Quarterly Cash Flows Research Articles

A Test of Income Smoothing Using Pseudo Fiscal Years

The purpose of our study is to further understand managerial incentives that affect the volatility of reported earnings. Prior research suggests that the volatility of fourth-quarter earnings may be affected by the integral approach to accounting (i.e., “settling up” of accrual estimation errors in the first three quarters of the fiscal year) or earnings management to meet certain reporting objectives (e.g., analyst forecasts). We suggest that another factor affecting fourth-quarter earnings is managers’ intentional smoothing of fiscal-year earnings. For each firm, we create pseudo-year earnings using four consecutive quarters other than the four quarters of the reported fiscal year. We then compare the earnings volatility of pseudo years to the earnings volatility of the firm’s own reported fiscal year. We find evidence consistent with fourth-quarter accruals reflecting managerial incentives to smooth fiscal-year earnings. This conclusion is validated by several cross-sectional tests, the pattern in quarterly cash flows and accruals, and several robustness tests. Overall, we contribute to the literature exploring alternative explanations for the differential volatility of fiscal-year and fourth-quarter earnings. This paper was accepted by Brian Bushee, accounting.

What happens during flight to safety: Evidence from public and private real estate markets

AbstractLittle is known about the performance of public and private real estate investments during and after flight to safety (FTS). We document that public real estate securities offer a partial hedge during FTS events. In addition, periods with multiple FTS events forecast slower economic growth, resulting in weaker long‐run investment performance of real estate assets. Following FTS, the probability of a drop in real estate cash flows increases by up to 20%, with quarterly cash flow growth rates declining by as much as 27%. Private market real estate price appreciation over the four quarters following FTS is nearly wiped out.

A hyperbolic model of optimal cash balances

ABSTRACTWe develop a hyperbolic cash management model based on the Pearson Type IV probability density which minimises extreme variations in firm cash balances. Since the moments for the Type IV probability density are in general undefined and maximum likelihood estimation is compromised by the non-algebraic nature of the Type IV normalising constant, parameter estimation is implemented using the minimum method. Empirical analysis shows that the Type IV density is highly compatible with the quarterly cash flow data of a randomly selected sample of 100 large U.S. corporations. In contrast, around 60% of the 100 corporations return Jarque–Bera test statistics which are incompatible with the Gaussian probability density.

A New Measure of Managers' Intentional Income Smoothing

The purpose of our study is to further understand managerial incentives that affect the volatility of reported earnings. Prior research suggests that the volatility of fourth-quarter earnings may be affected by the integral approach to accounting (i.e., “settling up” of accrual estimation errors in the first three quarters of the fiscal year) or earnings management to meet certain reporting objectives (e.g., analyst forecasts). We suggest that another factor affecting fourth-quarter earnings is managers’ intentional smoothing of fiscal-year earnings. For each firm, we create pseudo-year earnings using four consecutive quarters other than the four quarters of the reported fiscal year. We then compare the earnings volatility of pseudo years to the earnings volatility of the firm’s own reported fiscal year. We find evidence consistent with fourth-quarter accruals reflecting managerial incentives to smooth fiscal-year earnings. This conclusion is validated by several cross-sectional tests, the pattern in quarterly cash flows and accruals, and several robustness tests. Overall, we contribute to the literature exploring alternative explanations for the differential volatility of fiscal year and fourth-quarter earnings.

What can reanalysis data tell us about wind power?

Reanalysis data sets have become a popular data source for large-scale wind power analyses because they cover large areas and long time spans, but those data are uncertain representations of “true” wind speeds. In this work we develop a model that systematically quantifies the uncertainties across many sites and corrects for biases of the reanalysis data. We apply this model to 32 years of reanalysis data for 1002 plausible wind-plant sites in the U.S. Great Plains to estimate variability of wind energy generation and the smoothing effect of aggregating distant wind plants. We find the coefficient of variation (COV) of annual energy generation of individual wind plants in the Great Plains is 5–12%, but the COV of all those plants aggregated together is 3.0%. The year-to-year variability (of interest to system planners) shows a maximum step change of ∼10%, and the wind power varies by ±7.5% over a 32-year period. Similarly, the average variability of quarterly cash flow to equity investors in a single wind plant is 29%, but that can be reduced to 18–21% by creating small portfolios of two wind plants selected from regions with low correlations of wind speed.

