Stock Return Predictability with Financial Ratios ... · Internal information of the company is reflected in its financial ... Financial ratios are one of the financial statement

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International Journal of Basic Sciences & Applied Research. Vol., 6 (3), 269-280, 2017

Available online at http://www.isicenter.org ISSN 2147-3749 ©2017

Stock Return Predictability with Financial Ratios: Evidence from PSX 100

Index Companies

Wasim Ud Din

MS (Management Sciences) from Department of Management Sciences, Comsats Institute of Information

Technology, Islamabad

*Corresponding Author Email: [email protected]

Abstract

The objective of the current study is to investigate the stock return’s

predictability by using financial ratios and control variable of PSX 100

Index companies during period from 2001-2014. The current study mainly

focuses on investigating better predictor of stock returns. The methodology

is based on Ordinary Least Square (OLS) to estimate the multiple linear

regression model. The correlation matrix shows that there is no

multicollinearity found between variables. The result of F-Limer test shows

that the panel data is appropriate while Hausman test shows that random

effect model is appropriate to estimate the model. The results reveal that all

variables are statistically significant but some variables have negative

impact on stock returns such as asset turnover ratio, EPS, inflation, interest

rate and GDP. However, debt ratio, return on sales, firm size, market

return and Tobin’s-Q have positive and significant impact on stock returns.

In conclusion, potential investors not only focus on huge returns for

investing in smaller market cap firms but also investing in large market cap

firms of PSX 100 Index companies due to reason that large firms benefit

from economies of scale. Furthermore, the stock returns are predictable

through financial ratios and control variables in PSX 100 Index Companies

and investors also set the investment criterion to see the firm size and

Tobin’s-Q when investing in large or small market cap companies to earn

excess returns.

Keywords: Predicator, Hausman test, Tobin’s-Q, PSX 100 Index Companies,

Criterion, Excess Returns.

Introduction

Financial ratios are classified into five basic types for convenience: liquidity, activity (efficiency), debt,

profitability and market based ratios. Liquidity, activity and debt ratios mainly measures risk, profitability ratios

mailto:[email protected]

Intl. J. Basic. Sci. Appl. Res. Vol., 6 (3), 269-280, 2017

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measure only return and market based ratios capture both risk and returns for investors. A single ratio doesn’t

usually provide suitable information to evaluate the total performance of companies. On the other hand, if ratio

analysis is concerned only with particular characteristics of a financial position of companies, only one or two ratios

may be enough. The financial ratios are used for comparison and calculated on annually basis by using financial

statements of companies. If they are not calculated on annually basis, then the effects of seasonality may lead to

inaccurate conclusions and investment decisions. It is necessary to use audited financial reports of companies for

ratio analysis. But, the data of unaudited financial reports may not reflect the accurate financial condition of

companies. By using financial ratios, we must be cautious when relating old companies to new companies or a

company to itself over a long-term period (Gitman Lawrence, 2004).

Financial ratio is a fraction that shows the relationship between one or more variables which is taken from

financial statements for comparison. Financial ratios are the most powerful of all tools used for the analysis and

interpretations of financial statements that can enhance informed judgments and investment decisions (Lasher,

1997). To evaluate the stock returns, the vital criterion for evaluating the performance of companies is to measure

the rate of stock returns. This criterion itself contains information for investors used to evaluate the performance of

companies. Analysts use ratios as a common tool to identify the weaknesses and strengths of a company. However,

this ratio is only realized the complications, signs and not the primary problem. If the ratio is high or low, it is

important to realize a situation in company; however, cannot obtain sufficient information (Ghale, 2015). The

investors have less information about the stock exchange markets and many studies suggests that find the criteria

that help them investing. If so, they can decide to investigate very cautiously and in a speedy way. However, to find

relevant accounting variables for investors leads to better investment decisions (Hejazi et al., 2011).

The basic aim of investors from investing in company’s stock is to raise profits which are attained by stock

returns of companies. The stock return is the essential element in selecting the sound investments decisions.

Furthermore, any investor in selecting their best investment decision is to achieve stock returns with more

efficiently, competently and a lesser amount of risk. Yet, the investors must need information about those stock

returns of companies that can increase profits. The information which exists about company’s stock is based on

internal or external information of that company. Internal information of the company is reflected in its financial

reports such as balance sheet, profit and loss statement and cash flow statement. The outside information of

companies are available in the stock exchange market. Consequently, these internal and factors can affect the stock

returns. Furthermore, external information determines the stock prices in the stock exchange market and has

influence on investment decisions made by investors (Modridi & Mousavi, 2009).

