Ranking the Listed Indian Engineering Companies based on Financial Performance of
via Factor analysis
Study conducted by Mahua Adak Mandal
Email Id: mahuaadak@gmail.com
The fruits of liberalization reached their peak in 2007, with India recording its highest GDP growth rate of
9% With this, India became the second fastest growing major economy in the world, next only to China.
Thus the industrial sector playing an important role in economic development. Engineering is the largest
segment of the overall Indian industrial sector. All types of engineering companies like that of chemical
engineering, civil mechanical, auto engineering, design & manufacturing service, casting forgings etc. plays
important role in the growth of an economy contributing to GDP and generating employment opportunities.
The Engineering industry sector recorded a growth of 9.2% (measured in terms of the Index of Industrial
Production) during the period April- Nov. 2007-08 over and above the growth of 11.6 % achieved in 200607. The engineering industry is the largest segment of the overall Indian industry. Thus it becomes
imperative to study such sector and how it’s performing.
Rapid economic growth in India has significantly increased demand for engineering goods. The focus of this
research is to find out different dimensions or factors which contribute to efficient financial performance in
engineering companies based on different financial ratios and develop an empirical model for ranking them,
based on overall financial performance. The arguments of (Brief & Lawson, 1992; and Peasnell, 1996), in
their study indicated that accounting-based measures of financial performance are a sufficient predictor of a
firm’s returns and market-based valuation. Therefore accounting based measures like financial ratios were
carefully chosen through extensive review of relevant literature. Appropriate financial ratio like that of
current ratio, networking capital ratio, operating ratio, net profit ratio, earning per share, return on equity
(ROE), ROCE (Return on capital employed), fixed asset ratio, Net sales / capital employed ratio, stock
turnover, debt equity ratio, proprietary ratio etc. as variables for determining the factors influencing the
financial performance. Initially 20 financial ratios were considered as variables for the study. But latter, due
to multicollinearity, high level of correlation between these variables, only 12 financial ratios were retained
and are considered for factor analysis. All data is extracted from the annual report of the companies, 62 NSE
& BSE listed engineering are taken into consideration for the study. The study is also objected to do
methodological contribution & development of approach in quantifying, measuring and rating the firm’s
performance.
Table 1: List of variables, which is financial ratios.
S.No.
Ratios
1 Current ratio= {Current asset/ Current liabilities}= CA/ CL
2 Networking capital ratio = {Net working capital / Net sales}
3 Operating ratio = {Operating cost/ Net sales}
4 Net profit ratio = (Net profit / Sales) * 100
5 Earning Share = {Reported net profit/ Shares in issue(in Lakhs)}
6 Return on equity = {Equity Dividend / Shares in issue(in Lakhs)}* 100
7 ROCE(Return on capital employed) = {(PBDIT -- Depreciation) / (Total liabilities – Investment)}
8 Fixed asset ratio = {Fixed asset/ (shareholder fund + long term fund)}
9 (Net sales / Capital employed)
10 Stock turnover = (Net sales – Operating profit) /{( Opening stock(Inventory) +
Closing stock(Inventory) – Stock Adjustments)/ 2}
11 Debt equity ratio = {Long term debt/ Net worth}
12 Proprietary Ratio = (Shareholders fund/ Tangible asset)
PBDIT- Profit before depreciation interest taxes.
Several types of financial ratios are taken as variables, as described above. The study uses multivariate data
analysis technique known as factor analysis, for the purpose of arriving at the dimensions of financial
performance. It is a correlation technique, where there is no distinction between dependent and independent
variables. The correlation is one of the most common and most useful statistics. A correlation is a single
number that describes the degree of relationship between two or more variables. Factor analysis is a
correlational technique to determine meaningful clusters of shared variance. It begins with a large number of
variables and then tries to reduce the interrelationships amongst the variables to a few numbers of clusters or
factors.
The financial tools used for this study is ratio analysis and statistical tools used is factor analysis and
statistical softwares use is Microsoft Excel and SPSS version 16 for Windows for determining the measures
of financial performance. The ratio analysis is one of the most powerful tools of financial analysis & to find
out the financial performance of engineering companies in midst of financial crunch and downtrend in India
and globally. It is used as a device to analyze and interpret the financial health of enterprise.
