A Dissertation on

Testing of Arbitrage Pricing Model

Submitted in partial fulfillment of the requirements of

The M.B.A Degree Course of Bangalore University

Submitted By

Himanshu Kumar Sinha

(REGD NO: 06XQCM6030)

Under the Guidance and Supervision of

Dr.Nagesh Malavalli

( Principal)

M.P BIRLA INSTITUTE OF MANAGEMENT

Associate Bharatiya Vidya Bhavan

#43 Race Course Road, Bangalore-560001

2006-2008

DECLARATION

I ,Himanshu Kumar Sinha, hereby declare that this project

report t i t led “TESTING ARBITRAGE PRICING MODEL" under the guidance and

supervision of Dr. N. S. Malavalli, pricipal MPBIM- BVB, in partial

fulfillment of the requirements for the award of “MASTER OF BUSINESS ADMINISTRATION” Degree at Bangalore University.

I further declare that this Project is the result of my own efforts

and that it has not been submitted to any other university or

institute for the award of a degree or diploma or any other similar

title of recognition.

PRINCIPAL’S CERTIFICATE

This is to certify that the project report titled “TESTING

ARBITRAGED PRICING MODEL”, has been prepared by Mr.

Himanshu Kumar Sinha bearing the Registration No.06XQCM6030

under the guidance and supervision of Dr. N. S. Malavalli of M. P.

Birla Institute of Management (Associate Bharatiya Vidya Bhavan),

Bangalore. This has not formed the basis for the award of any

degree/ diploma for any university.

Place: Bangalore Principal

Date: (Dr. N. S. Malavalli)

GUIDE’S CERTIFICATE

This is to certify that the project report titled “TESTING

ARBITRAGED PRICING MODEL”, has been prepared by Mr.

Himanshu Kumar Sinha bearing the Registration No.06XQCM6030

under the guidance and supervision of Dr. N. S. Malavalli of M. P.

Birla Institute of Management (Associate Bharatiya Vidya Bhavan),

Bangalore. This has not formed the basis for the award of any

degree/ diploma for any university.

Place: Bangalore Guide

Date: (Dr. N. S. Malavalli)

TABLE OF CONTENTS S.No Particulars Pg. No

1. Executive Summary 1 2. Introduction 2 3. Problem Statement 4 4. Review of Literature 5 5. Research Methodology 7 6. Introduction of Arbitrage Pricing Model 9 7. Introduction to sensex 24 8. Data and their interpretation 28 9. Finding and conclusion 29

10. Bibliography 30

Executive Summary

It is widely believed that stock market is related to macroeconomic fundamentals of an economy, as companies that are listed for trading in stock exchanges are the ones who contribute significantly to the economy's growth. The notion that macroeconomic factors can drive the movement of stock prices is now widely accepted. However, it was only in the past decade or so that attempts have been made to capture the effect of economic forces in a theoretical framework and calibrate these effects empirically according to testing arbitrage pricing model. Arbitrage pricing model tells about how economic factors are responsible for effecting share prices of stocks. But individual stock will not explain everything because of risk associated with their individual business. So I have taken sensex index to test APT model because it is a great portfolio of all companies where firm related risk is diversified so only macro economic related factors remain responsible for movement in sensex. So I have tried to find out any change in macro economic factors, move the sensex in same direction.

INTRODUCTION Macroeconomic Indicators and Stock Prices - Indian Evidence This paper attempts to study the relationship of stock returns with macro economic variables in Indian context. The data consists of 36 months from Jan 2003 to Dec 2005 comprising of four macro indicators. We have considered 4 macro variables for the study: Exchange Rate, inflation rate, FII and SGL

Background: It is widely believed that stock market is related to macroeconomic fundamentals of an economy, as companies that are listed for trading in stock exchanges are the ones who contribute significantly to the economy's growth. The notion that macroeconomic factors can drive the movement of stock prices is now widely accepted. However, it was only in the past decade or so that attempts have been made to capture the effect of economic forces in a theoretical framework and calibrate these effects empirically according to testing arbitrage pricing model.

Arbitrage pricing model tells about how economic factors are responsible for effecting share prices of stocks. But individual stock will not explain everything because of risk associated with their individual business. So I have taken sensex index to test APT model because it is a great portfolio of all companies where firm related risk is diversified so only macro economic related factors remain responsible for movement in sensex. So I have tried to find out any change in macro economic factors, move the sensex in same direction. A central issue in macroeconomics is the question of how financial markets are connected to the real side of the economy. The issue has gained momentum due to increasing cross border movement of funds as fund managers try to move to markets where possibility of higher returns vis-à-vis risk is high. The ongoing integration of international capital markets and the repeated occurrence of large financial crises have raised the great concern about the topic The co-integration of macroeconomic variables and stock market has been an extensive area of research in financial econometrics. In financial economics, there have been a number of studies concerning developed markets like US, Japan, UK and European markets This study also investigates the short run causal relationship between the stock market and other macroeconomic variables in India for the period of Jan 2003 to dec 2005.

Problem Statement: Volatile markets are characterized by wide price fluctuations and heavy trading within a short span of time. Volatility is a traditional worry of investors, and is associated with fast-growing stocks, high P/Es, smaller companies, Information Technology (IT) firms. Volatility of stock market is usually caused by company news, economic factors like changes in forex rates, inflation rates, interest rates etc. Share prices fluctuations affect the investor’s wealth creation. In this context, the study of the impact of economic events on the movement of share prices in stock market is undertaken.

