Prior studies have provided evidence that earnings outperform historical cash flows in predicting future cash flows. Later researchers revealed that the major accrual components of earnings each possess significant explanatory power in predicting future cash flows and they augment, rather than replace, the predictive ability of aggregate earnings. The degree of relationship of Earning, Sales & Cash Flows with Share Price was the Primary goal of this study though Prior study focused on predictive abilities of Cash Flow, Sales and Earning where as this study also predicted the abilities of Variables by Using share price as the dependent variable and as a proxy for future cash flows. The result showed evidence that Cash Flow did not appear significant with share price with the significance value of 0.061 , Sales also appeared insignificant with the significance value of 0.108 & Earning appeared significant with the significance value of 0.000 and relationship exist between Earning & Share Price.
Earnings occupied an important role in financial accounting research and finance, this emphasis on earnings in the literature exists for many reasons. It is the most widely accepted measure of firm performance.
Attention was also given to earnings because it was commonly used in evaluating management performance. Perhaps the biggest reason for the attraction to earnings, though, lies with the notion that earnings serves as a predictor of future cash flows. Many theories have represented that the accrual earnings represents the best predictor and of future cash flows than the historical cash flows. A company's existence depends on its ability to generate positive cash flows, and research demonstrated that share price directly related to an entity's cash flow prospects. Thus, because earning was viewed as a primary predictor of future cash flows, it was also a key determinant of share price.
Barth, Cram and Nelson (2001) showed that the predictive ability of earnings can be improved when disaggregated into its major accrual components. One of the components was sales revenue, which surprisingly ignored in the literature as a predictor of future cash flows and share price. The degree of relationship of earnings and cash Flow, sales with share price was a goal of this study.
Information in the Cash Flow statement helped investors, creditors and other users of financial statements to assess attributes Such as the firm's Liquidity financial flexibility and risk. Financial Statements gave information about firm's cash flows and users were interested in cash flows mainly as these affect their future cash flows. People were interested and they were concerned with assessing the entities ability to meet obligations and paying dividends where as accounting books stated that earning rather than Cash flows provides a better indication of an enterprise's to generate favorable cash flows. Short term cash flow predictions can also provide information to investors by trend in cash flows. A firm that raising owners equity/ capital and growing and recording large positive accruals relative to assets, where as a firm that was declining or downsizing and that also recorded also large negative accruals relative to assets. The reason behind the accounting rules that apply to growing and declining or downsizing firms differ fundamentally and this difference likely stems from the historical emphasis on reliability and conservatism in accounting.
A firm's ability to generate cash flow affects the value of its securities, so the ability to assess future cash flow was important for the investment community, both shareholders and creditors. While shareholders may be concerned with the stream of cash flows to perpetuity, many creditors were concerned solely the short term cashgenerating ability of a company.
The next Chapter of this study Describes Literature Review. Chapter 3 describes Theoretical Frame work and construction of hypothesis, Chapter 4 describes Research method, Chapter 5 describes Results, Chapter 5 describes Results which includes Tables and Chapter 6 describes conclusion.
The study relates to examined the relationship of, earning, cash flow from operations and sales with share price and the previous research predicted the comparative abilities of cash flow, earning and sales but this study only concern with the relationship of cash flow, earning and sales with share price.
In the finance literature that market forces determined share price equal to the discounted value of a stream of expected future cash flows (Hollister, Shoaf and Tolly 2002). Cash flows represent amounts investors expect to receive in the form of dividend payments or from the sale of their shares and not necessarily the annual operating cash flows generated by a firm. Consequently, it is in a very broad sense that share price was considered to embody a firm's future cash flows. Even if share price often thought of and evaluated in terms of cash flows, earning is also known to be extremely important to managers and analysts because of the key information it conveys about future prospects (Brigham and Ehrhardt 2002).
Richardson, Sloan, Soliman and Tuna (2006) provided a method that indicated accounting distortions and that were significant that contributed to accrual component earnings and evidence also showed that the accruals lower persistence that extends accruals as well to accruals that were unrelated to sales growth and wee systematically associated with earning manipulation.
Dechow and Ge (2006) found that earnings were more persistent than cash flows in high accrual firms, due to the growth of firms. If the firm was growing, allowable accruals adjustments were likely to reduce the effect of negative transitory cash flows on earnings, and consistent with this prediction, high accrual firms have high earnings persistence relative to that of cash flows.
Extending the idea that is developed by Dechow, Kothari and Watts (1998) that the accruals within earnings enable earnings to outperform operating cash flows in predictions, Barth, Cram and Nelson (2001) examined the major accrual components of earnings. The researcher disaggregated earnings into its cash flow component and six major accrual components of earnings (i.e. change in inventory, change in accounts receivable, change in accounts payable, depreciation, amortization, and other accruals). The Barth, Cram and Nelson (2001) premise for disaggregating earnings into these components was that aggregate earnings masks information contained in the individual components and that the each major component of earnings reflects different or unique information about future cash flows. Study results showed that each of the six accrual components were significant in forecasting the future cash flows and models with earnings disaggregated into the six accrual components and the cash flow component markedly outperformed models developed with aggregate earnings in terms of predicting future cash flows.
firms have large income decreasing special items in the following year and found some evidence consistent with this conjecture and interpret this finding as the reversal of earnings management in the prior year. However Chan, Chan, Jegadeesh and Lakonishok did not investigated or addressed the contemporaneous relation between special items and low accrual firms.
