Tangible market information and stock returns the nepalese evidence synopsis

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Tangible Market Information and Stock Returns: The
Nepalese Evidence
By Sudarshan Kadariya
Email: su.kadariya@gmail.com
ABSTRACT
The financial market has been suffering from the unforeseen and sudden economic
turbulences that have been directly or indirectly contributing for the stock returns
movements. Identifying such economic turbulences is not an easy task for the financial
economists, academicians and practitioners. Broadly, including the economic turbulences,
the market information can be divided into tangible and intangible parts and the study
focused on the tangibles. The descriptive and causal-comparative research designs are
employed for the study and the secondary database are used to testify the issues of the
study. Despite the total listed enterprises in NEPSE only the record of 826 firm years are
collected from 146 enterprises due to its availability. The variables selected for the
analysis are: earnings per share, market value per share, cash dividend in percentage,
total common stock outstanding, book value per share, sales volume or annual deposit,
and the cash flow. The study documented that only the three years of historical
accounting database are useful to find the market signals and, book to price and earnings
to price ratios have strong predictive power among the other price-scaled variables for
firm level stock returns.
1. INTRODUCTION
The financial market has been suffering from the unforeseen and sudden economic
turbulences that have been directly or indirectly contributing for stock returns movements.
Identifying such economic turbulences is not an easy task for the financial economists,
academicians and practitioners. Even though, the systematic analysis helps to grasp some
ideas and symptoms of the unwanted occurring. The financial community ultimately gets
benefitted from the pros of systematic studies. At the same time, the need of separate
discipline has been felt which solely deals with the management of financial assets, as a
result, the investment management and the portfolio theories are developed. The
evolution of the investment management and the portfolio theory has long history. The
development of investment management can be traced chronologically through three
different phases (Francis, 1986). The first phase could be characterized as the speculative

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phase before 1929. During the 1930s investment management entered in its second phase,
a phase of professionalism. Then, the investment industry began the process of upgrading
its ethics, establishing standard practices and generating a good public image. As a result,
the investment markets became safer places and the ordinary people also began to invest.
Investors began to analyze the securities seriously before undertaking investments. Then,
the investment community entered into its third phase, the scientific phase after the
Markowitz’s study in 1952.
Further, the various empirical evidences have been contributing to help create new
prospective of assets management and to extend the existing theories and principles. Such
as Stattman (1980), Chan, et.al (1991), Brav, et.al (2000), Daniel and Titman (2006)
among others documented the book-to-market equity effects on stock returns; earnings-
to-price effects by Basu (1977), earning effects by Jafee, et.al (1989), Fama and French
(1995) and La Porta (1996) among others; Banz (1981), Vassalou and Xing (2004) and
Fama and French (2008) depicted the size effects, similarly, cash flows effects by Berk,
et.al (1999) and Vuolteenaho (2002) among others are the major studies that documented
the firm specific accounting variables are the major sources of stock returns changes.
Whereas in the later period, more focus was given towards the behavioral aspects like
investors’ characteristics and behavioral issues and market behavior, news effects, media
effects, etc. In sum, the recent focus has shifted towards the intangibles rather than the
fundamental effects on stock returns. The studies on human psychology and behavioral
issues, Einhorn, et al. (1978) documented that people have great confidence in their
fallible judgment. Similarly, Einhorn (1980) conformed that the overconfidence in
judgment showed that the contribution of behavioral factors in stock returns. Ikenberry,
et.al (1995), Odean (1999), Kaniel, et.al (2008), Foucault, et.al (2011), and Doskeland
and Hvide (2011), among others, are the major studies documented that investor behavior
is the major aspect of stock returns movements. With these evidences, the general
learning in the investment community is that the event that burst out expectedly or
unexpectedly that has significant impact on investors’ mindset so that such information
plays crucial roles in individual investment decisions making, in totality on investment
performance.
After the evolution of the assets valuation models, there has been the considerable shift of
the literature towards predicting returns and developing the forecasting tools and
techniques. But, there is lack of consensus upon single model, tools and procedures. For
instance, Fama (1972) divided the stock returns into selectivity and risk, changes in

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expected future dividends or expected future returns (Campbell, 1991), cash-flow news
effect (Vuolteenaho, 2002) and Daniel and Titman (2006) proved that stock return is a
function of tangible and intangible return. These empirical evidences focused towards the
stock return decomposition which helps to identify the dimensions of returns. Nowadays,
stock returns forecasting became the central issue in Finance and the numerous studies
have been articulated to scan the manifestations of returns. Moreover, the volatile
economic environment also helps to justify these efforts. In the behavioral studies, De
Long, et.al (1990) depicted that the overreaction of prices is due to news, price bubbles
and expectations; sophisticated investors can earn superior returns by taking advantage of
under-reaction and overreaction without bearing extra risk (Barbaris, et al., 1998) and
asset prices are influenced by investor overconfidence (Daniel and Titman, 2000). Further,
the analysis of intangible information is made by Sun and Wei (2011) documented that
investors are overly sensitive to intangible information when they need to make more
subjective judgments. Similarly, many investors consider purchasing only stocks that
have first caught their attention (Odean, 2008). These evidences suggest that the
investment decisions are more than models and numbers so that the importance of
financial theories and behavior of the decision makers have been raised significantly. On
top of the behavioral evidences, number of studies revealed that the existence of
relationship of stock returns with, for instance, earnings, cash flows, dividends, returns
itself, market equity (size), book-to-market equity, leverage, etc. Size and book-to-market
equity provide a simple and powerful characterization of the cross-section of average
stock returns (Fama and French, 1992, Daniel and Titman, 1997) and on the contrary,
Kothari, et al., (1995) documented the relationship between book-to-market equity and
stock returns is weaker and less consistent.
Apart from the voluminous studies in the developed and western economies, limited
studies have been conducted in the developing and transitional economies like Nepal. The
positive relation between stock returns and size where as inverse relation between returns
and market-to-book value (Pradhan, 1993), the positive relation of stock returns with
earning yield and size whereas negative relation with book-to-market ratio and cash flow
yield and book-to-market value (Pradhan and Balampaki, 2004). These studies provided
the evidences that book-to-market equity and size are the major determinant of stock
returns even if the capital market is inefficient in nature.
The study of the stock returns and market information occupies an important place in
financial management. It has received much attention in recent years for identifying the

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market signals to achieve relatively higher stock returns. The evidences on the stock
returns and market information indicate that this area is useful for financial decision
making process. More specifically, the insight from the analysis of stock returns and
market information are useful to achieve the short-run stock returns while the market
became more volatile due to various influences like the news effect, political effect, the
fundamental information disclosure effect, etc. The momentum and trend in varying
circumstances, magnitude and directions help to pretend the future development of stock
market. In general, the market signals provide in-depth knowledge about the effects and
ranges of market information in different period of time. Thus, the market deserved the
need for extensive studies on market information and stock returns thus became an
important area of study in recent years. With this perspective, the study devoted to market
information and stock returns may be a rewarding one both for the academicians and
practitioners.
Now, it is important to realize that stock return is a function of multiple interacting factors
in the capital market. It has been gradually influencing by the defined and undefined
factors. The information available in the market could be disseminated by the
management or could be developed through the end of invisible sources. The magnitudes
of the information that incorporated in stock prices are determined by the nature and form
of the capital market. Along with the information effect, the variation in stock prices can
also be affected by the future prospects and the other unseen factors. Thus, the study
helps to enhance the knowledge by decomposing the stock returns and market
information. There are a number of ways to decompose the information that influence the
stock prices. For instance, Fama (1972) segregate the stock returns into selectivity and
risk; Campbell (1991) decompose the stock returns into a component that reflects
information about cash flows, and a second component that reflects information about
discount rates; Daniel and Titman (2006) decompose the returns into tangible and
intangible returns. Similarly, the study decomposes the information into two components;
the first one is firm’s past and current performance that is described in its financial
statements are treated as tangible information which is relatively concrete and, which is
by definition orthogonal to the tangible information is refer to as intangible information.
More specifically, the financial indicators that can be generated from the financial
statement of the enterprises are categorized into tangible parts and the other information
which is not tangible and orthogonal to the tangible information is categorized into
intangible parts. In light of the separation of market information into two components, the

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study also decompose the stock returns into tangible return –which is associated with past
performance or supported by the tangible information and intangible return – which is
unrelated to past performance of the firm itself or backed by the intangible information.
The decomposition results might be a useful procedure to grasp the far sights in the
capital market so that one can perform well than others and focused on the tangible part.
In light of the description of the market information, the study deals with the following
issues:
o What is the relationship between past tangible information and future returns?
o Is there relationship between past intangible returns and future returns?
o Is there association between the fundamentals to price scaled variables with the future
returns?
o Do the stock prices overreact to the past performance?
o What is the most predictable fundamental accounting growth measure in stock
exchange?
o How long the past fundamentals help to predict the market returns?
The basic objective of the study is to analyze the market information and stock returns in
Nepalese stock market. More specifically, the evaluation of the relationship between
stock returns and the fundamental measures is the objective of this study.
2. REVIEW OF LITERATURE
The stock market movement is one of the most popular areas in finance. The French
mathematician, Louis Jean-Baptiste Alphonse Bachelier is considered the first person to
formulate the stochastic model or the random process. Bachelier (1900) the seminal work
is now called Brownian motion. Brownian motion is the presumably random drifting of
particles suspended in a fluid or the mathematical model used to describe such random
movements, which is often called a particle theory. The mathematical model of Brownian
has several real-world applications. An often quoted example is stock market fluctuations.
However, the movements in share prices may arise due to unforeseen events which do not
repeat themselves. The Bachelier’s study primarily focused on; what is the probability
that a certain market price be attained before a certain date? And, the application of
probability theory to the stock exchange. The influences for price movements are
innumerable – the past, current and even anticipated events that often have no obvious
connection with its changes might influence the prices. Apart from the natural variations,
some artificial causes might also intervene for price movements. The stock price

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movements depend upon infinite number of factors. Thus, it is almost impossible to
predict the market prices accurately using the econometric and the mathematical
modeling. For instance, at the same time, some buyers believe an increase in the prices
whereas sellers trust a decrease. Therefore, it is just an imagination that one can win with
certainty in the stock market. Even if such happened, the combination will not be
persistent because the buyer believes in a probable rise, otherwise he would not buy, but
if he buys, it is because someone sells to him, whereas and the seller obviously believes in
a probable decline. With these explanation, it is logical to state that the dynamics of the
stock price movements is never be an exact science, but it is possible to study
mathematically and with the application of econometric model given that the static state
of the market at a given point of time.
Bachelier (1900) which laid the foundation of stock price predictability and the pioneer
work by Markowitz (1952), the portfolio theory, provided the basis for individual
investors to allocate their resources with due consideration of risk and return tradeoff.
Further, the portfolio theory extended to CAMP which explains the individual stock co-
movements with the overall markets that determine the performance of the stock or the
expected returns which helps to forecast the stock prices. Specially, after the evolution of
CAPM in 1960s, many studies have been carried out to determine the factors affecting the
stock returns. But, the review of major studies suggests that there is lack of consensus on
a single model, methodology and the process of determining the stock returns. For
instance, some evidences shows that stock returns is divided into selectivity and risk
factors whereas others proved that the changes in expected future dividends or expected
future returns leads the stock prices; the firm specific fundamental measures are the
sources of stock returns; the intangible components are the prime causes of stock returns;
the behavioral issues dictates the stock prices; the stock market itself determine its future;
among others, are the major areas of market information and stock returns which have
been continuously contributing for the stock price movements. These empirical evidences
clearly postulated that there are multiple factors that have been supplying variations in
stock returns. So that, there is absence of consensus among the existing evidences
regarding the single area of the study, methodology, tools and techniques which clearly
indicates the importance of further studies in this area of Finance.
Stock returns forecasting being the central issues in Finance, numerous studies have tried
to find the most reliable model, tool and technique that explain the majority of variability
of stock returns. To identify such variations, multiple qualitative and quantitative

