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Introduction. 2
Literature Review.. 4
Research Objectives. 7
Research Design. 9
Data collection and analysis. 11

5.1. Inflation. 13
5.2. Industrial production. 13
5.3. Interest rates. 13
5.4. Crude Oil prices. 14
5.5. Money supply. 14
5.6. Import. 14
5.7. Export. 15
5.8. Global Gold Price. 15
5.9. Exchange rate. 15
5.10. Gross Domestic product. 15
5.11. Unemployment rate. 15

Conclusion. 17
References. 18

1. Introduction
A large amount of investigation has been done to find the relationship between stock market returns and macroeconomic variables among various stock markets and different time horizons (Brown and Weinstein, 1983; Antoniou et al, 1998; Chen, Roll, & Ross, 1986). However, most of the literature in this area have focused on developed countries.
There are some financial economic theories which introduce appropriate models to investigate this relationship. Most popular and significant theories in explaining the stock returns are capital asset pricing model(CAPM) by Sharpe and Arbitrage Pricing Theory (APT) (Ross, 1976) which are based on the assumption that stock returns volatility can be explained by some of economic factors (Opfer and Bessler, 2004). In recent years the CAPM has been examined massively because of its inability to explain thoroughly the pricing of risky assets. In this paper Arbitrage Pricing Theory (APT) which is a multifactor model is used in order to link a range of macroeconomic factors and stock market returns.
To my knowledge this is the first effort which empirically test the Arbitrage Pricing Theory (APT) in Tehran Stock Exchange (TSE) for the period of January 2001 up to December 2015 on Quarterly basis. 11 macroeconomic variables (money supply, industrial production, crude oil price, consumer price index (CPI), import, export, gold price, exchange rate, interest rate, gross domestic product (GDP) , unemployment rate are tested against 20 major industry indices of Tehran stock exchange using ordinary least square (OLS) method. The model is conducted both including and excluding a dummy variable for the recession of 2008 in order to assess the sensitivity of our results to this extreme volatility event.
Iran has quite a well-educated labour. However, it is ranked as the fourth-least-attractive business location out of the 82 countries due to its weaknesses of the banking system, the tax administration, inconsistent policies and many other reasons (Economist Intelligence Unit, 2015). Iran’s political and economic climate is expected to change due to the lifting of international nuclear-related sanctions in January 2016. Nevertheless, Iran’s economy is almost vulnerable to oil and gas price changes due to its dependence on hydrocarbons and definitely low oil prices will affect the economic performance. With sanctions lifted, the economy will benefit from higher investment levels and capital increase specifically from foreign investors.
As summarised by Bloomberg website (2016), Iran’s stock market is the fifth-largest in the Middle East with a market capital of approximately $90 billion. Although many international investors were allowed to invest on Tehran’s bourse in the sanction period, financial sanctions made it almost impractical to transfer money in and out of the country. Lifting the sanctions authorize the nation’s banks to reconnect to the Swift system for international financial transactions, opening the doors to ‘One of the world’s hardest-to-enter stock markets’. ‘The benchmark fell about 11 percent last year after sinking 21 percent in 2014, the first annual decline since 2008’ and it is forcasted to rise in the next five years. Therefore, it is almost crucial to capture the effect of specific macroeconomic factors on stock market returns to improve the performance of Iran’s stock market. The outcomes of this study might be valuable to identify the relationship of economic variables and stock returns volatility. Consequently, an appropriate economic and financial policy can be derived to enhance both stock market and economic conditions in the country which started to ease after implementing the sanctions.
2. Literature Review
Brown and Weinstein (1983) tested the APT related to the bilinear paradigm introduced by Kruskal (1978) using similar data of Roll and Ross (R&R) (1980). Nevertheless, they formed 60 securities groups instead of 30 based on their industrial classifications instead of alphabetical using pre-specified factors model. Consequently, they rejected the five or seven factors APT model accepting a three factor version. Meanwhile, claiming the fact that ‘there are few rather than many economy wide factors that appear to be priced in the APT’.
Dhrymes et al. (1985) showed that published test of the APT by R&R (1980) possess some limitations. They critically explain that the assumption in RR that there are three to five factors is not strong; they dispute that the number of factors rise with the number of securities in the group. Moreover, they found that is almost difficult to identify the exact number of factors describing the return making process.