Richardson, Scott, Sloan, Soliman, and Tuna (2005) found that the less reliable accruals result in lower earnings persistence and investors did not fully anticipate the lower earnings persistence. Two accrual categories of low reliability were change in current operating assets (COA) and change in non current operating assets (NCOA). Change in COA is dominated by receivables and inventory. Change in NCOA is dominated by PPE and intangibles. Both changes of COA and NCOA could reflect write downs due to special items. Richardson, Scott, Sloan, Soliman and Tuna did not address whether there is any systematic difference between low and high accrual firms in terms of changes of COA and NCOA, nor did investigate the role of special items.
AlAttar and Hussain (2004) extended the research study work of Barth, Cram and Nelson (2001) to examined U.K. entities and found that disaggregating earnings into its major accrual components and cash flow component produced models with explanatory power superior to that of aggregate earnings relative to predicting future cash flows.
Jussi Nikkinen and Petri Sahlström (2004) extended the study of cash flow prediction model of Barth, Cram and Nelson (2001), which disaggregating earnings into cash flow and the components of accruals with the year and country effects, and examined the impact of accounting environment on cash flow prediction. The result performed well in countries where the accruals were used mainly to correct cash flow to better reflect current profitability of the firm, i.e. in countries with high information content of accruals. It implied that the cash flow prediction model by Barth, Cram and Nelson (2001) can be used in different kinds of accounting environments.
Hollister, Shoaf and Tully (2002) also expanded the work that done by Barth, Cram and Nelson (2001) by examining companies in the U.S., U.K., Germany, and Japan and included countries other than the U.S. because Hollister, Shoaf and Tully (2002) believed that the accrual components of earnings would be less important in predicting future cash flows for these countries compared to the U.S. This is because earnings in the nonU.S. countries must conform more to reported taxable income or subject to greater earnings management than in the U.S.Though Hollister, Shoaf and Tully (2002) discovered that, earnings disaggregated into the major accrual components and the cash flow component predicted future cash flows better than either current operating cash flows or aggregate earnings.
Dechow and Dichev (2001) test for the informativeness of working capital and results suggested that accruals correct for the timing problems of cash flows but at the cost of including errors in estimation that lead to lower accrual quality for earnings persistence.
Black (1998) studied the impact of the life cycle stages on firms' performances over a wide sample of firms. Looking at four life cycle stages (startup, growth, maturity, and decline), Black documented that accrual earnings are more value relevant in mature stages, whereas cash flows are more value relevant in stages characterized by growth. Growth industries include those with intangible intensive, high technology characteristics, which suffer from timing and matching problems.
Dechow, Kothari and Rayburn (1998) examined cash flow and the accrual process related to accounts receivable, accounts payable, and inventory to derive the prediction that current earnings were the best predictor of future cash flow. Study reported that the variation in firm in which cash flow forecast errors were based on the aggregate earnings which was lower significantly than that based on cash flow and Dechow, Kothari and Rayburn also reported that in firm specific regression of future cash flow on current aggregate earnings and cash flow, both have incremental explanatory power.
Das, Levine and Sivaramakrishnan (1998) examined the association between Value Line analysts' earnings forecasts and earnings predictability and found that as earnings become less predictable analysts' earnings forecasts become increasingly optimistic.
A research line has studied the performance of earnings based valuation relative to discounted cash flow and other discounting methods. The findings (Penman and Sougiannis, 1998, Francis, Olsson and Oswald, 2000) indicated that the over relatively short forecast horizons, ten years or less, valuation estimates using the earnings approach generate more precise estimates of value than discounted cash flow models and this advantage for the earnings based approach persists for firms with conservative or aggressive accounting, indicated that accrual accounting in the U.S. did a reasonably good job of reflecting future cash flows.
Ray, Pieter, May and Lynn (1998) evaluated the relation between the security returns and the funds based earnings component. Ray, Pieter, May and Lynn documented that the proxy for market expectations of the components that were based on the measures of historical serial and cross dependencies were substantially more accurate than random walk proxies. However, Ray, Pieter, May and Lynn detect significantly higher valuations of the operating cash flow component of earnings, relative to current accruals, when market expectations represented were using the dependency based predictions. Such differential valuation was not detectable for random walk representations.
Burgstahler and Dichev (1997) provided evidence that the quoted firms manage reported earnings to avoid earnings decreases and losses and found unusually low frequencies of small decreases in earnings and small losses and unusually high frequencies of small increases in earnings and small positive income and also found evidence that two components of earnings, cash flow from operations and changes in working capital were used to achieve increases in earnings. If earnings were managed than it could be expected that firms with negative earnings show more negative and volatile cash flows than firms with positive earnings.
Dechow, Sloan and Sweeney (1995) examined a sample of earnings manipulations subjected to SEC enforcement actions and found that these earnings manipulations were primarily attributable to accruals that reverse in the year following the earnings manipulations. Thus, the evidence was consistent with earnings management contributing to the lower persistence of the accrual component of earnings.
Even though Kaplan (1994) did not examine the relationship between sales and cash flows or share price, showed that sales performance measures, earnings, and stock returns are all key factors in top executive compensation and turnover. Dechow and Dichev (2002) and Francis (2004) note that Earnings quality can be affected by sales volatility. By and large the greater the sales volatility, the more unstable is the operating environment. Findings were in larger estimation errors for accruals and diminished earnings quality.
Significant research (e.g. Watts, 1977; Dechow, 1994, Bartov, Gold Berg and Kim 1997) suggested that the earnings reflect stronger correlation with value (i.e. share returns) than does current operating cash flows. Although Watts (1977), Dechow (1994) and Bartov, Gold Berg and Kim (1997) examined the value in terms of share returns, Dechow (1994) notes that the substitution of raw stock prices provides analogous results. Previous Study also shown that earnings better predicts future operating cash flows than does current operating cash flows (e.g. Greenburg, Johnson, Ramesh, (1986); Murdoch and Krause, (1989); Dechow, Kothari and Watts (1998). The reasoning for this is because accruals in earnings offset the negative correlation in cash flow changes to produce earnings changes that are much less negatively serially correlated. Dechow, Kothari and watts (1998) explained that is why earnings, rather than current operating cash flows, tends to be used in firm (i.e. share) valuations.
