A Study on Financial Ratio Analysis using Two way ANOVA
By Team 4
Abinesh Moorthy SAshwini B VBriyanga T GJayavignesh J TNithya VSriram Chandar P SYamini Soundarya B
Financial Analysis• Financial analysis is a process of selecting, evaluating, and interpreting financial data, along with other pertinent information, in order to formulate an assessment of a company’s present and future financial condition and performance.• Financial analysis is a scientific tool. It has assumed important role as a tool for appraising the real worth of an enterprise, its performance during a period of time and its pit falls.
Financial ratio analysis• Financial ratio analysis is the use of
relationships among financial statement accounts to gauge the financial condition and performance of a company.
• Ratio-analysis is a concept or technique which is as old as accounting concept.
Types of Ratio Analysis• Ratios generally hold no meaning unless they
are benchmarked against something else, like past performance or another company.
• Thus, the ratios of firms in different industries, which face different risks, capital requirements, and competition are usually hard to compare.
• Financial ratios can be classified into five types.
Objectives
• The main objectives are as follows:
– To understand whether the companies belonging to different sectors are performing on the same track or if they do have a significant difference in their performance.
– Also to identify whether the high sales turnover company is performing equal or better than the low sales turnover company of the same sectors based on their financial ratios.
Research Methodology• Research Design
– A research design is the arrangement of conditions for collection and analysis of data in a manner
that aims to combine relevance to the research purpose with economy in procedure.
– The methodology used in project is Descriptive Research.
– The main characteristic of this method is that the researcher has no control over the variables; one
can only report what has happened or what is happening.
• Data Sources:– The data was collected from Prowess.– Secondary data is a data previously collected for any purpose other than the one at hand.
Research Methodology
– Secondary data originating within the company includes documents such as annual reports,
reports to shareholders, product testing results perhaps made available to the news media and
house periodicals composed by the company’s personnel for communication to employees,
customers or others.
– Since our objective is to find out the relationship between different sectors of companies, we
used secondary data for our study.
– The main advantage of using secondary data is it cost effectiveness, less time consumption and
minimal requirement of other resources.
ANOVA• ANOVA is an abbreviation for the full name of the method: Analysis of Variance
Invented by R.A. Fisher in the 1920’s.
• The one-way Analysis of Variance can be used for the case of a quantitative
outcome with a categorical explanatory variable that has two or more levels of
treatment.
Assumptions:
• Below are the assumptions of two way ANOVA:• The populations from which the samples were obtained must be normally or approximately normally
distributed.• The samples must be independent.• The variances of the populations must be equal.• The groups must have the same sample size.
ANOVA TableSource of Variance
Sum of Square Degree of freedom
Column SSC C-1 MSC FC Value F1 Value (Tabulated)
Row SSR R-1 MSR FR Value F2 Value (Tabulated)
Interaction SSI (R-1)(C-1) MSI FI Value F3 Value (Tabulated)
Error SSE RC (n-1) MSE
Total SST N-1
Analysis and Interpretation
• Two way ANOVA was performed on the collected data for
each of the financial ratios to determine the relationship
between the two categorical variables i.e the Sector of the
company and the Size of the company based on sales.
ANOVA table for analyzing Quick Ratio
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model 286.985a 3 95.662 9.425 .000
Intercept 258.621 1 258.621 25.480 .000
Type_of_industries 78.085 1 78.085 7.693 .006
Type_of_company 66.656 1 66.656 6.567 .011
Type_of_industries * Type_of_company 60.938 1 60.938 6.004 .016
Error 1370.237 135 10.150
Total 2029.275 139
Corrected Total 1657.223 138
a. R Squared = .173 (Adjusted R Squared = .155)
ANOVA table for analyzing Current RatioSource Type III Sum of Squares df Mean Square F Sig.
Corrected Model 348.436a 3 116.145 13.593 .000
Intercept 339.078 1 339.078 39.684 .000
Type_of_industries 86.350 1 86.350 10.106 .002
Type_of_company 106.698 1 106.698 12.487 .001
Type_of_industries * Type_of_company 69.926 1 69.926 8.184 .005
Error 1153.493 135 8.544
Total 2024.604 139
Corrected Total 1501.929 138
a. R Squared = .232 (Adjusted R Squared = .215)
ANOVA table for analyzing debt to equity Ratio
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model 83.089a 3 27.696 4.922 .003
Intercept 114.648 1 114.648 20.373 .000
Type_of_industries 65.932 1 65.932 11.716 .001
Type_of_company 11.478 1 11.478 2.040 .156
Type_of_industries * Type_of_company 25.715 1 25.715 4.570 .034
Error 759.699 135 5.627
Total 972.992 139
Corrected Total 842.788 138
a. R Squared = .099 (Adjusted R Squared = .079)
ANOVA table for analyzing working capital turnover ratio
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model 64286.895a 3 21428.965 .473 .702
Intercept 7954.426 1 7954.426 .175 .676
Type_of_industries 17870.484 1 17870.484 .394 .531
Type_of_company 14300.040 1 14300.040 .315 .575
Type_of_industries * Type_of_company 13573.533 1 13573.533 .299 .585
Error 6120335.564 135 45335.819
Total 6197886.312 139
Corrected Total 6184622.459 138
a. R Squared = .010 (Adjusted R Squared = -.012)
ANOVA table for analyzing return on investment ratio
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model .776a 3 .259 10.458 .000
Intercept .139 1 .139 5.616 .019
Type_of_industries .266 1 .266 10.750 .001
Type_of_company .506 1 .506 20.461 .000
Type_of_industries * Type_of_company .049 1 .049 1.985 .161
Error 3.338 135 .025
Total 4.114 139
Corrected Total 4.113 138
a. R Squared = .189 (Adjusted R Squared = .171)
ANOVA table for analyzing Fixed asset turnover ratio
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model 6455.571a 3 2151.857 9.565 .000
Intercept 6526.039 1 6526.039 29.008 .000
Type_of_industries 1696.431 1 1696.431 7.541 .007
Type_of_company 4303.995 1 4303.995 19.131 .000
Type_of_industries * Type_of_company 2205.524 1 2205.524 9.803 .002
Error 30371.653 135 224.975
Total 39978.112 139
Corrected Total 36827.224 138
a. R Squared = .175 (Adjusted R Squared = .157)
ANOVA table for analyzing Total asset turnover ratio
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model 18.128a 3 6.043 6.834 .000
Intercept 82.468 1 82.468 93.273 .000
Type_of_industries .265 1 .265 .300 .585
Type_of_company .001 1 .001 .001 .978
Type_of_industries * Type_of_company 15.842 1 15.842 17.918 .000
Error 119.360 135 .884
Total 254.603 139
Corrected Total 137.488 138
a. R Squared = .132 (Adjusted R Squared = .113)
Conclusion• Ratio analysis is necessary to establish the relationship between two
accounting figures to highlight the significant information to the management or users who can analyze the business situation and to monitor their performance in a meaningful way.
• It is clear that ratios like quick, current, debt to equity, return on investment and fixed asset turnover ratio have a significant difference based on the size of the company and the ratios like working capital turnover ratio doesn’t have any significant difference between them based on the size of the sales turnover of the company.
Thank you