Financial Performance Drives Market Performance -An ... › jsi › 2017-116-13-22 › articles › 21 › 94.pdf · company which is listed on that particu lar stock exchange. However

Financial Performance Drives Market

Performance-An Evidence from Indian

Industries 1E. Geetha and

2Satish Kumar

1Department of Commerce,

Manipal University, Manipal.

[email protected] 2Department of Commerce,

Manipal University, Manipal.

Abstract

The use of technical analysis enables the practitioners such as investors,

financial analysts, and traders, to formulate a basic trendline, to help

identify how the prices of the stocks would change. Fundamental analysis,

on the other hand, uses the resources provided by the company’s financial

reports such as annual growth, Revenue, and expenses etc., to provide

evidence of price fluctuation in the near future. The paper aims to

understand the trends in the market fluctuation of three major sectors of

the Indian market. The paper investigates the dependence of the change in

the market price of a share due to factors such as Eps and Profit. A

correlation analysis is carried out to understand the extent to which the

earnings per share and the profits of the company affect the average prices

of the same company. Overriding the significance of the correlation

between the factors a regression analysis is also conducted to identify the

association between the prices and EPS/Profit. Further, a regression

analysis is conducted to analyze the relationship between Average Price of

a share and the following factors: Dividend Paid per share, Dividend Yield

per Share, Book value of the share, EPS, Profit and Return on Equity.

Key Words:Technical, financial, market price, EPS,DPS.

International Journal of Pure and Applied MathematicsVolume 116 No. 21 2017, 787-798ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

787

1. Introduction

The use of Technical and financial analysis to predict the movement of share

prices in the market has been an age-old trend. The use of such tools enables the

practitioners such as investors, financial analysts, and traders, to formulate a

basic trendline, to help identify how the prices of the stocks would change.

Fundamental analysis approach uses the resources provided by the company’s

financial reports such as annual growth, Revenue, and expenses etc. (Murphy,

1999). However, the technical analysis is solely based on the historic prices of

the share and involves the use of trend analysis and identification of recurring

patterns. This information is then used to predict the possible value of the shares

(Turner, 2007).

This paper focuses on highlighting the correlation between the Earnings per

share of a firm and Profits of the firm with the average prices of the same firm.

Three of the booming sectors of the Indian market were identified and 10 firms

each were chosen based on their market capitalization. A correlation analysis

was carried forward between the firm’s average opening price, average closing

price, average high price and average low price. Further regression analysis was

conducted to identify the dependence of the average share price of the firm with

the factors such as dividend yield ratio, dividend per share, the book value of

the share and the Eps of the firm specifically for the banking industry.

Earnings per share are the part of the profit that has been given out to each

common share after deducting the net taxes and preferred share dividends. It is

calculated by dividing the net income (fewer taxes and preferred dividend) by

the total number of shares outstanding in the market.

Accounting Profit is calculated as a difference between the revenue and the

expenses of a firm. Here explicit costs are considered i.e. those incurred due to

the production and sale of goods and services by the firm. The taxable income

and the financial performance of the firm.

2. Review of Literature

The effect of a change in price due to the dividend stream was first mapped by

Gordon (Gordon, 1959) since there have been multiple models developed in this

field. A positive and non-linear relationship was found between the stock prices,

its returns and the expected dividend yield from that investment made. The

prediction of the dividends, however, is based solely on the information that the

investor had ex-ante. (Litzenberger & Ramaswamy, 1982). Some researchers

also argue that changes in the financial markets are purely based on the

investment trends followed by the investors. Though the market patterns give a

basic understanding of the market trends the relevance of the prediction is not

absolute (Chitra, 2011).

