Literature Review On Stock Return And Arbitrage Pricing
Although an abundance of studies aimed at extending public’s understanding of stock performance related to macroeconomic environments have been conducted in the past, few of these studies were conducted on stock return by extending the well-established Arbitrage Pricing Theory (APT) in China. With the number of economic activities are rising to control and adjustment one country’s economy, these economic activities can not be ignore impact on stock market and significant relationship between macroeconomic variables and stock return have to be researched to improve the China’s stock market performance.
The two categories of shares are traded in Chinese stock markets: A shares and B shares. A shares are purchased only for Chinese citizens and B shares just are available by foreigners. Both of A and B shares are traded in the two official exchanges in China: Shanghai stock exchanges and Shenzhen stock exchanges. A share which is the main shares in Chinese stock market dominated in terms of market capitalization and level of activity. The number of B shares listed on both of two exchanges is less than one-third of the number of A shares. B shares account to less than 3% of the A shares' market capitalization and 2% of the A shares' annual trading value. Additional categories of shares in Chinese stock market include H shares and N shares, which are available only to foreign investors same with B shares but traded in Hong Kong and the New York Stock Exchanges, respectively.
Finding out the relationship between macroeconomic variables and stock performance in China is the purpose of this study. The four main macroeconomic variables are stated that interest rate, exchange rate, inflation and money supply. This relationship can be positive or negative, even no direct impact for each other.
Interest rate is a monetary policy tool for Chinese government control the economy. Increasing interest rate can be see as tighten monetary policy is implementing for the government; decreasing interest rate is a loosely monetary policy and encourage the residents’ consumption to increasing the market demand.
Exchange rate is a payment tool for China’s trade in the international. It can indicate that China’s import and export trade between China and other countries. China using the direct quotation formulates the exchange rate. The raised exchange rate indicated that Chinese currency is depreciated, it benefit for China improve its export. Otherwise, the decreased exchange rate is showed that Chinese currency is appreciated and the import is encouraged. When the export more than import, the assets and financial item in the balance of payment will obtain the favorable balance and the nation’s foreign exchange reserve is increased and vice versa.
Inflation is a serious problem in China recently, the each month’s consumer price index (CPI) which often as an indicator reflect to inflation is keeping increasing and the price of commodities and food also increased in 2010. The Chinese government often use tighten fiscal policy to measurement inflation.
Money supply is also a fiscal policy tool for Chinese government control the economy. Increasing money supply showed that the government implements loosely fiscal policy and increasing demand for customers. Decreasing money supply indicated that tighten fiscal policy is operated, it will reduce the market demand and price will also decrease.
All of these variables are changed or adjusted through macro control by Chinese government in the China’s economy. This study focus on these variables affect to stock market. To investigate weather the macro control can affect the stock market.
Such of this study will be of interest to both academics and financial investors. Specifically, this study investigates the four main macroeconomic variables on the stock return for these investors to obtain profit from the stock market. In other words, our study focused on the China’s stock market to stimulate the liquidity and actively, attract more capital into the China’s stock market.
Our study is based on China’s basic economy and the sample is selected from Shanghai exchange and Shenzhen exchange’s listed companies.
2.2 Underlying Theory
We use the Arbitrage Pricing Theory as our underlying research theory. The Arbitrage Pricing Theory (APT) was developed primarily by Ross (1976).It is a one-period model in which every investor believes that the stochastic properties of returns of capital assets are consistent with a factor structure. Ross argues that if equilibrium prices oﬀer no arbitrage opportunities over static portfolios of the assets,then the expected returns on the assets are approximately linearly related to the factor loadings.
Ross’ (1976) argument for the theory is based on the preclusion of arbitrage. Ross’ formal proof shows that the linear pricing relation is a necessary condition for equilibrium in a market where agents maximize certain types of utility. The subsequent work, which is surveyed below, derives either from the assumption of the preclusion of arbitrage or the equilibrium of utility-maximization. A linear relation between the expected returns and the betas is tantamount to an identiﬁcation of the stochastic discount factor (SDF).
