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Chapter Plan

• Preliminaries

• Literature Review

• Research Problem

• Significance of the Problem

Objectives

• Hypotheses

• Methodology

• Limitations

• Chapter Scheme

• References

1

C ED7

1. INTRODUCTION

1.1 PRELIMINARIES

0

One of the earliest and most enduring questions of modern theory of

finance is whether financial asset prices can be predicted. Perhaps

because of the obvious analogy between financial investments and

games of chance, mathematical models of asset prices have an

unusually rich history that predates virtually every other aspect of

economic analysis. That many prominent mathematicians and scientists

have applied their considerable skills for forecasting financial securities'

prices testifies to the fascination for and the challenges of this problem.

Indeed, modern financial economics is firmly rooted in early attempts to

beat the market - an endeavour that is still of current interest and a

matter of hot debate in publications, conferences and cocktail parties.

In general, stock pricing models provide the relationship between the not

so well defined variables for a given financial market. There have been

several attempts in this direction, but there is no unanimity in identifying

the variables, as researchers and investors are constantly bombarded es

with vast quantities of diverse information. This study, then, attempts to

2

identify the factors, which influence the stock prices more intensely than

others do.

Before attempting to identify these factors, it is advisable to take a

journey into the history of stocks. Though it is believed that recording of

financial transactions came into being as early as 9000 B.C. to 8000 B.C.,

there is no evidence to prove the existence of such a system. However,

from around 2500 B.C. to 1800 B.C., cuneiform — i.e. writing on clay

tablets with a reed similar to a stylus - came into use extensively,

especially for financial transactions [Edward Chancellor (1999)1 1 . During

this period in Mesopotamia, there was a substantial amount of economic

activity in agriculture, crafts, ranching, trading, etc. The first bond

transactions were documented in cuneiform, where silver had been lent

out to a business, and that loan had been transferred to another

individual. In addition, the earliest stock or share transactions were also

documented in cuneiform, for funding maritime trade expeditions.

Stock exchanges originally existed in the form of 'Euro-Fairs' trading in

agricultural and other commodities during the Middle Ages. Credit was

commonly given, and therefore supporting documents such as drafts,

notes and bills of exchange were created. These were the precursors to

modern stock and bond certificates.

During the Roman period, the empire contracted out many of its services

to private groups called publicani [Edward Chancellor (1999)1 1 . Shares

3

in publicani were called `socir (for large co-operatives) and 'particulae',

(for over-the-counter shares of small companies). Though the records

available of this time are incomplete, Edward Chancellor (1999) 1 states

in his book "Devil Take the Hindmost" that there is some evidence that a

speculation in these shares became increasingly widespread and that

perhaps the first ever speculative bubble in 'stocks' occurred.

During the seventeenth century, certificates of ownership of business

came into existence. The first company to issue shares of stock after the

Middle Ages was the Dutch East India Company in 1606 [Edward

Chancellor (1999)]. The innovation of joint ownership made a great

deal of Europe's economic growth possible. The technique of pooling

capital to finance the building of ships, for example, made the

Netherlands a maritime superpower. Before the adoption of the joint-

stock corporation, an expensive venture such as the building of a

merchant ship could be undertaken only by governments or by very

wealthy individuals or families.

Economic historians found the Dutch stock market of the 1600s

particularly interesting: there was clear documentation of the use of stock

futures, stock options, short selling, the use of credit to purchase shares,

a speculative bubble that crashed in 1695 and changes in trading

patterns. Edward Stringham et al (2008) 2 also noted that practices such as

short selling continued to occur during this time despite the government

passing laws against it. This was unusual because it shows individual

4

parties fulfilling contracts that were not legally enforceable and where the

parties involved could incur a loss. Stringham argues that contracts can

be created and enforced without state sanction or, in this case, in spite of

laws to the contrary.