A survey conducted in the Boston College (2007), 62% respondents preferred financial information and 38%

respondents preferred non-financial information which is mainly used in investment decisions (Shehzad & Ismail,

2014). Stocks are the most chosen investments since they offer higher stock returns than other instruments. So, the

elements determining stock returns are one of the most vital subjects in finance. Furthermore, the investors will

prefer to buy the stock of those companies when book value of stock is larger than market value of stock.

Moreover, the financial ratios are vital indicators for valuation of a company and investment decisions

(Emamgholipour et al., 2013). The difficult and interesting phenomena for both financial analysts and investors are

that stock returns have permanently been a source of debate in the financial markets. The valuation of correlation

between financial ratios and their impact on the stock returns which is one of the dilemmas often debated in this

field of study (Karami & Talaeei, 2013).

Stock return is a crucial problem as well as the strategically target to achieve by shareholders who wish to

maximize it. Therefore, this issue has had lots of different results of experimental researches in the world. Many

financial ratios are statistically significant and positively related with stock returns as backed by literature. There is

no agreement among authors that stock returns are unpredictable and some said that stock returns are predictable.

But forty years ago, stock returns are not predictable because overall efficiency of markets (Fama, 1970). Stock

returns are predictable through market based ratios (Kheradyar et al., 2011; Zeytinoglu et al., 2012;

Emamgholipour et al., 2013). Though, the stock returns are predicable by using financial ratios (Pontiff & Schall,

1998; Kheradyar et al., 2011). It is evident that stock return predictability is due to market inefficiencies in stock

market (Pesaran & Timmermann, 1995).

Financial ratios are one of the financial statement analysis methods used by financial analysts. They use

genuine data and financial statements analysis in order to study the predictability of stock returns which is based on

financial ratios. Furthermore, financial ratios are calculated from financial reports which are considered as

appropriate indices that can be used to facilitate the sound investment decisions by investors and benefit the users of

financial reports in order to inspect financial ratios of companies (Karami & Talaeei, 2013). So, stock returns are

predictable in inefficient markets instead of efficient market. In the age of globalization, the investors are becoming

smarter and risk averse by investing in stocks of high market capitalization companies. Without stock return


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predictability, the investors are unable to make sound investment decisions which can leads to lower the investor

confidence. The objective of the current study is to investigate the impact of financial ratios on stock returns and

also to investigate the long-term performance of stock returns.

Literature Review

There are a diverse number of literatures available on the impact of financial ratios on stock returns

performance. There are literatures which tried to highlight on the factors that may have impact of financial

ratios and control variables on stock returns performance. Having the view of stock returns predictability, the

following literature survey has been conducted.

Kheradyar et al (2011) studied the role of financial ratios that can predict stock returns in hundred companies

listed on the Malaysian Stock Exchange during period from 2000-2009. The dependent variable is yearly stock

returns and independent variables are dividend yield, earning yield and Book-to-Market (B/M) ratio. They applied

Generalized Least Squares methods to estimate the predictive regressions on panel data. The result shows that the

financial ratios can forecast the stock returns. The B/M ratio has the higher predictive power than dividend yield

and earning yield. Moreover, the financial ratios can raise the predictability stock returns when the ratios are

combined in the multiple predictive regression model. Most of the studies have been hypothesized to predict the

U.S. stock returns. Hence, predictive power of the financial ratios is still unknown in developing markets. Among

financial ratios, dividend yield, earing yield, and B/M ratios have a strong theoretical background and unique

features. In short, financial ratios can predict future stock returns in emerging MSE.

Fun and Basana (2012) examined the impact of Price-to-Earnings (P/E) ratio on stock return in forty-five

companies listed on the Indonesia Stock Exchange during period from 2005-2010. They used Hausman test on panel

data. The result of simple linear regression model shows that there is statistically insignificant relationship between

P/E ratio and stock returns. There is another study done by two scholars such as Maxwell and Kehinde (2012) who

investigated the impact of financial ratios on stock returns. The study was conducted on 50 companies listed in the

Nigerian Stock Exchange (NSE) during the period from 2001-2006. They used panel data and simple linear

regression model. There finding suggest that there is a significant linear relationship between P/E ratio and stock

returns. There is another similar study done by Khan et al (2012) who examined the financial ratios and stock

return’s predictability by using Pakistani listed companies. The purpose of their work is to investigate the ability of

dividend yield, earning yield and B/M ratio in order to predict stock returns. The sample of the study consists of 100

non-financial companies listed on the Karachi Stock Exchange. The seven years are used for analysis from 2005-

2011. To find whether earning yield, dividend yield and B/M ratio can predict stock returns. They use regression

analysis on panel data models. The results indicate that earning yield, dividend yield ratios has direct positive

association with stock return whereas B/M ratio has statistically significant and negative relationship with stock

returns. Thus, dividend yield, earning yield and B/M ratios can predict stock returns. Furthermore, as compare to

dividend yield and earning yield, B/M ratio has the highest predictive power. Moreover, when we combine these

financial ratios, the stock return’s predictability will improve.

