课程信息
4.5
1,569 个评分
284 个审阅
专项课程
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为14 小时

建议:4 weeks, 3 -5 hours per week...
可选语言

英语(English)

字幕:英语(English), 蒙古语

您将获得的技能

AccountingAnalyticsEarnings ManagementFinance
专项课程
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为14 小时

建议:4 weeks, 3 -5 hours per week...
可选语言

英语(English)

字幕:英语(English), 蒙古语

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 2 小时

Ratios and Forecasting

The topic for this week is ratio analysis and forecasting. Since ratio analysis involves financial statement numbers, I’ve included two optional videos that review financial statements and sources of financial data, in case you need a review. We will do a ratio analysis of a single company during the module. First, we’ll examine the company's strategy and business model, and then we'll look at the DuPont analysis. Next, we’ll analyze profitability and turnover ratios followed by an analysis of the liquidity ratios for the company. Once we've put together all the ratios, we can use them to forecast future financial statements. (If you’re interested in learning more, I’ve included another optional video, on valuation). By the end of this week, you’ll be able to do a ratio analysis of a company to identify the sources of its competitive advantage (or red flags of potential trouble), and then use that information to forecast its future financial statements. ...
Reading
9 个视频 (总计 101 分钟), 2 个阅读材料, 1 个测验
Video9 个视频
Review of Financial Statements (Optional) 1.111分钟
Sources for Financial Statement Information (Optional) 1.26分钟
Ratio Analysis: Case Overview 1.37分钟
Ratio Analysis: Dupont Analysis 1.413分钟
Ratio Analysis: Profitability and Turnover Ratios 1.518分钟
Ratio Analysis: Liquidity Ratios 1.610分钟
Forecasting 1.715分钟
Accounting-based Valuation (Optional) 1.815分钟
Reading2 个阅读材料
PDF of Lecture Slides10分钟
Excel Files for Ratio Analysis10分钟
Quiz1 个练习
Ratio Analysis and Forecasting Quiz20分钟
2
完成时间(小时)
完成时间为 2 小时

Earnings Management

This week we are going to examine "earnings management", which is the practice of trying to intentionally bias financial statements to look better than they really should look. Beginning with an overview of earnings management, we’ll cover means, motive, and opportunity: how managers actually make their earnings look better, their incentives for manipulating earnings, and how they get away with it. Then, we will investigate red flags for two different forms of revenue manipulation. Manipulating earnings through aggressive revenue recognition practices is the most common reason that companies get in trouble with government regulators for their accounting practices. Next, we will discuss red flags for manipulating earnings through aggressive expense recognition practices, which is the second most common reason that companies get in trouble for their accounting practices. By the end of this module, you’ll know how to spot earnings management and get a more accurate picture of earnings, so that you’ll be able to catch some bad guys in finance reporting!...
Reading
6 个视频 (总计 98 分钟), 2 个阅读材料, 1 个测验
Video6 个视频
Overview of Earnings Management 2.115分钟
Revenue Recognition Red Flags: Revenue Before Cash Collection 2.218分钟
Revenue Recognition Red Flags: Revenue After Cash Collection 2.317分钟
Expense Recognition Red Flags: Capitalizing vs. Expensing 2.419分钟
Expense Recognition Red Flags: Reserve Accounts and Write-Offs 2.523分钟
Reading2 个阅读材料
PDFs of Lecture Slides10分钟
Excel Files for Earnings Management10分钟
Quiz1 个练习
Earnings Management20分钟
3
完成时间(小时)
完成时间为 2 小时