Lorek and Willinger (1996) examined the time series properties and predictive abilities of cash flow data and found that the model clearly outperforms firm specific and common structure ARIMA models as well as a multivariate, cross sectional regression model popularized in the literature Findings were robust across alternative cash flow metrics (e.g. levels, per share, and deflated by total assets) and were consistent with the viewpoint espoused by the FASB that the prediction of cash flow can be enhanced by earnings and accrual accounting data.
Finger (1994) found out the earnings ability and predicted future earnings and the future cash flow from operations 1 one through eight years ahead using annual data from193587 for 50 firms. In this research time series method was used to test firmspecific predictive ability over the entire time period (hereafter insample regression tests) and then compare out of sample forecast errors to assess earnings' ability to improve earnings or cash flow forecasts up to eight years ahead. Thus earnings were a significant predictor of future earnings. The random walk provides better out of sample forecasts than do individually estimated models one year ahead for 52% of the sample firms, Out of sample forecasts show that random walk models outperform individually estimated earnings models for oneyear but not for four or eightyear horizons. Earnings, used alone and with cash flow, are a significant predictor of cash flow for the majority of firms. However outofsample forecasts show that adding earnings rarely improves cash flow forecasts. Cash flow is a better shortterm predictor of cash flow than are earnings, both in and out of sample, and the two are approximately equivalent long term.
Sloan (1996) studied the nature of the information contained in the accrual and the cash flow components of earnings, the extent to which this information is reflected in stock prices and found that earning performance attributable to the accrual component of earnings exhibited lower persistence than earnings performance attributable to the cash flow component of earnings, hence findings also indicated that stock prices act as if investors "Fixate" on earnings, failing to distinguish fully between the different properties of the accrual and cash flow components of earnings.
Juan M. Rivara (1996) found out the accuracy and the consensus among forecasters of earnings estimates for U.S. domestic and U.S. multinational corporations, and was observed that the accuracy of earnings forecasts is significantly lower for purely domestic firms than for U.S based multinationals. Like wise the level of consensus in earnings estimates submitted by financial analysts is significantly lower for U.S. domestic than for U.S. multinational firms.
The accounting profession requires that firms disaggregate net income into specific components, even though earnings disaggregation was important for assessing firm profitability, there was little empirical evidence that the classification scheme actually improves profitability forecasts by analyzing the accuracy improvements in out of sample forecasts of one year ahead return on equity (ROE) to examined the predictive content of earnings disaggregations (Fairfield, Patrica, Sweeney and Yohn 1996). Results showed that the classification scheme prescribed by the accounting profession did increase the predictive content of reported earnings and found forecasting improvements from earnings disaggregation. These improvements go beyond separating extraordinary items and discontinued operations from the other components of earnings. Further disaggregation of earnings (into operating earnings, nonoperating earnings and taxes, and special items) improves forecasts of ROE one year ahead.
Dechow, Sloan, Sweeney (1995) and Kasznik (1999) found that firms with higher (lower) earnings exhibited significantly positive (negative) discretionary accruals, suggested earnings management varies with earnings or that the Jones (1991) model used to estimate nondiscretionary accruals is misspecified.
Cheng, Liu and Schaefer (1996) studied the Earning Permanence and the Incremental Information Content of CFO. Cheng findings suggested that the accounting earnings decreases and the cash flow from operations increases.
Lipe and Kormendi (1994) demonstrated that actual persistence measures derived from lower order ARIMA models did not capture all the value relevant time series properties of actual earnings because lower order models ignore a number of negative autocorrelations at higher lags that, though small individually were significant when considered in the aggregate.
Hopwood and McKeown (1992) studied the time series properties of quarterly operating CFs per share and earnings per share for a sample of manufacturing companies. The study found the timeseries properties of CFs were quite different from those of earnings. Results indicated a pattern of autocorrelation that is much stronger in the earnings series than in the CF series. In CF predictive ability tests, Hopwood and McKeown (1992) compared firm specific ARIMA models to the premier ARIMA models attributed to Brown and Rozeff (1979) and Griffin (1977). The former models exhibited a slight advantage over the premiers. Unfortunately, these premier ARIMA models, which were originally identified on earnings data, may not have served as a useful benchmark because Hopwood and McKeown's (1992) autocorrelation patterns for the CF series differed from the typical patterns for earnings series.
Freeman and Tse (1992) studied the relation between nonlinear abnormal returns and the unexpected earnings in which both argued that as the absolute value of unexpected earnings increases, the "persistence" of earnings declines (Brooks and Buckmaster (1976), unexpected earnings from a linear model would predominantly reflect the effects of transitory, rather than permanent, earnings (because a linear model heavily weights the coefficient on high magnitude transitory earnings). Freeman and Tse (1992) showed that forcing a linear specification on an abnormal return unexpected earnings model biases the slope coefficient on unexpected earnings toward zero.
Livnat and Zarowin (1990) examined the cash flow components and reported that the disaggregation of cash flow into its financing and operating components significantly improves the degree of association of cash flows with security returns.
Lorek and Willinger (1989) examined the differences in the auto regressive parameters of the Foster and Brown and Rozeff ARIMA models across firmsize strata. Onestepahead quarterly earnings forecasts were generated by a set of best fitting timeseries models. Tests also indicated that large and medium size firms generated one step ahead forecasts that were significantly more accurate than smaller firms at the .05 level and obtained similar predictive findings on the significance of the sizeeffect in a supplementary analysis of the non seasonal and volatile growth and inconsistent strata membership firms.
