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Macroeconomic Factors and Hong Kong Stock Returns Dissertation


Abstract

Numerous studies have been carried out on the causal relationship between macroeconomic factors and stock variables. However, these studies have not reached common ground on the causal relationship and direction of the said variables. This study aims at addressing the causal relationship between the selected microeconomic variables (inflation, exchange rates, monetary supply and real economy) in the Hong Kong stock market. The data used covers the period between the years 1991 to 2011. Granger causality model is used to establish these relationships. The study findings reveal that inflation, interest rate and foreign sale do Granger cause stock prices.

On the other hand, stock prices do Granger cause monetary supply, interest rates, exchange rates, foreign sales and purchases. Also, interest rates and foreign transaction negatively and positively determine stock prices in Hong Kong stock exchange. Lastly, the evidence tends to favour stock returns in the prediction of macroeconomic variables.

Introduction

Overview

This chapter covers the background of the study, problem statement, research objectives and hypotheses and the significance of the study.

Background to the study

Investment is generally described as the commitment of financial resources on a particular venture. The biggest challenge facing most investors or portfolio managers is the determination of the value of an investment; whether it is worth its price (Abdalla & Murinde, 1997, p.25). Investors need to consider the intrinsic value of any investment using the prevailing market price before committing financial resources. Since the estimation of financial asset value is not easy, numerous methods of valuation have been developed over time (Bodnar & Wong, 2000, p.4).

These valuation methods use various inputs, for instance, cash flows, rates of inflation, interest rates, rate of returns, exchange rates, risk premiums among others. These inputs greatly influence the aggregate return on investment. Also, since companies are based in different environments, they are exposed to different macroeconomic variables. Thus, both internal and external conditions a firm is subjected to must be taken into consideration during the process of investment valuation (Bodnar & Wong, 2000, p.5).

Theoretically, macroeconomic factors are believed to be the main cause of stock market volatility. Thus, they are considered to be the key indicators of stock returns (Chatrath, Ramchander & Song, 1997, p.2). According to Chatrath, Ramchander and Song (1997, p.3), the volatility of market portfolio returns and the ratio of anticipated profits to anticipated aggregate revenue to the economy are inversely proportional. Nardari and Scruggs (2005, p. 1) argue that increased uncertainty concerning future return is significantly related to the economic downturn. Nonetheless, Nishat and Shaheen (2004, p. 620) state that stock prices are the principal indicators and that the change in stock prices always occurs before changes in business activities.

According to a study conducted by Chatrath, Ramchander & Song (1997, p. 440), the evidence tends to favour stock returns in the prediction of macroeconomic variables. Therefore, stock prices are significant indicators for the impending economic conditions or business cycle. This means that stock prices normally start to declines prior to favourable economic conditions and vice versa. Furthermore, interest rates have a significant effect on business operations. Any rise in the interest rates, everything else held constant, will lead to an increase in the capital cost. Thus, companies have to up their game in a high-interest rate surrounding. Or else, the high-interest rate will consume all the profits.

According to Maysami and Koh (2000) analysis, based on stock portfolio instead of single stocks, found that interest rates negatively impact on the entire portfolio returns. However, Tursoy, Gunsel and Rjoub (2008. p.15) stress that there is no evidence to support the relationship between the interest rate and stock returns. Flannery and Protopapadakis (2001, p. 8) show that overnight interest rates have a causal relationship with stock prices and that stock returns also have a causal relationship with the overnight interest rates and the government rates.

Zhao (1999, p. 508) indicate that the Chinese common stock returns are inversely correlated to the expected inflation component, and almost certainly to the unexpected component of the inflation rate. He argues that the inverse relationship between inflation and real stock returns is as a result of the substitute effects. Stock returns are determined through estimation of the most significant real variable, and the inverse relationship between stock return and inflation is brought by the inverse correlation between inflation and real activities in the economy.

Statement of the problem

In modern society, investing in equity seems to be a more and more popular way of making more money for investors. Besides, issuing stocks is also an efficient way of raising funds. Therefore, the waves of stock price affect both individual investors and enterprises (Daohua, 2005). According to some works of literature, stock prices are normally affected by two main factors: internal and external factors. Internal factor is the operational condition of a firm/company. On the other hand, external factors/macroeconomic factors include inflation, exchange rate, interest rate, GDP and so on (Dornbusch & Fischer, 1980, p.960).

Macroeconomic factors considerably affect the stock index (Daohua, 2005). According to a number of works of literature, the correlation between various macroeconomic variables and stock returns is well defined, particularly in advanced economies. Although considerable relationships between macroeconomic variables and stock prices have been, researchers have not reached a common ground regarding the relationship signals or the direction of causality (Hondroyiannis & Papapetrou, 2001, p.24). This paper explores the causal relationship between macroeconomic factors and stock returns. The study is narrowed down to the Hong Kong stock market. To establish the macroeconomic causes of the Hong Kong Stock Exchange (SEHK), the study uses SEHK17 Index observed from 1991 to 2011. The relationship between macroeconomic variables and stock returns are tested in this study using annual data.

Objectives of the study

The general objective of this study is to explore the causal relationship between macroeconomic factors and stock returns. In line with the general objective, the study examined the following specific objectives:

  1. To establish the relationship between each macroeconomic variables and stock returns.
  2. To find out whether macroeconomic variables does Granger cause stock prices.
  3. To establish whether stock returns do Granger cause macroeconomic variables.

Research Hypotheses

To meet the above objectives, the following hypotheses are tested:

  • H0: The macro-economic variables do not Granger cause the SEHK-17 Index
  • H1: The macro-economic variables do Granger cause the SEHK-17 Index

Justification of the Study

The researcher is a post-graduate student at the local university. The findings of this study are of great value for policymakers and regulatory authorities, especially the Capital Market Board. It provides the policymakers with up to date information regarding Hong Kong Stock market, which is significant for future decision making and regulatory purpose. Also, the study will add to the researcher’s career development over and above the intended academic purpose.

Scope of the Study

The scope of this study is in line with the general objective, which is to explore the causal relationship between macroeconomic factors and stock returns. Using primary data and applying statistical techniques, the study explained the variables to meet the research objectives.

Literature Review

Introduction

In this chapter, the significant literature materials of the research are reviewed, classifying financial models linked to the relationship between macroeconomic factors and stock returns.

The relationship between Macro-Economic variables and Stock Returns

During the valuation process, emphasis should be placed on both the external and internal business environment. Reilly and Brown (2006, p. 361) assert that the company’s internal and industrial environment should be given a lot of attention during the valuation process. Therefore, this approach emphasizes the significance of internal and external environment in the valuation process.

The top-down approach stresses that internal and external environments have a great impact on the overall stock, irrespective of the nature and size of the company. On the other hand, the bottom-up approach argues that it is probable to acquire stocks with high returns irrespective of the prevailing economic conditions both internally and externally. Therefore, both internal and external business surrounding have a major impact on the stock values and return. Therefore, a number of macroeconomic variables are considered to be more significant to the risks that are familiar with most companies.