Reducing dimensionality of a set of financial ratios centers around developing some sort of structure or
grouping system for the ratios. A simple grouping system, such as those employed by introductory finance
texts, can be used to provide some structure to a financial ratio set. Analyzing empirical relationships among
financial ratios could be performed through correlation analysis. If two ratios are highly correlated, then the
user could consider one of the pair to be redundant, discarding it with little loss of information. If two ratios
are not highly correlated, then the user could consider each to measure a different aspect of firm
performance. Highly correlated ratios could be brought together into groups, where the groups would each
measure some different aspect of firm performance. In this way, the user could understand the relationships
and patterns among the financial ratios in a variable set. By uncovering the number of homogeneous groups
of ratios in a variable set, the size of the variable set could be reduced from the number of original variables
or ratios to the number of homogeneous groups. Instead of the user performing the groupings according to
the correlation coefficients, the grouping procedure could be performed via factor analysis.
Factor analysis takes a correlation matrix (or covariance matrix) among original variables as input and
constructs new variables where the number of new variables (called factors) to be retained is smaller than
the number of variables in the original data set. If the correlation coefficient between one of the original
variables and a factor is close to unity then that original variable can be used to represent the factor. In this
manner, a large set of variables can be reduced to a much smaller set, where the smaller set of variables is
then used for some predictive, explanatory, or descriptive purpose.
Table 2: Correlation matrix
The data for the empirical analysis were drawn from the financial statements of 62 light and heavy stock
exchange listed engineering companies. The use of financial ratios (indicators) is an accepted procedure in
evaluating the performance (success) of firms.
Table 3: KMO and Bartlett test of Sphericity produces Kaiser-Meyer-Olkin Measure of Sampling adequacy
and Barlett’s test.
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
Bartlett's Test of
Sphericity
Approx. Chi-Square
df
Sig.
.540
362.231
66
.000
Kaiser-Meyer-Olkin Measure of Sampling Adequacy generally indicates whether or not the variables are
able to be grouped into a smaller set of underlying factors. High values (close to 1.0) generally indicate that
a factor analysis may be useful with your data. If the value is less than .50, the results of the factor analysis
probably won't be very useful. So the value of KMO should be greater than 0.5 if the sample be adequate, as
indicated by the above KMO and Barlett’s test table, the KMO for this study is 0.540, which is adequate
indicating clearly that the data support the use of factor analysis and suggest that the data may be grouped
into a smaller set of underlying factors. Kaiser-Meyer-Olkin measure of sampling adequacy is an index for
comparing the magnitudes of the observed correlation coefficients to the magnitudes of the partial
correlation coefficients.
Another indicator of the strength of the relationship among variables is Bartlett's test of sphericity. Bartlett's
Test of Sphericity compares correlation matrix to an identity matrix. An identity matrix is a correlation
matrix with 1.0 on the principal diagonal and zeros in all other correlations. The correlation (R) matrix
represents the relationships between all items and is a complete matrix with 1.0 on the diagonal. The 1.0
indicates the perfect relationship the variable has with itself. The upper and lower elements of the matrix are
mirror images. R is population correlation matrix. I is Identity matrix, in Identity matrix diagonals are 1, and
rest all 0, so its 12 X 12 identity matrix. Variables are uncorrected with each other.
Bartlett value to be significant as we’re expecting relationships between variables.
H0: R = I
H1: R not equal I
Since significance value is less 0.05, reject the null hypothesis H0: R = I i.e reject H0 .000<0.05, so
population correlation matrix is not Identity matrix, therefore variables are correlated.
Bartlett's test of sphericity is used to test the null hypothesis that the variables in the population correlation
matrix are uncorrelated. The observed significance level is .0000. It is small enough to reject the hypothesis.
It is concluded that the strength of the relationship among variables is strong. It is a good idea to proceed a
factor analysis for the data.
Determinant of R matrix, vital for testing multicollinearity. Determinant of should be (greater than)>
0.00001. So here as per given data set of engineering companies, Determinant of population correlation
matrix is .002 Which satisfies one assumption of factor analysis, i.e is absence of multicollinearity.
Principle component analysis attempts to account for the inter-correlation among the observed variables in
terms of a much smaller number of hypothetical variates or factors. The procedure isolates a minimum
number of independent (orthogonal) factors that explain most of the variation retains the maximum amount
of information-contained in the original set of variables. The similarity of each variable with each of the
empirically derived factors is indicted by that variable’s factor loading, which is simply the correlation
between the original variable and the particular factor. Since the financial ratio groups should possess high
internal (within group) homogeneity and high external (between group) heterogeneity, each financial ratio
should have a high factor loadings on at most one factor (Pinches, Eubank, Mingo and Caruthers, 1975).