Bernard and Stober (1989) found no evidence that the stock prices respond in a systematic manner to the release of information about the cash flow and the accruals components of earning and the conjecture that the information content of these two components of earnings may not be systematically different.
Dharan (1987) examined the comparative abilities of accrual sales and cash collections of sales to predict future cash flows and found that when cash realization occurs in a period subsequent to sales realization, cash flow forecasts from earnings based on accrual sales are better than cash flow forecasts from earnings based on cash collections. This is because of accrual sales “provides information on management's expectations about future cash flows.
Financial analysts did not rely upon CF analysis; Analysts view it as an important supplementary tool useful in avoiding misleading inferences in the patterns of accrual based earnings numbers Dorfman (1987).
The magnitude of abnormal returns associated with good or bad news earnings signals is inversely related to firm size Freeman (1987) and speculated that findings might simply be due to differential time series properties of the earnings numbers of large and small firms an uncontrolled factor in research design and calls for future research to examine the possibility. The empirical evidence on the importance of the size effect in the above settings, led to consider whether controlling explicitly for firmsize leads to interfirm differences in predictive ability.
Theoretical and empirical work in accounting and finance has documented the importance of firm size when testing the information in security prices with respect to future earnings (Collins, Kothari and Rayburn 1987) and interested in assessing the information in security prices with respect to the predictive ability of earnings and found that that price based earnings forecasts outperform time series forecasts by the greater margin for the larger firms than smaller firm is of direct interest here. The study result implies that firmsize may help to explain interfirm differences in the predictive ability of quarterly earnings data and helps to motivate the consideration of firmsize as an independent variable in the current study.
Freeman (1987) provided evidence that the magnitude of abnormal returns associated with good or bad news earnings signals is inversely related to firmsize. Freeman speculated that the findings might simply be due to differential time series properties of the earnings numbers of large and small firms an uncontrolled factor in research design and calls for future research to examine the possibility.
Brown, Griffin, Hagerman and Zmijewski (1987) compared the accuracy of analysts to time series models based on historical earnings data. The attribute analysts' superiority to a timing advantage (more information was publicly available if the forecasts were made after the public announcement dates) and an informational advantage (more information is used by the analysts than historical earnings data).
Barth, Cram & Nelson (1986) studied the role of accruals in predicting future cash flows. Findings proved that disaggregating earnings into cash flow and the major components of accruals significantly enhances earnings predictive ability, findings also showed relation between cash flow next year and current cash flow and each component of accruals is significant and has a sign consistent with prediction.
The relationship between the earnings forecast error and predictability earnings because one of the evidence suggested that earning forecast optimism was not an effective mechanism for the gaining access to manager information ( Eames, Glover and Stice. 2001; Matsumoto 2002), earnings level to be an important control variable in examination the association between forecast error and earnings predictability, a lot of studies report an inverse relation between forecast error and the level of reported earnings (Brown 2001, Eames, Glover and Stice 2001, Eames and Glover 2002, Hwang, Jan and Basu 1996). This relationship reflects the earnings shocks and that was due to unanticipated events and earnings management.
Olsen and Dietrich (1985) demonstrated that the monthly sales announcements of major department and discount stores provide information for investors not only for the retail giants but also for their suppliers. The sales volume announcements for the retailers furnish information on the future cash flow prospects for their suppliers and, thus incorporated into the suppliers' share prices. Dharan (1987) investigated the comparative abilities of accrual sales and cash collections of sales to predict future cash flows and showed that, when cash realization occurs in a period subsequent to sales realization, cash flow forecasts from earnings based on accrual sales are better than cash flow forecasts from earnings based on cash collections. This is because accrual sales “provide information on management's expectations about future cash flows.
Greenberg, Johnson, and Ramesh (1986) used 196382 compustate data to test the ability of earnings and CFFO to predict future CFFO, for each firm two separate ordinary least squares regression models were used. The first model test used previous earnings against current CFFO (earnings model) and the second model used CFFO for lags of 1 to5 years against current CFFO (cash flows model).R square for the earnings and cash flows model were compared and the model with the higher R square was determined to be the better predictor. The results showed that earnings outperformed CFFO in predicting future CFFO and concluded that the study provides evidence in support of the FASB's assertions that the current earning was a better predictor of the future cash flows than was the current cash flows.
Similarly a supporter of Greenberg, Johnson, and Ramesh's (1986) similar previous findings found by Murdoch and Krause (1990) as well, however, the Singaporean study by Austin and Andrew (1989), whose approach was similar to that of Greenber, Johnson and Ramesh (1986) found that neither earnings nor CFFO proved to be superior in predicting future CFFO.
Bowen, Burgstahler and Daley (1986) examined relationships between signals provided by accrual earnings and various measures of cash flow, Findings indicated that Correlations between traditional cash flow measures and alternative CF measures that incorporate more extensive adjustments are low, 2nd the correlations between alternative measures of CF and earnings are, while the correlations between traditional measures of CF and earnings are high. These first two results were consistent with earnings and alternative measures of CF that incorporate more extensive adjustments conveying different signals. Finally, for four out of five cash flow variables, the results were consistent with the hypothesis that random walk models predict CF as well as model based on the other flow variables. An exception to this general result was that net income plus depreciation and amortization and working capital from operations appear to be the best predictors of cash flow from operations. Overall there results were not consistent with the FASB's statements that earnings numbers provide better forecasts of future cash flows than do cash flow numbers.