Karami and Talaeei (2013) examined stock return’s predictability by using financial ratios in the companies

listed in Tehran Stock Exchange during period from 1998-2007. The aim is to measure correlation between

financial ratios and their impact on predictability of stock returns. The stock returns are the dependent variable

while financial ratios including P/E ratio, B/M ratio, dividend yield and capital gain are the independent variables.

In order to test the research hypotheses, the panel data is used. The results of simple and multiple linear regression

model shows that B/M ratio and capital gain significantly affect predictability of stock returns. There is another

similar study done by Tahir et al., (2013). They studied the distinctive features of the companies have specific

power to predict the stock returns. This study was lead with an attempt to link the literature gap by presenting

empirical evidence about features of companies and their effect on stock returns. They used secondary data of 307

non-financial listed firms of Karachi Stock Exchange during period from 2000-2012. The dependent variable is

stock returns while independent variables are EPS, sales growth, market capitalization, and B/M ratio. First two

independent variables such as EPS and sales growth are used as proxies for size effect of companies while B/M

ratio is used for value effect of companies. They used multiple linear regression models by using panel data.

Furthermore, statistical and econometric techniques such as correlation matrix and multiple linear regression

analysis are applied for empirical testing. The result shows that independent variable of the study had significant

impact on stock returns except sales growth.

Jabbari and Fathi (2014) studied the stock return’s predictability by using financial ratios. The dependent

variable is stock returns and independent variables are current ratio, asset turnover ratio, fixed asset turnover ratio,

net income to sales ratio and return on assets. The methodology used in current study is Least Square regression

http://article.sciencepublishinggroup.com/html/10.11648.j.ijfbr.20160201.12.html


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technique in order to investigate the relationship between stock returns and financial ratios. This study provides

empirical evidences by selecting all listed firms of Tehran Stock Exchange during period from 2007-2012. The

result of F-Limer test shows that panel data is appropriate for analysis while result of Hausman test shows that fixed

effect model is suitable for estimation. The result of OLS regression under fixed effect method showed that the

financial ratios predicted 15% of the stock returns and all financial ratios are statistically significant impact on stock

returns. The overall validity of model is check by Fisher F-test value which is 9.37 and P-value of F-test is zero

which is lower than 5% level of significance. Hence, we reject null hypothesis and conclude that model is valid in

predicting stock returns through financial ratios. The Durban Watson test statistics value is 1.91 which signifies that

there is no problem of autocorrelation. The finding shows that all financial ratios have positive and statistically

significant impact on stock returns except fixed asset turnover ratio that have statistically significant but negative

impact on stock returns due to lowest value of its slope coefficient.

Petcharabul and Romprasert (2014) inspected the significant relationship between financial ratios and stock

returns of the Thailand Stock Exchange during period from 1997-2011. They used quarterly data which are

collected from annual reports of technology industry. The dependent variable is stock returns and independent

variables are current ratio, inventory turnover ratio, ROE, debt-to-equity and P/E ratio. The methodology used in

current paper is regression analysis by using ordinary least square in order to test the relationship between financial

ratios and stock returns. They found that only ROE and P/E ratios have a statistically significant relationship with

stock returns. In short, different countries used different methodology which is backed by literature to test the link

between stock returns and financial ratios which causes different results.

Ghale (2015) investigated the role of financial ratios in explaining stock returns and the correlation of stock

returns with financial ratios and earnings quality in Mobarakeh and Khuzestan steel company’s case study. The

dependent variable is stock returns and eleven financial ratios are used as independent variables such as current

ratio, inventory turnover ratio, receivables turnover ratio, debt ratio, return on assets, return on equity, liquidity ratio

of profit, earnings ratio, DPS, dividend ratio and cash flow per share. The methodology used in current study is

Spearman correlation test in order to investigate the relationship between stock returns and financial ratios. The

results of this study indicate that there was a positive relationship between current ratios, debt to asset ratio, ROE,

and profit on sale. Based on the findings, these financial ratios show small percentage of the variation in stock

returns.

Tehrani and Tehrani (2015) investigated the effect of financial ratios to predict company profits and stock

returns in Tehran stock Exchange based on sample of 252 companies. They used stock returns as dependent

variable and ten independent variables such as Return on Assets (ROA), margin sales, changes in ROA, and

changes in gross margin sales, changes in inventory turnover, changes in asset turnover, changes in debt-to-equity,

changes in debt, changes in current ratio and changes in annie ratio. They used panel data and fourteen years i.e.