Objectives of the study: 1. To study how share prices fluctuate with respect to economic factors 2. To enable the investors in exploring the investment opportunities using the economic indicators

Hypothesis: Null hypothesis (H0): Economic factors do not affect the movement of share prices.

Alternate hypothesis (H1): Economic factors affect the movement of share prices.

Review of Literature: In an early study, Geske and Roll (1983) found the linkage between macroeconomic variables and stock prices in US but found a negative relationship between stock prices and inflation. Chen, Roll and Ross (1986) found that economic variables like industrial production index, change in risk premium and inflation have a systematic influence on stock return and showed the existence of a long run equilibrium relationship. However, they also found that oil prices and consumption did not have significant effect on stock prices. In another study, Mukherjee and Naka (1995) found that Jam\pese stock prices are linked to money supply, inflation, real economic activity, long-term government bond rate, exchange rate and interest rate. In another study, Naka, Mukherjee and Tufte (1999) found that in Indian market, industrial production is the largest determinant of stock prices while inflation is the largest negative determinant. Lee (1992) showed a positive Ii relationship between stock returns and the real economy in US. Gjerde and Saettem , (1999) showed that the stock returns respond negatively to the change in the interest rate in Norway and found a positive relationship of stock returns with oil prices and real economic activity. Asian markets have been studied by Fung and Lie (1990), Leigh (1997), Granger, Huang and Yang (1998), Kwon and Shin (1999), Maysami and Koh(2000). In a study by Nath and Samanta, (2003a), it was found that the stock market and the exchange rate were no generally co integrated in India and some amount of causal effect could be noticed only late

in 1990s. In another study, Nath and Samanta (2003b) examined the changing pattern in extent of integration between foreign exchange and capital markets in India using daily data and found that in V AR-framework empirical results do not point much impressive causal relationship between returns except in some specific years. However, they found using Geweke's feedback measures strong bi-directional as well as contemporaneous causal relationship between these markets. From the existing literature, the linkage between macroeconomic variables and stock prices

have been established for major markets like US, Japan while for other markets the same

cannot be said for certainty.

Research Methodology: Secondary Data: Data collected was of NSE nifty for the period from jan2003 to dec 2005 . The data would be collected from websites like ,RBI, CSO “economictimes.com”,NSE India”, Business newspaper, and journals. Tools: Tool to be used is correlation analysis using coefficient of determination and T-test for hypothesis testing. The Reason for choosing nifty comprising of 50 scrips from a specified and non-specified list, is because the index has established a place amongst investors, chartists, technical analysts of the market, the newspapers and all other concerned with the securities market. Moreover, it has been widely accepted as a fair reflector of the trend of prices on the Mumbai stock market the following economic indicators and events will be taken into consideration for the period from 1 Jan 2003 to dec 2005. 1) FII: Flow in FIIs to the equity market in these 36 months would be considered for the study. 2) Inflation rates inflation for every month during period.

3) Foreign exchange rates for US $/ Re would be considered as majority of Foreign exchange transaction takes place in US $. Any significant changes (highest and lowest interest rates) for every three months during period.

4)SGL: Changes in YTM of govt. securities or treasury bills for one year period

Scope of the study: Since there are problems associated with volatile stock markets, the study can help the investors to take informed decisions regarding buying or selling of stock. Limitation of the study:

1. The study is restricted to NSE some companies 2. Only three Jan 203 to dec 2005 data will be taken for the study. 3. Only Economic factors will be considered. 4. Due to time & resource constraints only four economic factors like Government policies, FII, Interest rates, Inflation rate and foreign Exchange rates will be considered.

Arbitrage Pricing Model

The arbitrage pricing model (based on the arbitrage pricing theory) was first introduced academically in 1976- In 1988 it first became available in a commercially usable form, ft relies on risk factors of a pervasive economic nature.

1. All of the following are risk factors commonly considered by the arbitrage pricing model

a. Company-specific risk.

b. Confidence risk, measured as the difference between long-term corporate bonds expected returns and long-term government bond expected returns.

c. Interest rate (time horizon) risk.

d. Inflation risk.

2. Which of the following best describes (he arbitrage pricing model?

a. A linear model.

b. A multivariable model.

c. A discounted cash flow (DCF) model.

d. A build-up model.

3. The arbitrage pricing model does not specify its risk factors.

4.The arbitrage pricing model works better for individual stocks than for groups of stocks.

5.The arbitrage pricing model is used less than the build-up model, the Capital Asset Pricing Model, or the DCF model (see also about asset management).

6. The arbitrage pricing model would work well for estimating the cost of equity capital for a regional chain of doughnut shops.

The Arbitrage Pricing Theory Model

APT model was first described by Steven Ross in and article entitled The Arbitrage Theory of Capital Asset Pricing which appeared in the Journal of Economic Theory in December 1976. The APT assumes that each stock's (or asset's) return to the investor is influenced by several independent factors.

The APT Formula

Furthermore, Ross stated that the return on a stock must follow a very simple relationship that is described by the following APT formula:

Expected Return = rf + b1 x (factor 1) + b2 x (factor 2)... + bn x (factor n)

Where:

• rf = The risk free interest rate is the interest rate the investor would expect to receive from a risk free investment. Typically, US Treasury Bills are used for US dollars and German Government bills are used for the Euro

• b = the sensitivity of the stock to each factor • factor = the risk premium associated with each factor

The APT model also states that the risk premium of a stock depends on two factors:

1. The risk premiums associated with each of the factors described above 2. The stock's own sensitivity to each of the factors - similar to the beta concept

Risk Premium = r -rf = b(1) x (r factor(1) - rf) + b(2) x (r factor(2) - rf)... + b(n) x (r factor(n) - rf)

If the expected risk premium on a stock were lower than the calculated risk premium using the formula above, then investors would sell the stock. If the risk premium were higher than the calculated value, then investors would buy the stock until both sides of the equation were in balance. Arbitrage is term used to describe how investors could go about getting this formula, or equation, back into balance.