Since the days of the advancement of the stock market, there has been

a relentless effort to unravel the mystery of stock prices and the direction

of stock price movements. The few who could predict the direction

accurately have benefited from such predictions and created wealth. In

pursuit of this goal, several financial economists and market practitioners

have attempted to evolve methods and techniques, which would help

them to forecast stock prices accurately. However, their efforts were not

entirely fruitful and the solution to the mystery continued to elude the

players of the stock market.

The famous dramatist Oscar Wilde (1900) 3 once described a cynic as

one who "knows the price of everything, but the value of nothing". This

description holds good for some analysts and many investors who

subscribe to the theory of the 'big fool', which argues that the value of a

stock is irrelevant as long as there is a 'bigger fool' around willing to buy

the stock from them. While this may provide a basis for some profits, it

is a dangerous game to play since there is no guarantee that the latter

will still be around, when the time comes to sell.

5

Equity market professionals use a wide range of analyses to help them

make informed trading and investment decisions. Many wish to compare

current and historical market situations or review the past performance of

an instrument or index. They need tools that draw on real-time and

historical stock quotes to enable them to perform these types of

analyses.

Technical analysis charts track the historical evolution of stock quotes,

trading volumes and other indicators of activity. Technical analysts try to

identify buy and sell signals by looking at historical stock market actions.

They pay attention to recurring patterns in historical price movements, to

trends and their speed or momentum when making stock trading

recommendations.

Relative performance charts, which are also based on historical and real-

time stock quotes, enable users to compare the performance of stock

quotes against their peers or against sectors or indices over a selected

period. Other charts allow users to review how the market moved in the

past when certain fundamental levels were reached. An index-earnings

growth chart, for example, shows the relationship between earnings

growth and stock quotes for the index as a whole. These charts help

users identify buying and selling opportunities.

Institutions trading in the equity markets take data-feeds of real-time and

historical stock quotes to power their own deSktop applications, analytics •

6

and research databases. They use historical end-of-the-day stock

quotes for risk management and valuation purposes. Risk groups feed

historical end-of-day stock quotes into their systems to run their daily risk

reports. Mutual funds use historical end-of-day stock quotes to calculate

the valuation of their holdings.

Some foreign equity information products, tailored to the different needs

of different users, combine comprehensive news services, real-time

market data and powerful analysis tools. They supply real-time and

historical end-of-the-day stock quotes in flexible formats to enable

institutions to pump market information into their applications and publics

of organizations. They provide real-time equity quotes from several

exchanges over a decade or two.

Valuating common stock is a complex process, but certainly worth the

trouble for both investors and analysts. Over the years, two general

• approaches have been developed. One method called the discounted

cash flow approach estimates the stock's value based on the present

value of its future cash flows, such as dividends, operating cash flows or

free cash flows, while the other method values a stock based on its

current price relative to certain variables such as the company's

earnings, revenues or book value.

Both the discounted cash flow approach and the relative valuation

approach have certain factors in common. To start with, both techniques

7

are extensively impacted by the investors' required rate of return, because

this rate is essentially the discount rate used in many valuation models.

In addition, all asset valuation techniques are influenced by the

estimated growth rate of certain variables, such as dividends, earnings,

cash flows or sales. When one of the variables has to be estimated, the

result varies because variable inputs are likely to differ from one analyst

to another. In other words, when evaluating a stock, prices are likely to

be different because investors' required rates of return, as well as

estimates of growth rates of earnings like dividends might be different.

A postulate of sound investing is that an investor does not pay more for a

stock than its worth. This statement may seem logical and obvious, but

it is forgotten and rediscovered at some time in every generation and in

every market. There are those who are disingenuous enough to argue

that 'value is in the eye of the beholder, and that any price can be

justified if there are other investors willing to pay that price, which is

patently absurd. Perceptions may be all that matter when the asset is a

painting or a sculpture, but investors do not (and should not) buy most

assets for aesthetic or emotional reasons; stocks are acquired for the

cash flows expected on them. Consequently, perceptions of value have

to be backed by reality, which implies that the price paid for any stock

must reflect the cash flows it is expected to generate. The models of

valuation described in this study attempt to relate the stock value to the

level and the expected growth of cash flows and the risk attached to them.