2001 to 2014 by using ordinary least square (OLS) regression technique in order to test the Hausman test to

investigate the possible effects of financial ratios on stock returns and profits of companies. The results show that

profitability ratios and activity ratios can be proper predictive of oncoming efficiency in the stock market of Tehran.

However, there is not a meaningful relationship between financial ratios and companies’ profits; that this is due to

smoothing of profits in Iranian companies listed on Tehran Stock Exchange.

Anwaar (2016) investigated the impact of financial ratios by using firm’s performance variables on stock

returns. The aim of his study is to test the impact of firm’s performance on stock returns by using listed firms of

FTSE 100 Index during period from 2005 to 2014. There are five exogenous variables such as net profit margin,

ROE, EPS, ROA and quick ratio while endogenous variable is stock returns. The methodology is based on panel

regression analysis to analyze and interpret panel data. The result reveals that ROA and net profit margin have

positive and significant impact on stock returns while EPS has got negative and significant impact on stock returns.

If there are raises in EPS than all investors demands to achieve short-term profit and conscious for dividend and

also to sell their stocks in market because of decline of stock returns in near future and excess supply of stocks.

There is insignificant impact of ROE and quick ratio on stock returns.

In short, the literature examines that the significance of predictability of stock returns is not yet to be explored.

No specific study has been done in Pakistan to investigate the stock return predictability by using financial ratios

and control variables in PSX 100 Index Companies. This study tries to fill the gap in academic research as

combination of financial ratios and control variables that includes asset turnover ratio, debt ratio, ROS, EPS, while

control variables are firm size, market return, Tobin’s-Q, inflation, inflation and GDP that have impact on stock

returns; in order to investigate the significant predicator and impact on stock returns during period from 2001 to

2014.


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Research Hypothesis

The following are the research hypothesis of the current study.

H1: There is a significant relationship between asset turnover ratio and stock returns.

H2: There is a significant relationship between debt ratio and stock returns.

H3: There is a significant relationship between return on sales and stock returns.

H4: There is a significant relationship between EPS and stock returns.

H5: There is a significant relationship between firm size and stock returns.

H6: There is a significant relationship between market return and stock returns.

H7: There is a significant relationship between Tobin’s-Q and stock returns.

H8: There is a significant relationship between inflation and stock returns.

H9: There is a significant relationship between Interest Rates and stock returns.

H10: There is a significant relationship between GDP and stock returns.

Methodology

The current study is an applied research in terms of objectives and it is a descriptive and correlational one in

terms of nature and methodology. The current study is based on Ordinary Least Square (OSL) method on panel data

on sixty five listed companies of PSX 100 Index from the period 2001 to 2014 (Jabbari & Fathi, 2014; Tehrani &

Tehrani, 2015). Furthermore, OLS is used for estimating the unidentified parameters. This technique minimizes the

sum of squared vertical distances between the observed responses in the dataset and the responses predicted by the

linear approximation (Hunjra et al., 2014). Descriptive analysis, correlation analysis, F-Limer test and Hausman

test have been conducted in current study. The focus of current study is based on short panel having sixty-five

cross-sectional and fourteen-time series units which is a balanced panel.

Econometric Model

It is clear from below equation 1 that dependent variables of the model is stock returns while financial

variables are asset turnover ratio, debt ratio, return on sales, earnings per share. The control variables are of two

types such as firm’s specific and macroeconomic variables. The firm size, market returns and Tobin’s-Q are firm’s

specific variables while inflation, interest rates and GDP are macroeconomic variables. Based on the examination of

literature pertaining to the topic, the following econometric model is investigated by the using various financial

ratios and control variables.

Rit = αο + β1 ATRit + β2 DRit + β3ROSit + β4 EPSit +β5 FSit + β6MRit + β7 TQit + β8 INFt + β9 INTt + β10 GDPt +ϵit (1)

Where,

Rit = Stock Returns of ith firm in year t

αο = Intercept

β₁ to β10 = 10 slope coefficients of respective independent and control variables

ATRit = Asset Turnover Ratio of ith firm in year t

DRit = Debt Ratio of ith firm in year t

ROSit = Return on Sales of ith firm in year t

EPSit = Earnings Per Share of ith firm in year t

FSit = Firm Size of ith firm in year t

MRit = Market Return of ith firm in year t

TQit = Tobin’s Q of ith firm in year

INFt = Inflation of Pakistan in year t

INTt = Interest Rates of Pakistan in year t

GDPt = Interest Rates of Pakistan in year t

ϵit = Random Error Term of the ith firm in year t

i = 1,2,3……, 65

t = 1,2,3……, 14


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Research Variables and their Measurements

Table 1. List of Variable and their Measurements.