Factors Used in the Arbitrage Pricing Theory

It's one thing to describe the APT theory in terms of simple formulas, but it's another matter entirely to identify the factors used in this theory. That's because the theory itself does not tell the investor what those factors are for a particular stock or asset - and for very good reason. In practice, and in theory, one stock might be more sensitive to one factor than another. For example, the price of a share of ExxonMobil might be very sensitive to the price of crude oil, while a share of Colgate Palmolive might be relatively insensitive to the price of oil.

In fact, the Arbitrage Pricing Theory leaves it up to the investor, or analyst, to identify each of the factors for a particular stock. So the real challenge for the investor is to identify three things:

1. Each of the factors affecting a particular stock 2. The expected returns for each of these factors 3. The sensitivity of the stock to each of these factors

Identifying and quantifying each of these factors is no trivial matter and is one of the reasons that the Capital Asset Pricing Model remains the dominant theory to describe the relationship between a stock's risk and return.

Keeping in mind that the number and sensitivities of a stock to each of these factors is likely to change over time, Ross and others identified the following macro-economic factors they felt played a significant role in explaining the return on a stock:

• Inflation • GNP or Gross National Product • Investor Confidence • Shifts in the Yield Curve

With that as guidance, the rest of the work is left to the stock analyst.

APT versus the Capital Asset Pricing Model

As mentioned, the APT and the Capital Asset Pricing Model (CAPM) are the two most influential theories on stock and asset pricing today. The APT model is different from the CAPM in that it is far less restrictive in its assumptions. APT allows the individual investor to develop their model that explains the expected return for a particular asset.

Intuitively, the APT makes a lot of sense because it removes the CAPM restrictions and basically states "the expected return on an asset is a function of many factors and the sensitivity of the stock to these factors." As these factors move, so does the expected return on the stock - and therefore its value to the investor.

In the CAPM theory, the expected return on a stock can be described by the movement of that stock relative to the rest of the stock market. The CAPM theory is really just a

simplified version of the APT, whereby the only factor considered is the risk of a particular stock relative to the rest of the stock market - as described by the stock's beta.

From a practical standpoint, CAPM remains the dominant pricing model used today. When compared to the Arbitrage Pricing Theory, the Capital Asset pricing model is both elegant and relatively simple to calculate versus what's required by the APT formula

Factors under APT Model An important feature of the development of stock market in India in the last 15 years has been the growing participation of Institutional Investors, both foreign institutional investors and the Indian mutual funds. The Indian stock market has come of age and has substantially aligned itself with the international order. Over the last fifteen years the following developments have made the Indian stock markets almost on par with the global markets:

• Screen based trading systems replaced the conventional open outcry system of trading and everyone acclaims the contribution of the screen base trading in developing the culture of equity investing. • The replacement of the fourteen-day account period settlement system give way to rolling settlements on T+2 basis has brought down the settlement risk substantially. • Dematerialization of securities • Demutualization of exchanges • Derivatives trading

Infact, today we have one of the most modern securities Market among all the countries in the world. Along with these changes the market has also witnessed a growing trend of 'institutionalization' that may be considered as a consequence of globalization. More precisely the growing might of the institutional investors entities whose primary purpose is to invest their own assets or those entrusted to them by others and the most common among them are the mutual funds and portfolio investors. Today, giant institutions control huge sums of money which they move continuously. In European and Japanese markets,

institutions dominate virtually all trading. In the US, retail investors still remain active participants. An important feature of the development of stock market in India in the last 15 years has been the growing participation of Institutional Investors, both foreign institutional investors and the Indian mutual funds (since the pension funds are still restricted to fully participate in the stock market otherwise pension funds are big investors all world over). With the accelerating trends of reforms Indian stock market will witness more and more of institutionalization and the increasing size of money under the control, this set of investors will play a major role in Indian equity markets. The importance of institutional investors particularly foreign investors is very much evident as one of the routine reasons offered by market Pundits whenever the market rises it is attributed to foreign investors' money, no wonder we see headlines like "Flls Fuel Rally" etc., in the business press. This is not unusual with India alone as most developed economies of today might have seen a similar trend in the past. The increasing role of institutional investors has brought both quantitative and qualitative developments in the stock market viz., expansion of securities business, increased depth and breadth of the market, and above all their dominant investment philosophy of emphasizing the fundamentals has rendered efficient pricing of the stocks. This paper sets out with the objective of examining whether the institutional investors, with their war chests of money, set the direction to the market. The next section briefly outlines the growth of institutional investors' presence in Indian stock market followed by an explanation of the data and methodology employed by the study and finally we present the results and discussions.