The relationship between macro-economic variables and stock prices are demonstrated by numerous models, for instance, stock valuation models. From the stock valuation model, it is very clear that stock value and cash flow are more of the same. Therefore, any economic factor that impacts cash flow and the overall rate of return also has an impact on the share value (Nasseh & Strauss, 2000, p. 230). In addition, many researchers postulate that the volatility of the stock returns rises during the recession and declines during economic prosperity (Schwert, 1989, p. 1115; Nardari & Scruggs, 2005, p.4). Nardari and Scruggs (2005, p. 5) show that many incidents of high uncertainty vis-à-vis prospective returns are linked to the economic meltdown.

Stock returns and nominal interest rates (interest rates)

Numerous authors have reported a negative correlation between the above two variables in their studies. One of the most significant elements in the valuation process is the discount rate and always has to be determined at the initial stages. This rate corresponds to the volatility and time value of the stock. Time value of money corresponds to the risk-free rate. A risk premium corresponds to risk compensation, which is computed using a risk-free rate (Mui & Law, 1983, p.159). According to Stowe et al. (2007, p. 47), most investors regard the discount rate as the suitable rate of return.

Principle interest rate is positively correlated to the risk-free interest. When the interest rate goes up, the risk-free rate also rises. Consequently, this leads to an increase in the overall market rate. Ceteris paribus, the targeted prices of stocks would go down as a result of the increased rate of return. The reverse is also possible. When the interest rates decline, ceteris paribus, the stock prices will go up because of the drop in the required rate of return. In addition, the required rate of return would go up when the risk premium escalates (Mui & Law, 1983, p.160).

Generally, Interest rates and stock returns ought to be inversely correlated. Hondroyiannis and Papapetrou (2001, p. 440) indicate that the anticipated returns on common stocks are scientifically correlated to the general business risk and the interest rate risk. The results of the study show that the interest rate risk among small businesses is “downbeat”. In addition, the study reveals that the interest rate risk premium significantly influences the expected returns among businesses in the U.S and Mexico.

The impact of interest rate on stock prices and returns has also been studied in the developing economies (Al-Sharkas, 2004; Jordan & Tweneboah, 2008). As per Jordan and Tweneboah (2008), the relationship between stock prices and interest rate in budding economies, particularly in Africa is significantly negative. Maysami, Howe and Hamzah (2004, p. 48) disclose that short-term and long-term interest rates have a considerable positive and negative relationship, respectively in Singapore stock market. According to the study conducted by Abugri (2008) in a number of South and North American economies, the relationship of stock returns and interest rate is negative and significant. However, in Mexico, this relationship tends to be insignificant in describing the movement of returns. As to the Hong Kong case, the experiential results of Yu (1996, p. 52) show that interest rate growth impacts stock returns negatively and is significant in the short run. Wongbangpo and Sharma (2002, p. 29) explain that the real interest rate on deposits and interest rate differential variables have a very low negative correlation with stock returns.

Stock price and Money Supply

Both non-liberal and liberal monetary policies can have a two-pronged effect. In a liberal economy, the central government creates more liquidity by encouraging laissez-fair market, resulting in an increase and decrease in stock and bond prices, respectively. The reduced interest rate would force the required rate of return also to go down and therefore increase stock prices (Ozbay, 2009, p.6). In addition, an increase in money supply denotes surplus liquidity accessible to purchase stocks. In due course, this leads to a rise in stock prices because of the increase in demand for common stock and other consumer goods. In contrast, some experts argue that increased liquidity may lead to higher inflation, thus increases the nominal interest rate. The increased interest rate leads to an increased rate of return; this eventually forces stock prices to go down (Ozbay, 2009, p.7).

In a non-liberal monetary policy case, a decrease in money supply leads to a decrease in capital and operational liquidity among businesses and investors. Furthermore, this will increase the general interest rate and thus, the cost of capital. On the other hand, when the interest rate increases consumers becomes the most vulnerable, especially through the high cost of housing and other general goods. Nevertheless, inflation and money supply moves in the same direction and therefore, when they decline, an interest rate will go down. When this happens, stock prices will appreciate (Ozbay, 2009, p.8).

According to Reilly and Brown (2006, p. 362), a general increase in money supply could act as a pointer to changes in stock prices. However, a number of studies have questioned their finding. Beltratia and Morana (2006, P. 152) insist that financial policies that affect the volatilities of monetary supply and interest rates are the best in handling stock market volatilities. In addition, although macroeconomic volatility affects stock volatility, the evidence tends to favour stock returns in the prediction of macroeconomic variables.

Freris (1991) studying the Hong Kong stock market mentions that money supply is a strong risk factor contender. It has a considerable impact on both returns and volatility of the stock. Errunza and Hogan (1998, p. 362) state that stock returns are inversely correlated to the money supply. They point out that money supply volatility does Granger cause volatility in major European economies. Humpe and Macmillan (2007, p.5) state that stock prices among Asian giants (China and Japan) are inversely correlated to money supply; the situation is reversed (though insignificant) in the U.S.

On the other hand, Maghayereh (2002, p. 4) found a negative coefficient of money supply in Amman Stock Exchange, while Al-Sharkas (2004, p. 105) reports a positive effect of money supply (M2) on stock returns. Maysami, Howe and Hamzah (2004, p. 50) established a positive relationship between money supply (M2) and stock returns in the Singapore stock market. Abugiri (2008, p.340) report a negative response between stock returns and the money supply. He also indicates an insignificant relationship in some cases. Nishat and Shaheen (2004, p.622) established a long-term relationship between money supply and stock returns. In addition, his findings show that money supply does Granger cause changes in stock prices.

Hong Kong studies also do not escape contradictions. Ho (1983, p. 222) show that money supply is positively correlated to stock returns in the short-term dynamic model. He reveals that an increase in the money supply has a significant effect on SEHK20 Index. Yu (1996, p. 55) stresses that the relationship between money supply and stock returns does not exist. Furthermore, the findings of Muradoglu, Metin and Argac (2001, p.642) show no cointegration relationship between stock prices and monetary variables in a study they carried out for almost a decade.

Daohua (2000, p.3) state that stock returns have no causal relationship with the money supply. However, Ozturk (2008, p.65) contradicts this finding. Ozturk shows a unidirectional relationship between the two variables. Since different studies have a different opinion on this subject, no consensus has been reached regarding the relationship between the two variables. It depends on the economy and time.

Stock returns and Inflation level

The impact of inflation should be considered during the valuation process. This is because inflation level varies from one economy to the other. Theoretically, stock prices and stock valuation process is not supposed to be influenced by inflation (Fisher, 1930). Patra and Poshakwale (2006, p. 996) indicate that the sum of the anticipated inflation rate and anticipated real return gives the nominal interest rate.

