Accounting Analytics explores how financial statement data and non-financial metrics can be linked to financial performance. In this course, taught by Wharton’s acclaimed accounting professors, you’ll learn how data is used to assess what drives financial performance and to forecast future financial scenarios. While many accounting and financial organizations deliver data, accounting analytics deploys that data to deliver insight, and this course will explore the many areas in which accounting data provides insight into other business areas including consumer behavior predictions, corporate strategy, risk management, optimization, and more. By the end of this course, you’ll understand how financial data and non-financial data interact to forecast events, optimize operations, and determine strategy. This course has been designed to help you make better business decisions about the emerging roles of accounting analytics, so that you can apply what you’ve learned to make your own business decisions and create strategy using financial data.
This course is part of the Business Analytics Specialization
Accounting Analytics
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Syllabus - What you will learn from this course
Week
1Ratios 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
2 readings
PDF of Lecture Slides10m
Excel Files for Ratio Analysis10m
1 practice exercise
Ratio Analysis and Forecasting Quiz20m
Week
2Earnings 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
2 readings
PDFs of Lecture Slides10m
Excel Files for Earnings Management10m
1 practice exercise
Earnings Management20m
Week
3Big 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
2 readings
PDFs of Lecture Slides10m
Excel Files for Big Data and Prediction Models10m
1 practice exercise
Big Data and Prediction Models20m
Week
4Linking 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
2 readings
PDF of Lecture Slides10m
Expected Economic Value Spreadsheet10m
1 practice exercise
Linking Non-financial Metrics to Financial Performance20m
4.5
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Top Reviews
By FA•Jun 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 PB•Feb 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 ProfessorAccounting
Christopher D. Ittner
EY Professor of AccountingAccounting
About 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.
About the Business Analytics Specialization
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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