The predictive ability of accounting operating cash flows: a moving window spectral analysis

In this paper, evidence is provided on the predictive ability of quarterly operating Cash Flows (CFs). The inability of creditors and investors to anticipate future CFs based on historical CFs, with any degree of accuracy, may suggest that historical forecasting models are underspecified. Indeed, the discontinuities, variability, seasonality and trend in CF data may require additional, and as of yet, undisclosed variables, to enhance the predictability of extant forecasting models. In this study, Moving Window Spectral (MWS) analysis, a frequency domain approach, is applied to accounting time series data for the first time in an effort to assess the predictability of aggregate operating CFs. This method is adopted due to its ability to capture trend and multiple cyclical components in the data. Our results show that CFs can be reliably predicted using aggregate data on a firm-by-firm basis. In addition, our results outperform the results previously reported in the accounting literature. This research provides insight into the properties of accounting time series data not possible from a strictly time domain analysis. The implications of this and other findings for accounting and auditing are discussed.