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.
Ce cours fait partie de la Spécialisation Business Analytics
Analyse comptable
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À propos de ce cours
4.5
1,715 notes
Compétences que vous acquerrez
AccountingAnalyticsEarnings ManagementFinance
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
12 heures pour terminer
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 vidéos (Total 101 min), 2 lectures, 1 quiz
9 vidéos
Review of Financial Statements (Optional) 1.111 min
Sources for Financial Statement Information (Optional) 1.26 min
Ratio Analysis: Case Overview 1.37 min
Ratio Analysis: Dupont Analysis 1.413 min
Ratio Analysis: Profitability and Turnover Ratios 1.518 min
Ratio Analysis: Liquidity Ratios 1.610 min
Forecasting 1.715 min
Accounting-based Valuation (Optional) 1.815 min
2 lectures
PDF of Lecture Slides10 min
Excel Files for Ratio Analysis10 min
1 exercice pour s'entraîner
Ratio Analysis and Forecasting Quiz20 min
Semaine
22 heures pour terminer
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 vidéos (Total 98 min), 2 lectures, 1 quiz
6 vidéos
Overview of Earnings Management 2.115 min
Revenue Recognition Red Flags: Revenue Before Cash Collection 2.218 min
Revenue Recognition Red Flags: Revenue After Cash Collection 2.317 min
Expense Recognition Red Flags: Capitalizing vs. Expensing 2.419 min
Expense Recognition Red Flags: Reserve Accounts and Write-Offs 2.523 min
2 lectures
PDFs of Lecture Slides10 min
Excel Files for Earnings Management10 min
1 exercice pour s'entraîner
Earnings Management20 min
Semaine
32 heures pour terminer
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 vidéos (Total 92 min), 2 lectures, 1 quiz
7 vidéos
Discretionary Accruals: Model 3.119 min
Discretionary Accruals: Cases 3.213 min
Discretionary Expenditures: Models 3.311 min
Discretionary Expenditures: Refinements and Cases 3.414 min
Fraud Prediction Models 3.513 min
Benford's Law 3.615 min
2 lectures
PDFs of Lecture Slides10 min
Excel Files for Big Data and Prediction Models10 min
1 exercice pour s'entraîner
Big Data and Prediction Models20 min
Semaine
42 heures pour terminer
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 vidéos (Total 96 min), 2 lectures, 1 quiz
8 vidéos
Linking Non-financial Metrics to Financial Performance: Overview 4.114 min
Steps to Linking Non-financial Metrics to Financial Performance 4.216 min
Setting Targets 4.313 min
Comprehensive Examples 4.412 min
Incorporating Analysis Results in Financial Models 4.514 min
Using Analytics to Choose Action Plans 4.68 min
Organizational Issues 4.714 min
2 lectures
PDF of Lecture Slides10 min
Expected Economic Value Spreadsheet10 min
1 exercice pour s'entraîner
Linking Non-financial Metrics to Financial Performance20 min
4.5
300 avis24%
a commencé une nouvelle carrière après avoir terminé ces cours
25%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Meilleurs avis
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.
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.
Enseignants
Brian J Bushee
The Geoffrey T. Boisi ProfessorAccounting
Christopher D. Ittner
EY Professor of AccountingAccounting
À propos de Université de Pennsylvanie
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.
À propos de la Spécialisation Business Analytics
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.
Foire Aux Questions
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Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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