S.No. Variables Formulas

Dependent Variables

1 Stock Returns Rit = ln (Pt

Pt−1

)

Independent Variables

1 Asset Turnover Ratio ATRit = Total Sales

Total Assets

2 Debt Ratio DRit = Total Liabilitiess

Total Assets

3 Return on Sales ROSit = Net Income After Interest and Taxes

Net Sales

4 Earnings Per Share EPSit = TOtal Income Afetr Interrest and Taxes

Average Outsatnding Shares

Control Variables

1 Firm Size FSit = ln (Market Cap)

2 Market Return Rmt = ln (PSX 100 Index Pricet

PSX Index Pricet−1

)

3 Tobin’s Q TQit = Market Value of Firm

Total Assets of Firm

4 Inflation Consumer Price Index (CPI) is taken as proxy for Inflation.

5 Interest Rates A money market rates are taken as proxy for interest rates.

6 Gross Domestic Product GDPt = Consumer Spending + Govt. Spending

+Invesment on Capital + (Exports − Imports)

Results

Data analysis means actual results of data mining and information gathering in order to obtain reasonable

results to be used in investment decisions by investors (Ghale, 2015). In this section, data are collected, sorted and

then processed to test the research hypotheses. Furthermore, the financial ratios and control variable are collected

from annual reports of companies and World Bank website. Test and analysis are performed by using MS-Excel

and STATA 13 software. The result of descriptive statistics, correlation matrix, F-Limer and Hausman test and

Random Effect Model are briefly explained as under.

Descriptive Analysis

Table 2. Descriptive Statistics.

Measures Mean SD Minimum Maximum

Stock Returns 14.29 57.80 -301.67 214

Asset Turnover Ratio 8.41 31.56 0.000 36.68

Debt Ratio 0.60 0.23 0.000 1.17

ROS 5.55 13.30 -58.20 153.21

EPS 16.82 31.62 -47.58 367.92

Firm Size 22.55 1.69 15.50 26.75

Market Returns 21.85 38.01 -87.55 75.24

Tobin’s-Q 0.92 1.30 0.000 12.48

Inflation 8.98 4.60 2.91 20.29

Interest Rate 9.97 2.29 7.00 14.00

GDP 4.12 1.91 1.61 7.70


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It is clear from above Table-2 that mean value of stock return is 14.29% while variability of stock returns is quite

higher as compared to market return which is 57.80%. The average value of asset turnover ratio (ATR) is 1.58 times

and its variability is slightly lower than stock returns which is 31.05%. The minimum value of ATR is zero, which

is the signification in some companies that assets are not efficiently utilized to generate sales. Further, the

maximum value of ATR is 36.69 times that means the companies uses its assets efficiently to generate sales. This

measure having greater interest to management of companies because it shows the company’s operations are

financially efficient. The average value of debt ratios (DR) is 0.60% and its variability is very less which is only

0.23%. The minimum value of DR is zero which signifies that less money of others people’s in firms is not properly

generating profits. Furthermore, lower value of DR shows that some firms pay its total liabilities from its total

assets. The maximum value of DR is 1.17% which means that the firm could finance of its assets with debt and

more money of other peoples invested in firms is properly generating its profits. The higher value of debt ratio is

the indication of greater degree of indebtedness and having more financial leverage.

The average value of return on sales (ROS) is 0.05% and its variability around the mean value is slightly low

which 0.59% is. The minimum value of ROS is -1.20% while maximum value is 15.67%. The lower value shows

that some companies uses it net income after interest and taxes less efficiently to generate sales while higher value

signifies that the majority companies uses its net income after interest and taxes to generate its profits. It is due to

less financial risk found in higher value of ROS of PSX 100 Index companies. The mean value of EPS is 16.82 and

its variability is slightly lower than stock return which is 31.62%. The minimum value of EPS is -47.58 which

means some companies suffer losses due to ineffectively managing production capacity that’s why its value is

negative while highest value of EPS is 367.92 which means the positive growth in EPS is due to effectively

maximizing the utilization of production capacity and as well as better sales mix. The average value of firm size is

22.55 and its variability around mean value is 1.69%. The minimum value of firm size is 15.50 and maximum value

is 26.75. Furthermore, the fluctuation is due to dispersion between two positive values of firm size which are

actually positive market capitalization value regarding all listed non-financial firms of KSE 100 Index. The positive

value of firm size is the attaining the economies of scale by producing more on reducing the cost of production that

leads to improved efficiency of companies. The average value of market return is 21.85% and its variability around

mean value is 38.01%. The lowest value of market return is -87.55 while highest value is 75.24%. The fluctuation is

due to the dispersion between the higher and lower values. Furthermore, the negative value of market return is due

to financial crises occurred in 2008 and increased after financial crises of 2008. The average value of Tobin’s-Q is

