Macro Variable Model

The Macro Variable version of the APT uses observed factors assuming that stock prices react to news about macroeconomic and financial variables. Following the pioneering work of Chen et al (1986), there has been significant work in the literature. These works confirmed that stock market return is affected by macroeconomic and financial variables.

As most recent studies used this framework, it is important to first understand the true factor structure of this study. According to Chen et al. (1986) economic state variables have systematic effects on stock returns. From the perspective of the efficient market hypothesis and rational expectations, asset prices should depend on their exposures to the state variables that describe the economy.

Chen et al. (1986) correlated various macroeconomic variables with returns on five portfolios. They found that four macroeconomic variables were significant:

Unanticipated inflation; Twists in the yield curve; and

Changes in risk premium (spread between low grade bonds and high grade bonds). Chen et al. (1986) chose a set of economic state variables as candidates for sources of systematic asset risk. Several of these economic variables were found to be significant in explaining expected stock returns. The authors did not completely investigate the significant macroeconomic variables but selected some variables that showed some significance compared to other possible macro variables.

Beenstock and Chan (1988) presented a study proposing an alternative methodology for testing Arbitrage Pricing Theory (APT) in the context of the market for British securities. Using the macro variable model, they identified four macroeconomic variables for the UK market:

Interest rates;

Fuel and material costs; Money supply;

Inflation.

The arbitrage pricing theory (APT) with macroeconomic factors, put forward by Chen et al. (1986), was tested by Groenewold and Fraser (1997) using monthly Australian sectoral share-price indexes for the period 1980-1994. The inflation rate was found to be consistently priced. The significance of other factors was found to depend on their choice of sample period and estimation model. They found that: the rate of inflation, the short-term interest rate, and the money growth rate are priced factors. They found less support for output, employment, exchange rates and balance of payments.

Different sectors have a different factor structure in terms of APT. For example Faff and Chan (1998) identified a different set of variables determining gold industry stock. This paper

incorporates into one multifactor model three such variables - gold prices, interest rates and foreign exchange rates. Their paper applied this model over the period 1979 to 1992. They found that the only variables of significant explanatory power are the market and gold price factors.

He and Ng (1994) investigated whether size and book-to-market values of equity are proxying for macroeconomic risks found in Chen et al. (1986) multifactor models or are measures of stocks' risk exposure to relative distress. They found that the role of size includes stocks' risk exposures associated with the Chen et al. (1986) factors and that the Chen et al. (1986) multifactor model does not explain the book-to-market effect. They also found that size and book-to-market are related to relative distress and that relative distress can explain the size effect, but only partially the effect of book-to-market, on average stock returns.

Merville et al. (2001) examined the fundamental factors influencing the returns of constructed portfolios and selected equity mutual funds. Their results indicate that there are most likely three factors. These three stock returns factors can be associated with 1) market return, which also includes idiosyncratic return; 2) market capitalization; and 3) the investment opportunity set. Higher-order factors can also be uniquely identified with macroeconomic variables.

Shanken and Weinstein (2006) re-examined and tested the validity of the pricing of the five Chen et al. (1986) macrovariable factors. They found them to be surprisingly sensitive to reasonable alternative procedures for generating size portfolio returns and estimating their betas. Strong evidence of pricing is obtained only for the industrial production growth factor and, in another contrast, for the market index. In particular, the corporate-government bond return spread, an important factor in Chen at al. (1986) study, is insignificantly negative for the 1958-1983 period, corroborating the cross-sectional regression results.

Tursoy et al., (2008) tested the APT in the Istanbul Stock Exchange (Turkey) using monthly data between February 2001 and September 2005. In this paper, various macroeconomic variables which represent the basics of an economy were employed. They are: money supply, industrial production, oil price, consumer price index, import, export, gold price, exchange rate, interest rate, GDP, foreign reserve, unemployment rate and a market pressure index which is built by the authors. They tested these macroeconomic variables against 11 industry portfolios using ordinary least square technique. Their result indicates that there is not a significant relationship between stock return and these macroeconomic variables. However, each macroeconomic variable affects different industry portfolios to a different degree.

Humpe and Macmillan (2009) examined the effect of several macroeconomic variables on the stock prices in the US and Japan using monthly data between 1965 and 2005. They studied the relationship within the framework of a standard discounted value model and they applied cointegration analysis between industrial production, the consumer price index, money supply, long term interest rates and stock prices in the US and Japan. Using the US data they found a single cointegrating vector, between stock prices, industrial production, inflation and the long term interest rate. Stock prices are positively related to industrial production and negatively related to both the consumer price index and a long term interest rate. They also found an insignificant but positive relationship between US stock prices and the money supply. Using the Japanese data Humpe and Macmillan (2009) found two cointegrating vectors. For the first vector, stock prices were influenced positively by industrial production and negatively by the money supply. For the second cointegrating vector, industrial production was negatively influenced by the consumer price index and a long term interest rate. This study gives contrasting results and they explained these contrasting results

by the slump in the Japanese economy during the 1990s and consequent liquidity trap in the late 1990s and early 2000s.

The methodology of Chen et al. (1986), the macro variable model of the APT, is considered as the best and the most economically interpretable model. However, the evidence from the empirical studies above shows that this method does not explaining precisely the relationship between stock market return and the macroeconomic variables. The proposed multifactor model in this thesis follows the same methodology by applying a different set of data and tests the significance of the relationship.