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techniques occupy the major pie of the previous studies. Some studies prioritized the firm
level accounting variables whereas many others documented other variables such as
investor behavior, market behavior, media and political effects, etc. The effects of
individual investor behavior in stock returns have been documented by Lakonishok, et.al
(1994), Ikenberry et al. (1995), Barberis, et.al (1998), Klibanoff, et.al (1998), Odean
(1999), Chan (2003), Biais et.al. (2005), Barber and Odean (2008), Kaniel, et.al (2008),
Foucault, et.al (2011), Sun and Wei (2011) and Doskeland and Hvide (2011), among
others. The perception of individuals and the enterprises have been documented by
Loughran and Ritter (1995), Armen et.al. (2001), and, Brau and Fawcett (2006) among
others. Many academicians and the practitioners still believe on trends and the time bound
cycles that stock market assumed to be followed. Contrary, some others trust that stock
market exhibit the random walk behavior though both ideas have been supported by the
empirical evidences. Regarding the market behavior, some of the major studies are Fama
(1965), French (1980), Brown and Warner (1985), Ritter (1988), Schwert (1989), De
Long, et al (1990), Hasbrouck (1991), Jegadeesh and Titman (1993), Chan, et.al (2001),
Hirshleifer (2001), and, Baker and Wurgler (2002), among others.
On top of the stated intangible variables, the risk mismeasurement, credit rating, trading
halts, analyst’s coverage, and alike also have been fueling for the stock market
movements. Some major studies on intangible effects are Bernard and Thomas (1989),
Goh and Ederington (1993), Lee, et.al (1994), Brennan and Subrahmanyam (1995), Hong,
et.al (2000), Tumarkin and Whitelaw (2001), Conrad, et.al (2002), Vega (2006),
Worthington (2006), Epstein and Schneider (2008), and Hertzberg, et.al (2010), among
others. These studies also incorporated the internet message board effects, news effects,
corporate policy, and political effects on stock returns. These studies provided some
insight that there are some other dimensions in the stock market that have been
consistently providing information about the future movements so that these variables can
also be incorporated as an important factor for stock returns. For instance, On media
coverage, Merton (1987) documented the media coverage, public relations and other
investor marketing activities could play an important causal role in creating and
sustaining speculative bubbles and fads among investors; Tetlock (2007) revealed the
three primary contributions, firstly, high levels of media pessimism robustly predict
downward pressure on market prices, followed by high or low values of media pessimism
forecast high market trading volume and finally, low market returns lead to high media
pessimism. Similarly, high-media coverage stocks earn lower returns (Fang and Peress,

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2009) and Engelberg and Parsons (2011) showed that the presence or absence of local
media coverage is strongly related to the probability and magnitude of local trading. On
news effect - Campbell and Hentschel (1992) depicted that much of the variance of stock
returns is in fact due to other changes in expected excess returns, and not to news about
future dividends; the number of news stories and market activity are directly related
(Mitchell and Mulherin, 1994), among others. On investor overreaction and underreaction
- the systematic price reversals for stocks that experience extreme long-term gains or
losses and, excess returns in January are related to past performance (DeBondt and Thaler,
1987); contrarily to the above findings, Zarowin (1989) failed to support the overreaction
to earnings hypothesis and concluded the winner-loser effect is primarily a size effect.
Russo and Schoemaker (1992) showed that the overconfidence has remained a hidden
flaw in managerial decision making; Daniel, et.al (1998) described overconfidence
implies negative long-lag autocorrelations, excess volatility, and, when managerial
actions are correlated with stock mispricing, public-event-based return predictability;
overconfidence increases trading volume and market depth, but decreases the expected
utility of overconfident traders documented by Odean (1998); and, Hong and Stein (1999)
showed each news watcher observes some private information, but failed to extract other
news watchers’ information from prices; among others before 2000. Daniel and Titman
(2000) documented that asset prices are influenced by investor overconfidence; trading is
hazardous to your wealth (Barber and Odean, 2000); Under-confidence or pessimism
cannot survive in financial market, but moderate overconfidence or optimism can survive
and even dominate, particularly when the fundamental risk is large (Wang, 2001); Barber
and Odean (2001) documented that men are more prone to overconfidence than women,
particularly so in male-dominated realms such as Finance; A trader’s expected level of
overconfidence increases in the early stages of this career (Gervais and Odean, 2001); and,
high information uncertainty exacerbates investor overconfidence and limits rational
arbitrage (Jiang et.al, 2004), among others.
Apart from the voluminous studies in the developed and western economy, limited
studies were conducted in local context. Some of the major studies include the positive
relation between stock returns and size where as inverse relation between returns and
market-to-book value by Pradhan (1993). Stock returns is positively related with earning
yield and size, where as negatively related to book-to-market ratio and cash flow yield
and among the others, book-to-market value is found to be more informative by Pradhan
and Balampaki (2004), Baskota (2007) revealed that there is no persistence of volatility in

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Nepalese stock market and the stock price movements are not explained by the macro-
economic variables, Prasai (2010) documented a significant positive relationship between
size and stock returns and a significant negative relationship between book to market
equity and stock returns, among others.
Thus, the review of previous studies provides sufficient evidences on the controversy
among the existing studies for explaining stock returns. Such contradictions proved that
there is lack of consensus on single model, tool and technique or justify the need for
further studies in this area. Similarly, the impact of market information on stock returns is
inconclusive. More specially, in local context, the study on the market information and
stock returns is most probably a novel phenomenon. In addition, the concurrent
development of Nepalese financial sector and the gradual expansion of the economy
along with its sophistications deserve the study.
3. RESEARCH METHODOLOGY
3.1 Research design
The research designs adopted in this study consists of descriptive and causal-comparative
research design. The descriptive research design is a fact-finding operation searching for
adequate information. It is undertaken in order to ascertain and be able to describe the
characteristics of the variables of interest. It is a type of study, which is generally
conducted to assess the opinions, behaviors, or the characteristics of a given population. It
does not necessarily seek to explain relationships, test hypothesis, make predictions or get
the meanings and implications of a study rather it is a process of accumulating facts. The
descriptive research design is selected for the study to learn the profile of the respondents,
presentation and description of the data collection, and to describe the characteristics of
the investors in the Nepalese stock market. The causal-comparative research investigates
the possible causes affecting a particular situation by observing existing consequences
and searching for the possible factors leading to the results. This research is also known
as ‘ex post facto’ or ‘after the fact’ research (i.e. data are collected after all the events of
interest occurred). This is because both the effect and alleged causes have already
occurred. In other words, causal-comparative research is that research in which the
independent variable or variables have already occurred and in which researcher starts
with the observation of the dependent variable or variables. Then, analyze the
independent variables in retrospect for their possible relations to, and effect on the

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dependent variable or variables. This research design is selected for the study to delineate
the cause of one or more variables in stock returns.
3.2 Nature and sources of data
The study employed the secondary data to validate the issues of the study. Various tests
have been carried out by using annual dataset of listed enterprises of NEPSE upto mid-
July 2010. The annual database covered the period from July 16th
to July 15th
because of
the availability of annual reports as per the Nepali calendar. The secondary data are
collected from NEPSE and SEBON database. Further, some of them are collected directly
from the listed enterprises. The major sources of the secondary data are: the annual
reports of security market authorities and the listed enterprises. Despite the total listed
enterprises (176) in NEPSE and 1443 firm year at mid-July 2010, only the database of
826 firm years are collected (Details are presented in Appendix A) from 146 enterprises
due to its availability. The firm year is defined as the difference between the mid-July
2010 and listing date of the enterprise. The variables selected for the analysis are:
earnings per share (EPS), market value per share (MPS), cash dividend in percentage,
total common stock outstanding (size), book value per share (BPS), sales volume or
annual deposit (in case of financial institutions), and the cash flow (Details are presented
in Appendix B).
3.3 Selection of enterprises
The study incorporates 146 listed enterprises out of total 176 till July 15th
, 2010. Though,
the focus of the secondary data analysis is towards the study of the large sample or the
total listed enterprises in NEPSE from mid-January 1994 to mid-July 2010 are treated as
large sample suggested by Chan, et.al (1991), Devas, et.al, (2000), Asness, et.al (2000),
Grinblatt and Moskowitz (2004), Kaniel, et.al (2008), Hertzberg, et.al (2010), and
Foucault, et.al (2011) among others. The sample periods vary from stock to stock but
usually run from January 1994 to July 2010. The final date is the same for all stocks but
the initial date varies. The selected enterprises include both manufacturing and non-
manufacturing enterprises: insurance companies, financial institutions, hydropower and
trading firms of Nepal Stock Exchange and also the delisted securities are included. But,
some enterprises are not included in this large sample because of its unavailability of
required data.

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Table 3.1 presents the sector-wise distribution of listed (NEPSE) enterprises and selected
enterprises for secondary data sources. The figures corresponding to the listed enterprises
show the number of enterprises in each sector of the economy. The listed enterprises are
considered as the population of the study for the secondary data analysis. The figures
corresponding to the population indicate the percentile values. Whereas, in subsequent
row, figures indicates the number of selected enterprises based on the data availability.
The row total constitutes 146 enterprises. The majority enterprises were selected from the
merchant banking and finance sector and about 83 percent of population included for the
study.
Table 3.1: Sector-wise Distribution of Population and Selected Enterprises
Sector CB DB MF IC MC OS Total
Listed Enterprises 23 40 61 19 18 15 176
Population
Figures in percentage
100%
13.07 22.73 34.66 10.8 10.23 8.52
Selection
Figures in number
146
23 37 59 18 1 8
CB=Commercial banks, DB=Development banks, MF=Merchant Bank & Finance companies,
IC=Insurance companies, MC=Manufacturing companies, and OS=Others (Hotels, Hydro, Trading, Telecom & Film)
Source: www.nepalstock.com
The comprehensive details of the selected enterprises with trading symbol, date of listing,
study period and numbers of observations are presented in Appendix H. The overview of
the observation table is shown in Table 3.2.
The majority of the observations are collected from finance companies comprises 45.76
percent followed by commercial banks (21.67 percent). Because of the lack of proper data
availability of Nepalese manufacturing sector, its presence for the study is low which has
only 1.09 percent of total observations. The observable section shows the potential firm
year for secondary data collection which indicates 1443 firm year from 176 enterprises.
The next section explains the observed firm year which is 826 from 146 enterprises. The
proportion indicates the selection of enterprises out of the population, for instance, total
commercial banks are included for the study whereas only 5.56 percent of manufacturing
firms are considered. Similarly, the next column describes the ratio of observed firm
years out of its potentials, for example, 89.93 percent of the potential firm years under
development bank are considered for analysis followed by commercial banks whereas
manufacturing firms constitute only about six percent. Finally, the percentage column

