دانلود مقاله پیش‌بینی جریان نقدی با نگرش پویای غیر خطی
چکیده
مقدمه
مشخصات مدل
مطالعه تجربی بازار ایالات متحده
نتیجه گیری
منابع
Abstract
Introduction
Model specifcation
Empirical study of the U.S. market
Conclusion
References
چکیده
ما یک مدل جدید جعبه خاکستری را برای دریافت غیرخطی بودن و پویایی پارامترهای مدل جریان نقدی پیشنهاد می‌کنیم. مدل جعبه خاکستری ساختار مدل جعبه سفید ساده را حفظ می کند، در حالی که پارامترهای آنها به عنوان یک جعبه سیاه با یک Padé تقریبی به عنوان یک فرم عملکردی مدل‌سازی می‌شوند. نرخ رشد فروش و سن شرکت به‌عنوان متغیرهای برون‌زا مورد استفاده قرار می‌گیرند، زیرا قدرت توضیحی برای فرآیند پارامتر در نظر گرفته می‌شوند. روش‌های تخمین داده‌های پانل برای بررسی اینکه آیا آنها از رگرسیون تلفیقی بهتر عمل می‌کنند، که به طور گسترده در ادبیات موجود استفاده می‌شود، استفاده می‌شود. ما از مجموعه داده ایالات متحده برای ارزیابی عملکرد مدل‌های مختلف در پیش‌بینی جریان نقدی استفاده می‌کنیم. دو معیار عملکرد برای مقایسه قدرت پیش‌بینی خارج از نمونه مدل‌ها انتخاب شده‌اند. نتایج نشان می‌دهد که مدل جعبه خاکستری پیشنهادی می‌تواند عملکرد برتر، به‌ویژه در پیش‌بینی‌های چند دوره آینده را ارائه دهد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
We propose a novel grey-box model to capture the nonlinearity and the dynamics of cash flow model parameters. The grey-box model retains a simple white-box model structure, while their parameters are modelled as a black-box with a Padé approximant as a functional form. The growth rate of sales and firm age are used as exogenous variables because they are considered to have explanatory power for the parameter process. Panel data estimation methods are applied to investigate whether they outperform the pooled regression, which is widely used in the extant literature. We use the U.S. dataset to evaluate the performance of various models in predicting cash flow. Two performance measures are selected to compare the out-of-sample predictive power of the models. The results suggest that the proposed grey-box model can offer superior performance, especially in multi-period-ahead predictions.
Introduction
Future cash flows are critical for the survival of corporations. Reliable and accurate cash flow forecasting is important for academics and practitioners. For example, the value of a firm could be estimated by the sum of discounted future cash flows generated during its lifetime. One of the primary inputs of this valuation method is future cash flows. Also, when firms have larger accruals, more heterogeneous accounting choices than their peers in the same industry, higher earnings volatility, higher capital intensity, or poorer financial health, financial analysts prefer to provide cash flow forecasts to help their clients to make better investment decisions (Defond and Hung, 2003). Compared with accruals, cash flows are more difficult to be manipulated in earnings management; therefore, they could be used to monitor earnings transparency (McInnis and Collins, 2011). However, it is challenging to model the dynamics of future cash flows, which may be partially attributed to limited data and theory.
Conclusion
This paper proposes a cash flow forecast model which captures the nonlinearity and dynamics of the cash flow process. Our model incorporates heterogeneity across firms in cash flow prediction as we allow for a panel data setting which has both time-series and cross-sectional dimensions. The nonlinearity is captured numerically by a black-box model, and the linear form is captured by a white-box model. Therefore, our model is considered as a grey-box model, and it achieves a good balance between prediction accuracy and model complexity. Moreover, to incorporate the dynamics of cash flows, the parameters of the panel data models are treated as time-varying. No linearity restrictions are imposed on these time-varying parameters.