0.92 times and its variability around mean value is 1.30%. The minimum value of Tobin’s-Q is zero while higher

value is 12.48. The lower value of Tobin’s Q signifies that it is less than one. Hence, firm will do not replace

capital, while higher than one value is the signal of those companies will buy more capital to raise the market value

of firm. So, investor must set criterion to invest in those companies that have Tobin’s-Q value greater than one

which is more worthy and valuable firms for investments because market value of firm is greater than book value of

firm.

The mean value of inflation is 8.98% and its variability around mean value is 4.60%. The minimum value of

inflation is 2.91% in year 2003 while higher value is 20.29% in year 2008 and it drop down to 7.19% in year 2014

is due to strong monetary and fiscal policies. The average value of interest rates is 9.97% while its variability is

2.29% around mean value. The minimum value of interest rates is 7% in year 2003 and higher value is 14% in year

2009 and its value drop down further to 9.50% in year 2014 is mainly due to changing law and order situations in

Pakistan that externally influencing PSX 100 Index companies. The average value of GDP rate is 4.12% and its

variability around mean value is 1.91%. The GDP is fluctuating between 1.61% (2010) and 7.7% (2009). It is due to

instable economy that leads to poor economic reforms, poor governance of country and changing law and order

situation in Pakistan.


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Correlation Analysis

Table 3. Correlation Matrix.

Variables SR ATR DR ROS EPS FS MR TQ INF INT GDP

SR 1

ATR -0.012 1

DR -0.025 0.183 1

ROS 0.136 0.091 0.061 1

EPS 0.077 -0.083 -0.210 0.216 1

FS 0.098 -0.241 -0.075 0.156 0.259 1

MR 0.639 0.004 -0.005 0.098 -0.023 0.482 1

TQ 0.210 -0.134 -0.271 -0.018 0.391 0.353 0.069 1

INF -0.488 0.003 -0.011 -0.109 0.056 0.176 -0.599 -0.023 1

INT -0.206 0.005 0.049 -0.115 -0.010 -0.049 0.180 -0.140 0.346 1

GDP 0.288 -0.012 -0.044 0.139 0.026 0.126 0.161 0.161 -0.376 -0.763 1

Table-3 shows a slightly weak correlation found between stock returns and assets turnover ratio which is 0.012

while slightly higher and positive correlation is found between stock return and market returns which is 0.639. The

negative and weak correlation is found between a GDP and asset turnover ratio while higher correlation is with debt

ratio which is 0.1830. There is slightly strong but negative correlation found between GDP and interest rates which

is -0.763 and less than 0.80. In short, overall result of correlation matrix shows that there is no problem of

multicollinearity. Furthermore, the linear association between stock returns and financial ratios and control

variables are conduct by multiple linear regression analysis which will show better results because regression

analysis is more flexible and accurate than correlation matrix.

F-Limer and Hausman Test

Table 4. Result of F-Limer test and Hauman test.

Measures F-Limer test Hausman Test

Cross-section F-stat 2.01 ----

P-value of F-stat 0.00 ----

Cross-section Chi-square 130.17 1.50

P-value of Chi-square 0.00 0.6829

Decision Panel Data Random Effect Model

It is clear form above Table-4, the P-value of cross-section F-statistics and chi-square test statistics is less than 5%

level of significance for model. Hence, we reject null hypothesis of F-Limer test and conclude that panel ordinary

least square (OLS) is used to estimate the model. Furthermore, the P-value of Chi-square under Hausman test is

greater than 5% level of significance. Hence, we are not being able to reject Ho and conclude that results are

statistically insignificant and we use random effect model for estimating the model.


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Regression Analysis

Table 5. Results of Random Effect Model.

Variable Coefficient Std. Error t-Statistics P-Values

Intercept -242.4684* 40.5485 -5.9797 0.0000

Asset Turnover Ratio -0.2576**

0.1133 -2.2732 0.0233

Debt Ratio 21.0634**

10.2076 2.0635 0.0394

Return on Sales 0.7593* 0.1563 4.8576 0.0000

EPS -0.1345***

0.0694 -1.9382 0.0529

Firm Size 13.4790* 1.8553 7.2652 0.0000

Market Return 0.7327* 0.0501 14.6249 0.0000

Tobin’s-Q 3.9153**

1.7362 2.2551 0.0244

Inflation -3.3549* 0.4473 -7.5009 0.0000

Interest Rates -2.7822* 0.9803 -2.8379 0.0047

GDP -5.1201* 1.2481 -4.1021 0.0000

N= 910, R= 73.19%, R2 = 53.57%, F-stat = 13.02, Prob. (F-stat) = 0.0000 and DW- test = 2.06

Where ***

and ***

indicates the level of significance at 1, 5 and 10 percent.