The firm size independently explained a substantial portion of the variation in post announcement drifts in security returns due to potentially misspecified quarterly earnings expectation models Foster, Olsen and Shevlin (1984). (Ball and Watts 1972, Albrecht, Lookabill and McKeown 1977, Watts and Leftwich 1977 and Lev 1983 studied the Earnings ability to predict future earnings studied first or second order autocorrelations and or forecasts over one or twoyear horizons and provided evidence to support a random walk model that is uncorrelated earnings changes, However, random walk may not be descriptive of the earnings process whereas Ramesh and Thiagarajan (1989) rejected a random walk earnings model and Lipe and Kormendi (1993) showed that higher order, rather than random walk, models are descriptive of marketadjusted earnings' timeseries process. The magnitude of abnormal returns associated with good or bad news earnings signals is inversely related to firm size (Freeman 1987), speculates that these findings might simply be due to differential timeseries properties of the earnings numbers of large and small firmsan uncontrolled factor in his research designand calls for future research to examine the possibility.
Earlier additional information content of cash flows relies primarily on cross sectional regression models relating both earnings and cash flows to security return metrics that assumes a uniform relation between earnings (cash flow from operations) and security returns across observations. Ali (1994) however, conditions the incremental information content of unexpected earnings and cash flows from operations on their magnitude with respect to price and Ali (1994) found that changes in earnings (cash flows from operations) are not expected to persist and thus have reduced implications for returns.
Dechow and Dichev (1986) found out the new method of measuring working capital accruals and the earnings and illustrated the usefulness of analysis in two ways. First, Dechow and Dichev (1986) examined the relation between measuring the accrual quality and its firm characteristics. The process of accrual suggested a magnitude that estimate errors systematically related to business fundamentals as variations in the operation cycle and its length and Dechow and Dichev (1986) concluded accrual quality was negatively related to accrual of absolute magnitude, operating life cycle, loss incidence, sales of standard deviation, cash Flow, accrual, earning had a positive relationship with firm and its size. Dichow and Dichev concluded that the firm characteristics could be use as the instrument of accrual and its quality, accrual and their qualities which were depended on regression demanded a long time series of the data, and the cash flows estimation of accrual that it is making costly and infeasible for its practical application. Secondly this study illustrated the usefulness of analysis by the surveying the relationship between measuring the accrual and its quality earnings persistence. Those firms which have low accrual and its quality with low accrual quality and more accruals those were unrelated to cash flow realizations so it produced further noise, less persistence in the findings. In fact Dechow and Dichev (1986) found strong and positive relationship between the accrual and its quality earnings persistence as well though the quality of accrual was hypothetically, and related to magnitude of absolute accruals.
Sloan (1996) recognized that accruals levels were less a persistent then the cash flows and found that the accrual and its quality level were incremental each other in describing earnings persistence, with the accrual and its quality had more powerful determinant.
There were two widely held views about the motivation of management to managing and the each had quite different implications regarding the predictive usefulness of the resultant numbers .There are different views one of them view was earning management was motivated by the managers that attempted to sustained the stock prices and enhanced by using their personal funds to boost the economic performance of the firm. Managers manage earnings to reveal relevant information about the future prospects of firms. They showed that the earnings firms were classified into managing earning for the positive reasons and not much predictive future cash flow related to numbers and found that the firms earnings has been classified as managing earning as the reasons exhibited greater predictive abilities of future cash flows that related to restated numbers. (Collins and Lys 2007).
Chengand Dana (1996) studied persistence of cash flow components in the predicting future cash flows and the findings were components of cash flow from operating activities were persist differently. Findings were that the cash that was related to net sales, Cost of goods sold, operating expenses and interest has a grater impact on future cash flows, and about cash related to others has lower persistence and taxes had no persistence then they incorporated accrual components into regression model, found cash flow components were higher than accruals that however did not enhance performance of model as well, results were consist with ACCPA that cash flows should be distinguished.
Bowen, Burgstahler and Daley (1986) predicted CF from operations one and two years into the future by employing a set of alternative predictor variables including current net income, net income plus depreciation, working capital from operations and past values of CF from operations. Bowen, Burgstahler and Daley confined data analyses to annual numbers and employed a limited set of simple linear forecasts and did not attempt to identify multivariate CF prediction models. The study found that the differences in relative forecast errors of the net income and CF from operations predictor variables were not significant none of this study results were consistent with the FASB's assertion of the superiority of earnings as predictors of future cash flows. However the use of relatively short, annual data bases in conjunction with naive expectation models limits the generalizability of this study.
Lev (1983) examined cross sectional relationship between the set of economic characteristics and the first and second order autocorrelation coefficients of earnings changes, return on equity changes, and sales changes. The main objective was to see if characteristics of the studied time series were related to the firm's economic environment. Lev finding that there was associations between characteristics of the firm's economic environment and the first two autocorrelation coefficients in earnings changes can be viewed as consistent with result that a persistence measure from a higher order (2,1,0) ARIMA model of earnings is associated with the characteristics of the firm's economic environment.