Growing Clout of Institutional Investors on Indian Markets: Foreign Institutional Investors (Flls) A Grave Balance of Payments situation forced the policymakers to take a relook at allowing foreign capital Into the country and the year of 1991 marked the announcement of some fiscal disciplinary measures along with reforms on the external sector made, it possible for the foreign capital to reach the shores of the country. As on 31st March 2005 there were 685 (ISMR 2004-05 NSE, Mumbai) registered foreign institutional investors in the Indian stock market. As on that date the net cumulative investments made by Flls are around USD 35.9 billion representing around 6.55% of India's market capitalization. Ever since they were permitted to invest in India the investments made by them showed a gradual increase except in the 1998-99. The net inflows averaged around 1.1 bn per year and large net outflows are rare barring the year of 1998-99 where most South Asian countries were out of favour for a while. Foreign portfolio investment carries a sense of notoriety of its own because at the first sign of trouble this flows in reverse direction. The notoriety emanates from the very nature of FII investment - portfolio managers tend to restructure and rebalance their portfolios dynamically across the countries, their primary concern being their portfolio. Owing to their magnitude of flows, the direction of FII investment flows tends to make or break the fortunes of a market. FII flows to India are less Table I Since it is not statutorily binding on Flls to make public, the companies in which they are investing in, there is no publicly available information on this aspect. However, the overall investment that can be made by all Flls in any company's equity is monitored by Reserve Bank of India, it gives a caution notice, when the overall FII investment level reaches 22 percent in a company. Subsequently, all purchases have to be done by prior approval of Reserve Bank of India. From such monitoring reports it can be gauged that the Flls are Net Investments by Flls (Rs Cr.)

FII Inflows to India: Their Effect on Stock Market Liquidity and Volatility

Stock markets in India were opened to foreign capital flows in 1992, with its ramifications

(both positive and negative). This paper examines two consequences-liquidity (positive) and

volatility (negative) in the past decade on the Indian stock market(s). It finds that Foreign

Institutional Investment (FII) flows have enhanced liquidity of the Indian stock market. Stock

market liquidity is definitely higher post-liberalization.

There is not much evidence to support the hypothesis that FII inflows have led to volatility in

the returns in the Indian stock market(s). The paper uses Engel-Granger test of co-

integration to examine the impact of FII inflows on the Indian stock markets. Stock market liberalization is a decision by a country's government to allow foreigners to purchase shares in that country's stock market (Henry 2000). One of the immediate effects of episodes of capital inflows on the stock market is the boom that it causes in the stock price indices. In fact, in the nineties, the stock market boom in several emerging economies has coincided by the increase of capital inflows to these countries (Levine, 1997). The stock-market boom, typically, does not last for the entire period of capital inflows. They usually start with the initial surge in the capital inflows and end before the episode of capital inflows completely subsides. This has been true in the emerging markets of Asia, Latin America and Africa. However, whether this boom is good for the economy is an issue that has not yet been completely settled in the studies so far. A stock market boom has a 'wealth effect' on the investors in the stock market and this can lead to a rise in aggregate demand (through consumption). On the other hand, a consistent stock market boom can dampen the level and rate of savings in the country as agents move from a low-return deposit market to a high-return

stock M P market. Such shift in investor preference can be damaging in cases where the stock market boom is led by capital inflows. Sudden stops or reversals in these flows can leave the economy devoid of funds to sustain growth and development. This has found to be true in Mexico and Argentina (Levine, 1997). Given this fact, the consequences of such inflows on the stock market become an important aspect of any study of capital inflows to a country. These papers briefly examine the consequences and study two of these consequences viz., liquidity and volatility in some depth in the case of India. This paper is divided into two sections. Section 1 evaluates the impact of capital inflows on stock market liquidity and Section 2 examines the impact on stock market volatility.

Inflation

Inflation is an upward movement in the average level of prices. Its opposite is deflation, a downward movement in the average level of prices. The boundary between inflation and deflation is price stability.

Because inflation is a rise in the general level of prices, it is intrinsically linked to money, as captured by the often heard refrain "Inflation is too many dollars chasing too few goods". To understand how this works, imagine a world that only has two commodities: Oranges picked from orange trees, and paper money printed by the government. In a year where there is a drought and oranges are scarce, we'd expect to see the price of oranges rise, as there will be quite a few dollars chasing very few oranges. Conversely, if there's a record crop or oranges, we'd expect to see the price of oranges fall, as orange sellers will need to reduce their prices in order to clear their inventory. These scenarios are inflation and deflation, respectively, though in the real world inflation and deflation are changes in the average price of all goods and services, not just one.

We can also have inflation and deflation by changing the amount of money in the system. If the government decides to print a lot of money, then dollars will become plentiful relative to oranges, just as in our drought situation. Thus inflation is caused by the amount of dollars rising relative to the amount of oranges (goods and services), and deflation is caused by the amount of dollars falling relative to the amount of oranges. Thus, as shown by the article "Why Does Money Have Value?", inflation is caused by a combination of four factors:

1. The supply of money goes up. 2. The supply of other goods goes down. 3. Demand for money goes down. 4. Demand for other goods goes up.

Demand-pull inflation: This is basically when the aggregate demand in an economy exceeds the aggregate supply. It is also defined as `too much money chasing too few goods'. Bare-boned, it means that a country is capable of producing only 100 items but the demand is for 105 items.

It's a very simple demand-supply issue. The more demand there is, the costlier it becomes. Much the same as the way real estate in the country is rising.

Cost-push inflation: This is caused when there is a supply shock. The best example to describe cost-push inflation is the oil shock in the 1970s. When OPEC was formed, it squeezed the supply of oil and this caused oil prices to rise, contributing to higher inflation. This is similar to what has happened recently when the oil price hike increased inflation in many a country.

However, whether you believe that inflation can be caused by `cost-push factors,' it depends on whether you're a Keynesian or a Monetarist (Prof Friedman, et al). If you are the former, you would believe in the above two reasons; but, if you're a monetarist, you would believe that only money supply matters.

