# 会计代写|财务管理代写Financial Management代考|GMBA6006

#### Doug I. Jones

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础
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## 会计代写|财务管理代写Financial Management代考|Percent-of-Sales Forecasting

To paraphrase an old Danish proverb, “Forecasting is always difficult, especially with regard to the future.” One straightforward yet effective way to simplify the challenge is to tie many of the income statement and balance sheet figures to future sales. The rationale for this percent-of-sales approach is the tendency, noted in Chapter 2, for all variable costs and most current assets and current liabilities to vary directly with sales. Obviously, this will not be true for all of the entries in a company’s financial statements, and certainly, some independent forecasts of individual items, such as plant and equipment, will be required. Nonetheless, the percent-of-sales method does provide simple, logical estimates of many important variables.

The first step in a percent-of-sales forecast should be to examine historical data to determine which financial statement items have varied in proportion to sales in the past. This will enable you to decide which items can safely be estimated as a percentage of sales and which must be forecast using other information. The second step is to forecast sales. Because so many other items will be linked mechanically to the sales forecast, it is critical to estimate sales as accurately as possible. Also, once the pro forma statements are completed, it is a good idea to test the sensitivity of the results to reasonable variations in the sales forecast. The final step in the percent-of-sales forecast is to estimate individual financial statement items by extrapolating the historical patterns to the newly estimated sales. For instance, if inventories have historically been about 20 percent of sales and next year’s sales are forecast to be $\$ 10$million, we would expect inventories to be$\$2$ million. It’s that simple.

To illustrate the use of the percent-of-sales method, consider the problem faced by Suburban National Bank. R\&E Supplies, Inc., a modest-sized wholesaler of plumbing and electrical supplies, has been a customer of the bank for a number of years. The company has maintained average deposits of approximately $\$ 30,000$and has had a$\$50,000$ short-term, renewable loan for five years. The company has prospered, and the loan has been renewed annually with only cursory analysis.

## 会计代写|财务管理代写Financial Management代考|Interest Expense

One thing that bothers attentive novices about pro forma forecasting is the circularity involving interest expense and indebtedness. As noted earlier, interest expense cannot be estimated accurately until the amount of external funding required has been determined. Yet because the external funding depends in part on the amount of interest expense, it would appear one cannot be accurately estimated without the other.

There are two common ways around this dilemma. The more responsible approach is to use a computer spreadsheet to solve for the interest expense and external funding simultaneously. We will look at this approach in more detail in the section titled Forecasting with Spreadsheets. The other, more cavalier approach is to ignore the problem with the expectation that the first-pass estimate will be close enough. Given the likely errors in predicting sales and other variables, the additional error caused by a failure to determine interest expense accurately is usually not all that critical.

To illustrate, R\&E’s first-pass pro formas assumed a net interest expense of $\$ 90,000$, whereas the balance sheet indicates total interest-bearing debt of just over$\$2$ million. At a 10 percent interest rate, this implies an interest expense of about $\$ 200,000$, or$\$110,000$ more than our first-pass estimate. But think what happens as we trace the impact of a $\$ 110,000$addition to interest expense through the income statement. First, the expense is before taxes. At a 25 percent tax rate, the decline in earnings after tax will be only$\$82,500$ $[(1-0.25) \times \$ 110,000]$. Second, because R\&E Supplies distributes half of its earnings as dividends, an$\$82,500$ decline in earnings after tax will result in only a $\$ 41,250$decline in the addition to retained earnings. So after all the dust settles, our initial estimate of the external funding required will be about$\$41,250$ low. But when the need for new external financing is already almost $\$ 1.4$million, what’s another$\$41,250$ among friends? Granted, increased interest expense has a noticeable percentage effect on earnings, but by the time the increase filters through taxes and dividend payments, the effect on the external funding needed is modest. The moral to the story is that quick-and-dirty financial forecasts really can be quite useful. Unless you are naturally inclined toward green eyeshades or have the luxury of charging by the hour, you will find that simple forecasts are just fine for many purposes.

# 财务管理代考

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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