Brooks (1981) compared the predictive abilities of quarterly cash flows for a sample of 30 firms. The sample period was from 1964 to 1978. In this study univariate and transfer function Box Function was used to procedure to develop forecasting models. In this study cash was defined for the quarter as earnings from operations fro the quarter plus depreciation and amortization in the quarter plus a quarter of the annual change in the deferred taxes. The input series in his multivariate model was earnings before extraordinary items; the Brooks found the addition of earnings series to cash flow series in a multivariate setting did not improve the prediction of cash flows that were obtained from past cash flows series alone in a univariate setting. Thus there was no statistically significant difference between the two Box Jenkins forecasting models second examining the residuals from the earnings univariate model and cash flow univariate model, on a firm basis, the residual mean square error was smaller for the earnings model than cash flow model, thus indicating that earnings model “Fit” the earnings data better than cash flow model to cash flow data.
Foster (1981) examined the impact of earnings announcements on the security prices of other firms in the same industry. Foster documented statistically significant security returns for non announcing firms in ten industries at the time of "large" security returns for announcing firms within the same industry. These "intra industry information transfers" suggest news releases of other firms within an industry are used in the determination of a given firm's security price.
Khumawalla (1978) also used the BoxJenkins univariate procedure to provide the time series properties of quarterly Cash Flows for a sample of 29 airline companies. In this study khumawalla compared the aggregation of the data on the predictive ability of quarterly cash flows. Sample consisted of thirty airlines that was covering the period 196576. The Cash flow was defined as cash flow from operation, but did not included minority interest in the calculation of quarterly cash flow and the whole of subsidiary income was recognized under the method of equity accounting that was treated as unremitted earnings.
Albretch, Lookabill and Mckeown (1977) examined the timeSeries properties of undeflated earnings and earnings deflated by shareholders equity. The studied utilized only twenty  five observations for the 194775 period to avoid the problems of structural changes. Study sample consisted of 49 firms from three industries and Albretch, Lookabil and Mckeown also compared the one, two, and three years ahead predictions from the firm specific models with a random walk with a drift model. Utilizing five error measures, mean relative error, mean absolute relative error, mean squared relative error and average ranking and in this study Albratch, Lookabill and Mckeown found that random walk with a drift performed as well as the firm specific models for the undeflated earnings and random walk outperformed the firm specific models fro the deflated earnings.
Foster (1977) examined the behavior of quarterly earning, sales and expenses series of 69 forms over the 194674 period on a cross sectional basis, in this study the Foster examined the predictive ability of six forecasting models to forecast one period ahead for each quarter from 196274. The models included two simple seasonal quarter by quarter models, two simple adjacent quarter models, a model suggested by him , and firm specific models identified by using the Box Jenkins techniques. Foster suggested model had seasonal fluctuations and a trend in the time series and utilized three error metrics: average rank, mean absolute percentage error, and mean squared percentage error then Foster applied the Friedman analysis of variance test where the rank of one was assigned to the most accurate forecasts in any given period. In the end Foster concluded that the model had the lowest rank in each quarter. Moreover, Box Jenkins models were outperformed by his model when considering the sales and expenses series.
Griffin (1977) also examined quarterly earnings, sample consisted of 94 firms. In this study four models representing a broad range of linear auto regressive integrated moving average ARIMA models. Study presented some preliminary evidence and its implications for accounting research and security prices. By applying crosssectional analysis researcher concluded that “Quarterly earnings may be parsimoniously. One reflects the adjacent quarter movement and the other reflects the quarter to quarter movement over time”.
Following model was used to find the relationship of Earning, Cash Flow from operation and Sales with Share Price and to test the hypothesis that the association between Earning, Cash Flow from Operation, Sales and Share Price.
Share Price = α + β (Earning) + β (CFO) + β (Sales) + ū
Where Share Price values were taken from fiscal year that ends on 30th June from 2003 to 2008, Similarly Earning, CFO= Cash Flow From Operation and sales value were also taken from Fiscal year Financial reports ends on 30th June from 2003 to 2008 and the coefficients α and b are regression parameters for the independent variable and ū denotes the error term.
Same model was used by Charles, Waldron and Clark (2007), Abdullah (2009), Finger (1994) and Ali (1994) for examining the relationship with Share Price.
Using Quarterly data Charles, Waldron and Clark (2007) found that Earning predicts better than operating cash flow and sales predicts with greater accuracy than either operating cash flows or earnings.
Using annual data from 198992 Dyna (1997) found that CFFO (CFFIA) was a better predictor of one and twoperiod ahead CFFO (CFFIA) than earnings and CFFFA was a better predictor of twoperiod ahead CFFFA than the earnings.
Is there a significant relationship between Share Price with the three independent variables (i.e. Cash Flow, Sales, and Earning)?
Or
What is the relationship of Earning, Cash Flow & Sales with Share Price?
H1: There is an association between Cash Flow from Operation and Share Price.
H2: There is an association between Sales and Share Price.
H3: There is an association between Earning and Share Price.
In This Chapter 4.1 presents data collection, 4.2 presents methodological model.
To evaluate the relationship of Cash Flow from operations, earnings and current operating cash flows with share price, sample of 16 companies of Cement sector which are Listed at KSE (Karachi Stock Exchange), data obtained from Karachi Stock Exchange, SBP (State bank of Pakistan), Financial statements of companies and cement company website and collected the annual Share price value of each company from the Karachi Stock Exchange and company's website. The data collected included earnings (as measured by aftertax income from operations), operating cash flows, and net sales for the year of 2003, 2004, 2005, 2006, 2007 and 2008.
For example, financial year ends in June and share price values were taken as annual, Similarly Cash Flow, Sales and Earning. The variables were examined in SPSS in Pak Rupee.
To evaluate the relationship of variables the explanatory variables were regressed using Ordinary Least Square Regression Analysis (OLS). Share price was chosen as the dependent variable not only because of its obvious importance in the financial literature but also because it proxies for a firm's future cash flows in the broadest sense and earning, cash flow from operation and net sales were chosen as the independent variables.
In This Chapter Tables 1 presents Correlation Matrix, Tables 2 presents Model Summary, Table 3 Anova, and Table 4 presents Coefficients
The Correlation Matrix showed the relationship or association between the dependent variable and explanatory variable. The results of Correlation Matrix were as follows.
Share Price 
Cash Flow From Operation 
Sales 
Profit After Tax 

