according to Lastrapes and Koray (1990)

3.2 Data description:

Almost monthly secondary data (2002Ml-2010M3) will be collected from reliable resources of IMF, GSO and Thomson Reuters Specifically, the study will use Industrial Production as a proxy for GDP from GSO In general, there are 99 monthly observations for the empirical research And Vietnam stock performance in this study will be specified in form of a function of the selective variables below:

[3.16]

VN-Index or the price index ofHo Chi Minh Stock Exchange

Domestic credit aggregate

Nominal exchange rate VNDIUSD

Month-end money market interest rate

Inflation rate as a proxy of consumer price level (CPI)

Industrial Production calculated on monthly basic from annual Gross Domestic Production

Domestic deposit aggregate

Dow Jones Index representing Stock price index of US - a leading mature stock

Dow Jones Industrial Average is a price-weighted average of 30 blue-chip stocks that are generally the leaders in their industry It has been a widely followed indicator of the stock market since October I, 1928 (Bloomberg)

in the Eastern emerging market and alternative investment channel of foreign investors Prepared and published by SSE, SSE indices are the authoritative statistical indices widely followed and quoted at home and abroad in measuring the performance of China's securities market SSE Index Series consists of 17 indices, including 5 constituent indices, 2 composite indices, 7 class and sector

indices and 3 other indices In order to promote the long-term infrastructure construction and standardization process of the securities market, in June 2002, SSE restructured the original SSE 30 Index and renamed it SSE Component Index, or SSE 180 Index

All these variables are summarized in the table 3.1 below

SIGN Dependent Variable

Independent Variables

Time-series data of both dependent variable (VN Index) and independent macro factors will be employed in order to identify their stationary via Augmented Dicky-Fuller and Phillips Perron Unit Root Test; their lag length via the Schwarz Information Criterion (SIC) or Akaike

The main methodology throughout the study is the application of V AR model Then the econometric results through quantitative analysis present the implication of every interaction among macro factors, credit growth and stock price fluctuation

Depending on data availability, the study uses quantitative analysis to test hypothesizes

First, this research uses Unit Root (ADF and PP) in Eview to test whether 2 time series of stock performance and credit aggregate are stationary and co-integrated in first difference or not

p

i=l

As there are 2 variables tested each time, it is better off to apply EG approach instead of

Step1: Run OLS ofthe equation

In case it can satisfy the V AR prerequisite condition, we will go ahead to examine their causality

Second, Error Correction version of Granger causality will be used to test the relationship between HOSE stock performance and Credit growth rate (2002-20 1 0) As the routine V AR assumes both lags of independent and dependent variable are the same This kind of selection would lead to model misspecification Therefore, optimal lag length should be manually determined based on min value of AIC in restricted AR models

n

ft:.VN!t =a+ L)3i!':l.VN11 _; +Uyt [3.19]

i=l

n ft:.CRt =a + L 8;/t:.CR1 _; + Uxt [3.20]

i=l

to stock index

log VNI = F(log CR, log FX, log RAT, log INF, log IP, log DEP, log DOW, log SSE, log GOLD)

Upon checking their short-term causality in each bivariate relation, we keep testing such interactions in multivariate equation Thanks to the result of Granger cause test and historical empirical studies, 4 key independent macro variables will be selected for next estimation in relation to stock index They consist of credit growth rate, foreign exchange USDVND, industrial production and inflation rate First of all, we conduct Johansen Juselius cointegration test to verify whether there are any long-run cointegration vectors

If yes, VECM then is suitable for the study as the main model because of its ability to

Granger causality of the time series And from this stage, we can additionally draw the percentage of adjustment to the model equilibrium Furthermore, it is a full information maximum likelihood estimation allowing co-integration tests in a whole system of equation in one step without requiring a specific variable to be normalized Better than V AR of Granger, it can avoid carrying out errors over steps Due to research scope, the study aims to the influence of macro-economy on stock performance but not to that of individual financial instructions

Last, estimate the dynamic interaction among credit, macroeconomics and stock index in multivariate equation Moreover, apply VAR-base variance decomposition and impulse response functions to confirm their percentage mutual effects as well as relationship signs