International Journal of Pure and Applied Mathematics Special Issue

788

The use of the technical analysis to identify the profitability of an investment

made in a particular share helps investors identify and recognize the market

patterns and make a successful investment. The use of technical indicators

which are published by the firms themselves helps predict the trends and

movement of the shares (Pandya, 2013).The analysis of the right data in the

right manner under that guideline of technical analysis can help investors gauge

the short term and medium term movement in the share prices. This enables the

investor to take the right investment decision which is beneficial and

remunerative in nature (Boobalan, 2014).In a study done by Anup Kumar Saha

and Ashiquer Rahman Bhuiyan showed that there is a positive relationship

between Dividend Yield Ratio (1% sig.) and Earnings Per Share (5% sig.) with

the price of the share (Saha & Bhuiyan, 2014).

Some researchers have also found that factors such as the rate of exchange of

the currency of a country have an impact on the price of the shares for a

company which is listed on that particular stock exchange. However, there is no

dependency on the value of the dollar or the stock cannot be used to predict the

value of the stock in the near future. (Nieh & Lee, 2001).

3. Objective of the Study

Analyzing the market trends of the Banking, Pharma and Cement Sector.

Finding the relationship between the average prices of the company with

its EPS and Profits

To examine the correlation, dependence and significance between EPS

and Profit with that of the average prices of the company

Identifying the relationship between Average price and the following

factors: Dividend Paid per share, Dividend Yield per Share, Book value

of the share, EPS, Profit and Return on Equity.

4. Methodology

The paper has been completed in three parts as shown in the flow chart:

a. Identification of companies within the three industries

The market capitalization of each of the banks, cement factors, and pharma

companies was found and based on their market capitalization as on 6th

September 2016. The study restricted itself to the companies listed in the

national Stock Exchange of India. The following are the companies under each

of the sectors.

Identification of companies

Data sourcing for each company

Analysis and Interpretation of

data


789

Banking Pharmaceutical Cement

State Bank of India Sun Pharma UltraTechCement

Bank of Baroda Dr.Reddy's Labs Shree Cements

Punjab National Bank Cipla Ambuja Cements

Central Bank Aurobindo Pharm ACC

Canara Bank Cadila Health Dalmia Bharat

HDFC Bank Divis Labs Ramco Cements

ICICI Bank Piramal Enter Prism Cement

Axis Bank Torrent Pharma J. K. Cement

Kotak Mahindra Bank GlaxoSmithKline JK Lakshmi Cement

IndusInd Bank Glenmark Birla Corp

b. Data collection for each of the industries and its companies

Data related to the market capitalization of each of the firms was retrieved from

moneycontrol.com while data related to the shares prices and other economic

factors was sourced from the National stock Exchange of India’s online

historical data section. Under this, the information relating to the security wise

price and deliverable position data specific to the Equity shares of the company

was retrieved and analyzed. Company wise information for the previous 5 years

starting FY, 2011 and ending 31st August 2016 was taken into consideration for

the analysis.

c. Analysis and Interpretation of data

The data was analyzed in two ways

A correlation analysis was performed in order to identify the strength of

the relationship between the Average prices of the companies i.e

Average high price, Average low price, Average open price and Average

close price with that of the Earnings per share and the Net profit of the

company. The correlation was not only for each company but also for

the industry as a whole.

A multi-level regression analysis was performed on understanding the

extent of the relationship between the average price of the share and the

following factors: Dividend paid per share, dividend yield, the book

value of the share and the return on equity of the share. This analysis

was done only at an industry level and not company level.

5. Analysis and Findings

This study draws its data from 3 primary sectors banking, pharma, and cement.

10 firms in each of the sectors were identified based on their market

capitalization. Data pertaining to High price, Low price, Opening price and the

closing price was collected for the financial years starting 201, on a daily basis.