The Monetarists believe that if the central bank keeps the money supply constant, then increases in the price of a Good A will reduce the money available for other goods.

Therefore, the price of the other goods will fall and offset the price increase of Good A.

This was written for Business India, and was carried in its July 19, 2004 issue with the same

title. As someone once said with a dash of humor, `Inflation is when you pay fifteen dollars for the ten dollar haircut you used to get for five dollars when you had hair.' But the de nt inflation makes in your investments is far from humorous. In fact over the long-term the `damage' is significant enough to make the most unflappable investor sit up and take notice. First, let's understand inflation a little better. Simply speaking, an inflationary situation is where there is `too much money chasing too few goods'. As products/services are scarce in relation to the money available in the hands of buyers, prices of the products/services rise to adjust for the larger quantum of money chasing them. Let's understand this with the help of an example. Let's assume Rs 500 fetches you 1 gram of gold. Suppose there is a shortfall in the global supply of gold. The obvious implication is that gold prices will rise to adjust for the sustained demand at lower supply. This may sound complicated but it's a thumb rule of demand supply - high demand combined with limited supply leads to higher prices. Let's say gold prices rise by 10%. The revised rate of 1 gram of gold will be Rs 550.However, in real terms (i.e. in terms of the commodity in question) the value of the rupee would have declined from 1 gram of gold for Rs 500 to only 0.91 gram of gold for Rs 500. So the value of the rupee has eroded. In other words, the same quantum has money now fetches you fewer goods. Now you know why that haircut does not cost the same as it did even 2 years ago.

Exchange Rate:

Exchange rates (also known as the foreign-exchange rate, forex rate or FX rate) between two currencies specifies how much one currency is worth in terms of the other. For example an exchange rate of 102 Japanese yen (JPY, ¥) to the United States dollar (USD, $) means that JPY 102 is worth the same as USD 1. The foreign exchange market is one of the largest markets in the world. By some estimates, about 2 trillion USD worth of currency changes hands every day.

The spot exchange rate refers to the current exchange rate. The forward exchange rate refers to an exchange rate that is quoted and traded today but for delivery and payment on a specific future date.

Analysis of the influence of Re-$ exchange rate on share price: The volume of international transactions has grown enormously over the past 50 years .Exports of goods and services by the U.S. now total more than 10% of the GDP. Currencies must be bought and sold because the U.S.dollar is not acceptable means of payment in many other countries. The trading of currencies takes place in foreign exchange market whose primary function is to facilitate international trade and investment Abstract: The dynamic linkage between exchange rate and stock prices has been subjected to extensive research for over a decade and attracted considerable attention from researchers worldwide during the Asian crisis of 1997-98. The issue is also important from the viewpoint of recent large cross-boarder movement of funds. In India the issue is also gaining importance in the liberalization era. With this background, the present study examines the causal relationship between returns in stock market and forex market in India. Using daily data from March 1993 to December 2002, we found that causal link is generally absent though in recent years there has been strong causal influence from stock market return to forex market return. The results, however, are tentative and we need further in-depth research to identify the causes and consequences of the findings.

Treasury Bills

Treasury Bills are money market instruments to finance the short term requirements of the Government of India. These are discounted securities and thus are issued at a discount to face value. The return to the investor is the difference between the maturity value and issue price

Why G-secs?

Provident funds, by their very nature, need to invest in risk free securities that also provide them a reasonable return. Government securities, also called the gilt edged securities or G-secs, are not only free from default risk but also provide reasonable returns and, therefore, offer the most suitable investment opportunity to provident funds.

What are G-secs?

The Government securities comprise dated securities issued by the Government of India and state governments as also, treasury bills issued by the Government of India. Reserve Bank of India manages and services these securities through its public debt offices located in various places as an agent of the Government.

The yields on both the 364-day and the 91-day T-bill yields fell below the revised repo rates of 4.5 per cent. to 4.46 per cent, implying a flat short-term yield curve.

Traders said that rising inflation also failed to have any impact on the bond markets. Inflation, as measured by the wholesale price index, was close to about 4.3 per cent. The negligible impact was partly due to the impact of oil prices on the inflation numbers.

Netted for inflation, it continues to be in the 2.5-3 per cent range. Oil prices have been increasing world wide, and are now close to be about $28 a barrel (7.5 barrels to a tonne).

But another factor driving inflation was the demand pull effect, as is evident from the rising non-food credit offtake, traders said.

But falling interest rates and rising inflation have together begun leading to a narrowing of real interest rates, which the RBI has long been focusing on. Real yields are now less than one per cent.

Yet traders said that the current liquidity build-up was more than sufficient to offset the rising credit demand. The yields for the market favourite securities reflected this trend.

SENSEX - THE BAROMETER OF INDIAN CAPITAL MARKETS

Introduction

For the premier Stock Exchange that pioneered the stock broking activity in India, 128 years of experience seems to be a proud milestone. A lot has changed since 1875 when 318 persons became members of what today is called "The Stock Exchange, Mumbai" by paying a princely amount of Re1. Since then, the country's capital markets have passed through both good and bad periods. The journey in the 20th century has not been an easy one. Till the decade of eighties, there was no scale to measure the ups and downs in the Indian stock market. The Stock Exchange, Mumbai (BSE) in 1986 came out with a stock index that subsequently became the barometer of the Indian stock market. SENSEX is not only scientifically designed but also based on globally accepted construction and review methodology. First compiled in 1986, SENSEX is a basket of 30 constituent stocks representing a sample of large, liquid and representative companies. The base year of SENSEX is 1978-79 and the base value is 100. The index is widely reported in both domestic and international markets through print as well as electronic media. The Index was initially calculated based on the "Full Market Capitalization" methodology but was shifted to the free-float methodology with effect from September 1, 2003. The "Free-float Market Capitalization" methodology of index construction is regarded as an industry best practice globally. All major index providers like MSCI, FTSE, STOXX, S&P and Dow Jones use the Free-float methodology.