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exhibits the allocation of total observations into different sector of economy. Thus, the
study is based on 826 observations.
Table 3.2: Overview of sector-wise observations
SN Sector
Observable Observed Proportion
PercentageEnterprises
Firm
Yrs
Enterprises
Firm
Yrs
Selection Obs.
A Commercial Banks 23 201 23 179 100.00 89.05 21.67
B Development Bank 40 139 37 125 92.50 89.93 15.13
C Finance Companies 61 486 59 378 96.72 77.78 45.76
D Insurance Companies 19 179 18 87 94.74 48.60 10.53
E Manufacturing firms 18 265 1 9 5.56 3.40 1.09
F
Others (hydro, hotels,
trading, telecom & film)
15 173 8 48 53.33 27.75 5.81
Total 176 1443 146 826 82.95 57.24 100.00
Source: www.nepalstock.com
3.4 Method of analysis
The study intends to analyze the market information and stock returns, and to determine
the factors affecting investment decision making among others. The tangible information
is assumed as: earnings per share, market value per share, cash dividend in percentage,
total common stock outstanding, book value per share, sales volume or annual deposit,
and cash flows and its effects on stock returns is analyzed in the study.
The method of analysis constitutes the descriptive statistics, correlation matrix analysis,
portfolio formation, regression analysis, Kolmogorov-Smirnov test, stock returns
decomposition, the test of significance of econometric models using t-tests, f-tests, and
the detection and correction of autocorrelation, multicolinearity and heterocedasticity.
Further, the following regression models are formulated to exact the relationship of stock
returns on the tangible information. The firm specific variables and the market index
variables are treated as dependent and independent in order. The review of literature
suggests that book-to-market equity is a significant independent variable that explains
major variations in stock returns. Thus, book-to-market equity is assumed to be an
essential explanatory variable. Different regression models proposed for the study is
describes as below.
With the description of dependent and independent variables, the basic regression models
for the study are presented as follows:
Model 1: Log (Bit/Mit) = α + b1 BMi0 + b2  Bi + b3  Mi + ut ………………………………… (3.1)
Log (Bit/Mit) = α + b1 LogBMi0 + b2Log Bi + b3Log Mi + ut …………….. (3.1a)

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Priori sign (+) (+) (-)
Where,
Log (Bit/Mit) = log book to market equity of the ith firm at t periods
BMi0 = book to market equity of ith firm at 0 period
 Bi = change in book value of ith firm
 Mi = change in market price of ith firm
ut = random terms
The equation 3.1 decomposes the book-to-market equity which helps to illustrate the
good news effects and bad news effects on stock returns. The error term is added in the
above equation because the given variables do not always hold exactly. The priori sign
for book-to-market equity is positive as suggested by Chan, et.al (1991), The log book-to-
market ratio of the firm at time t can be expressed as its book-to-market ratio at time 0,
plus its change in book value, minus its change in the market value, that is,
Log (Bit/Mit) = BMi0 +  Bi -  Mi ……………………………………………………………….. (3.1b)
Now it is assume that, a time 0, all firms have the same log book-to-market ratio (bm0),
and that between time 0 and time t, information about the news arrives. Suppose that
some firms receive good news and some firms get the bad news about the ongoing
projects. Consistent with Bernard and Thomas (1989) and Zhang (2006) among others,
the study assume that poor earnings convey sufficiently bad information about the firm’s
future earnings, the market response to the bad earnings news inversely causes the stock
price to fall proportionately more. In other words, ︱  mi︱>︱  bi︱ resulting an
overall increase in bmi. On the other hand, good news has the opposite effect: change in
book value is positive, but change in market value is more positive, resulting a decrease
in bm. For example, if the book value is Rs 100, market value is Rs 200, then in case of
bad news about the earnings cause the decline in book value by Rs 10 and the market
value by more than decrease in book value, for instance Rs 30 so that the book-to-market
equity increases to 0.53 from 0.50. The same case can be explained in the opposite way
by increase in book value of the stock Rs 10 due to the good news information about the
earnings. The market perceived the good news proportionately more, for instance Rs 30
so that the new book-to-market ratio reaches to 0.48 from the original 0.50. In sum, the
bad news causes book-to-market equity to increase and the good news causes the book-
to-market equity to decrease. Under this interpretation, low bm firms are those that realize
higher earnings than high bm firms.

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Model 2: r(t-i,t) = E(t-i)[r(t-i,t)] + rT
(t-i,t) + rI
(t-i, t) + ut ……………………………………………… (3.2)
Where,
r(t-i,t) = firm level stock returns from t-i to t where i is the lag period
E(t-i)[r(t-i,t)] = the expected returns at t-i, to t, and
rT
= returns resulting from tangible information
rI
= returns resulting from intangible information
Model 2 in equation 3.2 indicates the decomposition of stock returns into tangible and
intangible components that can be attributable to tangible information and intangible
information from the period t-i to t. More specifically, the realized returns from t-i to t can
be explained by this model. Where E(t-i)[r(t-i,t)] is the expected returns at t-i, to t, and rT
and
rI
are the unanticipated returns resulting from tangible and intangible information
respectively during the same period.
In terms of BM decomposition, the equation 3.2 can also be expressed by replacing each
component with their proxy variables. If the accounting growth measures are taken as the
tangible information, then the distinction between tangible and intangible returns can be
viewed as a distinction between that portion of a stock’s returns that can be explained by
the accounting growth measures and that portion that is unrelated to those of fundamental
performance measures. With assuming the proxies of tangible and intangible
information – book value to lag book value and market price to lag-market price,
respectively. Thus, the extension of the equation 3.2 into the log-linear model is as below.
Model 3: log [Bt/Pt] = α + b0 log [Bt-i/Pt-i] + b1 [Bt/Bt-i] + b2 [Pt/Pt-i] + ut ……………(3.3)
r(t-i,t) = α + b0 log [Bt-i/Pt-i] + b1 [Bt/Bt-i] + b2 [Pt/Pt-i] + ut ……………..… (3.3a)
r(t-i,t) = α + b0 log [Bt-i/Pt-i] + b1 log [Bt/Bt-i] + b2 log [Pt/Pt-i] + ut ……….. (3.3b)
Where,
log [Bt/Pt] = log book to market equity at time t
log [Bt-i/Pt-i] = lagged log book to market equity at i lag periods
[Bt/Bt-i] = book value per share to lagged book value per share at i lag periods
[Pt/Pt-i] = market price per share to lagged market price per share at i lag
periods
r(t-i,t) = firm level stock returns from t-i to t where i is the lag period. For
instance, 5th
lagged period returns can be calculated as: ((MPPS - Lag5_MPPS) /
Lag5_MPPS) + Lag5_CD

15
The elements of this BM decomposition in equation 3.3 are directly related to those of the
tangible and intangible return decomposition given in equation 3.1. First, log [Bt-i/Pt-i]
serves as a proxy for the firm’s expected return between t-i to t. The log [Bt/Bt-i] captures
both the anticipated and unanticipated growth in book value from t-i to t. The
unanticipated component of this can be thought of as a proxy for the new tangible
information that arrives between t-i to t, while log [Pt/Pt-i] is equal to log return which
reflect all information, tangible as well as intangible. Similar to equation 3.1, the priori
expected sign for b1 is positive and b2 is negative as suggested by Daniel and Titman
(2006). The study use the log-linear model (log-log or double-log) because, first of all, it
measure the elasticity between the dependent and independent variables; secondly, the log
model transform the observed data into smaller scale because the log which is also known
as logarithm or common log has base 10; whereas by convention, ln means natural
logarithm which has base e (i.e. 2.1718).
Model 4: log [St/Pt] = α+ log [St-i/Pt-i] + rS
(t-i,t) + r(t-i,t) + ut ……..………………....... (3.4)
log [St/Pt] = α + b0 log [St-i/Pt-i] + b1 [St/St-i] + b2 [Pt/Pt-i] + ut ………………(3.4a)
r(t-i,t) = α + b0 log [St-i/Pt-i] + b1 [St/St-i] + b2 [Pt/Pt-i] + ut …………….….… (3.4b)
r(t-i,t) = α + b0 log [St-i/Pt-i] + b1 log [St/St-i] + b2 log [Pt/Pt-i] + ut …..………. (3.4c)
Where,
log [St/Pt] = log sales to price ratio at time t
log [St-i/Pt-i] = log sales to price ratio for the period i to t-i where i is the lag
periods
rS
(t-i,t) = sales returns for the period i to t-i where i is the lag periods
r(t-i,t) = firm level stock returns from t-i to t where i is the lag periods
[St/St-i] = sales to lagged sales ratio for the period t-i to t where i is the lag
periods
[Pt/Pt-i] = market price to lagged market price ratio for the period t-i to t
where i is the lag periods
ut = random terms
Similar to the book-to-market decomposition approach, it is also possible to decompose
the other accounting ratios that have been shown to predict stock returns. Equation 3.4
shows the decomposition of sales to price ratio, the log sales to price ratio into log sales to
price at t-i, to t period, log change in sales per unit of share which is viewed as the

16
another proxy for the tangible return and the next component is firm’s stock return at t-i,
to t period assuming firm returns only through market price of firm stock. The expected
sign of the coefficients are positive, positive and negative respectively for model 4.
Model 5: log [Ct/Pt] = α+ log [Ct-i/Pt-i] + rC
(t-i,t) + r(t-i,t) + ut …….………………....... (3.5)
log [Ct/Pt] = α + b0 log [Ct-i/Pt-i] + b1 [Ct/Ct-i] + b2 [Pt/Pt-i] + ut …..………… (3.5a)
r(t-i,t) = α + b0 log [Ct-i/Pt-i] + b1 [Ct/Ct-i] + b2 [Pt/Pt-i] + ut …...……….….… (3.5b)
r(t-i,t) = α + b0 log [Ct-i/Pt-i] + b1 log [Ct/Ct-i] + b2 log [Pt/Pt-i] + ut ….………. (3.5c)
Where,
log [Ct/Pt] = log cash flow to price ratio at time t
log [Ct-i/Pt-i] = log cash flow to price ratio for the period i to t-i where i is the lag
periods
rC
(t-i,t) = cash flow returns for the period i to t-i where i is the lag periods
[Ct/Ct-i] = cash flow to lagged cash flow ratio for the period t-i to t where i is
the lag periods
[Pt/Pt-i] = market price to lagged market price ratio for the period t-i to t
where i is the lag periods
ut = random terms
Model 6: log [Et/Pt] = α+ log [Et-i/Pt-i] + rE
(t-i,t) + r(t-i,t) + ut ……..………………....... (3.6)
log [Et/Pt] = α + b0 log [Et-i/Pt-i] + b1 [Et/Et-i] + b2 [Pt/Pt-i] + ut …..……….…(3.6a)
r(t-i,t) = α + b0 log [Et-i/Pt-i] + b1 [Et/Et-i] + b2 [Pt/Pt-i] + ut …...……….…..… (3.6b)
r(t-i,t) = α + b0 log [Et-i/Pt-i] + b1 log [Et/Et-i] + b2 log [Pt/Pt-i] + ut ….………. (3.6c)
Where,
log [Et/Pt] = log earnings to price ratio at time t
log [Et-i/Pt-i] = log earnings to price ratio for the period i to t-i where i is the lag
periods
rE
(t-i,t) = earnings returns for the period i to t-i where i is the lag periods
[Et/Et-i] = earnings to lagged earnings ratio for the period t-i to t where i is the lag
periods

