The results indicate that DY and EY ratios have a direct positive association with stock return whereas B/M ratio has a significant negative relationship with stock return. Therefore we can say that the above mentioned ratios are able to predict stock returns, furthermore, it can be seen that as compared to dividend yield and earnings yield the ratio of book to market has the highest predictive power. Moreover, when we combine these financial ratios the predictability of stock returns will enhance. Keywords: Financial ratios, Stock return, Karachi Stock Exchange, Dividend Yield, Earning Yield. 1.

Introduction Stock Market plays a very significant role in the economic growth of a country. According to A. Schrimpf (2010) there is significant economic aftermath of the existence of stock return predictability. S. Kheradyar et al, (2011), “The Analytics of Economic Time Series”, states that in stocks market share prices move randomly i. e. on certain day share prices are like to go down as they were like to up. Such random behavior worried some of the financial economists and followed by further research. Hence such random movement of share prices leads to a hypothesis called Random Walk Hypothesis.

The random walk hypothesis suggest that it is difficult to predict share prices because stock prices evolved, now it will be showing an upward trend but after some time such might be showing a downward trend. Hence predicting 100% accuracy of stock return is almost impossible. In contrast to Random, Walk Behavior is an efficient market hypothesis. According to the efficient market hypothesis, share prices are fairly priced in the stock market or prices of stock demonstrates information in the market is widely and equally available to all and no one in the market can outperform or can beat the market.

With the passage of time researchers tries to find out the most accurate variables for predicting stock prices, some were tended towards financial and some were towards profitability ratios i. e. book to market ratio, price to earnings ratio, 1 Research Journal of Finance, and Accounting ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online) Vol 3, No 10, 2012 www.iiste.org dividend yield, etc some were tended towards cash flow ratios like price to cash flow ratio, cash burn ratio, etc and some focused on a macroeconomic variables like interest rate, law and order situation, and inflation rate, etc.

In this research article, we have investigated 3 above mentioned ratios to determine whether they predict stock returns. This research study has used the stock return and the above mentioned financial ratios association at two samples as the foundation for the formulation of Eight hypotheses. On the grounds of their appropriate regression models, the eight hypotheses are divided into two sets. In this study we have used the two models of simple and multiple regressions to apply Predictive regression; it is an important tool for predicting stock returns. A set of panel data is used for the formulation of these two models.

For tackling the problem of heteroskedasticity and non-normality distributed residuals, we applied the generalized least squares method. 2. Literature Review Campbell and Shiller (1988) stated in their study that dividend yield has the ability to confine expected return and expectation about growth in dividend yield so dividend yield is a good predictor of stock return. Chan, L. Hamao, Y. Yakonishok, J. (1991), found that in the Japanese market fundamental variables like dividend yield, price to earnings ratio, a book to market ratio, and firm size have a significant impact on the expected earning/returns of stocks.

They notify that there is an indirect relationship between earning yield and stock returns in Japan. In a comparison to the size of the firm and earning yield, B/M and dividend yield (cash flow yield) are significantly related to returns of stocks. They further added that an important variable both economically and statistically is a book to market ratio and this needs to be observed because either the afterward half of the sample is judged or for the first time test is applied the book to market ratio shows its continuation. Mukerji, S. Dhatt, M. Kim, Y. 1997), on the Korean Stock market for a period of 1982-1992 establish a direct relationship between return of stocks and D/E, S/P, and B/M, moreover an indirect relationship between the size of the firm and the return of stocks. They demonstrated that P/E ratio is a less trustworthy indicator than B/M and S/P. Beta is a week proxy for assessment of risk when compared with debt to equity ratio. B/M and S/P are responsible for the direct relationship between the return of stocks and debt to equity. However, a P/E and B/M ratio becomes the base for the indirect relationship between the return of stocks and the size of the firm.