Due to is wide acceptance amongst the Indian investors; SENSEX is regarded to be the pulse of the Indian stock market. As the oldest index in the country, it provides the time series data over a fairly long period of time (From 1979 onwards). Small wonder, the SENSEX has over the years become one of the most prominent brands in the country. The growth of equity markets in India has been phenomenal in the decade gone by. Right from early nineties the stock market witnessed heightened activity in terms of various bull and bear runs. The SENSEX captured all these events in the most judicial manner. One can identify the booms and busts of the Indian stock market through SENSEX. SENSEX is calculated using the "Free-float Market Capitalization" methodology. As per this methodology, the level of index at any point of time reflects the Free-float market value of 30 component stocks relative to a base period. The market capitalization of a company is determined by multiplying the price of its stock by the number of shares issued by the company. This market capitalization is further multiplied by the free-float factor to determine the free-float market capitalization. The base period of SENSEX is 1978-79 and the base value is 100 index points. This is often indicated by the notation 1978-79=100. The calculation of SENSEX involves dividing the Free-float market capitalization of 30 companies in the Index by a number called the Index Divisor. The Divisor is the only link to the original base period value of the SENSEX. It keeps the Index comparable over time and is the adjustment point for all Index adjustments arising out of corporate actions, replacement of scrips etc. During market hours, prices of the index scrips, at which latest trades are executed, are used by the trading system to calculate SENSEX every 15 seconds and disseminated in real time.

Criteria for Selection and Review of SENSEX Constituents The scrip selection and review policy for BSE Indices is based on the objective of; · Improvement · Transparency · Simplicity

Qualification Criteria: The general guidelines for selection of constituent scrips in SENSEX are as follows

A. Quantitative Criteria:

1. Final Rank: The scrip should figure in the top 100 companies listed by Final Rank. The final rank is arrived at by assigning 75% weight age to the rank on the basis of six-month average full market capitalization and 25% weight age to the liquidity rank based on six-month average daily turnover & six-month average impact cost.

2. Trading Frequency: The scrip should have been traded on each and every trading

day for the last six months. Exceptions can be made for extreme reasons like scrip suspension etc.

3. Market Capitalization Weightage: The weight of each scrip in SENSEX

based on six-month average Free-Float market capitalization should be at least 0.5% of the Index.

4. Industry Representation: Scrip selection would take into account a balanced

representation of the listed companies in the universe of BSE. The index companies should be leaders in their industry group.

B. Qualitative Criteria: Track Record: In the opinion of the Committee, the company should have an acceptable track record Index Review Frequency: The Index Committee meets every quarter to review all BSE indices. However, every review meeting need not necessarily result in a change in the index constituents. In case of a revision in the Index constituents, the announcement of the incoming and outgoing scrips is made six weeks in advance of the actual implementation of the revision of the Index. While selecting scrip from SENSEX, only those scrips were taken for study, which was there in SENSEX on Feb 2005, and also from the day they are included in SENSEX from March 2002. (All the co-efficient have been calculated)

Data analysis and their interpretation I have taken data of 3 years f sensex from Jan 2003 to Jan 2005 of sensex in the term of their return with respect to their past month performance and next 2 years Jan 2006 to Jan 2008 have been tested so that calculated and real value there are any relation between them and we have tried the found out how this model work. We have gone for t – test so the it could be known the mean between these two pairs are near about or not Now have a look on data: We have analysed the sensex index from jan 2003 to jan 2005 and I have to applied regresstion on them.here sensex return have been kept dependent and macoeconomic factors have been kept independent vairibles Then we get a regresstion line with the help of of that we calculate two years index return return between jan 2006-dec 2007 and we campare this to actual return of index. Then we applied t-test to know what is the mean of both pairs.

SUMMARY OUTPUT

Regression Statistics Multiple R 0.816831 R Square 0.667213 Adjusted R Square 0.635007 Standard Error 66.91285 Observations 35 ANOVA

df SS MS F Significanc

e F

Regression 3 278277.

6 92759.220.7175

3 1.48E-07

Residual 31 138797.

2 4477.32

9

Total 34 417074.

8

Coefficien

ts Standard Error t Stat P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 3197.966 423.968

9 7.54292

51.68E-

08 2333.2754062.65

6 2333.27

5 4062.65

6

inflation 3.525453 9.61471

4 0.36667

30.71635

4 -16.083923.1347

9 -

16.0839 23.1347

9

FII 0.013133 0.01447 0.90757

30.37110

5 -0.016380.04264

5 -

0.01638 0.04264

5

EX RATE -64.7495 9.47652

2 -

6.832631.18E-

07 -84.077 -45.422 -84.077 -45.422 RESIDUAL OUTPUT Observatio

n Predicted Residual

s 1 114.0477 -3.65208 2 129.3037 -9.90106 3 139.1042 -23.3192 4 157.4553 -40.5003 5 178.2925 -52.5473 6 203.9621 -62.5596 7 229.7929 -80.1973 8 249.4067 -77.1067 9 262.4633 -80.1861

