LITERATURE REVIEW

This study relates to examine the relationship of cash flow from operations, earning and sales with share price and the previous research has predicted the comparative abilities of cash flow, earning and sales but this study is only concerned with the relationship of cash flow, earning and sales with share price.

In the finance literature that market forces determine share price equal to the discounted value of a stream of expected future cash flows (Hollister et al., 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 is considered to embody a firm’s future cash flows. Even if share price is often thought of and evaluated in terms of cash flows, earnings 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).

Various researchers examined value in terms of share return that Earnings reflect a stronger correlation with share return than does current operating cash flows (Watts, 1977; Dechow, 1994; Bartov et al, 1997) .It has been shown that earnings better predicts future operating cash flows than does current operating cash flows because accruals in earnings “offset the negative correlation in cash flow changes to produce earnings changes that are much less negatively serially correlated ( Dechow, et al 1998) that is why earnings, rather than current operating cash flows, tends to be used in firm share valuations.

Earnings quality can be affected by sales volatility (Dechow and Dichev (2002) and Francis et al. (2004). By and large the greater the sales volatility, the more unstable is the operating environment. This results in larger estimation errors for accruals and diminished earnings quality.

It gives an idea about how monthly sales announcements of major department and discount stores provide information for investors not only for the retail giants but also for their suppliers (Olsen and Dietrich (1985). The sales volume announcements for the retailers furnish information on the future cash flow prospects for their suppliers and, thus, are incorporated into the suppliers’ share prices. Dharan (1987) examined the comparative abilities of accrual sales and cash collections of sales to predict future cash flows. It is 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 (Dharan, 1987).

Greenberg, Johnson, and Ramesh (1986) used 1963-82 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) & the second model used CFFO for lags of 1-5 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. It was concluded that the study provides evidence in support of the FASB’s assertions that current earnings is a better predictor of future cash flows than is current cash flows.

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, it 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 is important for assessing firm profitability, there is 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 examine the predictive content of earnings disaggregations (Fairfield, Sweeney, & Yohn) .The results show that the classification scheme prescribed by the accounting profession does increase the predictive content of reported earnings. It was 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, non-operating earnings and taxes, and special items) improves forecasts of ROE one year ahead.

(Ball and Watts (1972), Albrecht, Lookabill & 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 two-year 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 Where as Ramesh and Thiagarajan (1989) rejected a random walk earnings model and Lipe and Kormendi (1993) show that higher order, rather than random walk, models are descriptive of market-adjusted earnings’ time-series process.

Finger (1994) found out the earnings ability to predict future earnings and future cash flow from operations1 one through eight years ahead using annual data from1935-87 for 50 firms. I use time-series methods to test firm-specific predictive ability over the entire time period (hereafter in-sample 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. He found that earnings are a significant predictor of future earnings, in sample, for 88% of the firms. 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 one-year but not for four- or eight-year horizons. Earnings, used alone and with cash flow, are a significant predictor of cash flow for the majority of firms. However, out-of-sample forecasts show that adding earnings rarely improves cash flow forecasts. Cash flow is a better short-term predictor of cash flow than are earnings, both in and out of sample, and the two are approximately equivalent long-term.

The nature of the information contained in the accrual and cash flow components of earnings and the extent to which this information is reflected in stock prices Sloan (1996). It is found that earning performance attributable to the accrual component of earnings exhibits lower persistence than earnings performance attributable to the cash flow component of earnings, hence results 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.

Lorek & Willinger (1996) the time series properties and predictive abilities of cash flow data. Results indicate that this model clearly outperforms firm-specific and common-structure ARIMA models as well as a multivariate, cross-sectional regression model popularized in the literature. These findings are robust across alternative cash-flow metrics (e.g., levels, per-share, and deflated by total assets) and are consistent with the viewpoint espoused by the FASB that cash-flow prediction is enhanced by consideration of earnings and accrual accounting data.

Bowen, Burgstahler & Daley (1986) examined relationships between signals provided by accrual earnings and various measures of cash flow, Findings indicate 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 are 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 are consistent with the hypothesis that random walk models predict CF as well as (and often better than) models based on other flow variables. An exception to this general result is 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 are not consistent with the FASB’s statements that earnings numbers provide better forecasts of future cash flows than do cash flow numbers.

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. It is found that changes in earnings (cash flows from operations) are not expected to persist and thus have reduced implications for returns.

Cheng, Liu & Schaefer (1996) investigated the Earnings Permanence and the Incremental Information Content of Cash Flows from Operations, findings suggest that the incremental information content of accounting earnings decreases, and the incremental information content of cash flows from operations increases, with a decrease in the permanence of earnings.