8

The proposed study is empirical in nature, aimed at studying the

relationship between corporate returns (cash returns) and stock returns

(market returns) so as to understand the relationship between earnings

(cash flows) and stock prices. Cash earnings are considered in the

place of accounting earnings (book profits) in order to avoid accounting

bias. The basic aim of this study is to convey to the participants of the

market that stock prices largely depend on fundamentals (earnings)

rather than on rumours and political or economic events in society. The

study also aims at suggesting to the participating firms that if they can

release forecast data relating to their earnings for a future period on a

continuous basis and disclose deviations thereof on completion of the

said period, the stock prices could respond to the changes in earnings

rather than to unanticipated elements. This information could make the

stock market more transparent and robust, which would put the

investors' confidence on a higher plane and hence the market would

become more vibrant.

The present work focuses on the Indian Stock Market and studies only

those stocks (large cap stocks), which are actively traded on the National

Stock Exchange, Mumbai or the Bombay Stock Exchange, Mumbai with

reference to the post liberalization period.

In India, it is generally believed that stock prices are not at all rooted in

any fundamental factors, but driven by rumours, grapevine,

manipulators, speculators, high net worth, institutional investors, etc.

9

However, in reality, although these factors do play some role in

influencing the stock prices, in the long term the fundamentals generally

influence the market price. Therefore, the proposed study is an attempt to

bring to light the significant factors that influence the stock prices.

Various economists, while trying to understand the fluctuation in stock

prices, are confronted with two major variables, viz. expected earnings

and expected rate of return (cost of capital). In developed economies,

there is a mechanism to evolve projected earnings for corporate sector.

However, in India there is no institutionalized mechanism to project

future earnings for corporate firms except their own in-house

mechanism. Therefore, such data are not available in the public domain.

If this study can establish the relationship explicitly, then the Regulating

Authorities could be convinced to include the projected earnings in the

disclosure norms. The second factor is risk free rate of return (cost of

capital), which is also critical for valuation along with earnings. However,

the cost of capital of a firm does not change as sadistically as the

earnings. Therefore, it is assumed that the cost of capital of a firm

remains stable during the short term. However, in the long term the cost

of capital should be incorporated in the valuation process. Since the

cost of capital is subject to the risk premium attached to it, it is

impossible to ascertain the cost of capital accurately and maintain it at

the same level for the entire period under study. Therefore, it is thought

prudent to take the risk free rate of return as the influencing factor

instead of the cost of capital.

10

If this study could establish that, there is a definite relationship between

accounting returns (cash basis) and stock returns and that such a

relationship could be used to establish future connection, then it would

be worthwhile to convey the findings to the regulatory authorities to bring

about changes in disclosure norms.

1.2 LITERATURE REVIEW

Eugene Fama (1991)4 in his paper discusses the various hypotheses on

efficient markets and their anomalies. The paper also redefines the

common definitions of efficient markets and investigates the joint-

hypothesis problem, the costs of information and various pricing models.

In this paper the author investigates two problems of market efficiency,

the first being information and transaction cost and the second, the joint

hypothesis problem. In another paper (1999) 6 the same author states

that stock prices fully reflect the most complete and best information

available. However, Eugene Fama himself acknowledges that his

reading of the market has been a stubborn obstacle for active investors

determined to find ways to beat the market.

Darius Palia and Jacob Thomas (1997) 5 write that a common belief

among practitioners is that unexpected changes in foreign exchange

rates shall affect the market value of certain firms. Given this common

belief, the inability to document a strong and systematic

11

contemporaneous relation between stock returns and exchange rate

changes is puzzling.

Paul Krugman (1999)7 argues that under efficient market hypothesis

(EMH), at any given time asset prices fully reflect all available

information. That seemingly straightforward proposition is one of the

most controversial ideas in all social sciences research, and its

implications continue to reverberate through investment practice. The

chief corollary to the idea that markets are efficient, that prices fully

.reflect all information, is that price movements do not follow any patterns

or trends. This means that past price movements cannot be used to

predict future price movements. Rather, prices follow what is known as

a 'random walk', an intrinsically unpredictable pattern.