It is clear from above Table-5 that the slope coefficient of all variables are positive in multiple linear regression

model except intercept, asset turnover ratio, EPS, inflation, interest rates and GDP but all statistically significant

and better predictor of stock returns. The coefficient of correlation (R) is 73.19% which signifies that there is

slightly strong but positive linear correlation found between dependent and independent variables. The coefficient

of determination (R2) is 53.57% which means that the 53.57% variations in stock returns are explained by financial

ratios and control variables. The closer the value of R2 to 1 the better the regression line describe the connection

between dependent and independent variables and in particular the predication made with equation will be more

accurate. The probability associated with the F-statistic is 0.0000 which can support the validity, usefulness and

statistically significant of model. The Durbin Watson statistics value is 2.06 which is slightly equal to 2. It implies

that there is no autocorrelation present in panel data which signifies that errors are independent. The below equation

2 is predicated multiple linear regression model of the current study.

Rit = – 242.47 – 0.2580 ATRit + 21.06 DRit + 0.7593 ROSit – 0.1345 EPSit + 13.48 FSit +

0.7327MRit + 3.92 TQit – 3.35 INFt – 2.78 INTt – 5.12 GDPt + ϵit (2)

In above equation 2 there are four independent variables such as asset turnover ratio, debt ratio, return on

sales, EPS while six control variables such as firm size, market return, Tobin’s Q, inflation, interest rates and GDP.

It is clear from the above equation 2 that the slope of model is negative but statistically significant. There is one unit

increase in asset turnover ratio, the stock returns will decrease by 0.2580 units. This is because of reduction in sales

to generate assets for PSX 100 Index companies. However, the negative impact of assets turnover ratio on stock

returns is due to slightly weak working capital management and high labor cost. If one unit increases in debt ratio,

the stock returns will increases by 21.06 units. The reason for increase in stock returns that the company’s debt

policy is quite satisfactory. The highest variation in stock returns is due to debt ratio because the companies fulfill

the short term and long term debts from total assets which is signals of no debt risk. According to Korajczyk et al

(1992) provide the signal of clustering equity issuance. Companies frequently issue equity in good times, as their

own equity price has an unusual rise, and hence, reduce the level of leverage. But, Levy (2000) stated that firm’s

managers are more likely to issue debt when their compensation is lower. If unit increases in EPS, the stock returns

will decreases by 0.1345 units. The reason for reduction in stock returns is due to fluctuation in stock returns which

is not balanced in reaction towards good news and bad news. However, the increase of investors for a particular

company is a good new to purchase the company shares but bad news for investor when company suffers losses and

company lose many investors in such regard. That’s why fluctuation in stock returns will happen in stock market

due to high inflation. According to Siegel (2002) who said that investors are concerned about the growth in EPS.

The result of the current study is consistent with Zeytinoglu et al (2012) and Emamgholipour et al (2013).

If one unit increases in firm size than the stock returns will increases by 13.48 units. It is better predicator for

potential investors that not only gives second highest variation in stock return by 13.48 units but also benefit from

economies of scale. Normally, larger firms intend to hold reserve more cash to maintain the high level and quality


278

of operations and investment chances. In large firm size, lower investor reaction leads to invest in stock of

companies as compared to small size firm where the investor reaction is more that leads to avoid investment. The

potential investors not only focus on huge returns for investing in smaller market capitalization companies but also

investing in large market capitalization companies. If one unit increases in market return than there will be 0.7327

units’ increases in stock returns. There is positive and statistically significant impact found between stock returns

and market returns. The stock returns is increased due to the fact that large market cap companies can earn excess

profits from their investment while small market cap companies fail to earn excess returns and their cost of capital

that behave differently with market returns of country as compared to small size of firm. But in large market cap

companies, the stock return will not behave differently with market returns. If one unit increases in Tobin’s Q than

there will be 3.92 units increases in stock returns. There is positive and statistically significant impact found

between stock returns of high market capitalization companies and market returns. Hence, the null hypothesis is

rejected. These companies have higher access to growth and investment opportunities due to reasons that firm’s

value is increasing that will directly increase the stock returns for investors. The results are consistent with

Davidson et al (2002).