The arbitrage pricing theory assumes that a security return is a linear function, not only of one, but also a set of common factors. The APT thus indicates that the risk premium for an asset is related to the risk premium for each factor and that as the asset’s sensitivity to each factor increases, its risk premium will increase as well. The APT predicted that the prices of all risky assets in the economy conformed to the condition of no arbitrage. No arbitrage mean that an individual holding a well diversified portfolio could not earn any additional return merely by changing the weights of the assets included in the portfolio, holding both systematic and unsystematic risk constant. The APT states that there is a set of underlying sources that influence all stocks returns. The stock return is a linear function of a certain number; say k, of economic factors, while these factors are unobservable and not meaningful.
The APT is a substitute for the Capital Asset Pricing Model (CAPM) in that both assert a linear relation between assets’ expected returns and their covariance with other random variables. (In the CAPM, the covariance is with the market portfolio’s return.) The covariance is interpreted as a measure of risk that investors cannot avoid by diversiﬁcation. The slope coeﬃcient in the linear relation between the expected returns and the covariance is interpreted as a risk premium. Such a relation is closely tied to mean-variance eﬃciency.
According to Chen, Roll & Ross (1986), these risk factors arise from changes in some fundamental economic and financial variables such as interest rates, inflation, real business activity, a market index, investor confidence etc.
The factor analysis-based empirical tests of the APT on US data have produced relatively mixed results. In their seminal paper, Roll and Ross (1980) tested the APT for the period 1962-72. They used daily data for individual equities listed on the New York Stock Exchange. They concluded that at least three and probably four priced factors were found in the return generating process.
Chen (1983) discovered that the APT seems to outperform the traditional CAPM when evaluated by explanatory power on stock returns. He investigated stocks using daily return data during the 1963-1978 period from the New York Stock Exchange. He compared the empirical performance of the APT with that of the CAPM.
More studies have found a number of critical issues when testing the theory. For example, it has been found that the number of factors seems to increase when the number of investigated securities increases. The APT does not specify the number of factors. Dhrymes, Friend, and Gultekin (1984) show that the number of factors extracted using a statistical procedure increases with the number of securities in the sample. Therefore, they argue, the number of pervasive factors may not be small. Trzcinka (1986) shows that while the number of statistically estimated factors increases with the sample size, the ﬁrst factor remains dominant. This suggests that we may have a one factor model, the one factor being the market. Roll and Ross (1984) argue that what matters is the number of priced factors and not the number of statistical factors extracted from the covariance matrix. While there is no clear answer about the number of factors, most of the literature uses 1, 5 or 10 factors. Lehmann and Modest (1987) show that of all the decision choices, the number of factors has the least affect on the model estimates.
An alternative to the traditional approach is to specify a priori, on the basis of the theory, the general factors that explain pricing in the stock market. In this case the common factors are first measured using prespecified macroeconomic variables, and asset sensitivities to these factors are estimated using time series regressions. In their seminal paper, Chen et al. (1986) found that the spread between long-term and short-term interest rates, expected and unexpected inflation, industrial production and spread between high and low-grade bonds are priced in the generating process of stock returns in the US stock market. These state variables have also been found to be important in a number of other studies on US data such as Chen (1989).
Martikainen,Yli & Gunasekaran (1991) tested APT for the Finish Stock Market using monthly data. They used two different approaches: an exploratory factor analysis and a pre-specified macroeconomic factor approach. They tested how many factors there were that affected finish stocks in the two time periods 1977-81 and 1982-86. In the first step of the test they used principal components analysis and varimax rotation to get the factor loadings. Then, OLS regressions were made where factor loadings were independent variables and the average return on stock was the dependent variable. The purpose was to find how many factors that were priced in the market. In the second step of the test they used 11 pre-specified macroeconomic factors to test the APT model. They used different stock market indices, price indices, interest rates and other national economic variables such as the GNP and money supply. They could find only one priced factor for the first subperiod. In the second subperiod all of the factors become priced. This was an encouraging result that supported the theory that the equilibrium stock returns were generated by an economic factor model.
Loflund (1992) found that international factors such as unanticipated changes in real exchange rates, inflation and unanticipated changes in future foreign economic activity or export demand should be important. National factors such as unexpected inflation, unanticipated changes in the short-term interest rate, the term structure of interest rates and unexpected changes in domestic real production should be important.
Booth (1993) tested the APT for US, Finnish and Swedish stock returns during the 也year 1977-86, using the monthly data. The study tested the intra-country stability of the factor patterns over time and across different samples. It investigated the cross-sectional similarities of the factor patterns of twelve 30-stock samples. It used transformation analysis to test the stability. The empirical evidence indicated that two stable common factors in different samples could be found. An interesting observation was that the factors were very often produced in different order in different samples. Another important finding was that there existed two common factors across the first US sample and Finnish and Swedish samples. Thus, the two common factors obtained have been international by nature. The results implied that for Finland the APT performed relatively poorly and for US and Swedish data one to two priced factors were identified.