Chen, Roll and Ross (1986) (CRR) have studied the impact of some macroeconomic variables to explain the U.S stock return in the period of 1958-1984. They examined seven macroeconomic variables (industrial production, term structure, risk premium, market return inflation, oil prices and consumption).In particular, industrial production, changes in the risk premium, changes in the yield curve and more weakly measures of unanticipated inflation and changes in expected inflation were found significant in explaining expected stock return during the tested period. As a result, stock returns are exposed to systematic economic news of that the news can be measured as innovations in state variables whose identification can be accomplished through simple and intuitive financial theory.  Poon and Taylor (1991) tested if the results in CR&R (1986) are applicable to U.K stock returns. They regressed monthly and annually data from Jan 1965- Dec 1984 including industrial production, the unanticipated, inflation, risk premium, term structure of return on value weighted market index. However, they concluded that macroeconomic factors do not affect stock market pricing indiscriminately as it is claimed in CR&R .The results demonstrate that macroeconomic factors may not be priced in UK as the way described in CRR. Koutoulas and Kryzanowski (1994) examined the hypothesis that Canadian equity market is segmented or integrated in relation to global North American equity market using IAPT (Solnik, 1983) and APT models for the period from March 1969 to March 1988. They showed Canadian equity market is only partly segmented and the North American macroeconomic factors influencing Canadian returns are the differential in the Canada/U.S. leading indicators and the interest rate on U.S. dollar deposits in London. Validity of the APT is investigated by Antoniou et al. (1998) for securities traded on London Stock Exchange. They concluded that there are five common factors to price the assets, although only unexpected inflation, the money supply and excess returns on the market portfolio carry the similar prices of risk in both random samples. Azeez and Yonezawa (2006) tested the effect off macroeconomic variables in the Japanese stock market during the bubble economy (comparing pre- and post- bubble periods) using APT model. They found there are four various risk factors (money supply, inflation, exchange rate and industrial production) influencing the stock returns in both sample periods. Furthermore, the risk of macroeconomic factors are not raised in the bubble period. Generally speaking, Mukherjee and Naka (1995), Booth and Booth (1997), Maysami and Koh (2000), Wongbangpo and Sharma (2002), Chen (2003), Ibrahim and Aziz (2003) and Chen et al. (2005) claimed that money growth, the rate of inflation, industrial production, reserves, exchange rates and interest rates are the most crucial factors in explaining the stock market changes. Rjoub et al. (2009) tried to test the APT model in the Istanbul Stock Exchange (ISE) from January 2001 to September 2005 on monthly basis. Consequently, the results proved the pricing relationship between the stock return and the examined macroeconomic variables. However, these findings indicated a weak explanatory power which means that there are other macroeconomic factors affecting stock market returns in ISE other than the tested ones.  A firm and industry analysis is implemented by Butt et al. (2010) in Karachi Stock Exchange. Initially, they came to this conclusion that stock returns have different behaviour among the firm and industry level and the effect of movements in economic variables on securities is more significant at the industry level. Secondly, manufacturing industries are less sensitive to macroeconomic factors than banking industry (financial sector).Owusu-Nantwi and Kuwornu (2011) concluded that only consumer price index (inflation) was statistically significant (among crude oil price, exchange rate and treasury bill rate) in explaining stock market returns in Ghana Stock Market. Nevertheless, Basu and chawla (2012) found 4 more factors (exchange rate, whole sale price index, gold prices and market index) influencing excess returns in Indian Markets and argued that in spite of some limitations of data availability, APT is approximately a good fit in India over the sample period.  There are some previous studies that Studied APT in Tehran Stock Exchange. Sabetfar, Fah and Mohd (2012) attempted to test APT in Iranian Stock market using monthly data from 1991 to 2008 (sanctioned economy) for non-oil based companies. Consumer price index (CPI), trade balance, exchange rate (Rial / US$), Money supply, central bank reserve, volume of stock
transaction of TSE, oil price, production of crude oil, Tehran price index (TEPIX), export of crude oil, profit rate proxy (ROE of banks) and gross domestic product(GDP) were considered as macroeconomic factors. The results indicate that APT is almost inappropriate in Iran over the study period. Moreover, it is claimed that non-oil base companies are more stable and they are not strongly affected by financial and economic sanctions. However; Samadi, Bayani and Ghalandari (2012) examined APT in Tehran Stock Exchange using monthly data from 2000 to 2010. They indicated the fact that gold price, inflation and exchange rate affect the stock returns, although oil price and liquidity had no effect over the sample period.
3. Research Objectives
This research has two main objectives:

The main goal of this paper is to empirically test the appropriateness of the Arbitrage Pricing Theory (APT) in Tehran stock Exchange (TSE) for the period of January 2001 up to December 2015 on Quarterly base.
We are aiming to investigate if there is a relation between 2008 global crisis and stock market returns in TSE.