Jing Liu and Jacob Thomas (1999) 8 have, in their paper, attempted to

derive and test a relation between current period unexpected returns and

unexpected earnings that incorporates revisions in forecasts of future

earnings. Their motivation was to emphasize the misspecification in

returns/earnings regressions that omits information currently available

about future earnings, and to offer a solution.

Pitabas Mohanty (2001) 9 believes that there is now considerable

evidence in the US that firm specific characteristics like size, price-to-

book value, market risk premium can capture the common variation in

stock returns. However, there is no consensus among researchers on

12

whether an investor can earn risk-adjusted excess returns by investing in

small stocks.

Tuomo Vuolteenaho (2001) 10 had used a Vector Autoregressive model

(VAR) to deconstruct an individual firm's stock return into two

components: changes in cash flow (expected cash flow news) and

changes in discount rates (expected returns news). By definition', a

firm's stock returns are driven by shocks to expected cash flows (cash-

0 flow news) and/or shocks to discount rates (expected-return news). He

says that there is a substantial body of research measuring the relative

importance of cash flow and expected return news for aggregate

portfolio returns, but virtually no evidence is available on the relative

importance of these components at the firm level.

Hossein Asgharian and Bjorn Hansson (2002) 11 have investigated the

ability of factor-mimicking portfolios to explain expected returns in

I multifactor asset pricing models. In particular, the usual manner of

constructing factor-mimicking portfolios may result in estimated asset

betas (coefficient of the predictor variables) that are quite different from

the asset betas against the underlying factors, which may seriously

affect the reliability of asset pricing models.

Pastor Lubos and Pietro Veronesi (2002) 12 show that uncertainty about a

firm's average profitability increases the firm's M/B ratio as well as its

idiosyncratic return volatility. They suggest that this uncertainty is

13

especially large for the newly listed firms, but it declines over time due to

learning. Their model therefore predicts that both the M/B and the return

volatility of a typical young firm would decline as the firm ages.

Moreover, this effect is stronger for firms that pay no dividends,

confirming another prediction of the model. The model is also endorsed

by the observation that M/B declines faster for younger firms.

G. P. Samanta and Kaushik Bhattacharya (2002) 13 in their paper have

discussed the issue of whether the spread between Earning to Market

Price (E/P) ratio and interest rate contains useful information about the

movement of stock market. The results of their study reveal that though

the spread seems to have reasonably strong causal influence on returns,

the causal model helps in achieving slightly better forecasts than the

random walk model. However, they are not clear as to whether the

spread can be used as a profitable business strategy.

Andrew Ang and Jun Liu (2003) 14 have developed a model to

consistently value cash flows with changing risk-free rates, predictable

risk premiums and conditional betas in the context of a conditional

Capital Asset Pricing Model (CAPM). Practical valuation is

accomplished with an analytic term structure of discount rates, with

different discount rates applied to expected cash flows at different

horizons.

14

John Y. Campbell and Motohiro Yogo (2003) 15 in their paper argue that

tests of the predictability of stock returns may be invalid when the

predictor variable is persistent and its innovations are highly correlated

with returns. They also suggest two methods to deal with the problem.

The first one is a pretest that determines predictability of stock, when the

conventional t-test is misleading and the second, a new test of

predictability that always leads to correct inference and is more efficient

when compared to existing methods.

Francis A. Longstaff and Monika Piazzesi (2003) 16 have attempted to

quantify the risk premium attached to the standard asset-pricing theory.

They have emphasized that equilibrium asset values can be expressed

as the expected product of a pricing kernel and the cash flows from

those assets.

Burton G. Malkiel (2003) 17 in his paper presents a defence of passive

financial investment (indexing) strategies in all types of investment

markets both nationally and internationally. He justifies the case of such

strategies by relying on the theory of efficient market hypothesis and

suggests that the information generally available about individual stock

or about the market as a whole is reflected in market prices immediately.

