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.
Este curso faz parte do Programa de cursos integrados Análises empresariaisAnálises Empresariais
Análise contábil
oferecido por
Informações sobre o curso
4.5
1,630 classificações
Habilidades que você terá
AccountingAnalyticsEarnings ManagementFinance
Programa - O que você aprenderá com este curso
Semana
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 vídeos (total de (Total 101 mín.) min), 2 leituras, 1 teste
9 videos
Review of Financial Statements (Optional) 1.111min
Sources for Financial Statement Information (Optional) 1.26min
Ratio Analysis: Case Overview 1.37min
Ratio Analysis: Dupont Analysis 1.413min
Ratio Analysis: Profitability and Turnover Ratios 1.518min
Ratio Analysis: Liquidity Ratios 1.610min
Forecasting 1.715min
Accounting-based Valuation (Optional) 1.815min
2 leituras
PDF of Lecture Slides10min
Excel Files for Ratio Analysis10min
1 exercício prático
Ratio Analysis and Forecasting Quiz20min
Semana
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 vídeos (total de (Total 98 mín.) min), 2 leituras, 1 teste
6 videos
Overview of Earnings Management 2.115min
Revenue Recognition Red Flags: Revenue Before Cash Collection 2.218min
Revenue Recognition Red Flags: Revenue After Cash Collection 2.317min
Expense Recognition Red Flags: Capitalizing vs. Expensing 2.419min
Expense Recognition Red Flags: Reserve Accounts and Write-Offs 2.523min
2 leituras
PDFs of Lecture Slides10min
Excel Files for Earnings Management10min
1 exercício prático
Earnings Management20min
Semana
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 vídeos (total de (Total 92 mín.) min), 2 leituras, 1 teste
7 videos
Discretionary Accruals: Model 3.119min
Discretionary Accruals: Cases 3.213min
Discretionary Expenditures: Models 3.311min
Discretionary Expenditures: Refinements and Cases 3.414min
Fraud Prediction Models 3.513min
Benford's Law 3.615min
2 leituras
PDFs of Lecture Slides10min
Excel Files for Big Data and Prediction Models10min
1 exercício prático
Big Data and Prediction Models20min
Semana
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 vídeos (total de (Total 96 mín.) min), 2 leituras, 1 teste
8 videos
Linking Non-financial Metrics to Financial Performance: Overview 4.114min
Steps to Linking Non-financial Metrics to Financial Performance 4.216min
Setting Targets 4.313min
Comprehensive Examples 4.412min
Incorporating Analysis Results in Financial Models 4.514min
Using Analytics to Choose Action Plans 4.68min
Organizational Issues 4.714min
2 leituras
PDF of Lecture Slides10min
Expected Economic Value Spreadsheet10min
1 exercício prático
Linking Non-financial Metrics to Financial Performance20min
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Melhores avaliações
por 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.
por 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.
Instrutores
Brian J Bushee
The Geoffrey T. Boisi ProfessorAccounting
Christopher D. Ittner
EY Professor of AccountingAccounting
Sobre Universidade da Pensilvânia
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.
Sobre o Programa de cursos integrados Análises empresariaisAnálises Empresariais
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.
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