Kothari and Shanken (1997) found for the US market that dividend yield and book to market ratios have a dependable proof for expected real return over a period 1926-1991, and there lies a track of time series variations. Pontiff and Schall (1998) stated that as for predicting power is concerned book to market ratio has some predictability power for predicting stock returns. Lewellen (2002) conducted his study in the US he found that the predictability power of dividend yield for predicting stock returns is more than P/E and B/M ratios.

Ang, A., and Bekaert, G., (2006), in their studies, tried to forecast interest rate and stock returns with the help of the predictive power of dividend yield. They found for short term forecasting, dividend yield predictive power is more than the long term forecasting. But as for the expected growth of cash flow prediction is concerned than dividend yield is a good predictive variable. Akyol, A. (2006), “analyzed the effect of a firm’s size, beta, and book-to-market value on the stock returns in Istanbul stock exchange.

He used data from July 1993 to December 2005 for Istanbul Stock Exchange and used Fama and French (1992) methodology to construct portfolios represented accurately by size-beta and then size-book-to-market, he found that book to market and Beta of firms have no effect on the stock return’s in Istanbul stock exchange. The size of the firm was the only variable that was negatively related to the stock returns in the Istanbul stock exchange. He also found that book to market, size, and beta are not related to January effects. Hjalmarsson, E. (2004), in his study tried to find out Global stock returns predictability.

He took twenty thousand monthly observations to form forty international stock markets. In which 24 were of developed economies and 16 were of developing economies. However, his study showed that dividend yield and price to earnings ratio has little power of predictability and defends his conclusion by adding that international results are showing deviation from the traditional view because the method used internationally may not count for the determination of variables. 2. 1 Hypotheses H1: the return of stock and DY has no association in time (t) and (t-1) respectively in sample one.

H2 return of stock and EY has no association in time (t) and (t-1) respectively in sample one. H3: the return of stock and B/M has no association in time (t) and (t-1) respectively in sample one. H4: the return of stock and DY has no association in time (t) and (t-1) respectively in sample two. H5: the return of stock and EY has no association in time (t) and (t-1) respectively in sample two. H6: the return of stock and B/M has no association in time (t) and (t-1) respectively in sample two. 2 Research Journal of Finance and Accounting ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)

Vol 3, No 10, 2012 ww.iiste.org H7: the return of stock and DY, EY, B/M combination has no association in time (t) and (t-1) respectively in sample one. H8: the return of stock and DY, EY, B/M combination has no association in time (t) and (t-1) respectively in sample two. 3. Research Methodology In order to check the predictability power of earning yield, dividend yield, and book to market ratios for predicting stock returns the study has taken a sample of 100 firms for a period of 2005-2011. We have applied certain screening criteria for companies to be included in the sample.

First, the firm must be listed on the KSE before Jan 1st, 2005. 2nd, for more than twelve months a stock must not be deferred. 3rd, for the study period of seven years a company stock must not be delisted. 4th, data must be available for all sample forms and variables. Finally, for a period of more than twelve months, the dividend yield of firms must not be zero. The study has divided the selected firms into two equal samples, which will reduce the effects of random sampling errors, and for the predictive regression two samples produce different estimation.

The study is based on secondary data, which is collected from, “State Bank of Pakistan”, the company’s annual reports, business recorder, and from “Karachi stock exchange”. Following S. Kheradyar et al, (2011) this study includes stock returns as a dependent variable while dividend yield, earnings yield, and B/M ratios have been taken as independent variables. 4. Measurement of Variables 4. 1 Stock Return Following Lewellen (2001) and S. Kheradyar et al, (2011) we have used stock return as a dependent variable.

Stock return is measured by dividing capital gain along with dividend per share on the market price per share. Following is the formula for stock returns. SRi = DPs + capital gain/market price 4. 2 Book to Market Ratio For finding the value of a company by comparison of the market value of a share to its book value, the study tends towards the book to market ratio. For finding the book value of a firm the study divide equity of a firm by its total number of outstanding shares. As for market price is concerned study tends towards the ongoing price of a share in the stock market.