Yearly data comprised of five or six years as available. 12420 data points were

collected for each of the sectors amounting to a total of 37260 data points. The

mathematical model used in this study is based on the assumption that the

Earnings per share (EPS) is a function of price–highest, lowest, opening and

closing. The same has been assumed for Net profit. This study, however,


790

restricts itself to identifying the correlation between the predictor and predicted

variables and the study limit itself to inferring about the significance of the

correlation and no further. The analysis is done sector wise as well as company

wise:

Table 5.1: Banking (Overall)

Correlation T- Observed (n=60) P Value

EPS Profit EPS Profit EPS Profit

Averages High price 0.04020 0.4132 0.3011 3.3958 0.7644 0.0012

Average Open price 0.16738 0.4282 1.2705 3.5466 0.2091 0.0007

Average low price 0.03884 0.4172 0.2908 3.4359 0.7722 0.0011

Average Close Price 0.16001 0.4385 1.2130 3.6513 0.2301 0.0005

t Critical for n=60, 2Tailed 2.0017

Findings: In a holistic way most banks have shown a positive correlation

between profit and the predictor variables.

Table 5.2: Pharmaceuticals (Overall)



Averages High price 0.1251 -0.0533 0.9521 -0.4030 0.3450 0.6884

Average Open price 0.1334 -0.0443 1.0166 -0.3349 0.3135 0.7389

Average low price 0.1253 -0.0528 0.9538 -0.3995 0.3441 0.6909

Average Close Price 0.1292 -0.0484 0.9840 -0.3658 0.3292 0.7158

t Critical for n-2=56,2Tailed 2.0032

Findings: Although the correlation recorded between Profit and the impacting

variables was negative, it was found to be insignificant ( P>0.05).

Table 5.3: Cement industry (Overall - Correlation)



Averages High price 0.7201 0.2458 7.7675 1.8979 1.87E-10 0.0628

Average Open price 0.7095 0.2502 7.5354 1.9340 4.51E-10 0.0581

Average low price 0.7207 0.2486 7.7811 1.9207 1.77E-10 0.0598

Average Close Price 0.7098 0.2443 7.5411 1.8857 4.41E-10 0.0645

t Critical for n-2=56, 2Tailed 2.0032

Findings: EPS exhibits a significant correlation with the impacting factors,

unlike profit.

The data did not have enough evidence to infer affirmatively about the

correlation between the predictor variables and the EPS for the banking and the

Pharma sectors, while a similar situation was observed for the profit variable in

the Cement industry’s data. This can also be attributed to the fact that

EPS/Profit is not solely affected by the variables considered but is also due to

the other impacting variables. Overriding the lack of significance and

considering the scope of this study which aims at understanding the association

between the prices and EPS/Profit, a regression analysis was conducted to give

the following results.


791

Figure 5.1: Regression Analysis for Banking Sector - EPS

The R2

value for the regression line is 0.3954 (p-value for the regression

coefficient is 0.0514) indicating a lack of significance at 5% loss.

Figure 5.2: Regression Analysis for Banking Sector - Profit

The R2



Figure 5.3: Regression Analysis for Pharma Sector - EPS

The R2



Figure 5.4: Regression Analysis for Pharma Sector - Profit


792

The R2



Figure 5.5: Regression Analysis for Cement Sector - EPS

The R2


coefficient is 0.6.59E-07) indicating a high significance at 5% loss.

Figure 5.6: Regression Analysis for Cement Sector - Profit

The R2


coefficient is 0.387) indicating a lack of significance at 5% loss. The regression

analysis reiterates the insignificance of EPS as an impacting factor for the

average price of the share. A study on factors affecting share prices indicate

other factors such as return on equity, book value per share, dividend per share,

dividend yield, price-earnings, firm size (Sharif, Purohit, & Pillai, 2015),

broadly classified into micro and macro factors (Islam, Khan, Choudhury, &

Adnan, 2014). Factors such as Book value, Earnings per share, Dividend cover,

Growth rate and Dividend yield have been studied and identified as those

impacting the share prices (Vijayakumar, 2010), (Qureshi, Abdullah, &

Imdadullah, 2012). Based on the aforementioned variables the initial model

fitted using multiple linear regression for the banking sector is as follows

Table 5.4: Regression Analysis - Banking Sector

Coefficients Standard Error t Stat P-value

Intercept 661.1808 238.0651 2.7773 0.0691

EPS 5.4611 1.6918 3.2280 0.0482

Profit 0.0420 0.0092 4.5955 0.0193

Dividend per share 4.3398 4.9439 0.8778 0.4446

Return on equity -1433.75 701.691 -2.0432 0.1336

Book value per share 0.0444 1.0968 0.0404 0.9702

Dividend yield -33991.2 10597.41 -3.2075 0.0491

The goodness of the model shows a value of 0.0351(p<0.05) with the adjusted


793

R2 = 0.8756, these indicating that the model is significant with an 87.56%

predictability power.