17
[Pt/Pt-i] = market price to lagged market price ratio for the period t-i to t where i is
the lag periods
ut = random terms
Model 7: r(t-i,t) = α + b0 [Bt-i/Pt-i] + b1 [St-i/Pt-i] + b2 [Ct-i/Pt-i] + b3 [Et-i/Pt-i] + ut …… (3.7)
Priori sign (+) (+) (+) (+)
Where,
[Bt-i/Pt-i] = book to price ratio for the period i to t-i where i is the lag periods
[St-i/Pt-i] = sales to price ratio for the period i to t-i where i is the lag periods
[Ct-i/Pt-i] = cash flow to price ratio for the period i to t-i where i is the lag
periods
[Et-i/Pt-i] = earnings to price ratio for the period i to t-i where i is the lag
periods
ut = random terms
The regression model 3.7 indicates that the price scaled variables as the independent
variables for the firm level stock return as dependent variable. The model shows the
independent effect of price scaled variables’ effect for the stock returns at ith lag periods.
Model 8: rt = α + b1 B/P(t-i,t) + b2 E/P(t-i,t) + b3 rB
(t-i,t) + b4 r(t-i,t) + b5 ι(t-i) + ut ………..(3.8)
Expected sign (+) (+) (-) (-) (-)
Where,
B/P (t-i, t) = book to price ratio for the period i to t-i where i is the lag periods
E/P (t-i, t) = earnings to price ratio for the period i to t-i where i is the lag
periods
rB
(t-i,t) = book returns for the period i to t-i where i is the lag periods
ι(t-i) = composite share issuance variable the period t-i where i is the lag
periods
ut = random terms
The regression model 3.8 is expected to yield the results from a set of Fama-MacBeth
regressions of stock returns on various components of the book-to-market decomposition
with addition of lagged stock return and composite share issuance measures as
explanatory variables. The dependent variable in model 5 is annual firm level log stock

18
returns and the independent variables are: log book to market ratio at time t, and t-i; log
book return from t-i to t; firm level past log return; and the composite share issuance
variable, respectively. The expected sign as suggested by Daniel and Titman (2006) are:
positive, positive, negative, negative, and negative, respectively for the above mentioned
variables.
The calculation of tangible and intangible information can also be possible with the
following regression model. With this model, the calculation of the portion of the stock
returns that cannot be explained by fundamental accounting variables is possible. The
tangible return is the portion of stock return that can be explained by fundamental
variables whereas the unexplained portion as the intangible return. The stock returns
decompose into tangible and intangible information as below.
Model 9: ri(t-i, t) = b0 + b1 B/P (t-i, t) + b2 ri
B
(t-i,t) + ui,t …………………………….. (3.9)
rB
ut = random terms
Where, (b0 + b1 BM (t-i,t)) is the proxy of expected return, (b2 ri
B
(t-i,t)) is tangible return and
ui,t is the proxy of intangible information. Model 5 and extension of the similar models by
accounting growth measures estimate the coefficients of the parameters so that estimated
tangible and intangible returns can be determined using the following equations.
The firm’s tangible return over certain the time period can also be calculated as;
ri
T(B)
(t-i,t) = bˆ0 + bˆ1. BP (t-i,t) + bˆ2.ri
B
(t-i,t) ……….…………………… (3.9a)
and the intangible return is;
r i
I(B)
(t-i,t) = uit ………………………………………………………... (3.9b)
The sum of the tangible and intangible returns is equal to the total stock returns.
With the same procedures, using the following regression equations, the total tangible and
intangible returns can be calculated.
Model 10: ri(t-i, t) = y0 + y1B/P (t-i,t) + y2S/P (t-i.t) + y3C/P (t-i,t) + y4E/P(t-i,t) + y5.ri
B
(t-i,t) +
y6.ri
S
(t-i, t) + y7.ri
C
(t-i, t) + y8. ri
E
(t-i, t) + ui,t …………………………..… (3.10)

19
Where,
S/P (t-i, t) = sales to price ratio for the period i to t-i where i is the lag periods
C/P (t-i, t) = cash flow to price ratio for the period i to t-i where i is the lag
periods
E/P (t-i, t) = earnings to price ratio for the period i to t-i where i is the lag
periods
rB
rS
rC
rE
ut = random terms
Specifically, in each year, the past return for each enterprise is broken up into three parts,
namely, an expected return (growth) component captured by the lagged accounting
measures, and unanticipated tangible and intangible return components.
The expected returns can be calculated as:
E(ri) = yˆ1B/P (t-i,t) + yˆ2S/P (t-i.t) + yˆ3C/P (t-i,t) + yˆ4E/P(t-i,t) ………………… (3.10a)
Total tangible return is defined as:
ri
T(Tot)
(t-i,t) = yˆ5. r i
B
(t-i,t) + yˆ6. r i
S
(t-i,t) + yˆ7. r i
C
(t-i,t) + yˆ8. r i
E
(t-i,t) ………. (3.10b)
and, the intangible return is defined as:
ri
I(Tot)
(t-i,t) = ui,t ……………………………………………………………. (3.10c)
Model 11: ri(t) = α + b0 B/P (t-i) + b1 rB
(t-i,t) + b2 rI(B)
+ b3 ι (t-i,t) + ut …………… (3.11)
Where,
ri(t) = firm returns for the period t.
B/P (t-i) = lag period book to market ratio where i is the lag period
rB
rI(B)
= Intangible book returns
ι(t-i,t) = composite share issuance variable for the period i to t-i where i is
the lag periods
ut = random terms

20
The regression model 3.11 is specified to find the independent effect of intangible
variables for the prediction of future returns. The first intangible variable is the variable
which is related to price scaled accounting measures assumed as the proxy of intangible
information, and the next proxy is composite share issuance measure. This model can
further be converted in terms of separate accounting growth variables with the similar
procedures.
ri(t) = α + b0 S/P(t-i) + b1 rS
(t-i,t) + b2 rI(S)
+ b3 ι (t-i,t) + ut …………............ (3.11a)
ri(t) = α + b0 C/P(t-i) + b1 rC
(t-i,t) + b2 rI(C)
+ b3 ι (t-i,t) + ut……………….... (3.11b)
ri(t) = α + b0 E/P(t-i) + b1 rE
(t-i,t) + b2 rI(E)
+ b3 ι (t-i,t) + ut……..................... (3.11c)
Where,
r S
rI(S)
= Intangible sales returns
r C
rI(C)
= Intangible cash flow returns
r E
rI(E)
= Intangible earnings returns
ut = random terms
The regression models (3.11, 3.11a, 3.11b, and 3.11c) are designed to find the
independent effect of intangible returns for the prediction of future returns. The price
scaled variables: book-to-market equity, sales to price, cash flows to price and earnings to
price is employed in the above models as the proxy of intangible information respectively.
The next proxy of intangible is composite share issuance measure.
Definition of key terms
The study broadly employed the tangible and intangible variables to determine the
relationship between the dependent and independent variables. The basic fundamental
variables are: earnings per share, market value per share, cash dividend, total common
stock outstanding, book value per share, sales volume or annual deposit, and cash flows.
The descriptions of major variables that are employed for the study are as follows:
a. Earnings per share:
It is the indicator of firms’ profitability, which is the ratio of earnings available to equity
holders to the number of common stock outstanding. It can be calculated using the
equation below:

21
EPS =
Earnings
No of common stock outstanding
b. Market value per share:
It is the market price per share which is determined by the free flow of demand and
supply of the equities in the secondary market. The study uses the market value per share
as at end of each year (i.e. July 15th
).
c. Cash dividend:
It is the annual rate of returns to shareholders in terms of cash out of annual earnings of
the enterprises and approved by the annual general meeting (AGM) of the board of
directors (BOD).
d. Size:
The market value of the outstanding number of common stock of the enterprises at the
end of July 15th
each year is considered as the size of the firm. Market value of equity is
the total market value of common stock outstanding at the end of period t. It has been
calculated based on the market price per share as follows:
MEit = Pit x Nit .............................................................................……… (3.14)
Where, MEit is the market value of equity outstanding of the firm i at the end of year t, Pit
refers the market price per share and Nit refers to the number of common stock
outstanding.
e. Book value per share:
Book value is the outcome of value of common equities divided by the number of
common stock outstanding. The sum of reserve and capital shown in audited balance
sheet is the measure of value of common equities. The book value per share can be
calculated as follows:
BVPS
Value of common equities
No of common stock outstanding
f. Sales volume:
It is the annual sales revenue of enterprises which representation of sales volume. In case
of financial institutions the annual total deposit is considered as the proxy of sales.
Similarly, for insurance companies the number of issued insurance policies which is
shown in annual reports is considered as sales.
g. Cash flows:

22
It is the audited annual cash flow of the enterprises during a year which was collected
from the annual report of the enterprises.
h. Book-to-market equity:
The book to market equity is the ratio of book value per share and the market value per
share at the end of fiscal year. It can be calculated as book value of equity divided by
market value of equity as follows:
BE/MEi,t =
BEit
MEit
………………………………………………….. (3.15)
Where, BE/MEit refers to the ratio of book value of equity to market value of equity of the
ith
firm at the end of year t, BEit is the book value of equity of the ith
firm at the end of
period t and MEit is the market value of equity of the ith
firm at the end of year t.
i. Sales-to-price:
It is the ratio of sales volume to market value per share at the end of the year t. It is also a
price scaled fundamental variable used for stock returns analysis.
j. Cash-to-price:
Cash flows can also indicate the annual operation of the form. The study uses the cash to
price ratio as an independent variable for regression analysis. It is derived as the annual
cash flow of the firm to the market price per share at the end of each period t.
k. Earning-to-price:
Earnings to price ratio is the next price scaled variable which is used as an independent
variables for study. It is defined as the ratio of earnings per share at the end of period t to
the market price per share which is calculated as follows:
E/Pit =
EPSit
MPSit
…………………………………………………………… (3.16)
Where, E/Pit refers to the earnings to price ratio of the ith
firm at the end of period t, EPSit
is the earnings per share, and MPSit is the market price per share at the end of year t.
l. Stock returns:
It is assumed that stock return is the sum of year ended capital gain and cash dividend of
the enterprises suggested by Fama and French (2002). The firms’ stock return is used as
dependent variable in cross-sectional regression analysis. Hence, the stock returns have

23
been defined as the rate of change in market price of common stock of a firm during
period t over the period t-1. The firm’s stock returns can be calculated as follows:
rit =
1
)1(
−
+−−
Pt
ndCashDividePtPt
. ……..……………………….. (3.17)
Equation 3.17 has rit which is the annual firm (ith
) return on equity for the year t, Pit is the
market price per share of equity at current year t and Pi(t-1) is the market price per share of
equity for the previous year end t-1.
m. Market returns:
It is assumed as the market rate of return is the overall stock market performance. It has
also been defined as the rate of change in NEPSE index during year t over the year t-1
and it can be calculated using the equation below:
rm =
NEPSEt - NEPSE (t-1)
NEPSE(t-1)
…………………………………………. (3.18)
In equation 3.15, rm is the annual return on overall common stock listed in NEPSE. The
stock returns is treated into two ways: first, the average stock returns which is based on
the average NEPSE index whereas the alternative measure is the stock returns as at mid-
July during the study periods for yearly, monthly and daily database.
n. Book/sales/cash flow/earnings returns:
It is calculated with the similar procedures as the capital gain (i.e. difference between
current year values and last year’s values divided by last year’s value). With the similar
fashion, sales return, cash flow return and earning return can be calculated.
o. Tangible and intangible components:
It is the parts of total stock returns. Total tangible and total intangible return is calculated
as per the relationship formed in model 10.
p. Composite share issuance measure:
It is defined with the following relationship,
ι(t-i,t) = log (MEt/MEt-i)………………………………………………… (3.19)
The composite share issuance measure can also be defined as the part of a firm’s growth
in market value that is not attributable to stock returns.
3.5 Limitations of the study