Recognition of macroeconomic factors which influence the stock market returns in Iran is an important fact because of massive volatility of these factors mainly due to political instability and huge amount of sanctions in recent years. Iran is not only an oil-based country but also participates in other significant industries. The chart below is given by Central Bank of Iran for 2012, illustrating the major industries in this country.
Table 1 Source: Central Bank of Iran
Consequently, it is almost crucial to ameliorate the fragile economy of Iran after lifting the sanctions in January 2016 by improving Iran’s capital markets and attracting small investments from local and foreign individuals to different industries. Undoubtedly,  Iran’s economy is strongly affected by performance of the stock market .According to Hakim(2008),making investment decisions is quite difficult in Iran due to the complexity of evaluating and quantifying risk. The purpose of first objective is to study if the Arbitrage pricing theory works in Iran markets in order to curb the systematic risk of macroeconomic factors on stock market returns to enhance the country’s economy and then the impact of 2008 global crisis is examined to test if huge global volatilities has an influence on Iran’s markets. In particular, do massive global movements have an impact on Iran’s Capital markets?
There are some previous studies that tested APT in Tehran stock exchange like Samadi et al. (2012) and Sabetfar et al. (2012). Since they used either short time frame work and a few of macroeconomic variables or time periods before 2010, their research is not reliable for recent years due to huge instability of stock market returns during nuclear-based sanctions. Furthermore, none of previous studies in APT are focusing on the effect of financial global crisis on stock returns and its volatility. To my knowledge this is the first paper which attempts to fill the following gaps: Testing the APT for 11 macroeconomic variables against 15 major industry portfolios in Tehran Stock Exchange during a 15 year period from 2000 to 2015 and this study aims to achieve empirical investigation of economic effects of global financial crisis in 2008.
4. Research Design
For the purpose of first objective, the following multiple regression is tested using OLS estimation method:
This gives 11 regression coefficients for each of twenty industry indices. The regression coefficients are the sensitivity of stock returns to movements in each macroeconomic variable demonstrating the average change in the stock returns by 1 percent change in a particular macroeconomic factor. Rjoub et al. (2009) did not used all chosen various macroeconomic variables in each industry portfolio. Therefore, initially, the original regression is estimated in this paper and then individual significance of each variable is examined in each portfolio in order to prevent the possible misspecification.
After specifying the macroeconomic variables, the equation of the model is implemented as:
= return for stock i
= constant term
= sensitivity of stock i to annual change in CPI
=annual change in CPI
= sensitivity of stock i to annual change in industrial production volume
= annual change in industrial production volume
= sensitivity of stock i to one year interest rate spread
= one year interest rate spread
= sensitivity of stock i to annual change in crude oil price
= annual change in crude oil price
= sensitivity of stock i to annual change in money supply
= annual change in money supply
= sensitivity of stock i to annual change in import
= annual change in import
= sensitivity of stock i to annual change in export
= annual change in export
 = sensitivity of stock i to annual change in gold price                                                                        
= annual change in gold price
 = sensitivity of stock i to exchange rate (national currency per US$)                                                                       
 = exchange rate (national currency per US$)
 = sensitivity of stock i to annual change in GDP                                                                       
= annual change in GDP
= sensitivity of stock i to annual change in unemployment rate
= annual change in unemployment rate
= disturbance term
For the purpose of second objective, given the data period extends over the crisis of 2008, estimations are conducted both including and excluding a dummy variable for this event in order to identify the sensitivity of the results to this extreme volatility event. All the regressions are performed with and without the dummy variable for the 2008 crisis. The 2008 crisis dummy variable take the value of 0, if data belongs to the period before 2008 crisis and take the value of 1 for 2008 and after the crisis period data.
5. Data collection and analysis
Secondary data is used in this paper which consists of quarterly time series observations regarding 11 macroeconomic variables namely, money supply(MS), industrial production(IP), crude oil price(COP), consumer price index (CPI), import(IMP), export(EXP), gold price(GP), exchange rate(ER), interest rate(IR), gross domestic product (GDP) , unemployment rate(UNEMP) and  15 major industry indices covering the year period from January 2001 up to December 2015 in Tehran Stock Exchange(TSE). Table 2. illustrates the resources which data is extracted from:


Money supply(MS)
Euromonitor Passport

Industrial production(IP)
Economist Intelligence Unit

Crude oil price(COP)
Bloomberg Terminal

Consumer Price index(CPI)
International financial statistics

Euromonitor Passport

Euromonitor Passport

 Global Gold price(GP)
Bloomberg Terminal

Exchange rate(ER)
Economist Intelligence Unit

Interest rate(IR)
International Financial Statistics

Gross Domestic Product(GDP)
Euromonitor Passport

Unemployment rate(UNEMP)

Industry Indices
Tehran Stock Exchange

Table 2 Data collection resources
The reason for using quarterly data instead of monthly is mostly due to unavailability of monthly data in Iran. Moreover, data for industry indices is only available for from 2008 to 2015 in TSE website. Therefore, data from 2000 to 2008 should be collected from statistical center of Iran in Rahi-ye-moayyeri St., Tehran, Iran. Table below demonstrates the 20 major industries which are applied in the model ? symbols are imaginary due to the alphabetical difference in Iran)

Industry name
Number of firms

Oil  products


Mass Construction

Non-metallic mineral


Chemical products




Rubber products

Sugar products

Metallic mineral

Tile and ceramic

Basic metal products



Food and beverage

Metal products




Table 3 industries classification (Resource: www.fipiran.com)
The stock returns are calculated as the quarterly change in the stock price by the following formula:
R(t) = log SP(t) – log SP(t-1),
Where SP(t) is the average stock price in quarter t and SP(t-1) is the average stock price in the previous quarter.
Arbitrage pricing theory does not exactly define which factors and how many factors should be concluded in the model. Therefore, macroeconomic factors are chosen almost similar as Rjoub et al. (2009) excluding foreign reserve and market pressure index due to unavailability of data in Iran.
5.1. Inflation
The main policy in every country is maintaining low inflation and full employment. An increase in the general level of prices is known as inflation which decreases the real value of money, thereby investors in the stock market are exposed to movements in inflation, because their expected excess return at the end of period relies on inflation during the holding period.  The empirical evidence to identify the relationship between inflation and stock market returns is mixed. Some studies support of the Fisher hypothesis that the  relationship  between  stock  market  returns  and  inflation  is  positive  (Choudhry, 2001; Boudhouch and Richardson, 1993; ;  Gultekin,  1983;Firth,  1979).However, Other studies claimed a negative relationship. (Ioannides et al, 2005; Spyrou, 2001). Consumer price index is a measure of inflation which is a weighted average of prices of a basket of consumer goods and services (Collins dictionary of economics). The annual change in CPI index is calculated by the following formula:
ACPI(t) = log CPI(t) – log CPI(t-12)
Where ACPI(t) is the annual change in CPI, Where CPI (t) is the consumer price index in quarter t and CPI (t-12) is consumer price index in the same quarter of the previous year.
5.2. Industrial production
Industrial production is one of the most significant measures of economic activity. It is given by:
AIP(t) = log IP(t) – log IP(t-12)
Where AIP(t) is the annual change in industrial production volume in quarter t, IP(t) is the Industrial Production Volume Index in quarter t and IP(t-12) is the Industrial Production Volume Index in the same quarter of the previous year.
5.3. Interest rates
Based on economic theory, rise in interest rates might lead stock prices to decrease. In particular, because high interest rates reduce the present value of future cash flows the investment is less attractive. The spread between active and passive one year interest rate is calculated by using the following formula:
IR (t) = lend (t) – dep (t)
Where IR(t) is the spread in quarter t, lend(t) is the annual percentage lending rate in quarter t and dep(t) is the annual percentage deposit rate in quarter t.
5.4. Crude Oil prices
The rise in oil prices have some negative impacts such as putting inflationary pressure on the economy , growing the costs of companies (production and transportation) and consequently,  uncertainty about the future of capital markets. However, Creti et al. (2014) indicated mixed influences of oil price movements on stock market activities, suggesting that impacts vary between oil-exporting and oil-importing countries. Spot crude oil prices are obtained from West Texas Intermediate Index (WTI). The annual change in quarterly oil prices are calculated by the following formula:
AOIL(t) = log COP(t) – log COP(t-12)
