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#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 14 hours to complete

Suggested: 4 weeks, 3 -5 hours per week...

#### English

Subtitles: English, Mongolian

### Skills you will gain

AccountingAnalyticsEarnings ManagementFinance

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 14 hours to complete

Suggested: 4 weeks, 3 -5 hours per week...

#### English

Subtitles: English, Mongolian

### Syllabus - What you will learn from this course

Week
1
2 hours to complete

## 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.

...
9 videos (Total 101 min), 2 readings, 1 quiz
9 videos
Review of Financial Statements (Optional) 1.111m
Sources for Financial Statement Information (Optional) 1.26m
Ratio Analysis: Case Overview 1.37m
Ratio Analysis: Dupont Analysis 1.413m
Ratio Analysis: Profitability and Turnover Ratios 1.518m
Ratio Analysis: Liquidity Ratios 1.610m
Forecasting 1.715m
Accounting-based Valuation (Optional) 1.815m
PDF of Lecture Slides10m
Excel Files for Ratio Analysis10m
1 practice exercise
Ratio Analysis and Forecasting Quiz20m
Week
2
2 hours to complete

## 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!

...
6 videos (Total 98 min), 2 readings, 1 quiz
6 videos
Overview of Earnings Management 2.115m
Revenue Recognition Red Flags: Revenue Before Cash Collection 2.218m
Revenue Recognition Red Flags: Revenue After Cash Collection 2.317m
Expense Recognition Red Flags: Capitalizing vs. Expensing 2.419m
Expense Recognition Red Flags: Reserve Accounts and Write-Offs 2.523m
PDFs of Lecture Slides10m
Excel Files for Earnings Management10m
1 practice exercise
Earnings Management20m
Week
3
2 hours to complete

## 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.

...
7 videos (Total 92 min), 2 readings, 1 quiz
7 videos
Discretionary Accruals: Model 3.119m
Discretionary Accruals: Cases 3.213m
Discretionary Expenditures: Models 3.311m
Discretionary Expenditures: Refinements and Cases 3.414m
Fraud Prediction Models 3.513m
Benford's Law 3.615m
PDFs of Lecture Slides10m
Excel Files for Big Data and Prediction Models10m
1 practice exercise
Big Data and Prediction Models20m
Week
4
2 hours to complete

## 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.

...
8 videos (Total 96 min), 2 readings, 1 quiz
8 videos
Linking Non-financial Metrics to Financial Performance: Overview 4.114m
Steps to Linking Non-financial Metrics to Financial Performance 4.216m
Setting Targets 4.313m
Comprehensive Examples 4.412m
Incorporating Analysis Results in Financial Models 4.514m
Using Analytics to Choose Action Plans 4.68m
Organizational Issues 4.714m
PDF of Lecture Slides10m
1 practice exercise
Linking Non-financial Metrics to Financial Performance20m
4.5
321 Reviews

## 23%

started a new career after completing these courses

## 28%

got a tangible career benefit from this course

## 10%

got a pay increase or promotion

### Top reviews from Accounting Analytics

By 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.

By 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.

## Instructors

### Brian J Bushee

The Geoffrey T. Boisi Professor
Accounting

### Christopher D. Ittner

EY Professor of Accounting
Accounting

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. ...