Share Price 
Pearson Correlation 
1 
.364(**) 
.406(**) 
.690(**) 
Sig. (2tailed) 
.000 
.000 
.000 

N 
96 
96 
92 
95 

Cash Flow From Operation 
Pearson Correlation 
.364(**) 
1 
.529(**) 
.618(**) 
Sig. (2tailed) 
.000 
.000 
.000 

N 
96 
96 
92 
95 

Sales 
Pearson Correlation 
.406(**) 
.529(**) 
1 
.483(**) 
Sig. (2tailed) 
.000 
.000 
.000 

N 
92 
92 
92 
92 

Profit After Tax 
Pearson Correlation 
.690(**) 
.618(**) 
.483(**) 
1 
Sig. (2tailed) 
.000 
.000 
.000 

N 
95 
95 
92 
95 
** Correlation is significant at the 0.01 level (2tailed).
Share Price and Cash Flow from Operation showed the relationship which was moderate and positive correlated and Pearson correlation was 0.364 and significant with 0.000 which was less than 0.01%.
Similarly Share Price and Sales showed that the relationship which was moderate and positive correlated and Pearson correlation was 0.406 and significant with the level of 0.000 which was less than 0.01%. One of previous study findings showed that Sales exhibited the strongest correlation with share price of all the predictor variables and weak relationship exist between Share Price and Cash Flow (Jordan, Waldron and Clark 2007).
Share Price and profit after Tax showed the relationship which was highly correlated and Pearson Correlation of 0.690 and significant 0.000 which was less than the level of 0.01.
Cheng and Hollie (1996) examined the determinations of Cash Flow components into future cash flow and found that Cash that was related to Sales, COGS, operating expenses and interest persist into future cash flows while cash related to other was lower persistence and was the Correlation Coefficient between Sales and Cost of goods sold was particularly high (0.939 and 0.907). The correlation of coefficient between EARN and CFO was 0.704 and 0.541, a correlation typical for these two variables that does not generally cause problems when they were in the same model.
Share Price=α+β1 (Cash Flow from Operation) + β 2 (Sales) + β 3 (Earning) +µ
Model 
Variables Entered 
Variables Removed 
Method 
1 
Profir After Tax, Sales, Cash Flow From Operation(a) 
. 
Enter 
2 
. 
Sales 
Backward (criterion: Probability of Ftoremove >= .100). 
3 
. 
Cash Flow From Operation 
Backward (criterion: Probability of Ftoremove >= .100). 
Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
1 
.712(a) 
.507 
.490 
21.34007 
2 
.701(b) 
.492 
.480 
21.53513 
3 
.693(c) 
.480 
.474 
21.66703 
Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
41152.641 
3 
13717.547 
30.122 
.000(a) 
Residual 
40075.067 
88 
455.398 

