Informações sobre o curso: 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.
Desenvolvido por: University of Pennsylvania
Ministrado por: Brian J Bushee, The Geoffrey T. Boisi Professor
Accounting
Ministrado por: Christopher D. Ittner, EY Professor of Accounting
Accounting
| Informações básicas | Curso 4 de 5 no Análises Empresariais Specialization |
| Compromisso | 4 weeks, 3 -5 hours per week |
| Idioma | English, Legendas: Mongolian |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
Programa
SEMANA 1
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 vídeos, 2 leituras
- Vídeo: Review of Financial Statements (Optional) 1.1
- Vídeo: Sources for Financial Statement Information (Optional) 1.2
- Vídeo: Ratio Analysis: Case Overview 1.3
- Vídeo: Ratio Analysis: Dupont Analysis 1.4
- Vídeo: Ratio Analysis: Profitability and Turnover Ratios 1.5
- Vídeo: Ratio Analysis: Liquidity Ratios 1.6
- Vídeo: Forecasting 1.7
- Vídeo: Accounting-based Valuation (Optional) 1.8
- Leitura: PDF of Lecture Slides
- Leitura: Excel Files for Ratio Analysis
Nota atribuída: Ratio Analysis and Forecasting Quiz
SEMANA 2
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 vídeos, 2 leituras
- Vídeo: Overview of Earnings Management 2.1
- Vídeo: Revenue Recognition Red Flags: Revenue Before Cash Collection 2.2
- Vídeo: Revenue Recognition Red Flags: Revenue After Cash Collection 2.3
- Vídeo: Expense Recognition Red Flags: Capitalizing vs. Expensing 2.4
- Vídeo: Expense Recognition Red Flags: Reserve Accounts and Write-Offs 2.5
- Leitura: PDFs of Lecture Slides
- Leitura: Excel Files for Earnings Management
Nota atribuída: Earnings Management
SEMANA 3
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 vídeos, 2 leituras
- Vídeo: Discretionary Accruals: Model 3.1
- Vídeo: Discretionary Accruals: Cases 3.2
- Vídeo: Discretionary Expenditures: Models 3.3
- Vídeo: Discretionary Expenditures: Refinements and Cases 3.4
- Vídeo: Fraud Prediction Models 3.5
- Vídeo: Benford's Law 3.6
- Leitura: PDFs of Lecture Slides
- Leitura: Excel Files for Big Data and Prediction Models
Nota atribuída: Big Data and Prediction Models
SEMANA 4
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 vídeos, 2 leituras
- Vídeo: Linking Non-financial Metrics to Financial Performance: Overview 4.1
- Vídeo: Steps to Linking Non-financial Metrics to Financial Performance 4.2
- Vídeo: Setting Targets 4.3
- Vídeo: Comprehensive Examples 4.4
- Vídeo: Incorporating Analysis Results in Financial Models 4.5
- Vídeo: Using Analytics to Choose Action Plans 4.6
- Vídeo: Organizational Issues 4.7
- Leitura: PDF of Lecture Slides
- Leitura: Expected Economic Value Spreadsheet
Nota atribuída: Linking Non-financial Metrics to Financial Performance
Perguntas frequentes
Como funciona
Trabalho
Cada curso é como um livro didático interativo, com vídeos pré-gravados, testes e projetos.
Ajuda dos seus colegas
Conecte-se com milhares de outros aprendizes, debata ideias, discuta sobre os materiais do curso e obtenha ajuda para dominar conceitos.
Certificados
Obtenha reconhecimento oficial pelo seu trabalho e compartilhe seu sucesso com amigos, colegas e empregadores.
Desenvolvedores
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.
Custo
| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
Classificações e avaliações
Well, I learnt a lot, to say the least. I pride myself on being a fast learner, but the scorching pace at which the course was taught really zapped me. As an ex-banker, I was very impressed by the fraud detection and earnings and expenses management prediction models. From my practical experience, I appreciated the importance given to non financial metrics as well. Everywhere, we work in silos, where our left hand does not know what the right hand is doing, or the 18th floor is completely oblivious to the goings on on the 17th floor. The presentations, too, were nicely done, with a lot of aesthetics and humour thrown in.
You earned your vacation, dude, although you made it one of the toughest courses to pass.
Pretty tough for the first two sections if you, like me, don't know anything about the basics of accounting or financial statements, but still doable. If you have the time, I do recommend taking an accounting overview first to ground you in basic concepts.
Second two sessions are great for folks like me who already work in what they call "big data" analytics.
The course content is so lucid that it levels up the learner irrespective of his/her functional/domain understanding. And the interesting part is practical applicability of learning in professional & personal area. Thank you Coursera; Brian J Bushee & Christopher D. Ittner
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