Lakshmi Narasimhan S. and H. K. Pradhan (2003) 18 find that the Indian

stock market has witnessed drastic changes during the past decade

under the broad stock market liberalization measures. In their study, the

15

authors have tested the validity of conditional CAPM for Indian stock

market and found that the risk premium changes with changing

economic conditions. The risk premium varies over time and it is

negatively correlated with the index of industrial production. They also

argue that the risk premium increases during a recessionary phase

rather than during an expansionary phase.

Ajay Pandey (2003) 19 believes that modeling and forecasting the volatility of

• capital markets are important areas of inquiry and research in financial

economics with the recognition of time-varying volatility, volatility clustering

and asymmetric response of volatility to market movements. This stream of

research has been aided by various conditional volatility (Autoregressive

Conditional Heteroskedasticity / Generalized Autoregressive Conditional

Heteroskedasticity - ARCH/GARCH type) models proposed to handle these

empirical regularities.

Jeremy J. Siegel (2003)20 defines a bubble as "a sharp rise in the price

of an asset or a range of assets in a continuous process, with the initial

rise generating expectations of further rises and attracting new buyers -

this concerns speculators interested in profits from trading in the asset

rather than its use or earnings capacity".

Eugene F. Fama and Kenneth R. French (2004) 21 argue that the capital

asset pricing model (CAPM) is still widely used in applications, such as a

estimating the cost of capital for firms and evaluating the performance of

16

managed portfolios. The attraction of the CAPM is that it offers powerful

and intuitively pleasing predictions about how to measure risk and the

relation between expected return and risk.

Torben G. Andersen, Tim Bollerslev, Francis X. Diebold and Clara Vega

(2005)22 have discussed how markets arrive at prices. There is perhaps

no question more central to economics. Their paper focuses on price

formation in financial markets where the question looms especially large.

How, if at all, is news about macroeconomic fundamentals incorporated

into stock prices, bond prices and foreign exchange rates?

Unfortunately, the process of price discovery in financial markets

remains poorly understood.

John Y. Campbell and Samuel B. Thompson (2005) 23 wrote that towards

the end of the last century, financial economists came to take the view

that aggregate stock returns are predictable. During the 1980s, a

number of papers studied valuation ratios such as the dividend-price

ratio, earnings price ratio or smoothed earnings-price ratio. Around the

same time, several papers pointed out that yields on short-term and

long-term treasury and corporate bonds were correlated with subsequent

stock returns.

Naiping Liu and Lu Zhang (2005) 24 state that recent studies have used

the value spread to predict aggregate stock returns to construct cash-

flow betas that appear to explain the size and value anomalies. Their

17

work shows that two related variables, the book-to-market spread (the

book-to-market of value stocks minus that of growth stocks) and the

market-to-book spread (the market-to-book of growth stocks minus that

of value stocks) predict returns in different directions and exhibit opposite

cyclical variations. More importantly, value spread mixes information on

the book-to-market and market-to-book spreads and appears less useful

in predicting returns.

to, Pandey I. M. (2005)25 explores the significance of profitability and growth

as drivers of shareholders wealth, measured by the market-to-book

value (M/B). The author has studied the relationship between

profitability (economic profitability) on the one hand and M/B ratio on the

other. He has used panel data, employed Generalized Method of

Moment (GMM) estimator and found that there is a strong positive

relationship between profitability and M/B ratio. Growth on the other

hand, is negatively related to M/B ratio.

Narasimhan Jegadeesh and Joshua Livnat (2006) 26 state in their paper

that there are significant positive associations between earnings

surprises and abnormal returns, around the preliminary earnings

announcements as well as in the post-earnings announcement period.

Since earnings is a summary measure of material economic events that

affect a firm in a given period, the intense focus on earnings surprises by

investors and academics is natural.

18

Lewellen Jonathan, Stefan Nagel and Jay Shanken (2006) 27 argue that

asset pricing tests are highly misleading in the sense that apparently

strong explanatory power, in fact provides exceptionally weak support for

a model. They offered a number of suggestions for improving empirical

tests and evidenced that several proposed models do not work as

satisfactorily as originally claimed.