24
The limitations of the study are as follows:
o The study is based on the selected variables such as book value, market value,
earnings, cash flows, common stock outstanding, and sales volume which were
suggested by Chan, et.al, (1991); Davis (1994); Daniel and Titman (1997, 2006); Berk,
et.al (1999); Vuolteenaho (2002); Jafee, et.al (1989); Fama and French (1992, 1993,
1995); La Porta (1996); Banz (1981); Daniel, et.al (2001) among others. But, failed to
incorporate the variables like: net stock issues, accruals and momentum (Fama and
French, 2008), and beta (Fama and French (1992) among others) for the study. The
excluded variables might also have the significant explanatory power with respect to
the cross-section of realized stock returns.
o Further, the study excluded the fundamental macro economic variables that also have
the significant explanatory power to predict the stock returns. For instance, Chen, et.al
(1986) documented the macro economic variables like: industrial production, inflation,
interest rate affect the stock returns. Similarly, T-bills, growth rate and the spread rate
(Chen, et.al 1991); the macro-economic volatility (Schwert, 1989), GDP deflator,
foreign direct investment, etc. Thus, the study would have been more meaningful if
such variables are included to analyze the stock returns.
o During the study period (1994 to 2010), there is the potential of 1443 firm year data
but, due to the lack of proper management of required data and the limitations of the
data providers, only 826 firm year’s data are able to organized. The infrastructure for
the database management seems measurable so that the study is suffered. Thus, in
case of the proper availability of firm specific database, the results of the study might
be more reliable and the study could be extended for the population study. Though,
the study of sample in many cases generates the more accurate, reliable and cost
effective outcomes, the study prefer to analyze the population as a whole to increase
the number of observations for the study.
o Generally, stock return is the sum of cash dividend and the capital gain. Cash
dividend is paid out from the annual earnings of the enterprises whereas capital gain
can be attained from the stock trading in the secondary market. The seasonal offerings
- bonus and rights share issues in most cases yield the capital gain but issuing
seasonal offerings is not only the basic condition to achieve the capital gain. The
study derived the annual stock returns of the enterprises using cash dividend and the
capital gain. But, it does not consider the bonus and right share adjustment while

25
determining the capital gain. Thus, it is expected that the results of the study would
more appropriate if the adjustment of seasonal offerings would also be made.
o The scope of the study helps to select the variables for the study. The most reliable
proxy of the firm size is market equity (Daniel and Titman, 1997). But, it can also be
measured by the sales volume of the enterprises (Davis, 1994) which is considered as
the firm specific fundamental variable. It is more appropriate to measure sales
volumes in monetary terms but the study assumed differently due to the lack of
required published database. In case of insurance companies, there is severe
unavailability of historical records regarding the collected of annual insurance
premium. Thus, the study assumed that the number of insurance policies issued during
a fiscal year is the proxy of sales volume of such enterprises rather than the insurance
premium in monetary terms. It would be a logical state if the study is able to replace
the numbers by the rupees.
o Further, the majority of the selected enterprises in the population are financial
institutions and they work as the financial intermediaries, mobilize the deposit
(current as well as the fixed account) for the various purposes. The study assumed that
the sales revenue of such financial institution is equal to the total annual deposit. Thus,
it would be more appropriate if the study could adjust the contribution of previous
long-term deposit into the current year deposit at least for the study periods.
o Due to the lack of published daily and monthly database of stock prices of listed
companies, the study employed the closing price of the enterprises as at the end of
each fiscal year (July 15th
). The analysis would have been more pervasive if the daily
and the monthly closing prices would have been included to determine the short run
stock returns. Rendleman, et.al (1982); Barber and Odean (2008); Foucault, et.al
(2011); Loughran and Ritter (1995); French (1980); Brown and Warner (1985); Ritter
(1988), among others use the daily returns whereas Grinblatt and Moskowitz (2004);
Banz (1981); Fama and French (1993, 1996); Chan (2003); La Porta (1996), among
others, employed the monthly returns files for the analysis. Thus, the analysis of the
study would have been extended if the daily and monthly database of the selected
enterprises would be included.
o The study period incorporates the inception of the organized stock exchange operation
in Nepal. But, the study failed to collect the sufficient observations basically before
2000 and for the year after mid-July 2010. Former is because of the lack of organized
sources and the later is because of Nepali fiscal year (July 16th
to July 15th
). As per the
regulatory provision and the administrative procedures, the listed enterprises are able

26
to publish its annual financials only after six months or more after completing the
fiscal year. Thus, the study failed to incorporate the latest (2010/011) financials of the
enterprises.
o The number of firm years of the selected enterprises is not similar. Though the study
period is considered as January 13, 1994 to July 15, 2010; the date of listing of the
enterprises, database management, the varying mandatory frameworks, imposed
formatting for regulatory submission for different sector of enterprises is different, etc
cause the variation on observed firm year of the listed enterprises. The variation
ranges from 1 year to 17 years basically as per the age of the firm. Thus, the
survivorship bias or the look-ahead bias as suggested by Fama and French (1996a),
Banz and Breen (1986), and Kothari, et.al (1992) among others is also exists for the
study.
4. PRESENTATION AND ANALYSIS OF DATA
4.1 Secondary Data Analysis
In a system approach, the capital market is a component of the whole economic system.
The capital market might be influenced by its own behavior and the other available
information from various sources. In open economic system, the financial market is a
mechanism that fuel for all the economic activities and gradually been influencing by
different kinds of information. The information actually carries some monetary values so
that the valuation of the financial instruments does not remain static for the long period of
time. Being the highly volatile characteristics of the capital market, many opportunities as
well as challenges emerges and disappears if it is not captured at the right time and the
right way. The performance in the capital market in the form of stock price tends to be
useful information for the investment decision makers. But, the magnitude of the
usefulness depends upon the form of the financial market and its growth level. The
financial investors generally use the concept of the investment theories those are
supported by the extensive evidences and, the overall market information can be broadly
classified into fundamental and behavioral information. The accounting growth measures
which is treated as the fundamental information might be the useful market information in
case of relatively static and growing economy. On the other hands, the behavioral issues
and the personal characteristics of the stock market participants are also very much

27
essential to increase the level of confidence and belief towards the sustainable
development of financial market.
The study employed some secondary database to examine the signals of the capital
market movements, different price scaled variables such as: book to market price ratio,
earnings to price ratio, cash flow to price ratio, sales to price ratio, along with the
individual variables and the stock returns series are used as the proxies of market
information because of their relatively static nature. The other information such as:
financial news coverage and political leadership effects are also considered for the
secondary analysis because these variables are not incorporated in the above accounting
growth measures. The news and politics has its own effects for stock returns thus are
treated as separate independent variables and placed them as the other market information.
Then, the secondary data analysis is presented in a specific order as: the profile analysis,
descriptive statistics, Daniel-Titman regression analysis, news and political effect analysis
for market returns, and, an extended analysis of news and stock returns: the graphical
presentation.
A. Profile Analysis
Table 4.1 incorporates the analysis of 176 enterprises across 14 years starting from 1997
to 2010. Panel A shows the movements of book to market ratio for 825 firm years where
the maximum mean ratio is 1.23 in 1998 and the minimum mean ratio is 0.29 in 2000
followed by 0.35 in 2009, similarly, 1.33 is the highest standard deviation for the year
1998 and the minimum is 0.16 for 2000. Panel B shows the firm year in first row, mean in
second and standard deviation in third row, the figures indicates that the mean book value
of the enterprises gradually decreases from 2000 to 2010 but the movement is volatile
before 2000. The maximum mean book value per share is Rs 293.41 and Rs 145.59 for
the year 1998 and 2007 respectively. The standard deviation on the other hands, indicates
that the highest Rs 215.02 in 1998 and lowest Rs 79.49 in 2009. The Panel C indicates the
average cash dividend of the enterprises and its instablility, as the analysis of 822 firm
years from 1997 to 2010, it ranges between 26.74 percent to 7.16 percent in 2000 and in
2008 respectively where as the standard deviation ranges 49.73 percent to 18.74 for the
year 2010 and 1999 in order. Panel D shows the features of cash flow in million which
shows highest in 2009 and lowest (negative) in 1998 for Rs 128.76 million and Rs – 5.10
million respectively and the standard deviation ranges between 35.43 and 888.76. The
cash flow to market price ratio is shown in Panel E which is in general upward sloping,

28
Table 4.1 Profile Analysis
This table presents the profile analysis of variables: book to market ratio, book value per share, cash dividend, cash flow, cash flow to
market price ratio, earnings to price ratio, earnings per share, market equity, market price per share, sales revenue, sales to market price
ratio, and stock returns. The measurements employed for the profile analysis are: ratios for Panel A, E (in thousands), F and K (in
million); Rs for Panel B, G, and I; millions for Panel D, H and J; and, percentage for Panel C and L. The study period covers from
1997:07 to 2010:07 indicate the firm year, mean and standard deviation for all variables.
Year 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Panel A: Book to Market Ratio (For 825 firm year)
F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133
Mean 1.00 1.23 0.54 0.29 0.35 0.56 0.72 0.81 0.80 0.76 0.53 0.36 0.35 0.55
SD 0.38 1.33 0.42 0.16 0.22 0.24 0.27 0.40 0.61 0.71 0.62 0.49 0.29 0.49
Panel B: Book Value Per Share (For 825 firm year)
F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133
Mean 221.88 293.41 189.01 228.28 188.64 173.04 164.49 165.40 164.75 152.52 145.59 149.10 156.47 157.15
SD 123.99 215.02 87.94 83.26 82.10 84.92 87.86 89.68 84.18 101.58 110.32 84.55 79.49 113.63
Panel C: Cash Dividend (For 822 firm year)
F.Year 3 8 10 13 18 36 44 59 74 86 101 111 128 131
Mean 20.00 25.00 23.00 26.74 18.35 12.43 15.76 10.61 11.95 13.74 9.86 7.16 9.42 12.69
SD 26.46 20.53 18.74 29.63 25.09 20.61 24.28 23.13 25.88 35.78 31.97 32.72 40.59 49.73
Panel D: Cash Flow (in Millions) (For 825 firm year)
F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133
Mean 102.36 -5.10 29.03 4.87 25.73 18.47 8.52 11.75 3.24 21.55 19.10 33.17 128.76 -8.34
SD 176.13 35.43 98.93 67.84 85.32 39.56 64.25 60.39 69.32 55.80 63.10 104.15 848.26 888.76
Panel E: Cash Flow to Market Price Ratio (in Thousands) (For 825 firm year)
F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133
Mean 164.98 -9.47 27.59 8.54 31.70 33.64 12.32 50.19 7.43 67.55 46.48 25.28 195.65 72.81
SD 275.45 54.14 114.42 47.31 68.95 68.58 128.09 334.04 253.88 226.11 342.58 109.85 1185.77 1758.85
Panel F: Earnings to Price Ratio (For 825 firm year)
F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133
Mean 0.31 0.19 0.15 0.08 0.06 0.08 0.08 0.09 0.14 0.05 0.01 0.05 0.06 0.08
SD 0.28 0.25 0.16 0.06 0.04 0.06 0.07 0.22 0.14 0.31 0.40 0.20 0.08 0.10
Panel G: Earnings Per Share (For 825 firm year)
F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133
Mean 72.38 53.07 49.73 62.85 42.32 30.86 26.27 25.77 32.81 21.24 19.94 28.64 31.33 29.95
SD 52.33 39.03 28.48 36.81 33.73 29.17 31.82 42.99 33.88 50.59 63.91 48.64 46.66 56.89
Panel H: Market Equity (in Millions) (For 825 firm year)
F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133
Mean 52.64 115.29 113.08 146.72 163.42 134.20 139.58 130.37 161.04 176.26 188.26 246.36 468.31 588.24
SD 59.36 122.08 115.51 131.20 160.54 164.52 168.56 166.71 209.72 230.74 239.73 329.59 1379.38 1381.76
Panel I: Market Price Per Share (For 825 firm year)
F.Year 3 8 10 13 18 36 44 59 74 86 101 112 128 133
Mean 294.33 527.75 609.60 1055.38 828.28 407.81 317.89 297.31 325.33 380.04 559.02 892.40 733.09 425.45
SD 293.68 585.28 516.51 617.65 623.67 384.50 345.09 349.21 409.02 592.42 883.02 970.09 833.75 550.09
Panel J: Sales Revenue (in Millions) (For 824 firm year)
F.Year 3 8 10 13 18 36 44 59 74 86 101 112 127 133
Mean 2070.22 2807.63 3120.36 4752.89 4708.80 2712.09 2536.60 2283.13 2185.83 2420.26 2520.40 2897.66 3941.62 4782.62
SD 3265.38 3464.42 3675.32 5192.15 5961.14 4783.75 4866.56 4824.93 4644.72 5273.94 5690.87 6807.87 8375.14 9379.37
Panel K: Sales to Market Price Ratio (in Millions) (For 824 firm year)
F.Year 3 8 10 13 18 36 44 59 74 86 101 112 127 133
Mean 4.14 7.08 5.25 3.75 4.34 5.39 6.60 5.94 5.38 5.99 3.31 2.30 4.70 10.64
SD 4.51 6.72 4.38 3.26 3.85 6.80 8.40 7.96 7.73 11.23 4.14 3.44 7.75 16.22
Panel L: Stock Returns (For 683 firm year)
F.Year 1 3 8 10 13 18 36 44 59 74 86 100 111 120
Mean 0.14 0.14 0.64 1.30 0.15 -0.26 -0.07 0.05 0.23 0.15 0.68 1.03 -0.01 -0.32
SD 0.00 0.19 0.42 0.78 0.51 0.30 0.18 0.21 0.34 0.29 0.67 1.36 0.44 0.29
negative in 1998 and highest in 2009, the figures are in thousand. Panel F shows the down
ward trend of earning to price till 2001 and took a upward movement upto 2005 and again
decreased sharply till 2007 and then started to move upwards. In figures, the highest mean
Source: Appendix B