The subset data set could contain as many factors as there are variables in the original data set. The
researcher, however, will only want to employ those factors that contribute substantially to explaining
variation in the original data set. For purposes of this study, factors with eigen values greater than one are
selected to represent the original data set. Other factors are discarded.
Table 4: Total variance Explained
Total Variance Explained
Component
1
2
3
4
5
6
7
8
9
10
11
12
Initial Eigenvalues
Total
% of Variance Cumulative %
3.074
25.617
25.617
2.278
18.980
44.598
1.524
12.699
57.297
1.313
10.942
68.239
1.103
9.195
77.434
.872
7.268
84.702
.671
5.593
90.295
.537
4.472
94.767
.300
2.499
97.266
.142
1.181
98.447
.125
1.042
99.489
.061
.511
100.000
Extraction Sums of Squared Loadings
Total
% of Variance Cumulative %
3.074
25.617
25.617
2.278
18.980
44.598
1.524
12.699
57.297
1.313
10.942
68.239
1.103
9.195
77.434
Rotation Sums of Squared Loadings
Total
% of Variance Cumulative %
2.699
22.489
22.489
1.916
15.964
38.453
1.795
14.960
53.414
1.470
12.249
65.662
1.413
11.771
77.434
Extraction Method: Principal Component Analysis.
Factor analysis of 12 ratios across the heavy and light engineering companies for 2008 resulted in the
identification of five groups or categories of financial ratios, which is termed as five dimensions of
performance. The number of factors extracted in each component solution was determined by requiring that
the unrotated eigenvalues of each factor exceed 1.00 and that the factor make a non trivial (insignificant)
contribution (in excess of 5 %) in explaining the common variance among the financial ratios, and by
inspecting all results for discontinuities.
Table 4 indicates total variance explained, rotation sums of square loadings column shows % of variance
explained by factor 1 is attributed to 22.489 %, factor 2 is attributed to 15.964 %, factor 3 is attributed to
14.960 %, factor 4 is attributed to 12.249 and factor 5 is attributed to 11.771. Almost 77% of the common
variation among the financial ratios is accounted by these five categories which is descriptively labeled as:
short term liquidity, returns to equity holders / profitability, assets utilization and returns, sales and long
term funds or solvency. The labels employed for the categories are intended to represent.
Table 5 Current and Previous Researches using factor analysis using ratios
Data Reduction in Factor Analyzed Financial Ratio Space
Variable Factor
% Reduction % Variation Still
S. No.Study
Space
Space
In Space
Explained
1 Pinches and Mingo (1973)
35
7
80
63
Pinches, Mingo, and Caruthers
2 (1973)
48
7
85
91, 92, 87, 92
3 Stevens (1973)
20
6
70
82
Libby
(1975)
Not
Reported
4
14
5
64
Pinches, Eubank, Mingo, and
5 Caruthers(1 975)
48
7
85
92
Current study
12
5
59
77
Literature review shows various studies were conducted by Pinches and Mingo (1973), Pinches, Mingo, and
Caruthers (1973), Stevens (1973), Libby (1975) and Pinches, Eubank, Mingo, and Caruthers(1 975) during
1970s specifically using factor analytic approach and reported the above findings, displayed in table 5.
Similarly the current study leads to reduction in the variable space to 5 from 12, which is 59% reduction in
space and the table 5 shows 77% of the common variation among the variables (financial ratios) is
accounted by these 5 factors.
Developing Financial Ratio Patterns
Table 6: Component which gives financial performance of engineering companies
Dimensions of financial performance
Component 1: Short term liquidity- Rotated component Matrix showed that there exists inverse
relationship between liquidity and sales volume. As the liquidity increases it lead to increase in working
capital, which is eroded away due to high input cost, so be liquidity situation is tighten further, resulting
decrease in net profit. Whereas increase in operating cost resulting decrease in net profit. Thus high cost
resulting in decrease in net profit.
Raw material prices fluctuate more violently than those of finished goods; wholesale prices than retail
Prices; prices of staples tend to fluctuate less violently than the prices of luxuries; and the prices of branded
goods less than those of bulk goods. Inventory of goods hedged by the sale of futures on a speculative
market or by customers' orders for future delivery are better protected than that carried to meet an
anticipated demand (Hardy and Meech, 1925).