Barth, Cram & Nelson investigated the role of accruals in predicting future cash flows and 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.

One of two researchers has re examined the association between earnings forecast error and earnings predictability because there is evidence suggesting that deliberate earnings forecast optimism is not an effective mechanism for gaining access to manager’s information ( Eames et al. 2002; Matsumoto 2002) ,For earnings level to be an important control variable in examinations of the association between forecast error and earnings predictability, there must be associations between earnings level and both forecast error and earnings predictability. Numerous studies report an inverse relation between forecast error and the level of reported earnings ( Brown 2001; Eames et al. 2002; Eames and Glover 2002; Hwang et al. 1996). The association reflects both earnings shocks due to unanticipated events and earnings management.

Dechow & Dichev suggested a new measure of one aspect of the quality of working capital accruals and earnings, they illustrated the usefulness of analysis in two ways. First, they examined the relation between measure of accrual quality and firm characteristics. The nature of the accrual process suggests that the magnitude of estimation errors will be systematically related to business fundamentals like the length of the operating cycle and variability of operations. It was found that accrual quality is negatively related to the absolute magnitude of accruals, the length of the operating cycle, loss incidence, and the standard deviation of sales, cash flows, accruals, and earnings, and positively related to firm size. Results suggest that these observable firm characteristics can be used as instruments for accrual quality. This is important because the regression based estimation of accrual quality demands long time series of data and the availability of subsequent cash flows, which makes it costly or infeasible for certain practical applications (e.g quality-of-accruals-based trading strategies). Second they illustrated the usefulness of analysis by exploring the relation between measure of accrual quality and earnings persistence. Firms with low accrual quality have more accruals that are unrelated to cash flow realizations, and so have more noise and less persistence in their earnings. Indeed, they find a strong positive relation between accrual quality and earnings persistence. Although the measure of accrual quality is theoretically and empirically related to the absolute magnitude of accruals, and Sloan (1996) documents that the level of accruals is less persistent than cash flows. Probing further, they found out that accrual quality and level of accruals are incremental to each other in explaining earnings persistence, with accrual quality the more powerful determinant.

There are two widely held views regarding management’s motivations to managing earnings and each has quite different implications for the predictive usefulness of the resultant numbers .One view is that earnings management is motivated by mangers attempt to sustain the overvaluation of the firm’s stock price and to enhance managers personal welfare by disguising the true underlying economic performance of the firm (opportunistic perspective). An alternative view is that managers manage earnings to reveal private value-relevant information about the future prospects of a firm (informational perspective). They shown that originally reported (managed) earnings of firms classified as managing earnings for opportunistic reasons are less predictive of future cash flows relative to the restated (unmanaged) numbers. Conversely, they find that originally reported (managed) earnings of firms classified as managing earnings for informational reasons exhibit greater predictive ability with respect to future cash flows relative to restated (unmanaged) numbers. (Badertscher , Collins and lys 2007).

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 et al., 1987) and interested in assessing the information in security prices with respect to the predictive ability of earnings, their finding that price-based-earnings forecasts outperform time-series forecasts by a greater margin for larger firms than smaller firms is of direct interest here. Their result implies that firm-size may help to explain inter-firm differences in the predictive ability of quarterly earnings data and helps to motivate the consideration of firm-size as an independent variable in the current study.

Foster et al (1984) report that firm-size independently explains a substantial portion of the variation in post announcement drifts in security returns due to potentially misspecified quarterly earnings expectation models.

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 time-series properties of the earnings numbers of large and small firms-an uncontrolled factor in his research design-and calls for future research to examine the possibility.

Bathke , Lorek & Willinger ( 1989) found out differences in the auto regressive parameters of the Foster and Brown and Rozeff ARIMA models across firm-size strata . One-step-ahead quarterly earnings forecasts were generated by a set of best fitting time-series models. Their 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 they obtained similar predictive findings on the significance of the size-effect in a supplementary analysis of the non seasonal and volatile growth and inconsistent strata membership firms.

Cheng&Dana examined the persistence of cash flow components in predicting future cash and the findings were that the cash flow components from various operating activities persist differentially. They found out that the cash related to sales, cost of goods sold, operating expenses and interest persists a great deal into future cash flows; cash related to other has lower persistence; and cash related to taxes has no persistence and then they incorporated accrual components into persistence regression model and found that the persistence of cash flow components are generally higher than those of accruals; however, accrual components do enhance model performance, their findings are consistent with the AICPA’s and financial analysts’ rationale for their recommendation that the financial effects of a company’s core and non-core cash flows should be distinguished.

Reference

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