Nominal interest rate (interest rate) is the prevailing interest rate, whereas the real interest rate is the interest rate that is adjusted following inflation. Experts posit that real interest rate can be stable in the long-run. Thus, changes in the interest rates are as a result of anticipated inflation and the dynamics of real interest rates (Ozbay, 2009, p.9). Fisher (1930) noted that nominal interest rate could be broken down into anticipated real rate and anticipated inflation element. He argued that the anticipated real returns is determined by factors that are observable, and is not related to presumed inflation. In other words, the real return on stocks and the anticipated inflation are independent, and that nominal returns on stock differ at an individual level with anticipated inflation.

Studies have not established a reliable link between inflation rates and nominal stock returns. In addition, the results of his study indicate that the regression coefficient between the two is largely negative. The inverse correlation between inflation and stock prices has been supported by many experts. This is because inflationary changes are normally accompanied by lower growth earnings and increased required real returns. In China, there is tangible experimental evidence that inflation increase is linked to equity risk premium and a decrease in stock prices (Zhao, 1999, p. 507). Increased inflation may force the government to employ precautionary measures which may increase nominal interest rates, thus increases the required rate of return.

In addition, inflation may falsify returns when historical data is used, especially when adjustments are not made to reflect the level of inflation in each period. Reported earnings that are pegged on depreciation derived from chronological costs (used as replacement costs) can give exaggerated returns. In the same way, a number of inventory management systems can also misrepresent the said values. Therefore, a firm operating in an environment with high inflation will greatly suffer if it does not factor in inflation (Solnik & McLeavy, 2009, p. 243).

Yu (1996, p. 50) explains that nominal stock returns and inflation in Tokyo, Hong Kong and Singapore are considerably linked to a negative trend, meaning that stocks are exceedingly lacking inflationary cover for the investors. Moreover, Yu (1996, p. 51) only indicates a unidirectional causal relationship between stock returns and interest. Flannery and Protopapadakis (2001, p. 12) point out that the consumer price index and producer price index also have a significant impact on stock values and returns. They report that stock prices are inversely correlated to the consumer price index (CPI) in American and a number of Asian stock markets. Similarly, in Canada and Mexico, inflation is the principal negative determinant of stock values/ prices (Humpe & Macmillan, 2007, p. 4). According to Humpe and Macmillan (2007, p. 4), inflation does Granger cause stock price movements in the American market. In addition, Maghayereh (2002) reports a constant relationship between stock returns and inflation.

However, Maysami, Howe and Hamzah (2004, p. 50) report a significant positive correlation between the consumer price index and stock returns. This is a complete contrast to the earlier studies that had established a significant negative correlation between anticipated inflation and stock returns. As for the case of Hong Kong, Tokyo and Singapore, Yu (1996, p. 52) argue that negative correlation between stock prices and inflation remains steady when other monetary variables are incorporated in the model. Ozturk (2008, p.3) report a negative causal relationship between stock returns and inflation.

Ho-Ki, Ho-Yin and Wu-Wing (2011, p.12) report a negative long-term relationship between inflation and stock prices. Their study concludes that the variables that represent real economic activity, for instance, industrial production index (IPI), level of employment and fixed investment significantly influence stock prices through inflation. Yu (1996, p. 53) point out that unexpected inflation has a positive impact on the returns of the developed portfolios. He adds that the relationship between inflation and stock returns is unstable in the long run and that there are disparities among nations in spite of the state of their level of development.

Stock Returns and Exchange Rate Relations

Researchers have never agreed whether exchange rates and stock returns have any form of relationship, particularly causal relationship. However, in many studies, two models have been fronted to explain the relationship between stock prices and exchange rate. These are stock-oriented model and Flow-oriented model. Goods market approach (Flow-oriented model) emphasizes on the relationship between exchange rate and current account (Dornbusch & Fischer, 1980, p. 962). Dornbusch and Fischer (1980, p. 963) came up with a model that determines exchanges rates by applying general price index, security indices and economic prospects. The model focuses on the causal relationship between foreign transactions and exchange rate fluctuations.

They argue that there is a connection between the exchange rate movement and the current account (Dornbusch & Fischer, 1980, p. 964). The flow-oriented model supposes that the exchange rate is considerably influenced by external/foreign transaction. Both stock oriented model and Flow-oriented model conceive that exchange rate movements impact global competitiveness and balance of trade, hence affect real economic variables like GDP and national income. In other words, the flow-oriented model argues that exchange rate impacts the level of business competition and the cost of borrowing fund. This ultimately influences stock values (Dornbusch & Fischer, 1980, p. 965).

On an aggregate level, the impact of changes in the exchange rate on stock returns would depend on the nature of the market and foreign transactions. Therefore, the goods market model posits a positive relationship between the two variables. The inference of positive correlation between exchange rate and stock prices originates from the assumption of employing quotation to direct exchange rate (Ho-Ki, Ho-Yin & Wu-Wing, 2011, p.16).

In contrast, the stock-oriented model put more emphasis on the role of capital account on business activities (Tahir & Ghani, 2004, p. 5). The stock-oriented model postulates a negative correlation between exchange rate and stock price. An increase in local stock price would encourage capital flows, which in turn create more demand for local currency and increase the exchange rate. A thriving stock market enhances the local currency through direct and indirect means.

An increase in stock price encourages both local and foreign investors to purchase local assets at the same time, disposing of overseas assets to acquire local currency (Stavarek, 2004, p. 3). Such shifts in demand and supply positively impact currency value. The indirect channel also depends on supply and demand shifts. An increase in the prices of stocks leads to increases in production (aggregate output). Increased wealth raises the demand for local currency, and consequently, the interest rate goes up to regulate borrowings. Increased interest rates attract external capital and lead to high foreign demand for local currency and, consequently, appreciation of the local currency (Stavarek, 2004, p. 4).

In fact, the exchange rate fluctuations equally affect exports and imports. When the local currency depreciates, imports become more expensive. If the extra cost of imports is passed to the consumers, then the earnings will not be affected by the currency fluctuation. However, this is not always the case. Increase in the prices of foreign goods will reduce their demand in the local market. In addition, demand for domestic products will increase more than the imports, thereby creating a substitution effect. Consequently, the number of companies that depend on imports will go down, while those exporting their products to foreign markets will increase (Solnik & McLeavey, 2009, p. 245).

Stavarek (2004, p. 5) states that the intensity and direction of a causal relationship between foreign exchange and stock market vary from country to country, regardless of their economic status. Shuangcheng and Guihua (2005, p. 23) postulate that there is a two-directional causality between the exchange rate and Chinese stock markets both in the short-term and long-term.

As for the Hong Kong case, the experimental findings of Yu (1996, p. 54) point out an expected increase in stock returns with the increase in exchange rates. The results of Bodnar and Wong (2000) report that companies that export their products abroad are highly susceptible to exchange rate. The findings of Ma and Kao (1990, p. 445) show that a stable long-term relationship between exchange rates and stock indices is achievable. Additionally, they report inconclusive evidence on the causality relationship between exchange rates and stock indices. Lee (2012, p.120) emphasizes that the relationship between stock returns and exchange rates is not certain, pointing out that the Hong Kong stock exchange is neither the result nor cause variable of exchange rate variables.

Real economy (industrial output) and Stock returns

The study uses proxies to represent the real economy. Studies have shown that industrial production generally affects the overall state of the economy and therefore, changes in industrial production would indicate economic transformations. Indeed, the productive capacity of any economy depends on the position and nature of its economy. The state of the economy influences the productive capacity of local firms. Thus, industrial production has considerable impact on aggregate wealth, thus a positive correlation between stock prices and industrial output (Fama, 1981, 547; Foresti, 2007).

Phillips and Xiao (1998, p.430) indicate that the large percentage of variations in stock returns (SEHK) can be described by periodic expected returns and prediction of real activity. They add that fluctuation in the stock market (SEHK) volatility over time is basically because of the fluctuations in the volatility regarding future returns. Errunza and Hogan (1998, p. 361) indicate that industrial production does Granger cause stock return in Europe.

Yu (1996) indicate that trade balance, employment level, and real estate are the strong risk factor candidates, and they only affect the stock return’s conditional volatility for SEHK (Hong Kong Stock Market). Freris (1991, p. 96) indicate that SEHK stocks are positively correlated with industrial production.

According to Nishat and Shaheen (2004, 628), there is a two-directional Granger cause between industrial production and stock returns. Therefore, industrial production significantly predicts stock prices in many. Many other authors also indicate that industrial production is positively and significantly correlated to stock returns (Maghayereh, 2002, p. 14; Al-Sharkas, 2004, p.107; Nishat, & Shaheen, 2004, p. 670). Abugri, 2008, p. 409 also indicates a positive correlation between stock returns and industrial production among the South American economies, except for Argentina and Mexico where industrial production does not seem to have a strong impact on the expected returns.

As for the case of Hong Kong, Ho-Ki, Ho-Yin and Wu-Wing (2011, p.27) indicate a positive correlation between stock returns and industrial production and a negative relationship between stock returns and balance of trade. In addition, the results show that stocks in Hong Kong stock exchange are neither the cause nor result of economic variables. The findings of Phillips and Xiao (1998, p. 455) is proof that there is a linear correlation between industrial production and stock returns.

Hong Kong Stock Exchange

Hong Kong Stock Exchange (SEHK) was established in 1891 and is based in Hong Kong, China. The market was operated by an association of stockbrokers during that time. Later on, in 1914, it changed its name to Hong Kong stock exchange which was a conglomerate of numerous stock exchanges that had come together either through merger or acquisition. The real merger took place after the Second World War when Hong Kong Stock Brokers Association joined the bandwagon. However, it retained the name Hong Kong stock exchange (SEHK, 2011).

Later on, in the late 70s, Hong Kong Stockholders association was founded, and it comprises of a number of entities such as Far East Exchange Limited, Kam Ngan Stock Exchange Limited, and Kowloon Stock Exchange Limited. In the late 80s, this association later merged with other exchanges but retained its name. In the early 2000s, Hong Kong Exchanges and Clearing, which is a holding company, was formed to oversee the operations of the Hong Kong Stock Exchange. Since the year 200, the Hong Kong stock exchange has been fairly liberalized (SEHK, 2011).

It is the 3rd largest stock exchange in China, and the 6th largest in the globe in terms of market capitalization (Ho-Ki, Ho-Yin & Wu-Wing, 2011, p.2). Hong Kong stock exchange is focused too much on stock trading, and very little attention is given to bonds and other securities. This is attributed to its historical background. In the early 60s, Hong Kong city was dominated by miniature financial markets and only accommodated small local companies. Therefore, the absence of large foreign companies was an obstacle to the development of a high-level security market. In addition, the Hong Kong government is self-sufficient and always operate within its means. As a result, the government does not rely on security floatation to finance the budgetary deficit (SEHK, 2011).

Hong Kong stock market is the oldest stock market in Asia and dates back to over 100 years. The stock market struggled a lot in the early 20th century, but since the late 60s, it has been developing at a faster rate. The market was established by the British colonialists, and since then, its operations have been carried out in a similar way to most European stock market (SEHK, 2011). The market is dominated by Chinese-owned companies and investors. However, with the expansion of the market to an international level, foreign companies and investors are also in large number.

Most of the practitioners and professional operating within this stock market are graduates from the U.S. Hong Kong stock exchange also incorporated global exchange rate system. This is an electronic system that was launched in the early 2000s. The rules and decision-making practices within the market are borrowed from the United States. Since the year 2003, the Hong Kong stock exchange has been under David Webb and numerous investor representatives (SEHK, 2011).

As per the end of November last year, the Hong Kong Stock Market had listed about 1500 companies with an aggregate market capitalization of HK$ 17 trillion. It operates under Hong Kong and Clearing, which is a holding company (SEHK, 2011; Lee, 2012, 120). Security exchange in Hong Kong city began in the late 19th century through informal security exchange. After a succession of mergers and acquisition, SEHK became the main trading joint for securities (Ho, 1983, p. 222). Currently, Hong Kong Stock Exchange ranks among the top stock exchanges in the globe and was ranked the first in terms of IPO funds in 2008, when it raised in excess of HK$248 billion from the new companies (Martig, 2009, p. 1). The average value of stock traded in Hong Kong stock exchange has always been on the rise, except in 2007/2008, where it dipped by about 7 per cent. Similarly, the average number of daily contracts also fell in that year. The total value of traded equity also fell by 10 per cent. The SEHK indices were being manipulated by the local economic conditions and the global economic crisis experienced during that time (Ho-Ki, Ho-Yin & Wu-Wing, 2011, p.3).

SEHK market indices are intended to compute stock prices and returns traded generally and on a sector-by-sector basis. The SEHK-20 Index is used as a yardstick in the Hong Kong stock exchange. Many studies for Hong Kong market have used SEHK-20 as a substitute for stock prices. However, in this study, we use SEHK-17 index. The SEHK-17 Index comprises of 17 stock chooses from the top companies, other than investment trusts, traded in Hong Kong stock exchange in accordance with the standard set by the SEHK (SEHK, 2011).

In 2011, the market value for SEHK-17 was approximately HK$10.5 trillion. This is approximately 60 per cent of the total market value of the Hong Kong stock market. Since the structure of the SEHK-17 is different from that of SEHK-20, the SEHK-17 price movement may possibly be poles apart. Compared to SEHK-20, the SEHK-17 could be less prone to speculations and manipulations since it comprises of the top companies in SEHK. This is the reason why the study opts for SEHK as a better alternative for stock returns. Furthermore, this helps us to compare the present results with the earlier studies using SEHK-20 (SEHK, 2011).

Research Methodology

Introduction

The methodology is the process of instructing ways of doing the research. It is, therefore, convenient for conducting the research and for analyzing the research questions. The process of methodology insists that much care should be given to the type and nature of procedures to be adhered to in accomplishing a given set of procedures or objectives. This chapter is pertinent to the Hong Kong Stock Exchange. To investigate the relationship between macroeconomic variables and the SEHK-17 index, the study emphasizes on causality among these factors applying Granger causality test. The causality test was developed to assess the causal relationship, among other statistical variables (Granger, 1969).

The following hypothesis has to be tried to meet the objectives of the study; establishing the causal relationship between the macroeconomic variables and stock return.

Null Hypothesis: The selected economic variables do not Granger cause stock returns.

Alternative hypothesis: The selected economic variables do Granger cause stock returns.

If we reject the null hypothesis, then we will conclude that the selected macro-economic variables do Granger cause stock returns in Hong Kong stock market.

Preliminary Analysis

Numerous economic series are used in the study to achieve the study objectives. These are IPI (Industrial Production Index), CDGDP (Currency Deficit to GDP), foreign Purchase (FP), foreign sale (FS), price indices, nominal interest rate, currency basket, money supply (MI, M2, M2Y, Central Bank Money), and Hong Kong Stock exchange-17 index to represent the stock market. FP, FS and price indices proxy goods market, while interest rate proxy money market.

Hong Kong and trade system have been fairly liberalized in the past three decades. Since the onset of the millennium, the exchange rate in China has always been determined through floatation. This means that the exchange rate regime fluctuates from time to time. This is one of the weaknesses of the study. The study uses a currency basket as an alternative to the exchange rate. The currency basket in our study comprises of a weighted average of the most common currencies in Hong Kong; that is the U.S. dollar, Hong Kong Dollar and Euro. 1 U.S. dollar and 0.125HKD are used over the period between January 1991 to December 2001; 1 U.S. Dollar, 0.125 HKD and 0.77 Euro over the period January 2002 to December 2011.

Weighted normal interest rate is used in place of the overnight interest rate. Furthermore, yearly interest rates from the central bank database are used instead of the treasury interest rate. Most of the relevant data is acquired from the Central bank of China’s database. Foreign purchase, foreign sale, and daily closing SEHK17 index are obtained from the Hong Kong stock exchange database. Stock prices are derived from the Hong Kong stock exchange monthly data.

Monthly data is used to determine the level at which SEHK-17 reflects the real economy in Hong Kong. All the data, except CD/GDP, have been transformed into the logarithm form. The study covers the period between 1991 to 2011 financial years. The tables below give summary statistics on levels and first- differences respectively. The mean nominal return on the stock is 1.379. In addition, China has normally experienced a relatively stable inflation rate, just like most European countries. However, the inflation rate in China was relatively higher than those of advanced economies. Nonetheless, this rate has fallen to below 1.8 per cent lately.

Table 1: Level specification.

Series DSEHK17 DCB DCBM CDGDP DCPI LFP DFS DIPI DM1 DM2 DM2Y DOIR DPPI DTIR
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
Jarque-Bera
0.018 0.019 -0.016 0.021 0.031 0.025 0.020 0.022 0.026 0.029 0.030 -0.098 0.020 – 0.009
0.023 0.015 0.019 0.001 0.020 -0.009 0.015 0.001 0.038 0.030 0.086 -0.002 0.020 -0.020
0.486 0.272 1.135 0.200 0.080 1.200 1.156 0.225 0.140 0.135 0.150 2.340 0.010 0.501
-0.500 -0.059 -4.452 -0.150 -0.065 -1.200 -1.245 -0.264 -0.010 -0.014 -0.030 -1.672 -0.040 -0.463
0.150 0.040 0.440 0.043 0.020 0.390 0.400 0.080 0.050 0.030 0.020 0.302 0.029 0.142
0.050 1.600 -8.00 0.330 0.910 0.286 0.040 0.141 -0.050 1.212 0.800 1.301 0.415 0.950
4.893 10.540 79.125 5.991 3.302 3.200 3.420 4.102 3.312 5.234 5.237 33.524 3.966 10.128
29.75 300.34 30,500.01 84.80 19.20 2.60 1.23 5.97 0.32 49.20 44.14 4,984.17 7.92 290.40

Table 2: First difference.

Series SEH17 CB CBM CD/GDP CPI FP FS IPI M1 M2 M2Y OIR PPI HKIR
Mean
Median
Maximum
Std. Dev.
Skewness
Kurtosis
Jarque-Bera
8.99 0.56 2.34 (0.03) 8.72 6.31 6.99 4.25 2.56 4.01 4.50 3.28 7.01 3.79
10.01 0.98 3.03 (0.04) 10.03 6.25 7.36 4.43 2.56 4.22 4.89 3.37 8.02 3.46
10.56 1.06 4.00 0.19 9.44 9.37 9.29 5.55 4.18 5.53 6.34 6.27 9.36 5.30
0.86 0.62 1.43 0.05 0.79 1.39 1.38 0.17 1.09 1.08 1.00 0.69 0.80 0.78
(0.50) (1.20) (1.12) (0.67) (0.90) (0.49) (0.30) 0.08 (0.45) (0.60) (0.81) 0.56 (0.91) 0.36
2.34 2.76 6.65 2.23 2.16 2.05 1.75 2.12 2.27 2.50 2.87 2.30 1.72 1.86
6.65 28.32 27.65 80.02 19.02 6.00 7.02 8.08 10.90 10.01 12.92 6.24 19.20 100.50

Unit Root Test

According to statistical literature, time-series data is normally understood to be non-stationery. Differenced series of prices/returns are traditionally used in the studies of stock returns. The possibility of data to be non-stationary can be because of overcrowded stationery data and reversible first difference. Correspondingly, numerous financial variables are well-known to exhibit these characteristics and therefore, it is necessary to carry out a univariate assessment to check whether the variables exhibit stationery or non-stationer characteristics. This is done to avoid the spurious regression problem prior to relationship analysis.

Key assumptions have to be made in time series analysis so as to conduct a legitimate statistical inference. The model must be believed to be covariance stationary. A stationary time series is a series with non-fluctuating mean and variance. The following conditions must be met: First, its expected value must remain invariable and fixed persistently. Second, its variance is required to remain invariable and fixed after a while. Lastly, its covariance must always remain invariable and fixed persistently. None-stationery time series lacks economic meaning since it yields spurious results.

Nonetheless, it is not advisable to change data into a stationary time series if it is non-stationary. Statistically, before carrying out any analysis, we must determine whether it is stationary or not. The conventional method of determining whether data is stationary or non-stationary is the Dickey-Fuller unit root test (DeFusco et al., 2007; p. 4005). Therefore, the study employs Augmented Dickey-Fuller test to test the existence of unit root in the used variables (level and log variables). This tests the following hypothesis:

  • Ho: The economic variables understudy has a unit root.
  • H1: The macro-economic variables understudy doesn’t have a unit root.

It tests the null hypothesis against the alternative. It is first conducted on variables in levels. The results of the test are shown in the table below.

Table 3: Augmented Dickey-Fuller Test Results.

A=0,

Series Statistic Prob t= -3.19

Ho(0)

A=0,

Series Statistic Prob t= -3.19

Ho(0)

SEHK17 -1.782823 0.4983 Not Rejection
FP -1.636363 0.393 8 Not Rejection
FS -1.836353 0.2366 Not Rejection
OIR -4.263883 0.3880 Not Rejection
HKIR -2.998787 0.1214 Not Rejection
CB 0.487873 0.8144 Not Rejection
CBM 0.537233 0.9358 Not Rejection
MI -1.243324 0.7469 Not Rejection
M2 – 3.222332 0.0571 Not Rejection
M2Y -2.635355 0.2272 Not Rejection
CPI -1.784325 0.5600 Not Rejection
PPI -0.724232 0.88972 Not Rejection
IPI -1.798343 0.54267 Not Rejection
CD,GDP 5.021287 0.0010 Not Rejection
SEHK17 -10.782323 0.0000 Rejection
FP -10.563763 0.0000 Rejection
FS -15.342625 0.0000 Rejection
OIR -6.9635783 0.0000 Rejection
HKIR 7.3633444 0.0000 Rejection
CB 8.3647474 0.0000 Rejection
CBM -3.2456341 0.0000 Rejection
MI -10.233323 0.0000 Rejection
M2 -5.735467 0.0000 Rejection
M2Y -7.000213 0.0000 Rejection
CPI -7.215653 0.0000 Rejection
PPI -6.802873 0.0000 Rejection
IPI -4.034126 0.4124 Rejection
CD,GDP -20.274648 0.0000 Rejection

The findings show unit root in log variables except for CD/GDP and OIR. Hence, the null hypothesis can not be rejected except for the overnight interest rate and currency deficit. We can model time series data with a unit root. This can be achieved through first difference autoregression (DeFusco et al., 2007; p. 4006).

Time series is differenced by generating a new series, for instance, Yt in which each period is equivalent to the difference between Xt and Xt-i. This kind of modelling is known as the first differencing since it takes away the value of the time series in the initial period from the present values. The results of the above test for the first difference are presented in section two of the table above. The last column in section two exhibit the same statistics when the test is carried out again on the first differences of the macroeconomic variables that had a unit root in the first level.

There is no known study that has opposed the unit root hypothesis, and, on numerous occasions, the first difference does not indicate unit root characteristics. In other words, there is no need to conduct a unit root test in the first difference data. Since the Augmented Dickey-Fuller test statistics for all the factors is lower than 5percent critical value, we do not reject the null hypothesis. That is, the first difference variables are stationary series.

Correlation Analysis

In finance study, it is always important to establish a relationship between two or more variables. There are numerous methods for examining the relationship between a given set of data. Scatter plots and correlation analysis are well known for this work. Scatter plots are two-dimensional graphs that are used to establish a relationship between two variables. On the other hand, correlation analysis depicts this relationship using a single digit. In summary, the correlation analysis assesses the direction and relationship between variables. The correlation coefficient is scaled between the positive one and the negative one. A correlation coefficient that is greater than zero depicts a positive linear relationship, and a coefficient that is less than zero depicts a negative linear relationship between two variables. A coefficient of zero depicts a non-linear relationship. The correlation coefficient of the two variables is arrived at by the covariance of the two variables by its standard deviation.

Despite its flaws, correlation analysis has been widely used to compute the linear relationship between two or more variables (DeFusco et al., 2007; p. 4005). This is an additional limitation to this study. The table below shows a correlation matrix of the chosen macroeconomic variables and the SEHK-17. In the right-hand side are the correlation test levels in log form.

Table 4: Correlation between SEHK-17 Index and Macroeconomic series.

Series Correlation t Ho
Coefficient Statistic Hypothesis
SEHK17
Series Correlation t Ho
Coefficient Statistic Hypothesis
DSEHK17
CB 0.82 15.20 Reject
CBM 0.86 17.33 Reject
CPI 0.90 17.33 Reject
FP 0.91 24.34 Reject
FS 0.99 49.20 Reject
IPI 0.98 46.69 Reject
MI 0.79 16.20 Reject
M2 0.94 26.39 Reject
M2Y -0.98 -18.70 Reject
OIR 0.90 23.20 Reject
PPI -0.95 -30.20 Reject
TIR -0.50 -6.06 Reject
CD/GDP -0.45 -6.00 Reject
DCB – 0.14 -1.39 Not Reject
DCBM 0.11 1.47 Not Reject
DCPI 0.39 0.44 Not Reject
DFP 0.54 7.69 Reject
DFS 0.39 5.68 Reject
DIPI -0.09 -1.21 Not Reject
DMI 0.09 0.12 Not Reject
DM2 0.07 0.91 Not Reject
DM2Y -0.19 -0.25 Not Reject
DOIR -0.20 -2.21 Reject
DPPI 0.08 1.01 Not Reject
DTIR -0.40 -5.02 Reject
DCDGDP 0.07 -0.70 Not Reject

The study established a strong correlation between stock returns and the chosen macroeconomic variables. However, the series computed in this study generally increases or reduces in long-ran regardless of the fact that they may seasonally change. If such a scenario arises, there will be a risk of getting significant correlation results from data that are not related. Correlation of this nature is referred to as spurious correlation. Spurious correlation can be avoided by using the first differenced log data.

Stock returns versus money supply

According to the study results, there is a low positive correlation between DSEHK17 and respectively DCBM, DM1, DM2 and low negative correlation between DSEHK17 and DMY2 used to represent money supply. Nonetheless, the study reports an insignificant correlation between the money supply and stock returns. In other words, no relationship exists between stock prices and monetary supply in Hong Kong stock market. This result is consistent with Yu (1996, p. 55), Ozturk (2008, p.65), and Maysami, Howe and Hamzah (2004, p. 50) who established a positive but insignificant relationship between stock prices and money supply. In addition, Abugiri (2008, p.340) established an insignificantly negative correlation between stock returns and money supply in South America.

Theoretically, it is argued that excess liquidity can either reduce the interest rate, which in turn reduces the cost of doing business or can increase the rate of inflation in the economy. The liquidity fluctuation ultimately affects the common stock. Nonetheless, the concrete relationship between money supply and stock returns is an experimental question, and the impact differs from time to time.

Inflation versus stock returns

In contrast with the findings from well-established economies, the study shows a low, insignificant positive correlation between stock returns and inflation rates, consistent with Fisher (1930). The correlation coefficients between DSEHK17 and DCPI or DPPI used to represent inflation are 0.398 and 0.087. These findings show that there is no correlation between stock inflation and stock returns in Hong Kong. This is consistent with Zhao (1999, p. 508).

In theory, the relationship between stock returns and inflation rate impacts the ability of businesses to protect themselves against the effects of; an economically sound market should have a high positive relationship with inflation. Yu (1996, p. 50) show that stock indices in Tokyo, Hong Kong and Singapore have a negative correlation, which means that they are not good inflation hedges. Maghayereh (2002, p. 7), Humpe and Macmillan (2007, p. 4) and McLeavy (2009, p. 244) representing both developing and developed economies report a negative correlation between inflation and stock returns. However, Maysami, Howe and Hamzah (2004, p. 50) report a positive correlation between stock returns and inflation.

The actual link between inflation and stock returns is still also unclear and depends on the economy to economy and time. Therefore, the general notion that stock returns and inflation are negatively correlated can not be said to the ultimate. Furthermore, even this is a fact in many cases; some sectors in the economy have a number of macroeconomic variables that responds very positively to inflation. In such a case, the stock returns will also be affected positively by inflation (Mui & Law, 1983, p.162).

Real economy versus Stock returns

IPI and stock returns have a correlation of 0.0879, whereas stock returns and the CD/ GDP have a correlation of 0.0687. This means that their relationship is positively and negatively low, respectively. However, the study has established no relationship between stock returns and real economy proxies. This finding is consistent with Yu (1996). On the contrary, (Fama, 1981, 547), Phillips and Xiao (1998, p.425), and Freris (1991, p. 96) among others report a positive correlation between industrial production and stock returns. It is only Abugri who reports an insignificant relationship between the two variables in two South American markets.

Theoretically, increased industrial production means more cash flow and returns on the stock. This is in line with the theories that posit that stocks represent an investment for the future; for this reason, economic growth should translate into increases stock returns. This is because the increase in industrial production generally leads to higher returns in most companies.

Exchange rate movement versus Stock returns

The study reveals that the relationship between rate proxies and stock return does not exist in the Hong Kong stock exchange; it is insignificant. This is consistent with studies of Abugri (2008, p. 403), and Adam and Tweneboah (2008, p. 8). Shuangcheng and Guihua (2005, p. 23), and Maysami, Howe and Hamzah (2004, p. 51) indicate a positive relationship between stock prices and exchange rate in Singapore and Hong Kong. Therefore, we can also make a conclusion that there is no enough proof to claim a positive or negative relationship between the two macroeconomic variables (stock returns and exchange rate). In other words, there are numerous pieces of evidence supporting both the stock-oriented model and the flow-oriented model.

Interest rate versus Stock returns

The study shows a negative (but significant) relationship between DSEHK17 and DOIR and DTIR. This means that there is a significantly negative relationship between stock and interest rate in Hong Kong. This is consistent with Tahir and Ghani (2004, p. 5) and Adam and Tweneboah (2008, p. 8). The negative relationship is as a result of the effect of the discount rate. In other words, interest rate fluctuation affects the company’s value and shares through the fluctuations in the required rate of return. It also affects the cost of doing business.

Granger Causality Test

Throughout the study, we have argued that that the selected macroeconomic variables considerably impact stock prices. Thus, it is imperative for investors to consider them as risk factors before making any investment decision. Therefore, the impact of macroeconomic variables on stock prices has been studied all over the globe. Despite numerous studies, researchers have not come to a common ground on the direction of causality between the variables. Hence, it is significant to experimentally test whether these macroeconomic factors have an impact on the Hong Kong stock exchange.

The study adopts the causality test to investigate the relationship between macroeconomic variables and returns and vice versa. This is applied to the first differenced logarithm data. However, this test has a number of limitations. The first limitation is the problem of autocorrelation. This arises when two variables are found to be correlated with another variable, but in reality, only one variable causes the third. This may occur when the first two variables are highly correlated. In addition, this Granger causality is very susceptible to lag length.

The above weaknesses can be avoided by carrying out Vector Autoregression estimates on the selected macro-economic variable. Akaike information criterion is then used to choose the most suitable lag length for each variable used in the case study. The lag length selected can then be applied to the individual macroeconomic variables. The study would test the following hypothesis in this part.

HO: The macroeconomic factors do not Granger cause the SEHK17 index

H1: The macroeconomic factors do Granger cause the SEHK17 index

When F-table less than F computed then the null hypothesis is not rejected. We can then proceed to test causality between macroeconomic variables and stock returns (SEHK17).

Causality test between stock prices and money supply

This involves SEHK17 versus MI, M2, M2Y, and CB, as shown in the table below.

Lag HoF-statistic Probability a =0.05
3 DCBM does not Granger cause DSEHK17 1.5998 0.1663 Not Reject
3 DM1 does not Granger cause DSEHK17 0.0278 0.8898 Not Reject
3 DM2 does not Granger cause DSEHK17 0.8096 0.4234 Not Reject
3 DM2Y does not Granger cause DSEHK17 2.1899 0.0989 Not Reject
3 DSEHK17 does not Granger cause DCBM 5.8060 0.0048 Reject
3 DSEHK17 does not Granger cause DMI 2.7969 0.0378 Reject
3 DSEHK17 does not Granger cause DM2 0.8605 0.3759 Not Reject
2 DSEHK17 does not Granger cause DM2Y 4.8956 0.0042 Reject

The results of the study indicate that monetary variables do not Granger cause stock prices. This means that expansion of monetary variables does not result in growth in stock investment. Therefore, they do not predict share values and returns in SEHK. This is consistent with Errunza and Hogan (1998, p. 362). Nonetheless, the study can conclude in the case of Hong Kong that, with the exception of M2, there is a causal relationship between stock returns and monetary supply in that order. In other words, we can conclude that SEHK17 is a significant indicator of the money supply.

Causality test between stock prices and inflation

The causal relationship between stock returns and inflation is highlighted in the table below.

Lag HoF-statistic Probability a =0.05
1 DCPI does not Granger cause DSEHK17 3.9689 0.0379 Reject
3 DPPI does not Granger cause DSEHK17 1.5975 0.1982 Not Reject
3 DSEHK17 does not Granger cause DCPI 0.2938 0.4769 Not Reject
3 DSEHK17 does not Granger cause DPPI 2.9359 0.0054 Reject

The results of the study tend to be paradoxical. It shows inflation (represented by CPI) does Granger cause stock returns. This is in line with Nishat and Shaheen (2004) who indicated a single directionality between the two variables. Thus, the CPI index can be regarded as a principle signal in predicting stock returns. On the contrary, the study findings exhibit a one-directional causality between stock returns and production price index in that. In this case, stock returns can be used as a signal when predicting inflation. However, Ho-Ki, Ho-Yin and Wu-Wing (2011, p.28) pointed out that there is no causal relationship between inflation and stock returns. Therefore, our results may be affected by the inflation proxies used.

Causality test between stock returns and the real economy

Lag HoF-statistic Probability a =0.05
12 DIPI does not Granger cause DSEHK17 0.6892 0.5896 Not Reject
1 CD/GDP does not Granger cause DSEHK17 12.5673 0.0032 Reject
8 DFP does not Granger cause DSEHK17 1.2398 0.3241 Not Reject
8 DFS does not Granger cause DSEHK17 2.2784 0.01746 Reject
3 DSEHK17 does not Granger cause DLIPI 1.5633 0.2234 Not Reject
3 DSEHK17 does not Granger cause CD/GDP 1.3566 0.2754 Not Reject
3 DSEHK17 does not Granger cause DFP 3.8705 0.0043 Reject
2 DSEHK17 does not Granger cause DFS 8.8354 0.0002 Reject

The findings of the study clearly show that there is no causal relationship between industrial output and stock returns, and therefore none of these variables can be used to predict each other. This is consistent with Errunza and Hogan (1998, p. 362). In contrast, IPI is effective on stock returns through inflation, meaning that it can be significant in predicting stock prices. This is also consistent with Nishat and Shaheen (2004). There is also a single-directional causality moving from in the direction of CD/GDP to stock prices. This implies an increase in current deficit leads to deterioration of the economy, thus fall in stock prices. Lastly, the study indicates a causal relationship between stock returns and foreign transactions. The causality with FS and FP are unidirectional and bidirectional, respectively. This implies that stock market growth encourages foreign investment and vice versa.

Causality test between stock returns and exchange rate

Lag HoF-statistic Probability a =0.05
1 DCB does not Granger cause DSEHK17 1.0943 0.4322 Not Reject
3 DSEHK17 does not Granger cause DCB 9.5585 0.0021 Reject

The results indicate a one-directional causal relationship between the two variables. Stock return do Granger cause exchange rate. This is consistent with a number of works of literature such as Ho-Ki, Ho-Yin and Wu-Wing (2011, p.32) and Tabak (2006), at the same times contradicts other findings like Humpe and Macmillan (2007, p.5) and Foresti (2006). Due to the above contradictions, the causal relationship between the two variables is still not clear up to now.

Causality test between interest rate movements and stock returns

Lag HoF-statistic Probability a =0.05
3 DOIR does not Granger cause DSEHK17 2.9773 0.0031 Reject
3 DHKIR does not Granger cause DSEHK17 1.6071 0.3234 Not Reject
3 DSEHK17 does not Granger cause DOIR 5.8466 0.0041 Reject
3 DSEHK17 does not Granger cause DHKIR 9.1382 0.0026 Reject

The study indicates two-directional causality between OIR and stock returns. This is consistent with Ho-Ki, Ho-Yin and Wu-Wing (2011, p.37). Similarly, Ozturk (2008) indicates that the interest rate has a considerable effect in predicting stock market prices. In theory, interest rate increase generally has a negative impact on asset values. Similarly, the study shows a causative effect running from OIR to stock prices.

Conclusion

Abundant researches have carried out to determine the causal relationship between economic factors and stock returns globally. This is crucial because a number of these factors are used to predict the inherent value of securities. Theoretically, macroeconomic factors are believed to be the main cause of stock market volatility. As a result, they are regarded to be significant in predicting stock prices. In our case study, the selected macroeconomic variables are examined over the period between 1991-2011. According to a number of works of literature, the causal relationship between various macroeconomic variables and stock returns is well defined, particularly in advanced economies. This is massively opposed by other researchers, and this is confirmed by the study results. Some works of literature reveal that in most cases, the overall level of stock prices tend to be high during economic prosperity compared to the recession. The study points out that characteristically, change in stock prices takes place just before any changes in business activities occurs.

According to the findings of the study, the relationship between the above macroeconomic variables and stock returns, with the exception of foreign investor transaction, is statistically insignificant. Stock returns have a non-constructive and a constructive causal relationship with nominal interest rates and FS with respect to the.

With regard to casual relationships, the study indicates a two-way causal relationship between stock returns and interest rate. This means that with other factors held constant, the two variables can be used to predict each. The findings of the study also indicate a one-sided affair between stock returns and nominal interest rates in that order. The study indicates that inflation and stock returns have a causal relationship. This is against the economic principle of stock neutrality with respect to inflation. On of the reason given is that inflation may falsify returns when historical data is used, especially when adjustments are not made to reflect the level of inflation in each period. When inflation shoots up interest rates in general also increases.

Tabak (2006, p.15) analysis of South American stock prices, show that long-term relationship between the two variables does not exist. However, there is a slight causality between them. In addition, his study shows a non-linear causality from exchange rate to stock prices. Horobet and Llie (2007) contradict Tabak’s findings. They record no relationship between exchange rates and stock returns in Romania. However, the use of the Johansen-Juselius method indicates a relationship between the two variables in Romania and Brazil. However, some researchers have pointed out that there is no causal relationship between inflation and stock returns. Therefore, our results may be affected by the inflation proxies used in the study.

With regard to the money supply, the study shows that monetary variables have no effect on stock values and returns. However, according to the findings, it can be construed that money supply impacts stock return through consumer price index and overnight interest rates. Thus, the money supply is a weak indicator of stock returns in the Hong Kong stock market. The study results show unidirectional causal relation from GDP to stock returns. The findings of the study also indicate that monetary variables do not Granger cause stock prices. This means that expansion of monetary variables does not result in growth in stock investment. Therefore, they do not predict share values and returns in the Hong Kong Stock Exchange. Nonetheless, the study can conclude in the case of Hong Kong that, with the exception of M2, there is a causal relationship between stock returns and monetary supply in that order. In other words, we can conclude that the Hong Kong Stock Exchange index is a significant indicator of monetary supply.

Furthermore, the analysis shows that industrial production is neither the result nor the cause variable of stock prices. Thus both can not be used as indicators to predict each other. According to the findings to some researchers, stock returns have a significantly positive response on industrial production, especially in the Chinese economy. Additionally, causality movement between stock returns and real economy runs in that order. The findings of the study clearly show that there is no causal relationship between industrial output and stock returns, and therefore none of these variables can be used to predict each other. In contrast, IPI is effective on stock returns through inflation, meaning that it can be significant in predicting stock prices. There is also a single-directional causality moving from in the direction of CD/GDP to stock prices. This implies an increase in current deficit leads to deterioration of the economy, thus fall in stock prices. Lastly, the study indicates a causal relationship between stock returns and foreign transactions. This means that stock market growth encourages foreign investment and vice versa.

A number of studies have also indicated a positive and significant long-run relationship between stock prices and independent macroeconomic variables. They conclude that independent variables are very effective on stock prices through inflation. In summary, these studies show a positive correlation between stock returns and the real economy in both developed and developing economies, as conjectured. During awful economic times, business cash flows and required rate of returns are heavily affected, and this normally has a negative impact on share values. In addition, when the interest rates increase to a level where most companies would find it very hard to repay their debts, then their continued existence may be under threat. In such a scenario, business owners will insist on a higher risk premium. Consequently, the share value will deepen even further.

Industrial production index (IPI), level of employment and fixed investment significantly influence stock prices through inflation. Some studies point out that unexpected inflation has a positive impact on the returns of business portfolios. He adds that the relationship between inflation and stock returns is unstable in the long run and that there are disparities among nations in spite of the state of their level of development. In line with the study findings, it may be agreed that the causal relationship running from returns on the stock is superiors than the causal relationship running from macroeconomic variables to returns on the stock. Therefore, the SEHK index can be considered as a signal and a pointer to economic changes in Hong Kong.

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IvyPanda. (2021) 'Macroeconomic Factors and Hong Kong Stock Returns'. 12 February.

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