Usually current ratio indicates the firm’s ability to meet short-term obligations. The current ratio represents a
"benchmark" in the finance literature for measuring the company's ability to meet maturing obligations.
When we say that an expansion of sales requires matching assets to support it, for a going concern which has
already gone on stream, this dominantly means the current assets because fixed assets like plant and
machinery, etc, have already been installed with a designated capacity and these are not going to change
within a reasonably long period. However, a minimum amount of capital expenditure many have to be
continuously committed for proper upkeep of the existing technological structure of the enterprise
(Bhattacharya, 1992).
Commonly used measure of financial performance is cash flow (Gombole and Ketz, 1983). Cash flow is a
more reliable measure of any organization's financial viability than net income because it is based on cash,
not accrual, basis of accounting. As described in Walker and Petty (1978) research, large corporations have
more liquidity, as reflected by the current ratio. This conclusion is cited in their work, is consistent with
Gupta (1969) findings, in which the current and quick ratios were seen to increase as the firm size becomes
larger. In addition, this difference would not appear to be the impact of any single factor. With respect to
profitability, small firms are more profitable than large corporations, because of higher profit margins and
more efficient management of fixed assets. Ability to produce greater earnings on the sales and more
efficient utilization of fixed assets in carrying out the operation represent the two distinguishing elements.
Component 2: Returns to equity holders / Profitability
Return on equity is calculated as net income available to common stockholders divided by common equity.
This most important ‘bottom line’ accounting ratio measured as ratio pf net income to common equity.
Positive relationship between earnings per share (EPS) and return on equity (ROE). As the earnings per
share increase it leads to increase in earnings. As the earning increases, funds are utilized for paying out
more dividends to shareholders. Shareholders usually invest to get a return on their invested money and
ROE reflects it.
Firm performance is said to be a function of owner or manager control plus additional factors or variables.
Return on equity or return to the equity holders, theoretically, measures the owner's (stock-holder's) return
on investment (McKean and Kania, 1978). For purposes of determining profitability, items in the income
statement to other items in the income statement (for example, net income to sales) or to items in the balance
sheet (net income to owners' equity) are taken into consideration (Lasman & Weil, 1978).
Cleverley (1990) utilized return on equity (ROE), defined as the ratio of net income to equity, as an overall
financial performance measure. He pointed out that ROE is affected by four financial ratios: operating
margin (operating income/operating revenue); total asset turnover (operating revenue/total assets); nonoperating revenue (non-operating revenue/net income); and equity financing (fund balance/total assets).
Cleverley asserted that high ROE firms generate funds through the combined effects of asset utilization,
profit margin, non-operating income, and debt capacity. Using similar methods as Cleverley, Coyne (1986)
evaluated the financial performance of multi-hospital systems. Using equity growth or return on equity as
the overall performance measure, Coyne noted that leverage and profitability are the primary ratios affecting
this measure.
Component 3: Assets Utilization and returns
There exist positive relationship between fixed asset and working capital. As engineering companies invest
in fixed asset like plant, machinery, land and building etc. the return from such investment tend to increase.
The assets of a firm also have a natural categorization based on liquidity. Cash or cash like (marketable)
securities are liquid assets. Long-term investments (such as plant and machinery) which may only produce
liquid assets in the future may be called "illiquid" assets.
There are two components of the return on asset (ROA) ratio - total margin (the ratio of net income to total
revenues) and total asset turnover ratio (the ratio of total revenues to total assets). The product of these
components yields the ROA ratio. By analysing these components, one can determine whether earning the
highest net profit margin for each firm or utilizing the full capacity of all of the firm's assets generates the
highest rupees revenues per asset, thereby bolstering the firm's financial performance (Ozcan and McCue,
1996).
For example, in the retail industry, a low profit margin is compensated by high turnover or utilization. On
the other hand, in the steel industry, a high profit margin on rupees revenues is counteracted by a low
turnover per asset. The two components cannot always be analysed as if they acted independently. For
organizations with high fixed costs, the components may interact. In these cases, a higher activity of services
will lower the fixed cost per service which, in turn, will raise the profit margin. Thus, the componentsspecifically, net income to total revenues and total revenues to total assets-can provide additional
information about the strengths and weaknesses of a firm.