10 299.3921 -89.1262 11 306.961 -48.9485 12 308.1575 -24.2302 13 294.8294 85.8468

3

14 303.0745 72.7281

6

15 265.1858 108.582

4

16 256.8944 145.758

1

17 289.5133 36.8652

9 18 274.35 -38.6864

19 239.6774 15.2998

4

20 242.3694 19.1306

5

21 234.7845 61.1263

5

22 275.9733 38.4591

9 23 350.9968 -27.1168 24 373.0224 -6.72244 25 387.5811 -21.4601 26 428.9691 -25.8241 27 419.4903 -41.8425 28 392.8603 -43.6078 29 414.1646 -45.7717 30 408.998 -30.7306 31 430.9375 -7.94885 32 413.1952 -14.6005

33 395.1638 62.5885

9

34 339.652 96.5180

1

35 286.1855 153.682

6

t-Test: Paired Two Sample for Means

Variable

1 Variable

2 Mean 517.75 463.0606Variance 2432.37 22373.41Observations 24 24Pearson Correlation -0.12838 Hypothesized Mean Difference 0 df 23 t Stat 1.639659 P(T<=t) one-tail 0.057342 t Critical one-tail 1.713872 P(T<=t) two-tail 0.114684 t Critical two-tail 2.068658

SBI inf FII GOV SEC

EX RATE

286 4.2 458 5.72 47.95 301 5.3 289 5.87 47.74 279 6.5 368 5.61 47.67 283 6.6 358 4.98 47.39 313 6.5 591 5.11 47.11 357 5.3 797 5.23 46.69 403 4.71 605 4.82 46.22 429 3.94 974 4.76 45.95 431 4.8 1195 4.69 45.84 480 5.13 1749 4.67 45.4 467 5.36 1031 4.48 45.15 620 5.7 1869 4.55 45.32 604 6.49 1283 4.48 45.45 589 6.13 1120 4.24 45.27 633 4.77 2002 4.44 45.96 565 4.5 1098 4.42 45.89 449 5.01 -105 4.64 45.17 439 6.74 -97 4.6 45.5 439 7.61 -259 4.88 46.05 467 8.46 1049 5.16 46.32 467 7.85 -696 5.45 46.05 501 7.26 1021 5.73 45.73 598 7.5 3218 5.78 45.03 600 6.7 1067 5.79 44.21 661 3.9 -31 5.68 43.61 704 5 2628 5.73 43.57 633 5.3 1875 5.51 43.58 635 5.9 -18 5.68 43.64 674 5.49 531 5.76 43.4

720 4.31 1046 5.78 43.52 795 4.28 2133 5.86 43.4 885 3.66 1688 5.93 43.55 898 4.11 1624 5.92 43.84 801 4.71 1624 5.97 44.73 904 4.2 500 6.2 45.3 935 4.1 978 6.6 45.64 910 4.08 2027 6.67 44.23 870 4.25 1948 6.69 44.22 940 3.9 2206 6.3 44.33 937 3.86 3617 5.7 44.82 918 4.78 3972 6.2 45.22 761 4.87 4902 6 45.88 740 4.83 7418 6.5 46.36 872 5.12 -2044 6.8 46.44 969 5.37 7794 6.8 46.02

1041 5.51 11591 6.9 45.35 1193 5.4 1757 6.99 44.72 1267 5.67 6852 7.44 44.47 1202 6.36 8506 7.52 44.21 1138 6.36 -3234 7.63 44.02

980 6.6 56450 7.89 43.79 1026 6.28 4372 7.93 42.01 1093 5.46 -2796 7.82 40.55 1395 4.52 -380 7.58 40.28 1567 4.7 818 7.13 40.67 1571 4.14 1831 7.68 40.17 1731 3.39 2154 7.69 39.17 1896 3.11 3453 8.4 39.36 2249 3.25 3328 9.57 39.31 2315 3.7 -1803 7.87 39.37

SUMMARY OUTPUT

Regression Statistics Multiple R 0.847168 R Square 0.717693 Adjusted R Square 0.681266 Standard Error 105.5404

Observations 36

Coefficients Standard

Error t Stat P-value Intercept 3852.014 748.7123 5.144853 1.42E-05 inf -26.72 14.9706 -1.78483 0.084079 FII 0.037146 0.02285 1.62566 0.114147 GOV SEC 83.89326 30.39044 2.760514 0.009605 EX RATE -79.8961 15.32591 -5.21314 1.17E-05 t-Test: Paired Two Sample for Means

Variable 1 Variable

2 Mean 1232.542 1090.196 Variance 195360.6 210424.4 Observations 24 24 Pearson Correlation #N/A

Hypothesized Mean Difference 0

df 23 t Stat 3.405663 P(T<=t) one-tail 0.001213 t Critical one-tail 1.713872 P(T<=t) two-tail 0.002425 t Critical two-tail 2.068658 Return on sensex index inf FII