Big Data and Prediction Models

This week, we’ll use big data approaches to try to detect earnings management. Specifically, we're going to use prediction models to try to predict how the financial statements would look if there were no manipulation by the manager. First, we’ll look at Discretionary Accruals Models, which try to model the non-cash portion of earnings or "accruals," where managers are making estimates to calculate revenues or expenses. Next, we'll talk about Discretionary Expenditure Models, which try to model the cash portion of earnings. Then we'll look at Fraud Prediction Models, which try to directly predict what types of companies are likely to commit frauds. Finally, we’ll explore something called Benford's Law, which examines the frequency with which certain numbers appear. If certain numbers appear more often than dictated by Benford's Law, it's an indication that the financial statements were potentially manipulated. These models represent the state of the art right now, and are what academics use to try to detect and predict earnings management. By the end of this module, you'll have a very strong tool kit that will help you try to detect financial statements that may have been manipulated by managers....
Reading
7 个视频 (总计 92 分钟), 2 个阅读材料, 1 个测验
Video7 个视频
Discretionary Accruals: Model 3.119分钟
Discretionary Accruals: Cases 3.213分钟
Discretionary Expenditures: Models 3.311分钟
Discretionary Expenditures: Refinements and Cases 3.414分钟
Fraud Prediction Models 3.513分钟
Benford's Law 3.615分钟
Reading2 个阅读材料
PDFs of Lecture Slides10分钟
Excel Files for Big Data and Prediction Models10分钟
Quiz1 个练习
Big Data and Prediction Models20分钟
4
完成时间(小时)
完成时间为 2 小时

Linking Non-financial Metrics to Financial Performance

Linking non-financial metrics to financial performance is one of the most important things we do as managers, and also one of the most difficult. We need to forecast future financial performance, but we have to take non-financial actions to influence it. And we must be able to accurately predict the ultimate impact on financial performance of improving non-financial dimensions. In this module, we’ll examine how to uncover which non-financial performance measures predict financial results through asking fundamental questions, such as: of the hundreds of non-financial measures, which are the key drivers of financial success? How do you rank or weight non-financial measures which don’t share a common denominator? What performance targets are desirable? Finally, we’ll look at some comprehensive examples of how companies have used accounting analytics to show how investments in non-financial dimensions pay off in the future, and finish with some important organizational issues that commonly arise using these models. By the end of this module, you’ll know how predictive analytics can be used to determine what you should be measuring, how to weight very, very different performance measures when trying to analyze potential financial results, how to make trade-offs between short-term and long-term objectives, and how to set performance targets for optimal financial performance....
Reading
8 个视频 (总计 96 分钟), 2 个阅读材料, 1 个测验
Video8 个视频
Linking Non-financial Metrics to Financial Performance: Overview 4.114分钟
Steps to Linking Non-financial Metrics to Financial Performance 4.216分钟
Setting Targets 4.313分钟
Comprehensive Examples 4.412分钟
Incorporating Analysis Results in Financial Models 4.514分钟
Using Analytics to Choose Action Plans 4.68分钟
Organizational Issues 4.714分钟
Reading2 个阅读材料
PDF of Lecture Slides10分钟
Expected Economic Value Spreadsheet10分钟
Quiz1 个练习
Linking Non-financial Metrics to Financial Performance20分钟
4.5
284 个审阅Chevron Right
职业方向

20%

完成这些课程后已开始新的职业生涯
工作福利

83%

通过此课程获得实实在在的工作福利
职业晋升

10%

加薪或升职

热门审阅

创建者 FAJun 12th 2018

One of the most practical courses I have taken in Coursera. Highly recommended for professionals in Business, Strategy, and Finance & Accounting departments, as well as stock market investors.

创建者 PBFeb 5th 2016

The course makes accounting interesting and especially the examples are very illustrative. Virtual students bring some fun. The 4th week is however really integrated in the course structure.

讲师

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Brian J Bushee

The Geoffrey T. Boisi Professor
Accounting
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Christopher D. Ittner

EY Professor of Accounting
Accounting

关于 University of Pennsylvania

The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies. ...

关于 Business Analytics 专项课程

This Specialization provides an introduction to big data analytics for all business professionals, including those with no prior analytics experience. You’ll learn how data analysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations, and you’ll develop basic data literacy and an analytic mindset that will help you make strategic decisions based on data. In the final Capstone Project, you’ll apply your skills to interpret a real-world data set and make appropriate business strategy recommendations....
Business Analytics

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