In tests of the APT, the necessity to generate unanticipated components in the factors is readily seen if we consider the model of Ross(1976), which assumes the return generating process is a unction of k systematic risk factors:
Y = βo + β1X1 + β2X2 + β3X3 + …..+ βnXn + ε
βi= Coefficient for the independent variables
Multiple regressions use to construct Arbitrage Pricing Theory (APT).
E (Ri) = λo + λ1b1 + λ2b2 + λ 3b3 + …..+ λ nbn
2.3 The dependent variable: stock return
Stock return is the return after investors invested their capital into the stock market for a period; it can be as profits or losses for financial investors. If investors expected that stock price will rise in the future and profits will be occurred from stock market, they will into the stock market and buy more stocks there, then sell these stocks at higher stock prices, the differences as the profit for these investors. If some investors hold stocks in the stock market and have a sign indicated that the stock price will decline in short time, the investors will sell their stock in hand quickly to reduce the losses in the stock market even exit the market.
There are many pervious studies on the some factors effect on the movement of stock return: Basher and Sadorsky’s (2006) explanation of the impact of oil price changes on the stock market returns of 21 emerging economies, then found strong evidences of the effect of oil prices being positive and statistically significant at the 10% level to stock market returns for most of these countries studied. Diacogiannis, Tsiritakis, and Manolas (2001) researched Greek stock market from 1980 to 1992 and its relationship to 18 macroeconomic variables; they obtain significant high loadings between stock returns and 13 of the 19 macroeconomic variables for both periods: 1980-1986 and 1986-1992. Wongbangpo and Sharma (2002) showed the relationship between the stock returns for the ASEAN-5 countries of Indonesia, Malaysia, the Philippines, Singapore, and Thailand and five macroeconomic variables. The observation for both of short and long run relationships between respective stock indexes and the macroeconomic variables of gross national product (GNP), consumer price index (CPI), money supply, interest rate, and exchange rate. They found that in the long-run all five stock price indexes had positively related to growth in output and negatively to the aggregate price level. In Philippines, Singapore, and Thailand, a negative long-run relationship was occurred between stock prices and interest rates, but for Indonesia and Malaysia were noted a positive relationship between stock prices and interest rates.
So, there are many studies were researched in many emerged and emerging market in kinds of countries. The stock return had significant relationship with some macroeconomic variables were obtained from these studies. For different country, different relationship between macroeconomic variables and stock performance, in this study, we are looking for these macroeconomic variables impact to the stock market in China; it will be helpful for investors make decision for investments in Chinese stock market through observing macroeconomic environment’s change or development.
2.4 Independent Variables
2.4.1 Interest Rate ( BLR)
Interest rates are among the most closely watched variables in the economy. Their movements are reported almost daily by the news media. They directly affect our everyday lives and have important consequences for the health of the economy. The higher the interest rate, the higher the discount factor, and lower the stock prices. Martikainen (1991) used this variable in testing the APT model. The stock returns and production growth.
Economic theory predicts that the short-term and long-term interest rates have a negative impact on stock returns. An increase in interest rates may raise financing costs, and then reduce future corporate profitability and stock prices.
Bulmash and Trivoli (1991) investigate the time-lagged interactions between US stock prices and selected economic variables using an autoregressive procedure. Their results indicate that both short-term and long-term interest rates have negative impacts on the stock prices. And this result also supported by Abdullah and Hayworth (1993) but moreover, long-term interest rates are found to relate more closely to stock returns than do short-term interest rates.
Nevertheless, Mukherjee and Naka (1995) found a positive relation between Japanese stock prices and the short-term interest rates represented by call money rates.
H1: Interest rate have negative relationship with China’s stock return.
2.4.2 Exchange Rate
The impact of exchange rate changes on the economy will depend to a large extent on the level of international trade and the trade balance. Hence the impact will be determined by the relative dominance of import and export sectors of the economy.
