Cash Flow Forecasting: Dealing with Serial Correlation and Idiosyncratic Heterogeneity

• In Event: Research with an Impact on Scholarship: Reporting, Valuation, and Markets

Abstract

We extend the literature on statistically-based, annual cash-flow prediction models by introducing estimation procedures that, in essence, combine the favorable attributes of both cross-sectional estimation via the use of “local” cross-sectional data for firms of similar size and time-series estimation via the capturing of firm-specific variability in the beta parameters for the independent variables. Extant literature (Dechow et al. (1998), Barth et al. (2001), and Kim and Kross (2005), among others) has pursued cross-sectional versus time-series estimation procedures in a mutually exclusive fashion. Doing so has not allowed statistical models to realize their potential in terms of predictive performance. We provide empirical evidence that the prediction of cash flows from operations (CFO) is enhanced by jointly adopting features specific to both cross-sectional and time-series modeling simultaneously.

Authors

• Hongtao Guo, Salem State University

• Yushan Hu, Boston College

• Zhijie Xiao, Boston College