Table 5.5: Regression Statistics for the Banking Sector

Regression Statistics

Multiple R 0.9791

R Square 0.9585

Adjusted R Square 0.8756

Standard Error 126.2246

Observations 10

ANOVA

df SS MS F Significance F

Regression 6 1105152 184192 11.5607 0.0351

Residual 3 47797.94 15932.65

Total 9 1152950

Ignoring the insignificant variables in the model, namely dividend per share,

return on equity and book value of the share, the revised model showed a

reduction in the predictability power

Table 5.6: Regression Analysis (revised) for Banking Sector


Multiple R 0.9209

R Square 0.8482



Observations 10

ANOVA


Regression 3 977895.5 325965.2 11.1724 0.0072

Residual 6 175054.5 29175.75

Total 9 1152950

Hence, the best fit for the average price is given by the equation (as in table)

It can be inferred that EPS, profit, dividend per share and book value of the

share impact average price positively, whereas Return on equity and Dividend

Yield Ratio impact negatively. This inference is limited by the fact that it is

based on data from 10 firms only and cannot be completely generalized. Based

on the aforementioned variables the initial model fitted using multiple linear

regression for the Cement sector is as follows:

Table 5.7: Regression Analysis for Cement Sector


Intercept -319.2382 488.1401 -0.6540 0.5598

EPS 24.2266 14.0005 1.7304 0.1820

Profit -0.0371 0.3382 -0.1097 0.9196

Dividend yield -20617.1188 54589.7536 -0.3777 0.7308


794

Return on equity -1380.1720 5699.2583 -0.2422 0.8243

Book value per share 1.6221 1.3511 1.2005 0.3161

Dividend per share 173.7658 221.5947 0.7842 0.4902

The goodness of the model shows a value of 0.0098(p<0.05) with the adjusted

R2 = 0.9826, these indicating that the model is significant with a 98.26%

predictability power.

Table 5.8: Regression Statistics for Cement Sector


Multiple R 0.9913

R Square 0.9826



Observations 10.0000

ANOVA


Regression 6.0000 40670907.6588 6778484.6098 28.2398 0.0098

Residual 3.0000 720097.8866 240032.6289

Total 9.0000 41391005.5454

Hence, the best fit for the average price is given by the equation (as in table)

It can be inferred that profit, return on equity and dividend yield per share has a

negative effect on the average price, while EPS, Book value of the share and

dividend per share has a positive impact. This inference is limited by the fact

that it is based on data from 10 firms only and cannot be completely

generalized. Based on the aforementioned variables the initial model fitted

using multiple linear regression for the Pharmaceutical sector is as follows:

Table 5.9: Regression Analysis for Pharmaceutical Sector


Intercept 1142.4089 680.5302 1.6787 0.1918

EPS 87.3825 16.7528 5.2160 0.0137

Profit -1.1854 0.3054 -3.8808 0.0303

Dividend per share -784.7280 125.2017 -6.2677 0.0082

Dividend yield 78917.7904 34305.1090 2.3005 0.1049

return on equity 386.4339 2888.2329 0.1338 0.9020

book value of the share -1.8691 1.3516 -1.3829 0.2607


795

The goodness of the model shows a value of 0.0278 (p<0.05) with the adjusted

R2 = 0.8942, these indicating that the model is significant with an 89.42%

predictability power. Hence, the best fit for the average price is given by the

equation.