If a firm offers a high return and having high book value then its market value, the firm is riskier and in the future returns of stock will be lowered than today. The following formula is used for calculating book to market value: B/M = Book Value per share Market value per share Lewellen (2001) states that as compared to P/E ratio B/M has higher predictive power for predicting stock return. But when a study compares B/M ratio with dividend yield than dividend yield is good forecaster than B/M ratio. 4. 3 Dividend yield Following S.

Kheradyar et al, (2011) second independent variable in this study is Dividend yield which is calculated as dividing dividends per share on the market price per share. If the market price is lower than the dividend yield will be higher and give a riskier signal for investment. Contrast to a higher dividend yield is a low dividend yield; such happens when the market price per share is higher than the dividend yield and gives an optimistic view for investment.

The following formula demonstrates how to calculate dividend yield: Dividend Yield (%) = (Dividend per Share / Market rate per share) x 100 4. Earnings Yield The empirical literature lay foundations of the predictive power of earning yield on stock return, and find out the association between earning yield and stock return is considerable because earning yield plays as a risk factor in relation to stock return. Moreover, the earning yield can demonstrate the efficiency of the market that has an important role in emerging markets, thus this study uses earning yield as the empirical predictor of stock return. Following S. Kheradyar et al, (2011) we have measured earning yield as earning per share divided by the price of the share.

Regression Model In this research article we have investigated three financial ratios EY, DY, and B/M to determine whether they predict stock returns. This research study has used the stock return and the above mentioned financial ratios association at 3 Research Journal of Finance and Accounting ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online) Vol 3, No 10, 2012 www.iiste org two samples as the foundation for the formulation of Eight hypotheses. On the grounds of their appropriate regression models, the eight hypotheses are divided into two sets.

In this study we have used the two models of simple and multiple regressions to apply Predictive regression; it is an important tool for predicting stock returns. A set of panel data is used for the formulation of these two models. For tackling the problem of heteroskedasticity and non-normality distributed residuals, we applied the generalized least squares method. Following S. Kheradyar et al, (2011) we have used panel models to formulate predictive regressions. Hence we have used a simple regression model to test the first 6 hypotheses which are formulated on the basis of the association between each financial ratio and future stock returns.

The simple regression model has the following form: SR it = ß0 + ßi Xi (t-1) + eit Where, SR it= in time period t, the return of ith stock, ß0= the estimated constant, ßi= ith stock predictable coefficient, Xi (t-1) = in period t-1 financial ratios of the ith stock, eit = error term. Similarly following S. Kheradyar et al, (2011) we have used multiple regression models to test the other two hypotheses H7 and H8, these two hypotheses are formulated on the basis of the relationship between combined financial ratios and future stock returns.

The model has the following form: SR it = ß0 + ßi1 DYi (t-1) + ßi2 EYi (t-1) + ßi3 B/Mi (t-1) + eit Where, SR it= in time period t, the return of ith stock, ß0= the estimated constant, ßi1= for DY the Ith stock predictable coefficient, ßi2= for EY the Ith stock predictable coefficient, ßi3= for B/M the Ith stock predictable coefficient, DYi (t-1) = is ith stock DY factor in period of time t-1, EYi (t-1) = EY factor of ith stock in period of time t-1, B/Mi (t-1) = B/M factor of ith stock in t-1 time period, eit = error terms. 6.

Results and Discussion For the first 6 hypotheses the predictive regression results are summarized in Table 1. The coefficient of dividend yield in Table 1 demonstrates a positive relationship of dividend yield in period (t-1) and stock returns in period (t) in both samples that is when dividend yield increases by one unit it will cause an increase of 0. 021 and 0. 010 units in stock returns of two samples respectively. As for the p-value of the coefficient of Dividend yield is concerned it is 0. 016 in sample one which is less than 0. 5, so the relationship is statistically significant and the null hypothesis H1 is rejected, however in sample two the association is insignificant so hypothesis H4 cannot be rejected.