A critical assessment of the time-series literature in accounting pertaining to quarterly accounting numbers

This paper summarizes, critiques, and synthesizes the time-series literature in accounting pertaining to quarterly accounting numbers. It reviews work on quarterly earnings, quarterly balance sheet and income statement subcomponents, and quarterly cash-flows from operations (CFOs). Several salient findings emerge. First, the premier ARIMA models attributed to Foster (1977), Griffin (1977) and Brown and Rozeff (1979) were identified on relatively small samples dominated by “old economy” firms. It appears that the descriptive validity of these ARIMA structures must be called into question when analyzing more current databases replete with high-technology, regulated, and financial-service firms (Lorek & Willinger, 2007). Second, the use of ARIMA-based analytical review procedures in audit settings is not cost effective. Third, recent evidence (Lorek & Willinger, 2008, 2011) supports the univariate Brown & Rozeff (100) × (011) ARIMA model as the best statistically-based prediction model for quarterly CFO, a finding of considerable import to analysts, investors, and researchers.

Multi-Step-Ahead Quarterly Cash-Flow Prediction Models

SYNOPSIS: We provide new empirical evidence supportive of the Brown-Rozeff ARIMA model as a candidate univariate statistically based expectation model for multi-period-ahead projections of quarterly cash flows. It provides 1- through 20-step-ahead projections of quarterly cash flows that are significantly more accurate than those generated by the premier multivariate quarterly time-series, disaggregated-accrual regression model popularized by Lorek and Willinger (1996). We also find that both quarterly earnings and quarterly cash flow from operations are modeled by the same Brown-Rozeff ARIMA structure, although the autoregressive and seasonal moving-average parameters of the quarterly earnings model are significantly larger than those of the cash-flow prediction model. This finding is consistent with Beaver (1970) and Dechow and Dichev (2002), among others, who argue that accounting accruals induce incremental amounts of serial correlation in the quarterly earnings time series vis-a`-vis the time series of quarterly cash flows. Such findings may be of interest to analysts who wish to derive multi-step-ahead cash-flow predictions, and accounting researchers attempting to adopt a statistical proxy for the market’s expectation of quarterly cash flows. Finally, we propose a forecasting schema by which statistically based cash-flow forecasts are adjusted upwards or downwards via qualitative assessments regarding the economy, industry, and firm by analysts employing fundamental financial analysis.

The reliability of mandatory cash expenditure forecasts provided by Australian mining exploration companies in quarterly cash flow reports

PurposeThe purpose of this study is to examine the usefulness of pre‐production cash expenditure forecasts issued by Australian mining explorers in their quarterly cash‐flow reports.Design/methodology/approachUsefulness is determined by examining compliance and the reliability of forecasts (accuracy and bias) for a sample of 1,760 forecasts issued by 481 explorers in 2005/2006. The cross‐sectional variation in reliability is examined using regression analysis.FindingsThe findings reveal a high level of compliance but significant inaccuracies (median forecast error of around 50 percent of actual expenditure for exploration and evaluation expenditure and 85 percent for development expenditure), and some evidence of forecast bias. Forecast inaccuracy is more prevalent in firms that have poorer performance, greater financial slack, greater cash‐flow volatility, no financial leverage, and for firms that are smaller, in the pre‐development stage, and in the mineral (non‐oil and gas) sub‐industry.Research limitations/implicationsThe analysis of forecast usefulness is confined to compliance and reliability. Further research could consider the value‐relevance and predictive ability of these forecasts.Practical implicationsThe findings question the usefulness of mandatory forecasting by showing that the information role of forecasts in capital markets is impaired when firms have little discretion over the forecast decision, timing and specificity.Originality/valueThis is the first study to examine mandatory cash expenditure forecasts and makes a significant contribution to the small literature on mandatory financial forecasts.

Cash‐based related party transactions in new economy firms

This paper investigates associations between related party transactions (RPTs) and governance and performance factors of new economy firms. Previous research has examined the related party transactions of large U.S. firms. In contrast, we focus on smaller, newly listed Australian firms. Referred to as 'commitments test entities' (CTE), these firms are distinguished by the unique Australian Securities Exchange listing requirements applying to them, and associated additional (quarterly cash flow) reporting requirements. While strong corporate governance characteristics may be expected to constrain the amounts of payments and loans to related parties, we find only weak evidence to support that proposition. The results show that financial condition dominates the decision to engage in RPTs and suggest that external monitoring (associated both with larger firm size and the quarterly reporting phase) are a more effective restraint on the magnitude of RPTs for these high-risk CTE firms. The findings are generally consistent with the 'conflict of interest view' proposed by Gordon et al. (2004a, 2004b), suggesting related party transactions do not serve shareholders' interests. The findings suggest that external monitoring may be a more effective control over RPTs than internal corporate governance mechanisms in this institutional context of small 'cashbox' firms. Since RPTs may not be in the best interests of shareholders, extending mandatory RPT disclosures to all periodic cash flow reports warrants further consideration by regulators. This study contributes to the limited research on the effects and implications of related party transactions.

Time-series properties and predictive ability of quarterly cash flows

Abstract We provide descriptive and predictive evidence on the time-series properties and predictive ability of quarterly cash flows from operations (CFO) reported in accordance with Statement of Financial Accounting Standards (SFAS) No. 95. Previous work such as Hopwood and McKeown [Hopwood, W.S., & McKeown, J.C., (1992). Empirical evidence on the time-series properties of operating cash flows. Managerial Finance, 18, 62–78] and Lorek, Schaefer and Willinger [Lorek, K.S., Schaefer, T.F., & Willinger, G.L., (1993, January). Time-series properties and predictive ability of funds flow variables.” The Accounting Review, 68, 151–63], among others, has analyzed a proxy series (PCFO) for quarterly cash flows constructed via a relatively simplistic algorithm. We provide new evidence documenting that: (1) the time-series properties of quarterly CFO are at variance with the exclusively seasonal characterization of quarterly PCFO exhibiting both adjacent (quarter-to-quarter) and seasonal (quarter-by-quarter) relationships, (2) the Brown–Rozeff ARIMA model significantly outpredicts the random walk with drift model, seasonal random walk with drift model, and a disaggregated-accrual model, the multivariate time-series regression model (MULT) originally popularized by Lorek and Willinger [Lorek, K.S., & Willinger, G.L., (1996, January). A multivariate time-series prediction model for cash-flow data. The Accounting Review, 71, 81–101], (3) the forecast errors of larger firms are significantly smaller than the forecast errors of smaller firms, and (4) the predictive ability of the Brown–Rozeff model is robust when tested upon an expanded sample of firms (n = 745; 8940 firms/quarter predictions) obtained by eliminating the considerable data requirements of MULT.

Measuring macroeconomic exposure: The case of Volvo Cars*

AbstractVolvo Cars' economic exposure to exchange rates and other macroeconomic variables is estimated using quarterly cash flows as the firm's target variable. We discuss first several issues relating to management's view of the macroeconomic environment, as well as the firm's objective and structure. These issues must be addressed before multiple regression analysis can be implemented with the purpose of estimating exposures. the use of cash flow exposure coefficients for evaluating exposure and choosing currency denomination of liabilities is illustrated, and an out‐of‐sample analysis of the estimated exposure coefficients is carried out.