EX RATE

GOV SEC

-2.6 4.2 458 47.95 5.72 1.02 5.3 289 47.74 5.87

-7.15 6.5 368 47.67 5.61 -2.91 6.6 358 47.39 4.98 7.46 6.5 591 47.11 5.11

13.42 5.3 797 46.69 5.23 5.13 4.71 605 46.22 4.82

11.91 3.94 974 45.95 4.76 4.92 4.8 1195 45.84 4.69

10.17 5.13 1749 45.4 4.67 2.81 5.36 1031 45.15 4.48

15.74 5.7 1869 45.32 4.55 -2.44 6.49 1283 45.45 4.48

-0.5 6.13 1120 45.27 4.24 1.35 4.77 2002 45.96 4.44 1.16 4.5 1098 45.89 4.42

15.84 5.01 -105 45.17 4.64 0.7 6.74 -97 45.5 4.6

7.82 7.61 -259 46.05 4.88 0.42 8.46 1049 46.32 5.16

7.5 7.85 -696 46.05 5.45 1.6 7.26 1021 45.73 5.73 9.9 7.5 3218 45.03 5.78 5.9 6.7 1067 44.21 5.79 0.8 3.9 -31 43.61 5.68

0.87 5 2628 43.57 5.73 -3.2 5.3 1875 43.58 5.51 -5.2 5.9 -18 43.64 5.68 9.1 5.49 531 43.4 5.76 7.1 4.31 1046 43.52 5.78 6.1 4.28 2133 43.4 5.86

2.21 3.66 1688 43.55 5.93 10.6 4.11 1624 43.84 5.92 -8.5 4.71 1624 44.73 5.97

11.35 4.2 500 45.3 6.2 6.9 4.1 978 45.64 6.6 5.5 4.08 2027 44.23 6.67 4.5 4.25 1948 44.22 6.69

8.76 3.9 2206 44.33 6.3 6.7 3.86 3617 44.82 5.7

-13.6 4.78 3972 45.22 6.2 2.02 4.87 4902 45.88 6

1.2 4.83 7418 46.36 6.5 8.8 5.12 -2044 46.44 6.8 6.4 5.37 7794 46.02 6.8

4.07 5.51 11591 45.35 6.9 5.67 5.4 1757 44.72 6.99

0.6 5.67 6852 44.47 7.44 2.2 6.36 8506 44.21 7.52

-8.1 6.36 -3234 44.02 7.63

1 6.6 5645 43.79 7.89 5.76 6.28 4372 42.01 7.93

4.8 5.46 -2796 40.55 7.82 0.7 4.52 -380 40.28 7.58 6.1 4.7 818 40.67 7.13

-1.5 4.14 1831 40.17 7.68 12.8 3.39 2154 39.17 7.69

14.72 3.11 3453 39.36 8.4 -2.4 3.25 3328 39.31 9.57 4.7 3.7 -1803 39.37 7.87

SUMMARY OUTPUT

Regression Statistics Multiple R 0.178967 R Square 0.032029 Adjusted R Square -0.09287 Standard Error 6.470032 Observations 36 ANOVA

df SS MS F Significan

ce F Regression 4

42.93945

10.73486

0.256439 0.9035

Residual 31 1297.70

141.861

31 Total 35 1340.64

Coefficients Standard Error t Stat P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 34.33927 45.8989

20.7481

50.4600

08 -59.2722127.95

07

-59.27

22127.95

07

inf -0.55637 0.91775

5

-0.6062

30.5487

75 -2.428151.3154

02

-2.428

151.3154

02

FII -9.3E-06 0.00140

1

-0.0066

10.9947

67 -0.002870.0028

48

-0.002

870.0028

48

EX RATE -0.45042 0.93953

7

-0.4794

10.6350

11 -2.366621.4657

74

-2.366

621.4657

74

GOV SEC -1.27381 1.86305

-0.6837

20.4992

31 -5.073522.5259

11

-5.073

522.5259

11

RESIDUAL OUTPUT Observatio

n Predicted Return on sensex index

Residuals

1 3.114236 -

5.71424

2 2.407311 -

1.38731

3 2.101652 -

9.25165

4 2.974723 -

5.88472

5 2.988727 4.47127

3

6 3.690787 9.72921

3

7 4.754785 0.37521

5

8 5.377817 6.53218

3 9 5.036003 -0.116

10 5.070932 5.09906

8

11 5.304246 -

2.49425

12 4.94158 10.7984

2

13 4.538084 -

6.97808

14 5.126677 -

5.62668

15 5.309621 -

3.95962

16 5.525219 -

4.36522

17 5.29668 10.5433

2

18 4.236394 -

3.53639

19 3.149451 4.67054

9

20 2.186141 -

1.76614 21 2.2939 5.2061

22 2.393728 -

0.79373

23 2.491458 7.40854

2

24 3.313088 2.58691

2

25 5.291473 -

4.49147

26 4.609164 -

3.73916

27 4.724959 -

7.92496 28 4.165095 -9.3651

29 4.394321 4.70567

9

30 4.966543 2.13345

7

31 4.925314 1.17468

6

32 5.117656 -

2.90766

33 4.749996 5.85000

4

34 3.951604 -

12.4516

35 3.696047 7.65395

3 36 3.08459 3.81541

Findings After studing all data and their result we find that however adjusted R of sensex is low. but when we go for T- test then we find that difference between means of both pairs calculated and actual is very less.so it proves that there is significant relationship between sensex index and macro economic factors. This significant difference reject null hypothesis and accept alternative hypothesis that APT MODEL IS accepted. SO APT MODEL IS TESTED.

CONCLUSION With the help of this research report we can say that there are significant relationship between economic factors and stocks return. In this report the model has been tested that tell about how economic factors are responsible for share return and ultimately influence the the return of sensex . So this report tells investors before going for investment in stocks is very necessary to analyse the all economic factors.and investors could take good decstion before investment.

Bibliography 1)nseindia 2) moneycontrol.com 3)rbi.org.com 4)bse sensex 5) google.com 6)busineesline 7)The economic times