دانلود ترجمه مقاله مدل پیش بینی جریان نقدی فصلی با روند های موجود – مجله الزویر

• فهرست مطالب:

 چکیده
۱ مقدمه
۲ پیشینه پژوهشی
۳ بررسی اجمالی در تحقیقات برجسته
۴ معادلات ساختاری مدل های پیش بینی CFO سه ماهه آماری
۵ ملاحظات روشی و توصیه هایی برای تحقیقات آینده
۵ ۱ داده CFD بر اساس SFAS 95
۵ ۲ تک متغیره ها در مقابل مدل های پیش بینی CFO چند متغیره
۵ ۳ مقطع در برآورد سری زمانی
۶ بحث و نتیجه گیری

• بخشی از ترجمه:

در حالی که گام های قابل توجهی در توسعه آماری بر اساس توسعه نقدی سه ماهه برداشته شده است، جریان پیش بینی مدل ها نیاز به تحقیقات بیشتر برای مدل های موجود دارد. به خصوص چرا که متغیرهای مدل سری زمانی ARIMA و مدل مبتنی بر رگراسیون در چند گام به پیشرو پیش بینی CFO سه ماهه را دارد. در حالی که چنین یافته هایی مطابق با تحقیقات بوده و تحقیقات آینده بررسی هر دو مجموعه را به طور بالقوه غنی از متغیرهای مستقل مبتنی بر اقلام تعهدی و ساختار با تاخیر ذاتی در مدل چند متغیره می داند که ممکن است به نسل بعدی ازجریان پول های نقد و مدل های پیش بینی منجر شود به طور خاص تحقیقات آینده در ساختار تاخیر مجاور و فصلی بر اساس مدل سری زمانی با استفاده از مدل رگراسیون MULT است. علاوه بر این به طور منظم، پارامترهای شخصی در حال حرکت به طور متوسط به سمت سری زمانی برای سری  CFO سه ماهه است که ممکن است به عنوان پروکسی برای تداوم CFO  در یک زمینه ارزیابی باشد.

• بخشی از مقاله انگلیسی:

Introduction This paper provides an overview and synthesis of research devoted to statistically based, quarterly cash-flow prediction models, a topic of considerable interest to academic researchers in accounting and finance, financial analysts, standardsetting boards like the Financial Accounting Standards Board (FASB) and the International Accounting Standards Board (IASB), and retail investors. It is timely to undertake this task due to recent empirical findings (e.g., Lorek & Willinger, 2011) that have considerably altered the understanding of: (1) the key time-series properties of reported quarterly cash-flow from operations (CFO), (2) the joint impact of quarter-to-quarter (adjacent) and quarter-by-quarter (seasonal) autocorrelation in the time series of quarterly CFO, (3) and the recommended structural form(s) of statistically based quarterly CFO prediction model(s). Finally, it also provides a set of recommendations for future research seeking to refine extant statistically based quarterly CFO prediction models. The current paper is directly related to Nurnberg (2006) and Herrmann, Saudagaran, and Thomas (2006). Nurnberg provides a cautionary note pertaining to the use of the CFO series in valuation contexts similar to those that we describe. He questions the inherent superiority of CFO vis-à-vis the earnings series. While there is no question that earnings numbers are more readily subject to manipulation by opportunistic managers, the CFO series, nevertheless, may also be managed using acquisition and disposition transactions as discussed by Nurnberg. Such distortions serve to undermine the pristine nature of the CFO series and reduce the usefulness of CFO numbers in the valuation contexts described herein. Moreover, they provide a cautionary note and underscore a potential limitation of employing CFO numbers in firm valuations. Herrmann et al. (2006) argue that the use of fair value measures for property, plant, and equipment vis-à-vis historical cost may serve to enhance the predictive value of accounting series. If Hermann et al.’s analytical reasoning is substantiated via empirical testing, then the inherent advantages of CFO versus earnings in valuation settings may be mitigated. Herrmann et al. (2006) suggest further that the use of historical costs for valuing property, plant, and equipment in the United States, unless such assets are impaired, may serve to undermine the usefulness of accounting numbers for economic decisions. To the extent that fair value measures replace historical costs across diverse asset classes, the importance of the predictive performance of the CFO models described herein may be diminished. 2. Background In some ways, the current paper is reminiscent of O’Hanlon’s (1995) work summarizing the univariate time-series properties of earnings data. Both papers summarize extant work on the modeling of two different but important financial series – earnings and cash flows. While O’Hanlon’s study focuses primarily upon earnings, both annual and quarterly, the current paper is focused on statistically based prediction models for quarterly CFO data. Kim and Kross (2005) observe that valuation models employed by analysts typically favor the use of CFO as an input series as opposed to net earnings thereby enhancing interest in specifying the time-series properties of CFO. Therefore, the objects of prediction, both of considerable importance to accounting and finance researchers and practitioners, are different. Additionally, the current paper discusses very recent research efforts highlighting novel advancements in the modeling of quarterly CFO as well as providing suggestions for future modeling efforts. Finally, the current paper provides an important linkage between the modeling of quarterly net earnings and quarterly CFO by citing recent evidence by Lorek and Willinger (2011) that the Brown–Rozeff (100) × (۰۱۱) ARIMA time-series model originally popularized on quarterly earnings data some forty years ago provides the most accurate multi-step ahead quarterly CFO predictions currently.1 Bowen, Burgstahler, and Daley (1986) discuss several reasons why accounting researchers andmembers ofthe investment community should be interested in cash-flow forecasting. Such interest may be linked to a multitude of decision settings that rely upon CFO predictions as inputs including risk assessment, the accuracy of credit-rating predictions, and firm valuation.