Jacob K. Thomas and Huai Zhang (2006) 28 state that their study is

motivated by the apparent gap between predictions regarding the

determinants of market price to earning ratios (P/E ratio) and empirical

evidence. While P/E ratio should be positively related to expected

growth rate and negatively related to risk and the level of interest

rates, prior evidence suggests weak relations at the portfolio level.

1.3 RESEARCH PROBLEM

The above studies illustrate that various attempts have been made to

ascertain the value of stocks by identifying the unexpected earnings,

dividend/price relationship, book value/market value relationship,

discounted value of dividends, earning/market value relationship etc.

However, no attempts have been made to relate accounting returns

(cash flows) to stock returns and use this relationship as a benchmark to

predict the stock prices. This relationship could also be collated with the

cost of capital as the latter has undergone a radical change vis-à-vis the

integration of Indian economy with the global economy.

19

1.4 SIGNIFICANCE OF THE PROBLEM

If the research community identifies the relevant variables that influence

the stock returns and communicates the same to the investing

community in specific, and market participants in general, it will benefit

them all in arriving at a fair market value of stocks. It will also enable the

market participants to bring about transparency in market operations and

help to build confidence in the investing community. This will lead to

create stability in the market and make markets less volatile.

1.5 OBJECTIVES

1.5.1 To establish the relationship between accounting returns (cash

basis) and risk free rate of return (as independent variables) with

market returns (as dependent variable).

1.5.2 To determine the expected stock price based on the relationship

established under 1.5.1.

1.6 HYPOTHESES

1.6.1 There is a significant relationship between the earnings and risk-

free rate of return of the firm on the one hand and stock price on

the other.

1.6.2 Earnings and risk free rate of return influence the stock price.

20

1.7 METHODOLOGY

For the purpose of this study, the following three different techniques

have been used. A brief description of these techniques is given

hereunder. At the same time, a detailed explanation for all the three

techniques is given in Chapters 2, 3 and 4.

4IL

1.7.1 Multivariate Regression Model:

The Multivariate Regression Analysis (MRA) technique is an

extension of simple regression analysis. The regression that

measures the relationship between two variables becomes a

multiple regression when it is extended to include more than one

independent (predictor) variable such as X1, X2, X3, X4, etc, in

trying to explain the dependent variable Y. In the case of simple

regression analysis, the R 2 measures the strength of the

relationship, but an additional R 2 statistic called the adjusted R2

is computed to counter the basis that will induce the R2 to keep

increasing as more independent variables are added to the

regression. Like simple regression, multivariate regression is a

powerful tool that allows the examination of the determinants of

any response variable.

21

1.7.2 Probabilistic Growth Model:

This tool is newly developed and is used to forecast the price of

stocks. In this model, it is assumed that stock price is a function

of growth rate, subject to occurrence of such a growth rate. To

capture non-linear behaviour of stocks, it is necessary to

ascertain the lognormal growth rate instead of the simple growth

rate. Therefore, the lognormal growth rate is derived for all the

observations. Another important part of this model is that it lays

emphasis on the probability of occurrence of such a growth rate,

which is calculated by using the cumulative probability for

standard normal distribution.

1.7.3 Artificial Neural Network Model:

The third tool is the Artificial Neural Network. An artificial

neural network is an information-processing model that is

inspired by the way human nervous systems process

information. The key element of this model is the new

structure of the information processing system. It comprises

of a large number of interconnected processing elements

(neurons) working in harmony to decipher a particular

problem. An artificial neural network is configured for a

specific application, such as pattern recognition or data

classification, through a learning process. Neural networks,

with their remarkable ability are able to derive meaning from

complicated or imprecise data. These can be used to mine

22

patterns and discover trends that are too complex to be

noticed by either humans or other computer software

programs.

1.8 LIMITATIONS

The main limitation of the study is timely availability of data. These

models cannot be used as a black box but should be used judiciously.

These are user-specific techniques; therefore, the user should have a

thorough knowledge of the techniques used in this study.

1.9 CHAPTER SCHEME

The chapter scheme given below has been followed in presenting the

details of the study conducted:

Chapter 1: This chapter covers introduction encompassing the

preliminary background of the study, a literature review,

the research problem and its significance, research

objectives, hypotheses, methodology, limitations and the

chapter scheme.

Chapter 2: In this chapter, the Multivariate Regression model along

with sources of data, type of data used, sampling design,

sample size, data analysis, results and interpretations are

23

discussed. Relevant references made in the chapter are

stated at the end.

Chapter 3: This, chapter covers the explanation of the Probabilistic

Growth model, including sources of data and type of data

used, sampling design, sample size, data analysis and

finally results and interpretations. References made in the

chapter are stated at the end.

Chapter 4: This part discusses the various aspects of the Artificial

Neural Network model, including sources of data, type of

data used, sampling design, sample size, data analysis

and results and interpretations. References made during

the discussion are given at the end of the chapter.

Chapter 5: Finally, in this chapter, all the observations made during

the entire study are summarized, conclusions are drawn,

recommendations are made and scope for further

research is suggested.

24

1.10 REFERENCES

1 Chancellor, E. (1999), "Devil Take the Hindmost: A history of financial

speculation", Penguin Books.

2. Stringham, E. Boettke P. and Clark J. R. (2008), "Are regulations

the answer for emerging stock markets", Evidence from the Czech

Republic and Poland" Quarterly Review of Economics & Finance,

Elsevier, Vol. 48, pp. 541 - 566.

3. Wilde, 0. (1900), "Nothing ... except my genius" Alastair Rolfe

(Compiler), Stephen Fry (Introduction) 1997, Penguin Books.

4. Fama, E. (1991), "Efficient capital market II", Journal of Finance, Vol.

46, pp. 1575 - 1617.

5. Palia, D. and Thomas J. (1997), "Exchange rate exposure and

firm valuation: New evidence for market efficiency", Harvard

(Financial decisions and control workshop) and Stanford

(Accounting Summer Camp), USA, http://www.som.yale.edu/Faculty/

jkt7/papers/fx.pdf.

6. Fama, E. (1999), "Think you can beat the market? Eugene Fama

still says you can't", Capital Ideas, Vol. 2, pp. 57 - 58.

7 Krugman Paul (1999), "Market Efficiency", http://www.deanlebaron .

com/book/ultimate/chapters/mkt eff.html, www.web.mit.edu/krugman/,

Paul Krugman's website www.ssrn.com .

25

8. Liu, J. and Thomas J. (1999), "Stock returns and accounting

• earnings", The Journal of Accounting Research, Vol. 38, pp. 71 - 101.

9. Mohanty, P. (2001), "Efficiency of the market for small stocks"

presented for NSE Research Initiative, Research Paper No. 1,

Mumbai, http://www.nseindia.com/content/research/res_papers.htm.

10. Vuolteenaho, T. (2001), "What drives firm level stock returns",

Working Paper No. W-8240, NBER, MA, USA.

4

11. Hossein, A. and Bjorn H. (2002), "'A critical investigation of the

explanatory role of factor mimicking portfolios in multifactor asset

pricing models", EFA Berlin Meetings Discussion Papers, SSRN:

http://ssm.com/abstract=302338.

12. Lubos, P. and Veronesi P. (2002), "Stock valuation and learning

about profitability", The Journal of Finance, Vol. 58, pp. 1749 - 1789.

13. Samanta, G. P. and Bhattacharya, K. (2002), "Is the spread

between E/P ratio and interest rate informative for future

movement of Indian stock market"? NSE Research Initiative,

Research Paper No. 7, NSE, Mumbai, India.

14. Ang, A. and Liu J. (2003), "How to discount cash flows with time-

varying expected returns", Working Paper No. 10042, NBER, MA, USA.

• 15.

Campbell, J. Y. and Motohiro Y. (2003), "Efficient tests of stock return

predictability" Working Paper No.10026, NBER, MA, USA.

26

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