Where AOIL(t) is the annual change in oil prices in quarter t, COP(t) is the oil price in quarter t and COP(t-12) is the oil price in the same quarter of the previous year.
 5.5. Money supply
Money supply is an important factor to determine the level of spending in the economy and it should be controlled by implementing appropriate monetary policy. Money supply include either assets possessing ready liquidity or assets that are less liquid but are nevertheless significant in underpinning spending(Collins dictionary of economics). The anticipated liquidity hypothesis suggests that if there is an unanticipated growth in money supply, it lowers stock prices due to the market participants’ expectations from Federal Reserve to slow the money growth (Urich and wachtel, 1981). Money supply is given by the million units of national currency and its annual change is calculated by the following formula:
AMS(t) = log MS(t) – log MS(t-12)
Where AMS(t) is the annual change in money supply in quarter t, MS(t) is the money supply in quarter t and MS(t-12) is the money supply in the same quarter of the previous year.
5.6. Import
Import is given by the million units of national currency and its annual change is calculated by the following formula:
AIMP(t) = log IMP(t) – log IMP(t-12)
Where AIMP(t) is the annual change in import in quarter t, IMP(t) is the import in quarter t and IMP(t-12) is the import in the same quarter of the previous year.
5.7. Export
Export is given by the million units of national currency and its annual change is calculated by the following formula:
AEXP(t) = log EXP(t) – log EXP(t-12)
Where AEXP(t) is the annual change in export in quarter t, EXP(t) is the export in quarter t and EXP(t-12) is the export in the same quarter of the previous year.
5.8. Global Gold Price
Global gold prices are quoted as US Dollars per Troy Ounce which its annual change is calculated by the following formula:
AGP(t) = log GP(t) – log GP(t-12)
Where AGP(t) is the annual change in gold price in quarter t, GP(t) is the gold price in quarter t and GP(t-12) is the gold price in the same quarter of the previous year.
5.9. Exchange rate
The fee for exchanging currency of one country for currency of another is the exchange rate. Currency movements are expected to make the share market of an import-based country stronger (positive effect) and depress that of an export-based economy (negative effect) (Obben et al. 2007). It is given by national currency per US$.
5.10. Gross Domestic product
‘The total money value of all final goods and services produced in an economy over a one-year period’ (Collins dictionary of economics). GDP is given by the million units of national currency and its annual change is calculated by the following formula:
AGDP(t) = log GDP(t) – log GDP(t-12)
Where AGDP(t) is the annual change in GDP in quarter t, GDP(t) is the GDP in quarter t and GDP(t-12) is the GDP in the same quarter of the previous year.
5.11. Unemployment rate
Unemployment rate is the percentage of the labour force that is unemployed which is used to examine the state of the economy performance which is given by the number of persons who are unemployed as a percent of the total number of employed and unemployed persons and is calculated by the following formula:
AUNEMP(t) = log UNEMP(t) – log UNEMP(t-12)
Where AUNEMP(t) is the annual change in unemployment rate in quarter t, UNEMP(t) is the unemployment rate in quarter t and UNEMP(t-12) is the unemployment rate in the same quarter of the previous year.

6. Conclusion
A huge number of previous studies proved that there is a relationship between macroeconomic factors and stock market returns. However, results can be different in various countries and time periods. In this paper, linear regression is employed to examine the impacts of macroeconomic factors on stock returns in Tehran Stock Exchange from January 2001 to December 2015 on Quarterly base. Macroeconomic factors used in this study are employment rate, GDP, consumer price index, exchange rate, crude oil price, gold price, money supply, industrial production, interest rate, export and import; and return on the 20 major industry indices listed in TSE. The analysis is based on stock portfolios rather than single stocks. Moreover, the effect of 2008 global financial crisis on Iranian markets is studied. The findings will have significant implications both for companies and investors due to the fragile economy of Iran after lifting the sanctions. This study can be used as a guide in forecasting equity market viability, to decide whether it is useful to invest in it and for the purpose of risk management. It may provide some knowledge about appropriate monetary policies to stabilize the market.
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