Component 4: Sales
As net sales/capital employed ratio decreases the stock turnover ratio also tend to decrease. Resulting high
closing stock and lower stock turnover. Low stock turnover leads to block in working capital due to low
sales. Low sales resulting, high closing stock, and low stock turnover. Closing stock should be low so that
stock rotate fast, thus this will lead to high stock turnover and thus enhance the liquidity position. Thus leads
to sufficient generation of working capital.
Most firms value sales growth. The business press and corporate annual reports frequently include
statements like: "We plan to double sales in the next five years," or "Our objective is to be a $2 billion
company within 7 years." The popular business press contains many examples of companies that focus on
sales growth as a key to profitability (Brush, Bromiley, Hendrickx, 2000). Windows of opportunity and
product life cycles have been shortening in recent years, placing pressure on firms to stay competitive. Many
firms have responded to this pressure by setting goals of reducing new product development (NPD) cycle
time and/or improving product performance, often by setting up fuzzy gates between stages, cross-functional
teams, or both (Calantone & Benedetto, 2000).
While a higher sales turnover implies a better utilization of asset base of the firm and hence higher
efficiency, a higher profit margin implies that the firm enjoys significant market power and hence can reap
what economists call ‘producer surplus’ or ‘rents’.
Component 5: Long term funds or Solvency
There is inverse relationship between long term debt and equity. As the debt equity ratio decreases
proprietary ratio tend to increases. Debt equity ratio indicates the solvency of the company. High debt equity
ratio leads to lower solvency. And lower level of debt leads to better solvency. The extent to which a firm
uses debt financing, or financial leverage, has certain implications. Leverage is borrowing that allows a
company to purchase more assets than its stockholders are able to pay for through their own investment
(Stice & Stice, 2008). Creditors look to the equity, or owner-supplied funds, to provide a margin of safety,
so higher the proportion of total capital provided by stockholders, the less the risk faced by creditors. In the
other scenario if the firm earns more on investment financed with borrowed funds than it pays in interest, the
return on the owner’s capital is magnified or leveraged. According to Brigham & Ehrhardt (2004), firms
with relatively high debt ratios have higher expected returns when the economy is normal, but they are
exposed to risk of loss when the economy goes into recession.
Other factors, described in previous researches, which could be expected to have some influence on the
performance indicator, are time, the debt-equity ratio, and firm size. Time as a trial allows for repeated
measures on the same firm. Debt-equity ratio is indicated by the ratio of net worth to total assets (NWITA)
and is used as a leverage effect. It would be reasonable to assume that a decline of NWITA will prompt
stockholders to demand a larger ratio of net in-come to net worth (NIINW) to compensate for greater risk
(McKean and Kania, 1978). In assessing solvency, generally various coverage ratios and at the debt-equity
ratio are considered.
The debt-equity ratio is debt divided by equity. The numerator of the debt-equity ratio can include all
liabilities, all but current liabilities, or only long-term, interest-bearing debt; a given company's debt may be
reduced to the extent that the company holds debt of other companies or of the government. The numerator
of the conventional debt-equity ratio used in this study consists of long-term debt. The denominator of the
debt-equity ratio is similarly variable. It sometimes includes only owners' equity, sometimes only long-term
sources of capital (long-term debt plus owners' equity) and sometimes all the items on the right-hand side of
the balance sheet (Lasman & Weil, 1978).
Rating of engineering companies based on overall Financial performance.
To find overall financial performance of engineering companies through ratio analysis, total rotations sums
of squared loading been multiplied by each factor and all of them added.
Overall Performance = FAC1_1*2.699+FAC2_1*1.916+FAC3_1*1.795+FAC4_1*1.470+FAC5_1*1.413
Table 6: List of companies as per over all financial performance of engineering in descending order.