29
ratio is 0.31 early in 1997 and lowest 0.01 in 2007 whereas the standard deviation of
earning to price ratio shows the highest 0.40 in 2007 followed by 0.31 in 2006 and lowest
0.04 in 2001. Similarly, Panel G shows the annual movements of earning per share of 825
firm year, the general trend indicates the downward movements with some spikes in 2000,
2005 and then showed the upward slope 2007 onwards. The maximum mean earnings per
share is Rs 72.38 in 1997 and minimum Rs 19.94 in 2007 followed by Rs 21.24 in 2006.
The standard deviation on the other hands indicates the high point Rs 63.91 in 2007 and
the low point Rs 28.48 in 1999. Market equity is shown in Panel H which indicates the
upward movement early from the beginning which was started from mean value of
market equity Rs 52.64 million and reached to Rs 588.24 million in 2010 with the same
fashion the standard deviation also started from Rs 59.36 million to Rs 1381.76 million
during the study period. Similarly, the average market price per share is shown in Panel I
which exhibit the U-shape with the highest point of Rs 1055.38 followed by Rs 892.40 in
2000 and 2008 respectively whereas the highest value of standard deviation is Rs 970.09
and lowest is Rs 293.68 for the year 2008 and 1997 respectively. On the other hands,
Panel J shows the yearly features of the sales revenue of 824 firm years which indicates
another U-share during 2000 to 2010 and prior to this period the movement is increasing
till 2000. Panel K similarly indicates the sales to market price ratio, the figures are in
million, the trend line does not shows the smooth movement but potray the ups and
downs where the highest point of mean sales to price is 10.64 million in 2010 and lowest
is 2.30 million in 2008. Finally, the stock returns movements for the period covering 1997
to 2010 is shown in Panel L which constitute 683 firm years with four negative average
return figures. Among the various mean points, the highest is 130 percent in 2000
followed by 103 percent in 2008, after these peak points the stock returns experienced the
sharpe decline. Similarly, the lowest point is negative 32 percent for the year 2010
followed by negative 26 percent in 2002. Standard deviation on the other hands shows the
highest 136 percent in 2008 followed by 78 percent in 2000.
The graphical presentation of the figures presented in Table 4.1 are shown in the Figure
4.1 which shows the graph of four price scaled variables and eight other accounting
variables for the period mid-July 1997 to mid-July 2010. The database consist of 176
enterprises and the maximum 825 firm year and the minimum 683 firm year. In aggregate
the movement of majority of the selected variable exhibit the downward movement and
three out of twelve variables indicate the upward movements namely, market equity (size),
sales to price ratio and the sales revenue.

30
B. Summary Statistics
Figure 4.1 Graphical presentation showing the trends of the variables: book to market ratio, book value per share, cash dividend,
cash flow, earnings per share, cash flow to market price ratio, earnings to price ratio, market equity, market price per share, sales to
market price ratio, sales revenue, and stock returns for the period covering 1997:07 to 2010:07. The figures show the trends of
respective variables employed for the study.
Source: Appendix B

31
Table 4.2 presents the summary statistics of the secondary database which is used to
describe the characteristics of the variables selected for the study. The number of
observation contained maximum of 826 firm years and minimum of 822 firm years
during the study period. Average earnings per share for the whole period is Rs 29.15 with
median value Rs 21.18, the minimum and maximum values are Rs 444.08 (negative) and
Rs 626.00 respectively, the standard deviation is Rs 48.87 and the first and third quartiles
are Rs 11.02 and Rs 36.69 in order. The market price per share in second row shows the
mean value Rs 545.07, the share price ranges between Rs 44 to 6830 during the study
period and the standard deviation is Rs 716.68. Taking the figures of book value per share,
mean is Rs 160.29, median is Rs 138.21, and the maximum and minimum values are Rs
1005.86 and Rs 364 (negative) in order. The standard deviation shows the magnitude of
variation of the variable which is Rs 97.77 represents the stability of book value as
compare to market price per share. On the other hands, cash dividend represents the mean
value is 11.78 percent with median 1.05 percent and the maximum is 560 percent whereas
the standard deviation is 35.23 percent. The figures of sales and cash flow are shown in
sixth and seventh row whereas market equity is in fifth indicates that the mean value of
Rs 287.78 million, minimum value is Rs 8 million and maximum value is Rs 15000
million, the standard deviation shows Rs 815.04 million. The stock returns is shown in
twelfth row which indicates average returns 5.59 percent with median 2.08 percent, the
maximum return is 80.21 percent with 9.54 percent as standard deviation of the whole
study period. The price scaled variables: book to market ratio, earnings to price ratio, cash
flow to price ratio and sales to price ratio is placed in eighth to eleventh rows respectively.
The mean values are: 0.56, 0.07, 66.21 and 5.59 for the stated price scaled variables
respectively where are median values are: 0.47, 0.06, 5.35 and 2.08, these median values
divide the whole series into two equal parts. Similarly, the standard deviations in number
are: 0.53, 0.21, 866.73 and 9.54. The unit of measurement for cash to price ratio is in
thousand and for sales to price ratio is in million.
Table 4.3 shows the correlation coefficients of the variables are considered for the study.
Among the total correlation coefficients, nine sets of variables which have no significant
correlation and the remaining nineteen pairs have significant positive correlation at 95
percentage confidence interval. Among the significant correlations, the log cash flow and
log market equity has the correlation coefficient 0.61 is the highest value followed by
0.58 for log market equity and log composite share issuance variable whereas the lowest
correlation coefficient is 0.10 which describes the movements of both variables in the

32
same direction. There are total four negative correlations among the variables, out of
them 0.08 (negative) is the highest correlation between log composite share issuance and
Table 4.2
Summary Statistics
This table presents the summary statistics of the variables used for the study. The five point scale with median, standard deviation,
number of observations per variables, unit of measurement and the name of the variables are presented in columns and individual
variables are shown in rows. The first three variables: earnings per share, market price per share and book value per share are
measured in Rs, the cash dividend and stock returns are in percentage terms, the market equity, sales revenue and cash flow are
measured in millions in Rs, book-to-market ratio, earnings to price ratio, cash flow to price ratio, and sales to price ratios are
measured in times where cash flow to price ratio is in times in thousand and sales to price ratio is in times in millions. All the
variables are measured for the period 1997:07 to 2010:07.
Variables Unit N Mean Median Minimum Maximum
Quartile Std.
Dev.Q1 Q3
Earnings per share Rs 826 29.15 21.18 -444.08 626.00 11.02 36.69 48.87
Market price per share Rs 826 545.07 295.00 44.00 6830.00 174.75 626.75 716.68
Book value per share Rs 826 160.29 138.21 -364.00 1005.86 114.36 183.35 97.77
Cash dividend Percent 823 11.78 1.05 0.00 560.00 0.00 10.53 35.23
Market equity Million (Rs) 826 287.78 92.07 8.00 15000.00 48.00 320.00 815.04
Sales revenue Million (Rs) 825 3200.67 598.07 0.01 50094.73 269.82 1776.33 6776.75
Cash flow Million (Rs) 826 31.38 1.53 -9523.19 9327.70 -0.36 18.59 492.67
Book to market ratio Times 826 0.56 0.47 -1.44 4.91 0.23 0.76 0.53
Earnings to price ratio Times 826 0.07 0.06 -3.52 1.60 0.03 0.11 0.21
Cash flow to price ratio in '000' 826 66.21 5.35 -17968.28 12777.67 -1.68 48.46 866.73
Sales to price ratio in Million 825 5.59 2.08 0.00 80.21 0.91 5.65 9.54
Stock returns Percent 822 5.59 2.08 0.00 80.21 0.91 5.65 9.54
Source: Appendix B
Table 4.3
Correlation Matrix
This table shows the correlation coefficients of the variables employed for the study which are: earnings per share
(EPS), market price per share (MPPS), book value per share (BVPS), sales, cash flow, market equity (ME), stock
returns (Rt) and composite share issuance (CSI) variable. The strength of the correlation coefficient is measured at
5 percent level of significance. The Pearson correlation is used for the analysis. The study period ranges from
1997:07 to 2010:07. The figures in parenthesis are p-values.
LogMPPS LogBVPS LogSales LogCashFlow LogME LogRt LogCSI
LogEPS 0.41 0.57 0.22 0.10 -0.04 0.09 -0.08
(0.00) (0.00) (0.00) (0.02) (0.28) (0.09) (0.10)
LogMPPS 0.40 0.31 0.42 0.40 0.30 0.35
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
LogBVPS 0.07 0.14 -0.05 0.02 -0.07
(0.05) (0.00) (0.16) (0.64) (0.18)
LogSales 0.21 0.39 0.15 0.26
(0.00) (0.00) (0.00) (0.00)
LogCashFlow 0.61 0.08 0.30
(0.00) (0.18) (0.00)
LogME 0.18 0.58
(0.00) (0.00)
LogRt 0.06
(0.35)
Source: Appendix B

33
the log earnings per share followed by 0.07 (negative) between log book value per share
and log composite share issuance variable. Further, there is negative correlation between
log market equity and log earnings per share and log book value per share.
C. Daniel-Titman Regression Results
I. Book to Market Decomposition
Table 4.4 presents the book-to-market equity decomposition using Fama-MacBeth
regressions. The relationship between the dependent variable and the change in book
value is assumed to have positive whereas market value is negative. The existence of the
stated relationship between the variables proves the information effect on stock price. In
the first regression estimates, the evidence proves that the priori sign is as expected and
significant at 5 percent level. While taking the independent effect of lagged book to
market effect and the changes in book value and changes in market value, the priori sign
is disappeared in model 3 in case of changes in market price. The figures in Panel A
shows that a unit change in BMio leads to 60.80 percent change in Bit/Mit, 0.20 percent
change in △Bi and 0.10 percent (negative) change in △Mi while taking independent
effect the magnitude decreases. The final column indicates the observations retained in
the analysis to generate the Kolmogovor-Smirnov test (p-values) in accepted level. Panel
B shows that the elasticity between the variables i.e. 1 percent change in Log BMi0, Log
△Bi and Log △Mi leads to 88.30, 12.00 and 18.60 (negative) percentage changes in
dependent Log Bit/Mit respectively and the explanatory power of the independent
variables is 98 percent as shown in model 4. Thus, the coefficient indicates that the
relationship between the variables is persistence taking the mutual effects of the selected
variables.
Further, the book to market decomposition can be made by replacing the independent
variables and replacing the dependent by firm level stock returns taking 2 to 5 lagged
periods. It is expected to have the same priori sign as presented in Table 4.4. The basic
regression model 3.2 is transformed into three different versions in Panel A, Panel B and
in Panel C which is shown in the Table 4.5 respectively. The first column indicates the
regression models with lagged periods in parenthesis (i.e. 2 to 5) and for each models the
first row shows the coefficients and the subsequent row indicates p-values. Similarly,
seventh column shows the p-values of ANOVA tests, the next column indicates the
coefficient of determination, then K-S rest column shows the normality test values (p-

34
values) and finally, N indicates the number of observations in each model which is ranges
between 403 to 89 observations.
Table 4.4
Regression Analysis for Book to Market Decomposition
This table shows the book-to-market decomposition. The dependent variable is log book-to-market equity at time
t. The BMio is the lagged book-to-market equity of the firm i at the period 0 to t. △bi is change in book equity at
0 period to t and △mi is change in market price at 0 period to t. R-square is the coefficient of determination, N is
the number of observations and the p-values in the Model Sig. column. The study period covers 1997:07 to
2010:07. The p-values are presented in the parenthesis.
α b1 b2 b3 Model Sig R-square
K-S Test of
residual N
Panel A: Log (Bit/Mit) = α + b1 BMi0 + b2△Bi + b3△Mi + ut
Model 1 bi -0.640 0.608 0.002 -0.001 0.000 0.95 0.05 437
p (0.000) (0.000) (0.000) (0.000)
Model 2 bi -0.484 0.461 0.000 0.84 0.20 279
p (0.000) (0.000)
Model 3 bi -0.149 0.000 0.001 0.000 0.17 0.20 279
p (0.000) (0.333) (0.000)
Panel B: Log (Bit/Mit) = α + b1 LogBMi0 + b2Log△Bi + b3Log△Mi + ut
Model 4 bi 0.081 0.883 0.120 -0.186 0.000 0.98 0.20 50
p (0.000) (0.000) (0.000) (0.000)
Model 5 bi -0.027 0.701 0.000 0.866 0.05 280
p (0.002) (0.000)
Source: Appendix B
Table 4.5 presents the regression estimates of the extension of book to market
decomposition. In Panel A, the interpretation is very much similar to the estimates
available in Table 4.4 Panel A but the coefficients are much stronger. For instance, taking
4 lag periods in model 3, the elasticity is 0.914 followed by 0.873 and 0.857 for taking 5
lag and 2 lag periods respectively between log book-to-market and lagged log book-to-
market ratio. Further, 1 unit change in Bt/Bt-i leads to 39.80 percent change, 37.70 percent,
and 33.70 percent changes in log book to market prices by taking 4 lag, 2 lag and 3 lag
periods, in order. Similarly, 1 percent changes in Pt/Pt-i leads to the highest 28.7 (negative)
percent changes in dependent variable in model 1, 19.10 (negative) percent changes in
model 2 and the least effect is 14.50 (negative) percent in model 3.
The R2
values ranges between 96.30 percentages to 82.90 percentages. While replacing
the dependent variable in Panel B by rt-i,t and maintaining no changes in independent
variables, the coefficient indicates the highest 0.23 unit (negative) changes to firm level
stock returns while changing 100 percent in lagged log book-to-market ratio for 3 lagged
periods followed by 0.175 units (negative) in 2 lagged periods. The estimates do not
retain the expected priori signs where it is negative for b1, unstable for b2 and positive for
b3. While explaining the coefficient of b2, it is shown that the highest 0.007 (negative)
unit effects on dependent variable whereas the lowest is 0.001 (negative) unit effects for


35
firm level stock returns for 3 and 5 lagged periods, in order. Under Panel C, all the
regression coefficients carry the similar interpretation as: a 100 percent change in
independent variables leads to -0.266, -0.094 and 2.006 unit change firm level stock
returns respectively in case of model 9 (taking 2 lag periods). Similarly, in 3 lag periods,
0.281 unit significant changes for lagged log book-to-price, the coefficient is insignificant
for log Bt/Bt-i and 2.299 unit significant changes for log Pt/Pt-i variable. The coefficient of
determination is highest for 5 lagged periods in model 12 followed by model 11. The
analysis is based on 403 observations for model 9 and 95 observations for model 12 and
the K-S test values in Panel B and C are of the dependent variables rather than the
residuals. From the Table 4.5, it is concluded that the firm level stock returns is
negatively affected by the lagged book-to-market ratio and positively by market price to
lagged market price ratio but the relationship between returns and the book to lagged
book values is inconclusive.
Table 4.5
Regression Analysis for Book to Market Decomposition: An Extension
This table shows the book-to-market decomposition with an extension of firm level stock returns. The dependent variable is log
book-to-market equity at time t for Panel A and firm level stock returns from t to t-1 period for Panel B and Panel C. The Bt-i/Pt-i
is the lagged book-to-market equity of the firm for the period t-i to t. The Bt/Bt-i is book to lagged book value ratio and Pt/Pt-i is
the ratio of price to lagged price at t-i to t period. R-square is the coefficient of determination; N is the number of observations
and the p-values in the Model Sig. column, similarly the p-values of K-S test of residuals and dependent variables are presented
in the second last column. The study period covers 1997:07 to 2010:07. The p-values are presented in the parenthesis.
α b1 b2 b3 Model Sig R-square
K-S Test of
Res/DV (p) N
Panel A: log [Bt/Pt] = α + b1 log [Bt-i/Pt-i] + b2 [Bt/Bt-i] + b3 [Pt/Pt-i] + ut
Model 1
(i=2)
bi -0.112 0.857 0.377 -0.287 0.000 0.963 0.200 322
p (0.000) (0.000) (0.000) (0.000)
Model 2
(i=3)
bi -0.239 0.727 0.337 -0.191 0.000 0.829 0.200 185
p (0.000) (0.000) (0.000) (0.000)
Model 3
(i=4)
bi -0.420 0.914 0.398 -0.145 0.000 0.934 0.058 146
p (0.000) (0.000) (0.000) (0.000)
Model 4
(i=5)
bi -0.268 0.873 0.288 -0.148 0.000 0.934 0.054 200
p (0.000) (0.000) (0.000) (0.000)
Panel B: r(t-i,t) = α + b1 log [Bt-i/Pt-i] + b2 [Bt/Bt-i] + b3 [Pt/Pt-i] + ut
Model 5
(i=2)
bi -1.044 -0.175 0.003 1.068 0.000 0.882 0.146 401
p (0.000) (0.000) (0.739) (0.000)
Model 6
(i=3)
bi -0.971 -0.230 -0.007 1.012 0.000 0.870 0.200 287
p (0.000) (0.000) (0.605) (0.000)
Model 7
(i=4)
bi -0.959 -0.030 0.004 0.995 0.000 0.988 0.056 169
p (0.000) (0.157) (0.445) (0.000)
Model 8
(i=5)
bi -0.955 -0.019 -0.001 0.998 0.000 0.971 0.087 89
p (0.000) (0.534) (0.829) (0.000)
Panel C: r(t-i,t) = α + b1 log [Bt-i/Pt-i] + b2 log [Bt/Bt-i] + b3 log [Pt/Pt-i] + ut
Model 9
(i=2)
bi 0.109 -0.266 -0.094 2.006 0.000 0.822 0.161 403
p (0.000) (0.000) (0.145) (0.000)
Model 10
(i=3)
bi 0.141 -0.281 -0.136 2.299 0.000 0.827 0.064 297
p (0.000) (0.000) (0.056) (0.000)
Model 11
(i=4)
bi 0.033 -0.166 -0.076 3.106 0.000 0.964 0.074 124
p (0.003) (0.000) (0.024) (0.000)
Model 12
(i=5)
bi 0.036 -0.038 -0.060 3.166 0.000 0.965 0.070 95
p (0.014) (0.298) (0.093) (0.000)
Source: Appendix B


36
II. Sales to Price Decomposition
The next regression estimates is the decomposition of sales to price ratio as similar to the
book-to-market decomposition process. The ratio is decomposed into log sales to price
ratio at t-i to t period as base, and the ratio of sales to lag sales and the ratio of price to
lag price for the lag period 2 to 5 years. Table 4.6 shows the estimated parameters of
twelve the regression models employed for different lag periods with the changes and
modification of concerned variables. In each Panel, the estimate starts from the lag period
Table 4.6
Regression Analysis for Sales to Price Decomposition
This table shows the sales to price decomposition with an extension of firm level stock returns. The dependent variable is log
sales to price ratio at time t for Panel A and firm level stock returns from t to t-1 period for Panel B and Panel C. The Bt-i/Pt-i is
the lagged book-to-market equity of the firm for the period t-i to t. The Bt/Bt-i is book to lagged book value ratio and Pt/Pt-i is
the ratio of price to lagged price at t-i to t period. R-square is the coefficient of determination; N is the number of observations
and the p-values in the Model Sig. column. The study period covers 1997:07 to 2010:07. The p-values are presented in the
parenthesis.
Α b1 b2 b3
Model
Sig
R-
square
K-S Test of
Res/DV (p) N
Panel A: log [St/Pt] = α + b1 log [St-i/Pt-i] + b2 [St/St-i] + b3 [Pt/Pt-i] + ut
Model 1
(i=2)
bi 0.482 0.979 0.000 -0.214 0.000 0.970 0.065 317
p (0.000) (0.000) (0.000) (0.000)
Model 2
(i=3)
bi 1.511 0.812 0.000 -0.154 0.000 0.842 0.075 199
p (0.000) (0.000) (0.000) (0.000)
Model 3
(i=4)
bi 1.970 0.729 0.000 -0.092 0.000 0.674 0.200 235
p (0.000) (0.000) (0.000) (0.000)
Model 4
(i=5)
bi 0.924 0.915 0.000 -0.107 0.000 0.801 0.050 227
p (0.000) (0.000) (0.000) (0.000)
Panel B: r(t-i,t) = α + b1 log [St-i/Pt-i] + b2 [St/St-i] + b3 [Pt/Pt-i] + ut
Model 5
(i=2)
bi -0.894 -0.002 0.000 0.985 0.000 0.867 0.200 380
p (0.000) (0.718) (0.428) (0.000)
Model 6
(i=3)
bi -0.801 -0.006 0.000 0.942 0.000 0.876 0.064 296
p (0.000) (0.411) (0.209) (0.000)
Model 7
(i=4)
bi -0.803 0.005 0.000 0.923 0.000 0.848 0.061 210
p (0.000) (0.615) (0.283) (0.000)
Model 8
(i=5)
bi -0.784 -0.006 0.000 0.964 0.000 0.885 0.053 155
p (0.000) (0.572) (0.171) (0.000)
Panel C: r(t-i,t) = α + b1 log [St-i/Pt-i] + b2 log [St/St-i] + b3 log [Pt/Pt-i] + ut
Model 9
(i=2)
bi 0.001 0.000 0.020 2.533 0.000 0.998 0.200 57
p (0.642) (0.634) (0.000) (0.000)
Model 10
(i=3)
bi 0.009 -0.001 -0.003 2.663 0.000 0.997 0.200 65
p (0.058) (0.356) (0.080) (0.000)
Model 11
(i=4)
bi 0.022 0.000 -0.005 2.584 0.000 0.992 0.200 47
p (0.031) (0.810) (0.172) (0.000)
Model 12
(i=5)
bi 0.183 -0.026 -0.050 3.620 0.000 0.977 0.200 113
p (0.000) (0.000) (0.000) (0.000)
Source: Appendix B
2 year to 5 years. The dependent variable for Panel B and Panel C is the firm level stock
returns whereas the basic independent variables are the same for all estimates. The
hypothesized priori sign for the parameters b1 and b2 are positive and negative for b3. In
Panel A, all the parameters are statistically significant at 95 percent confidence level. The
coefficient of b1 measure the elasticity between the dependent and log lagged sales to

37
price ratio where the elasticity is 0.979 for 2 lag, 0.915 for 5 lag, 0.812 for 3 lag and
0.729 for 4 lag periods. But, the coefficients are significantly nil for the variable sales to
lagged sales ratio and the negative relation as per priori for the price to lagged price ratio.
Taking 2 lag period, the coefficient 0.214 (negative) indicates that the movement of price
to lagged price ratio from 0 to 1 leads a 21.40 (negative) percentage change in sales to
price ratio, followed by -0.154 for 3 lag periods and least effect is -0.092 for taking 4 lag
periods. The R-square values ranges from 97 percent to 67.40 percent and the number of
observations in Panel A ranges and 317 to 199 observations. The p-values of K-S test are
the result of the residual analysis. The Panel B shows the relationship between firm level
stock returns and sales to price and its components. From model 5 to model 8, it is shown
that there is minimal and uncertain effect of lagged sales to price effect on stock returns,
no effect of sales to lagged sales ratio and significant positive effect of price to lagged
price ratio for firm level stock returns. More specifically, 0.985 unit changes on firm
returns when 1 unit changes in price to lagged price ratio taking 2 lag periods, 0.964 unit
changes, 0.942 unit changes and 0.923 unit changes taking 5 lag, 3 lag and 4 lag periods
respectively and all of them are significant at 5 percent. Further, the Panel C indicates that
a 100 percent changes in Pt/Pt-i generates 2.533 unit changes in rt-i, t taking 2 lag periods,
2.584 units when taking 4 lag periods, 2.663 unit changes taking 3 lag periods and 3.62
unit changes in dependent variable while taking 5 lag periods. The coefficient of
determination values ranges between 0.998 and 0.977, the numbers of observations in
Panel C are relatively low because of the normality test. The p-values of K-S test indicate
the analysis of residuals in case of Panel A and C and the analysis of dependent variable
in case of Panel B.
In sum, there is consistent negative relationship between sales to price and price to lagged
price ratio and consistent positive relation between firm level stock returns and price to
lagged price ratio whereas inconclusive relation and least effects of lagged sales to price
and sales to lagged sales ratio for stock returns.
III. Cash Flow to Price Decomposition
The decomposition of cash flow to price ratio is shown in Table 4.7 which is divided into
three panels. The normality test is of the dependent variable is shown Panel B and the
residual analysis is shown in Panel A and Panel C. The expected priori sign is proved in
Panel A and when the dependent variable changes to firm returns there is consistently
positive sign for price to lagged price ratio. The average coefficient for b1 is 0.585 that

38
measure the elasticity between cash flow to price and lagged cash flow to price ratio, b2
constitute 0.024 which describes 1 unit change in cash flow to lagged cash flow ratio
Table 4.7
Regression Analysis for Cash flow to Price Decomposition
This table shows the cash flow to price decomposition in Panel A and the extension of firm level stock returns in Panel B and
C. The dependent variable is log cash flow-to-price ratio at time t for Panel A and firm level stock returns from t to t-i period
for Panel B and Panel C. The Ct-i/Pt-i is the lagged cash flow-to-price ratio of the firm for the period t-i to t. The Ct/Ct-i is cash
flow to lagged cash flow value ratio and Pt/Pt-i is the ratio of price to lagged price at t-i to t period. R-square column indicates
the coefficient of determination; N is the number of observations and the p-values in the Model Sig. column. The study period
covers 1997:07 to 2010:07. The p-values are presented in the parenthesis.
α b1 b2 b3
Model
Sig R-square
K-S Test of
Res/DV (p) N
Panel A: log [Ct/Pt] = α + b1 log [Ct-i/Pt-i] + b2 [Ct/Ct-i] + b3 [Pt/Pt-i] + ut
Model 0
(i=1)
bi 0.434 0.959 0.121 -0.266 0.000 0.963 0.200 221
p (0.000) (0.000) (0.000) (0.000)
Model 1
(i=2)
bi 2.486 0.558 0.000 -0.211 0.000 0.489 0.200 247
p (0.000) (0.000) (0.000) (0.000)
Model 2
(i=3)
bi 2.849 0.468 0.000 -0.155 0.000 0.414 0.200 182
p (0.000) (0.000) (0.000) (0.000)
Model 3
(i=4)
bi 2.737 0.489 0.000 -0.110 0.000 0.406 0.200 134
p (0.000) (0.000) (0.000) (0.000)
Model 4
(i=5)
bi 2.755 0.452 0.001 -0.075 0.000 0.283 0.200 102
p (0.000) (0.000) (0.000) (0.042)
Panel B: r(t-i,t) = α + b1 log [Ct-i/Pt-i] + b2 [Ct/Ct-i] + b3 [Pt/Pt-i] + ut
Model 5
(i=2)
bi -0.944 0.012 0.000 0.953 0.000 0.850 0.059 282
p (0.000) (0.055) (0.736) (0.000)
Model 6
(i=2)
bi -0.993 0.005 0.000 1.014 0.000 0.985 0.061 247
p (0.000) (0.288) (0.074) (0.000)
Model 7
(i=3)
bi -0.968 0.020 0.000 0.978 0.000 0.898 0.059 195
p (0.000) (0.146) (0.577) (0.000)
Model 8
(i=4)
bi -0.998 0.000 0.000 1.001 0.000 0.999 0.089 84
p (0.000) (0.489) (0.000) (0.000)
Model 9
(i=5)
bi -0.978 0.013 0.000 1.022 0.000 0.918 0.059 90
p (0.000) (0.464) (0.845) (0.000)
Panel C: r(t-i,t) = α + b1 log [Ct-i/Pt-i] + b2 log [Ct/Ct-i] + b3 log [Pt/Pt-i] + ut
Model 10
(i=1)
bi -0.008 0.002 0.000 1.976 0.000 0.996 0.092 69
p (0.163) (0.077) (0.785) (0.000)
Model 11
(i=2)
bi -0.123 0.050 0.031 2.885 0.000 0.967 0.085 132
p (0.003) (0.000) (0.001) (0.000)
Model 12
(i=3)
bi -0.042 0.012 0.001 3.389 0.000 0.976 0.066 71
p (0.398) (0.284) (0.965) (0.000)
Model 13
(i=4)
bi 0.017 0.009 -0.007 3.505 0.000 0.959 0.199 83
p (0.851) (0.660) (0.677) (0.000)
Model 14
(i=5)
bi 0.072 -0.025 -0.030 4.349 0.000 0.963 0.093 67
p (0.557) (0.368) (0.246) (0.000)
Source: Appendix B
leads to 2.40 percent change in cash flow to price ratio, similarly, the average coefficient
if b3 is -0.163 with the similar interpretation as b2 and the average coefficient of
determination is 51.10 percent. In Panel B, on average 0.993 unit changes in firm returns
because of 1 unit changes in price to lagged price ratio and a100 percent change in lagged
cash flow to price ratio leads to 0.010 unit change in firm returns on an average. An
average R-square is 93 percent and average p-value for K-S test is 0.065 that describe all


39
the regression models in Panel B are normally distributed. Likewise, the regression
coefficient in Panel C shows the similar meaning as a 100 percent change in lagged cash
flow to price and price to lagged price ratio leads to 0.002 and 1.976 unit changes in firm
level stock return taking lag period 1. Similarly, 0.05, 0.03 and 2.885 unit changes in firm
returns in case of lag period of 2, the figures in regression model 11 are significant at 5
percent. In sum, while taking the independent variable effect, the price to lagged price
ratio has the substantial explanatory power for firm level stock returns during the study
periods.
IV. Earnings to Price Decomposition
The separation of earnings and price from the variable earnings price ratio is shown in
Table 4.8 which is divided into three Panels. The first section describes the
decomposition of earnings to price ratio into lagged earnings to price ratio and the
independent effect of earnings and price variables. The various studies in the financial
market literatures proved that earnings have significant effects for price movement in
different market context. This table replicates the similar findings in Nepalese scenario as
well. When taking 1, 2 and 3 lag periods, there is significant effect of lagged earnings to
price ratio for firm level stock returns as shown in Panel B but there is insignificant and
negative effect for 4 and 5 lag periods. Likewise, the earnings to lagged earnings have
minimal and insignificant effect for stock returns but the coefficients of b3 indicates the
positive and significant effect on stock returns. The price to lagged price effect is also
strong, consistent and significant in Panel C but there are unreliable effect of lagged
earnings to price ration and earnings to lagged earnings variables for firm level stock
returns as shown in model 11 to model 15. Taking a look in Panel A, all the regression
coefficients are significant at 5 percent level, the relationship is strong while taking 1 lag
periods and as on the increment of the lag periods to 2, 3, 4 and 5, the magnitude is
started to decrease gradually for price to lagged price ratio, the similar manner for lagged
earnings to price ratio except model 5 and the similar pattern for earnings to lagged
earnings ratio. K-S test column indicates the p-values for normality where Panel A and C
is the test of regression residuals and for Panel B, the values shows the normality test of
dependent variable. With these evidences, it is concluded that for the firm level stock
returns there is significant effect of lagged earnings to price ratio up to three years and the
maximum a 100 percent change in lagged earnings to price ratio generates a 0.032 unit
changes in firm level stock returns.

Tangible market information and stock returns the nepalese evidence synopsis

Tangible market information and stock returns the nepalese evidence synopsis

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