Statistical

Model

The

following regression model will be applied for estimation

ROE=

?0 + ?1 (FL)+ ?2 (OL)+ ?3 (Age)+?4 (AU)+?5 (NDTS)+ ?6(Pre/Post)+e

Where;

ROE=Return

of Equity

FL=

financial leverage

OL=Operating

leverage

?0

= Constant/Intercept

?1=Coefficient/Slop

of FL

?2=Coefficient/Slop

of OL

?3=Coefficient/Slop

of Age

?4=Coefficient/Slop

of AU/Asset Turnover

?5=Coefficient/Slop

of NDTS/Depreciation.

?6=Coefficient/Slop

of Pre/Post

3.3 Type of Research

The

explanatory/ co-relational research is carried out on the groundwork prepared

by the descriptiveresearch. During the descriptiveresearch, the researcher tries to

enrich the field prepared by exploratory research by finding some additional features

and facets. While in explanatory research,

the researcher attempts to connect the

ideas to understand the cause and effect. Here the researcher wants to know

“what is going on.”

Our research objectives are also

similar nature through which we will find the impact of leverages on

profitability in the presence of both adverse and favorable economic

conditions. Hence, we can conclude that our proposed research will be

explanatory.

3.4

Research Paradigm

In

the positivistic research paradigm, the research

must be value-free, i.e. the research is assumed to be free of subjective

bias. Since our proposed research is also based

on the objective reality and going to be free from subjective bias. Therefore,

our research paradigm will be positivistic. Other reasons due to which our

research falls in the positivistic research paradigm are; firstly, the study will generate the hypothesis/questions

and test them and, secondly, the research will use the quantitative methods to

analyze and evaluate the data.

3.5

Nature of Research Study

Quantitative

Research is used to quantify the problem by way of a collection of numerical data type or data that can be transformed into usable statistics. It is

used to quantify attitudes, opinions, behaviors, and other defined variables

and generalize results from a larger sample population. Quantitative Research

uses measurable/quantifiable data to formulate facts and uncover patterns in

research. Quantitative data collection methods are much more structured as

compared to the qualitative data collection methods, i.e. surveys focused interviews, questionnaires or observations, etc. Since our research will be making use of

data available in the numeric

form, therefore, the proposed study

will be quantitative.

3.6

Population and Sample Frame

The population of 29 firms listed under

the head of “Chemical Sector” on Pakistan Stock Exchange (PSX) will be included in the analysis. In addition to the

29 chemical sector firms, 14 more firms

having chemical related products will also be

included in the analysis. Hence

our population and sample will comprise 43 firms.

3.7

Sample Size and Sampling technique

The all twenty-nine (29) listed chemical

sector firms on PSX will be included in

the sample In addition to these 29 listed companies, 14 more firms having similar business nature like

pharmaceuticals & plastic, etc will

also be included by taking unbalanced

panel data to improve the study results. Hence the total firms under analysis will become 43. The study

will comprise the twelve years period data from 2004 to 2015

3.8

Data Collection Sources

All

the necessary data for this study will be collected/ gathered from the secondary

sources available on the internet. For

this study, the data will be used from

State bank of Pakistan (SBP)’s publication under the head “Financial Statement

Analysis of Joint Stock Companies listed on KSE/PSX.”

3.9

Data analysis techniques

Appropriate Panel Data Regression

model will be used to estimate the coefficients and relationships between the

dependent and independent variables. The statistical analysis software “Stata” will

be used for the regression and estimation.

3.10

Variables Description

3.10.1 Dependent Variable

Profitability =ROE= (EBT/Equity)

The most relevant independent variable that may be considered as the measure of the financial performance and

efficiency is Profitability. Profitability

can be regarded as main independent variable that determines

capital structure because of the well-known

postulate that is represented by POT. According to the POT, the mostly the

firms try to fulfill its capital needs from the internal sources, and if the firms

need additional capital beyond the internal

arrangements, the firms go towards the

outside arrangements, i.e. debt and

equity issues. Therefore, according to POT, the financial leverage and

profitability are related negatively. Similarly,

according to the TOT, the firm identifies the target debt ratio by comparing

benefit from and cost of financial leverage.

Hence, according to the TOT, the profitability and financial leverage are

related positively.