S. No. CoName
1 Gujarat Toolroom
2 Thermax
3 R.J. Shah
4 Hercules Hoists
5 Alfred Herbert (India)
6 AIA Engineering
7 Bharat Heavy Electricals
8 International Combustion (India)
9 BEML
10 Eimco Elecon (India)
11 Texmaco
12 TRF
13 Praj Industries
14 Kabra Extrusion Technik
15 Engineers India
16 GG Dandekar Machine Works
17 OM Metals Infraprojects
18 GMM Pfaudler
19 Premier
20 ABG Infralogistics
OverAll_Per
formance
16.89
10.14
9.13
8.87
8.08
7.74
5.03
3.78
3.75
3.26
3.01
2.78
2.30
1.95
1.94
1.68
1.65
0.84
0.60
0.53
S. No. CoName
21 Manugraph Industries
22 Mazda
23 Avery India
24 TIL
25 Hittco Precision Tool Tec
26 Electrotherm (India)
27 Action Construction Equipment
28 Skyline Millars
29 Kilburn Engineering
30 Reliance Industrial Infrastructure
31 Ecoboard Industries
32 Suzlon Energy
33 Incon Engineers
34 Wires and Fabriks (SA)
35 Cenlub Industries
36 Shanthi Gears
37 Kalindee Rail Nirman (Engineers)
38 Josts Engineers Company
39 Hindustan Dorr-Oliver
40 Punj Lloyd
41 Pitti Laminations
OverAll_
Performa
nce
0.07
-0.04
-0.41
-0.51
-0.53
-0.73
-0.80
-0.83
-0.96
-1.04
-1.20
-1.25
-1.30
-1.37
-1.46
-1.46
-1.54
-1.69
-1.69
-1.71
-1.81
S. No. CoName
42 GEI Industrial Systems
OverAll_
Perform
ance
-1.93
43 Fluidomat
-2.00
44 Flat Products Equipments (India)
-2.01
45 Axtel Industries
-2.05
46 Ion Exchange (India)
-2.18
47 Shriram EPC
-2.25
48 UB Engineering
-2.29
49 Batliboi
-2.41
50 Elecon Engineering Company
-2.44
51 Vulcan Engineers
-2.53
52 Petron Engineering Construction
-2.57
53 Mcnally Bharat Engineering
-2.74
54 Sanghvi Movers
-2.91
55 LG Balakrishnan and Brothers
-3.00
56 Permanent Magnets
-3.16
57 Mundra Port and Special Economic Zone-4.15
58 UT
-4.34
59 Stewarts and Lloyds of India
-5.49
60 United Van Der Horst
-5.54
61 BGR Energy Systems
-6.91
62 Artson Engineering
-8.78
The top ten companies in terms of overall financial performance Gujarat toolroom, Thermax, R. J. Shah,
Hercules Hoists, Alfred Herbert (India), AIA Engineering, Bharat Heavy Electricals, International
combustion (India), BEML and Eimco Elecon (India). And the bottom top companies are Mcnally Bharat
Engineering, Sanghvi Movers, LG Balakrishnan and Brothers, Permqanent Magnets, Mundra Port and
Special Economic Zone, UT, Stewarts and Lloyds of India, United Van Der Horst, BGR Energy Systems
and Artson Engineering.
The various limitation of the study are cross-sectional stochastic frontier model does not effectively handle
statistical noise, panel data models do (Ruggiero, 2007). The study suffers from all the limitations of
financial ratio analysis. Due financial crisis during year 2007-08, the generalization of the study is avoided.
Sample data is constrained by availability of 2008 financial statement data, random nature of the sample
may have been biased because of the rejection of certain companies in the original sample of 62 because of
data limitations.
Various implication of this study is, it will lead to methodological contribution & development of approach
in quantifying, measuring and rating the firm’s performance for academic research. Business and managerial
Implication is that it will lay the foundation for applied research. Techniques can be useful to equity analyst,
general public for choosing better investment option etc. Many parties like brokerage houses, investment
bankers, lending institution, commercial bankers, institutional investors, and private equity firms can get
benefited. Government may base its policies relating to industries on the basis of the information available
from various units. In the absence of reliable economic information, governmental plans and policies may
not prove successful. Thus it will serve as policy Implication. Facilitate government, investors, shareholders
etc. in knowing the relative position of particular firm in compared to its competitors with engineering
industry. Different parties like the creditors, suppliers, investors, financial institutions; shareholders and the
management are interested in knowing the financial performance and position of a firm for different
purposes.
There is further scope for research, where current cross-sectional study, this can extended to time series.
Where time series data will capture efficiency more efficiently. Longitudinal study to evaluate the
relationships tested in the hypothesized model. A cross sectional comparison (across industries within years)
of factor patterns can be performed for each of the ten years. In order to aid this cross sectional comparison,
an examination of time series (within industries across years) & stability of ratio patterns can be performed.
Conclusion
This study ranked 62 listed engineering companies, based on financial performance, computed using
multivariate data analysis technique factor analysis, employing financial ratios as variables. The inference
were drawn based on empirical data indicates that the imperative dimensions of performance are short term
liquidity, returns to equity holders / profitability, assets utilization and returns, sales and long term funds or
solvency.