If unit increases in inflation than there will be 3.35 units decline in stock returns. There is negative and

statistically significant impact found between stock return and inflation. Hence, the null hypothesis is rejected. The

decrease in stock returns is due to decline in actual profits that shareholder earns in the form of EPS. However,

stock returns are not only negatively affected by the inflation but also decrease the savings and increased the

consumption. If the savings of investors are decreased so the investment in PSX 100 Index companies also

decreased. This result is inconsistent with Samadi et al (2012). There is statistically significant and positive impact

of inflation on stock returns. Some companies offer high stock return will lead to high stock prices (Ahmad et al.,

2013). The result of current study is also consistent with Abbas et al (2015) who investigated the impact of inflation

on stock returns. They result shows that there is negative and significant relationship between stock returns and

inflation. The negative effects are however most pronounced and comprise a decline in the actual value of money

and other monetary variables over time and vice versa. As a result, uncertainty over future inflation rates may

reduce investment and savings, and if inflation levels rise quickly than there may be shortages of goods as

consumers begin to hoard out of uneasiness that is the prices may increase in the future (Kimani & Mutuku, 2013). It

is desirable to keep the inflation below 6% because it may be beneficial for the achievement of maintainable economic

growth and investment (Nasir & Saima, 2010). Hence, investors must sift through the confusion to make wise

investment decisions on how to invest in periods of inflation. Majority non-financial firm’s stocks seem to perform

better during periods of high inflation.

If one unit increase in interest rates than there will be 2.78 units decline in stock returns. There is negative and

statistically significant impact found between stock return and interest rates. Hence, the null hypothesis is rejected.

This result is consistent with Leon (2008). One unit increase in GDP than there will be 5.12 units decline in stock

returns. There is negative and statistically significant impact found between stock return and gross domestic

product. The negative impact of GDP on stock return is due to high price-to-earnings ratio. Furthermore, the higher

price-to-earnings ratio is the indication of greater investor’s confidence in buying the shares of companies (Gitman

Lawrence, 2004). Economic growth happens from high individual savings rates, greater participation of

employment force and change of technology and monopolies of companies. The results are consistent with previous

work done by Ritter (2005) who focused on cross-country correlation actual stock returns and GDP growth during

period from 1900-2002 is negative. Furthermore, the result is consistent with Abbas et al (2015) which signifies that

GDP has a negative and significant impact on stock returns.

Discussion and Conclusion

Current study has empirically examined the impact of financial ratios and control variables on stock returns by

taking Pakistan Stock Exchange (PSX) 100 Index Comapnies during the period from 2001 to 2014. The stock

returns are predictable by using financial ratios (Kheradyar et al., 2011). But to fill the gaps of previous studies by

using control variables. Ten variables are found to be better and significant predictor among the various variables

such as asset turnover ratio, debt ratio, return on sales and earnings per share, and control variables such as firm

size, market returns, Tobin’s-Q, inflation, interest rates and GDP. I have used F-Limer test and Hausman test to

estimate the random effect model on panel data. Furthermore, all variables are statistically significant but some

variables have negative impact on stock returns performance such as asset turnover ratio, earnings per share,

inflation, interest and gross domestic product. However, the debt ratio, return on sales, firm size, market return and

Tobin’s-Q have positive impact on better performance of stock returns. In addition, potential investors not only


279

focus on huge returns for investing in smaller market capitalization companies but also investing in large market

capitalization companies of PSX 100 Index companies due to reason that large firms benefit from economies of

scale rather than the small market capitalization companies.

Empirically, results of the current study are similar with Kheradyar et al (2011). Zeytinoglu et al (2012) and

Emamgholipour et al (2013) and Ghale (2015). The results of multiple linear regression model show that 53.57%

variation in stock returns are explained by financial ratios and control variables. The linear relationship between

stock returns and financial ratios and control variables is 73.19% which shows that there is slightly strong

correlation exits between dependent and independent variables. Hence, overall model is valid and significant which

is specified by P-value of F-test of random effect model which is zero. Hence, it is concluded that financial ratio

and control variables are statistically significant and slightly strong relationship are observed and predictability of

stock returns is 53.57%. Furthermore, the stock market is inefficient that’s why stock returns are predictable in

listed firms PSX 100 Index. Additionally, it is scrutinize from literature that Karachi Stock Exchange is not an

efficient market that’s why stock returns are predictable in listed firms of PXS 100 Index. Moreover, modern

investor also set an investment criterion that is based on firm size and Tobin’s-Q to earn excess returns in market.

Conflict of interest

The authors declare no conflict of interest.

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Stock Return Predictability with Financial Ratios ... · Internal information of the company is reflected in its financial ... Financial ratios are one of the financial statement