Kanas (2000) was one of the first studies which analysed volatility spillovers from stock returns to exchange rate changes in the USA, the UK, Japan, Germany, France and Canada. He found evidence of spillovers from stock returns to exchange rate changes for all countries except Germany, suggesting that the asset approach to exchange rate determination is valid when formulated in terms of the second moments of the exchange rate distribution for the countries included in his analysis. Volatility spillovers from exchange rate changes to stock returns were insignificant for all countries.
Turgut,Nil and Husam (2008) have a study on the Istanbul Stock Exchange from year 2001 up to 2005. They used 13 macrovariables to test the relationship between these variables and the stock return. For the exchange rate variable they used ordinary least square (OLS) indicated that there is no significant relationship between the exchange rate and the stock return.
Wu (2005) who examines volatility spillovers between stock prices and exchange rates for Japan, South Korea, Indonesia, Philippines, Singapore, Thailand and Taiwan for the period 1997-2000, splitting the sample into crises and recovery periods. He found a bi-directional relationship between the volatility of stock returns and exchange rate changes during the recovery period in all countries except South Korea, as well as significant contemporaneous relationships between the two markets for most of the countries. Furthermore, he found volatility spillovers increased in the recovery period.
H2: Exchange rate have no relationship with the stock return.
In the presence of inflation, the value of the contingent claims will be revised upward. Therefore, proportionate increases in prices should not affect the real rates of return on equity (Day 1984). However, the monetary assets of the firm (ie. cash, securities, receivables and debt) will be independent of fluctuations in the price level. Hence, it is only the real component of the firm that will be hedged against changes in inflation (Hong 1977). Surprisingly, empirical tests have found a negative relationship to exist between inflation and nominal stock returns Fama & Schwert(1977) and Gultekin (1983)..
Two channels can explain this negative correlation between these variables. First, according to Fama (1981), such relations are induced by the negativeinflation–real activity via the quantity theory of money and money demand theory, and stock returns are positively related to real variables like capital expenditures and output as explained in the theory of finance. Second, increased inflation may enhance the nominal risk-free rate and thus the discount rate. This, in turn, may decrease stock prices since stock prices can be viewed as the discounted value of expected dividends.
But the effects of inflation on stock prices are empirically controversial. Abdullah and Hayworth (1993)founda positive relationship between stock returns and inflation because equity is a hedge against inflation.
H3: Inflation have negative relationship with China’s stock return.
2.4.4 Money Supply ( M2 )
Exploring this variable ,Monetary Portfolio Theory suggests that changes in money supply alters the equilibrium position of money, thereby altering the composition and price of assets in an investor’s portfolio (Cooper 1974; Rozeff 1974). In addition, changes in money supply may impact on real economic variables, thereby having a lagged influence on stock returns (Rogalski & Vinso 1977). Both of these mechanisms suggest a positive relationship between changes in money supply and stock returns.
This result also supported by other researchers. The money supply can be linked to stock prices through portfolio substitution or inflationary expec-tation (Abdullah & Hayworth 1993; Cheung and Lai 1999). An increased money supply could enhance stock prices via the liquidity effect, i.e. higher liquidity in the economy reduces the interest rate and, consequently, raises stock prices. In addition, changes in the money supply lead to output, and since the money supply and output are positively correlated, the money supply and the stock prices have a positive relationship.
On the other hand increase in money supply growth would indicate excess liquidity available for buying securities, resulting in higher security prices. Empirically, Hamburger and Kochin (1972) and Kraft and Kraft (1977) found a strong linkage between the two variables, while Cooper (1974) and Nozar and Taylor (1988) found
Money supply also influences stock returns through inflation. Here, due to the positive relationship between inflation and money supply, an increase in money supply reduces stock prices (Dhakal 1993). Furthermore, portfolio theory suggests that an increase in money supply results in a portfolio shift from non-interest bearing money assets to financial assets including stocks cited by Charles K.D. Adjasi (2009)
H4: Money supply have positive relationship with China’s stock return.
As a conclusion that there are two main variants of the multifactor model have been proposed in the multifactor model like APT. The first variant implicitly assumes perfect integration, and models returns as a linear relation to a number of global risk sources (Ferson and Harvey 1994; Dumas and Solnik 1995; Harvey1995). The second type of multifactor model assumes complete segmentation whereby returns are assumed to be determined solely by a number of local priced factors (Merton 1973; Ross1976; Chen, Roll & Ross 1986; Jorion1991; Ely & Robinson 1997). In our study we main focus on the local priced factors.
2.5 Reasearch Framework
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