課程信息
4.5
1,556 個評分
283 個審閱
專項課程

第 4 門課程(共 5 門),位於

100% online

100% online

立即開始,按照自己的計劃學習。
可靈活調整截止日期

可靈活調整截止日期

根據您的日程表重置截止日期。
完成時間(小時)

完成時間大約為14 小時

建議:4 weeks, 3 -5 hours per week...
可選語言

英語(English)

字幕:英語(English), 蒙古語...

您將獲得的技能

AccountingAnalyticsEarnings ManagementFinance
專項課程

第 4 門課程(共 5 門),位於

100% online

100% online

立即開始,按照自己的計劃學習。
可靈活調整截止日期

可靈活調整截止日期

根據您的日程表重置截止日期。
完成時間(小時)

完成時間大約為14 小時

建議:4 weeks, 3 -5 hours per week...
可選語言

英語(English)

字幕:英語(English), 蒙古語...

教學大綱 - 您將從這門課程中學到什麼

1
完成時間(小時)
完成時間為 2 小時

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 個視頻(共 101 分鐘), 2 個閱讀材料, 1 個測驗
Video9 個視頻
Review of Financial Statements (Optional) 1.111分鐘
Sources for Financial Statement Information (Optional) 1.26分鐘
Ratio Analysis: Case Overview 1.37分鐘
Ratio Analysis: Dupont Analysis 1.413分鐘
Ratio Analysis: Profitability and Turnover Ratios 1.518分鐘
Ratio Analysis: Liquidity Ratios 1.610分鐘
Forecasting 1.715分鐘
Accounting-based Valuation (Optional) 1.815分鐘
Reading2 個閱讀材料
PDF of Lecture Slides10分鐘
Excel Files for Ratio Analysis10分鐘
Quiz1 個練習
Ratio Analysis and Forecasting Quiz20分鐘
2
完成時間(小時)
完成時間為 2 小時

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 個視頻(共 98 分鐘), 2 個閱讀材料, 1 個測驗
Video6 個視頻
Overview of Earnings Management 2.115分鐘
Revenue Recognition Red Flags: Revenue Before Cash Collection 2.218分鐘
Revenue Recognition Red Flags: Revenue After Cash Collection 2.317分鐘
Expense Recognition Red Flags: Capitalizing vs. Expensing 2.419分鐘
Expense Recognition Red Flags: Reserve Accounts and Write-Offs 2.523分鐘
Reading2 個閱讀材料
PDFs of Lecture Slides10分鐘
Excel Files for Earnings Management10分鐘
Quiz1 個練習
Earnings Management20分鐘
3
完成時間(小時)
完成時間為 2 小時

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 個視頻(共 92 分鐘), 2 個閱讀材料, 1 個測驗
Video7 個視頻
Discretionary Accruals: Model 3.119分鐘
Discretionary Accruals: Cases 3.213分鐘
Discretionary Expenditures: Models 3.311分鐘
Discretionary Expenditures: Refinements and Cases 3.414分鐘
Fraud Prediction Models 3.513分鐘
Benford's Law 3.615分鐘
Reading2 個閱讀材料
PDFs of Lecture Slides10分鐘
Excel Files for Big Data and Prediction Models10分鐘
Quiz1 個練習
Big Data and Prediction Models20分鐘
4
完成時間(小時)
完成時間為 2 小時

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 個視頻(共 96 分鐘), 2 個閱讀材料, 1 個測驗
Video8 個視頻
Linking Non-financial Metrics to Financial Performance: Overview 4.114分鐘
Steps to Linking Non-financial Metrics to Financial Performance 4.216分鐘
Setting Targets 4.313分鐘
Comprehensive Examples 4.412分鐘
Incorporating Analysis Results in Financial Models 4.514分鐘
Using Analytics to Choose Action Plans 4.68分鐘
Organizational Issues 4.714分鐘
Reading2 個閱讀材料
PDF of Lecture Slides10分鐘
Expected Economic Value Spreadsheet10分鐘
Quiz1 個練習
Linking Non-financial Metrics to Financial Performance20分鐘
4.5
職業方向

20%

完成這些課程後已開始新的職業生涯
工作福利

83%

通過此課程獲得實實在在的工作福利
職業晉升

10%

加薪或升職

熱門審閱

創建者 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.

創建者 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.

講師

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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

關於 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. ...

關於 Business Analytics 專項課程

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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