It can be inferred that profit, book value of the share and dividend per share has

a negative effect on the average price, while EPS, return on equity and dividend

yield per share has a positive impact. This inference is limited by the fact that it

is based on data from 10 firms only and cannot be completely generalized.

Table 5.10: Regression Statistics for Pharmaceutical Sector


Multiple R 0.9822

R Square 0.9647



Observations 10.0000

ANOVA


Regression 6.0000 6634763.0347 1105793.8391 13.6786 0.0278

Residual 3.0000 242523.9430 80841.3143

Total 9.0000 6877286.9777

6. Conclusion

The information related to the prices of the shares i.e. high price, low price,

open price and close price, EPS and Profits was retrieved and a correlation

analysis was made. It was found that the data did not have enough evidence to

infer affirmatively about the correlation between the predictor variables. It was

also found that EPS/Profit is not solely affected by the variables considered but

is also due to the other impacting variables. Overriding the significance of the

correlation a regression analysis was conducted for the same factors but at an

industry level. It was found that in the case of the Pharma industry and the

profits there is a negative relationship, also a high significance was found in the

case of regression for EPS in the Cement Sector.

Broadening the spectrum of the study a regression analysis was conducted

between the average price of the shares and the other relating factors such as

dividend per share, dividend yield, the book value of the share, return on equity

ratio, Eps, and profit.

This regression model was applied only at the industry level and not for each

company individually. An 87.56% predictability power was found in the

analysis for the banking sector, 98.26% predictability power for the cement

sector and 89.42% predictability power for the pharmaceutical sector.


796

References [1] Boobalan C., Technical analysis in select stocks of indian companies,

International Journal of Business And Administration Research Review (2014), 26-36.

[2] Chitra R., Technical Analysis on Selected Stocks of Energy Sector, International Journal of Management & Business Studies (2011), 42-46.

[3] Gordon M.J., Dividends, Earnings and Stock Prices, Review of Economics and Statistics 41(2) (1959), 99-105.

[4] Litzenberger R.H., Ramaswamy K., The Effects of Dividends on Common Stock Prices Tax Effects or Information Effects?, The Journal of Finance (1982), 429-443.

[5] Murphy J.J., Technical analysis of the financial markets: A comprehensive guide to trading methods and application, Prentice Hall Press (1999).

[6] Nieh C.C., Lee C.F., Dynamic relationship between stock prices and exchange rates for G-7 countries, The Quarterly Review of Economics and Finance 41 (2001), 477–490.

[7] Pandya H., Technical Analysis for Selected Companies of Indian IT Sector, International Journal of Advanced Research 1 (2013), 430-446.

[8] Saha A.K., Bhuiyan A.R., Predictability of Share Prices through Corporate Annual Reporting: A Focus on the Dhaka Stock Exchange, Journal of Applied Management Accounting Research 12(2) (2014), 75-86.

[9] Turner T., A Beginner’s Guide to Day Trading Online, Adams Media, 2nd Edition, 2007.

[10] Islam M.R., Khan T.R., Choudhury T.T., Adnan A.M., How Earning Per Share (EPS) Affects on Share Price and Firm Value, European Journal of Business and Management 6(17) (2014), 97-108.

[11] Qureshi M.A., Abdullah A., Imdadullah M., Stock Prices’ Variability around Earnings Announcement Dates at Karachi Stock Exchange, Economics Research International (2012).

[12] Sharif T., Purohit H., Pillai R., Analysis of Factors Affecting Share Prices: The Case of Bahrain Stock Exchange. International Journal of Economics and Finance 7(3) (2015), 207-216.

[13] Vijayakumar D.A., Effect of Financial Performance on Share Prices in the Indian Corporate Sector: An Empirical Study, Management and Labour Studies 35 (2010).


797

798

Documents

Financial Performance Drives Market Performance -An ... › jsi › 2017-116-13-22 › articles › 21 › 94.pdf · company which is listed on that particu lar stock exchange. However