The coefficient of earning yield in Table 1 demonstrates a positive relationship of earning yield in period (t-1) and stock returns at period (t) that is when earning yield increases by one unit it will cause an increase of 0. 013 and 0. 008 units in stock returns in the two samples respectively. As for the p-value of the coefficient of earning yield is concerned it is 0. 19 and 0. 010 in the two samples respectively which are less than 0. 05, so the relationship is statistically significant, therefore we will reject hypothesis H2 and H5. The negative coefficient of Book to market value in table 1 notifies an inverse relationship of B/M and stock returns in both samples that is if B/M ratio increasing the stock return will be decreasing and vice versa. The p-value of the coefficient of B/M value 0. 000 indicates that the relationship is statistically significant in both samples, so hypothesis H3 and H6 have been rejected.

S. Kheradyar et al, (2011) found that DY has a negative influence on stock return and a positive association between EY and stock return. He also found a positive impact of B/M on stock return in (2) (1) 4 Research Journal of Finance and Accounting ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online) Vol 3, No 10, 2012 www.iiste.org sample 2 but a negative one in sample 1. It can also be noticed by looking at the adjusted R-square that B/M has the highest predictive power, and this result is also supported by S. Kheradyar et al, (2011). Insert Table 1 Here) Now we will test to see whether stock return predictive power increases with the combination of EY, BM, and DY. We will reject H7 and H8 because it can be seen in Table 2 that the predictive regressions are statistically significant. Thus we can say that stock return can be predicted by the combination of EY, BM, and DY. Also, we can say that as compared to the other two ratios, the variations of the ratio of book to market have a greater impact on stock return, because in both samples it has the highest coefficient.

Similarly, by looking at the adjusted R-square we can say that in the two samples stock return predictive power increases when the combination of EY, BM and DY increases. (Insert Table 2 Here) 6. Conclusion Literature regarding the predictability of stock returns has changed over the last 20 years. With evolution researchers and economists separated price to earnings ratio, dividend yield, inflation, and book to market ratio, beta, industry returns, interest rate, and size of firms from amongst other variables which were considered important for predicting the return of stocks.

Presently strong evidence is present regarding variables for predicting stock returns. The analysis showed that financial ratios have significant power of predictability for forecasting returns of stock and they predict the future stock return of the Pakistani market, and B/M has higher predictive power as compared to other ratios. Similarly, the predictability of the stock return is enhanced by the combination of financial ratios.

References:

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Mat Nor, (2011). Stock Return Predictability with Financial Ratios. International Journal of Trade, Economics, and Finance, 2(5): 391-396.

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Chan, L. Hamao, Y. Lakonishok, J. (1991). Fundamental and Stock Returns in Japan. The Journal of Finance, 17391764.

Mukerji, S. Dhatt, M. Kim, Y. , (1997). A Fundamental Analysis of Korean Stock. Financial Analyst Journal, 53: 7580

Kothari, S. P., Jay A. Shanken, (1997). Book-to-Market, Dividend Yield and Expected Market Returns: A TimeSeries Analysis.

Journal of Financial Economics 44: 169-203. J. Pontiff, and L. Schall, (1998).

Book-to-Market Ratios as Predictors of Market Returns. Journal of Financial Economics, 49: 141–160. Lewellen, J., (2002).

Predicting Returns with Financial Ratio. National Bureau of Economic Research, MIT working paper no. 4374-02 Ang, A. and Bekeart, G., (2006).

Stock Returns Predictability. The Review of Financial Study, 651-707. E. F. Fama and K. French, (1992).

The Cross-Section of Expected Stock Returns. Journal of Finance, 47: 427-465 Lewellen, J. , (2001). Predicting Returns with Financial Ratios. Journal of Financial Economic, 209-235.

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