Total 
81227.708 
91 

2 
Regression 
39952.918 
2 
19976.459 
43.075 
.000(b) 
Residual 
41274.790 
89 
463.762 

Total 
81227.708 
91 

3 
Regression 
38976.275 
1 
38976.275 
83.024 
.000(c) 
Residual 
42251.433 
90 
469.460 

Total 
81227.708 
91 
Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 
Collinearity Statistics 

B 
Std. Error 
Beta 
Tolerance 
VIF 

1 
(Constant) 
20.480 
3.162 
6.476 
.000 

Cash Flow From Operation 
4.12E009 
.000 
.206 
1.896 
.061 
.477 
2.097 

Sales 
1.33E009 
.000 
.146 
1.623 
.108 
.693 
1.443 

Profit After Tax 
3.60E008 
.000 
.763 
7.268 
.000 
.508 
1.968 

2 
(Constant) 
23.541 
2.562 
9.189 
.000 

Cash Flow From Operation 
3.03E009 
.000 
.151 
1.451 
.150 
.528 
1.895 

Profit After Tax 
3.75E008 
.000 
.796 
7.657 
.000 
.528 
1.895 

3 
(Constant) 
23.018 
2.552 
9.019 
.000 

Profit After Tax 
3.26E008 
.000 
.693 
9.112 
.000 
1.000 
1.000 
Model 
Beta In 
t 
Sig. 
Partial Correlation 
Collinearity Statistics 

Tolerance 
VIF 
Minimum Tolerance 

2 
Sales 
.146(a) 
1.623 
.108 
.170 
.693 
1.443 
.477 
3 
Sales 
.093(b) 
1.073 
.286 
.113 
.767 
1.304 
.767 
Cash Flow From Operation 
.151(b) 
1.451 
.150 
.152 
.528 
1.895 
.528 
Model 
R 
R Square 
Adjusted R Square 
1 
.693(c) 
.480 
.474 
Adjusted R Square value .474 suggested that there was 47.4% variation in Share Price due to linear relationship with Earning, Cash Flow From operation and Sales which was 69.3% explained.
48% increaseed by dependent variable (Share Price) due to the Independent Variable (Cash Flow from Operation, Earning & Sales).
Goodness of fit (i.e. R Square) provides an indication of the variation in the dependent variable that is being explained by the independent variable(s) in a regression model (e.g. Greenburg et al. 1986; Murdoch and Krause, 1989; McBeth, 1993).
Lorek and Willinger (1996), Cheung and Krishnan (1997), and Neter and Wasserman (1974) note, however, that models with higher R Squares may not necessarily be the best predictors, but in Model 3 F Value improves by 83.024 while R Square Decreases From 50.7% to 48.0%.
Model 
Sum of Squares 
F 
Sig. 

1 
Regression 
38976.275 
83.024 
.000(c) 
Residual 
42251.433 

Total 
81227.708 
There is no Correlation between the Share Price, Cash Flow from Operation Sales & Earning because Significant value is less than 0.05 and our result is Significant.
In model 3 the F value improves by 83.024 and the level of significance 0.000 which is less than 0.05 that means and on the other side Coefficient of determination decreases by 48.0 due to improvement in F value from 30.122 to 83.024.
Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 
Collinearity Statistics 

B 
Std. Error 
Beta 
Tolerance 
VIF 

1 
(Constant) 
23.018 
2.552 
9.019 
*.000 

Profit After Tax 
3.26E008 
.000 
.693 
9.112 
.000 
1.000 
1.000 
A. Dependent Variable: Share Price
Share Price=20.480+ 4.12(Cash Flow from Operation)+1.33(Sales)+3.60(Profit After Tax).
Backward method has been used for this study; backward regression method excludes two variables i.e. sales and cash flow.
Share Price= 23.018+3.26(Profit after Tax)
Profit After Tax is assumed at 1% level of Significance, the significance value of Profit after Tax is 0.000 that is less than the assumed level of significance which means it is significant and the relationship exist between profit After Tax & Share Price.
Similarly (Finger 1994) also found earnings as a significant predictor of future earnings, in sample, for 88% of the firms.
Cheng & Hollie (1996) studied determination of Cash flow components into future cash flows and studied test the significance of the Coefficients and aggregate Cash flows was significantly positive in predicting the equation. Cash flow from operation explained 28.69% variation in the next period cash flows and found that the coefficient for Cash flow from operation has an average of .529 with tstatistic of 27.34 and the finding suggested that more than 50% of the current year's cash flows will persist into next year's cash flows.
Previously studies did not focus on sales as predict variable of share price because entity's of operating cash flows derives eventually from its sales. The accrual components in the previously studies included the change in accounts payable, change in inventory, depreciation and others. This study empirically test the relationship of cash flows, sales , earning with share price and whether earnings , sales or cash flows measure are a better predictor of share price though earning is a determinant of share price. The results support the following conclusions.
Source: Essay UK  http://turkiyegoz.com/freeessays/finance/importantroleinfinancialaccountingresearch.php
This Finance essay was submitted to us by a student in order to help you with your studies.
This page has approximately words.
If you use part of this page in your own work, you need to provide a citation, as follows:
Essay UK, Important role in financial accounting research. Available from: <http://turkiyegoz.com/freeessays/finance/importantroleinfinancialaccountingresearch.php> [191218].
If you are the original author of this content and no longer wish to have it published on our website then please click on the link below to request removal: