Detail New evidence pertaining to the prediction of operating (in Review of Quantitative Finance and Accounting Volume 32 (2009) Issue 1) Bibliografi Author: Lorek, Kenneth S. ; Willinger, G. Lee Topik: Predictive ability; Operating cash flows; Cross-sectional prediction models; Time-series; Prediction models; Operating cycles Bahasa: (EN )     Penerbit: Springer     Tempat Terbit: New York    Tahun Terbit: 2009     Jenis: Article - diterbitkan di jurnal ilmiah internasional Fulltext: New evidence pertaining to the prediction of operating cash flows.pdf (186.41KB; 0 download) Abstract We investigate the ability of past operating cash flows (Model 1) and past earnings (Model 2) to generate predictions of operating cash flows from 1990 to 2004. We employ actual cash flow numbers reported in accordance with Statement of Financial Accounting Standards (SFAS) No. 95 in our primary analysis rather than using an algorithm to approximate operating cash flows (i.e., Kim and Kross J Acc Res 43:753–780, 2005; Dechow et al. J Acc Econ 25:133–168, 1998, among others). We derive out of sample predictions of operating cash flows both cross-sectionally similar to the approach of Kim and Kross (2005) and on a firm-specific time-series basis consistent with Dechow et al. (1998). Our predictive findings suggest: (1) cash-flow based models (Model 1) provide significantly more accurate predictions of operating cash flows than earnings-based models (Model 2); (2) time-series models significantly outperform cross-sectional models; (3) larger firms exhibit significantly more accurate cash-flow predictions than smaller firms; (4) firms with relatively shorter operating cycles exhibit significantly more accurate cash-flow predictions that firms with longer operating cycles consistent with Dechow (J Acc Econ 18:3–42, 1994); (5) we find no evidence of increased predictive power for either the cash-based or earnings-based prediction models across 1990–2004; (6) we also provide supplementary analyses to assess the impact on predictive performance when descriptive goodness-of-fit criteria are used instead of out-of-sample forecasts to assess predictive performance, and (7) we re-estimate the CFO prediction models using algorithmic CFO data instead of actual data. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Lihat Sejarah Pengadaan  Konversi Metadata   Kembali

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