Hence

the profitability is taken to be the Return on Equity (ROE). The ROE is measured as Earnings before Taxes (EBT) divided by Total Shareholders’ Equity of

the firm

3.10.2

Independent Variables

a).

FL = (Debt/Equity)

We

will use the ratio of debt to equity as financial leverage

b).

OL =C M/EBIT=Gross Profit/EBIT

OL

is the degree to which a company’s operating costs are fixed. The OL is measured as the ratio of contribution margin

(CM) to earnings before interest & Taxes (EBIT). Since the CM calculation requires the data from cost accounts and those

are not readily available on the secondary sources. Therefore, we will

use the “Gross Profit” Figures from financial data as the proxy of CM.

3.10.3

Control Variables

We

will introduce three control variables in our model to investigate some

overlapping relationship effects (if any). These three control variables are;

assets utilization (assets turnover), age, and non-debt tax shields

(depreciation & investment tax credits)

3.10.4

Dummy Variable (Pre and Post Crisis Measurement)

We

will introduce one dummy variable named “Pre/Post” in our model corresponding

to Pre-Crisis and Post Crisis Period. The variables “Pre /Post” will take the

value “0” for the years falling in the Pre-Crisis Period. Similarly, the value taken by this

variable will be “1” for the years falling in the Post-Crisis Period.

RESULTS

CHAPTER No. 04

4.1 Background

The

study has utilized the data collected from the Pakistan Stock Exchange (PSX)

formerly known as Karachi Stock Exchange (KSE). The total number of companies

included in the analysis was 43. The 29 companies

relate directly to the chemical sector while other 14 companies whose products

are either of chemical oriented or utilized

by the chemical companies as input. The study has included these additional

firms in the analysis to improve the

analysis results. A comprehensive list of these companies/firms appends in the

annexure at the end.

The

data used for this analysis is taken from

the SBP’ s publication titled “Financial Statements Analysis of Joint Stock

Companies list on PSX for the period from 2004 to 2015. Therefore our analysis

confined the 12 years period.

Our

data consists of both types, i.e. Cross Section Data (CSD) and Time-Series

Data (TSD). This combination of both types is

referred as Panel Data (PD). The CSD consists of the data gathered from

multiple individuals at the same time. Whereas,

the TSD is a data type collected from the same individual at different times.

The PD data type exhibits the qualities of both

the types, i.e. CSD & TSD. Therefore, in the PD data type, the data is

gathered from multiple individuals at different times.

The

OLS (Ordinary Least Square) Model could

not be applied to

Panel Data due to correlated errors occurring in the presences of time-series

and cross-sectional

components.

As

we know that the model form for OLS is “Yi= a+bXi+e” whereas for Panel Data is “Yit=a+bXit+et” where “i” stands for

cross section and “t” for time series

Therefore,

we have used the Panel Linear Model (PLM) Instead of OLS model.

1).

Our data consists of an unbalanced panel. It is due to the facts that

the “Financial statement analysis of joint stock companies” published by the

SBP does not include substantially all

the firms throughout the analysis period, i.e.

from 2004 to 2015. Since this is a prolonged

period and companies are added/ frequently removed from to/from the listing of

stock exchange during a short period. Similarly, some firms are listed

again after some period either under the same name or changed one. Therefore,

the number of firms mentioned in the

first few years’ analysis is not the same as having

been included in the last few years’ of analysis. While in the balance panel

data the number of the firms and data variables necessarily

have to remain same throughout the analysis period.

2).

Similarly our analysis comprises random

effect model because, in the fixed effect

model, it is assumed that every firm included in the analysis is substantially

same. While in the real since the every firm has

different set of characteristics and merits/demerits. Therefore, the firms included in the analysis can’t be 100% of

the same nature. This required applying a

random effect model rather than fixed effect model.

4.2

Regression Method

To

analyze the data, the “Stata” Statistical Software has been used. The “Enter” method has been applied rather than the

“Step” method. The dependent variable is

ROE% and other six variables, i.e., FL, OL, AU, Age, NDTS and Pre/Post are

taken as independent variables. Regression has produced the following results.

4.3

Results

4.3.1

Tabular Form

The

actual results produced by running Stata

Multiple Regression are given in the seven tables given below. The

detailed discussion about these results is

provided in the next chapter