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