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Ca. 14 Stunden zum Abschließen

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

Englisch

Untertitel: Englisch, Mongolisch

Kompetenzen, die Sie erwerben

AccountingAnalyticsEarnings ManagementFinance

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Ca. 14 Stunden zum Abschließen

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

Englisch

Untertitel: Englisch, Mongolisch

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1
2 Stunden zum Abschließen

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 (Gesamt 101 min), 2 Lektüren, 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 Lektüren
PDF of Lecture Slides10m
Excel Files for Ratio Analysis10m
1 praktische Übung
Ratio Analysis and Forecasting Quiz20m
Woche
2
2 Stunden zum Abschließen

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 (Gesamt 98 min), 2 Lektüren, 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 Lektüren
PDFs of Lecture Slides10m
Excel Files for Earnings Management10m
1 praktische Übung
Earnings Management20m
Woche
3
2 Stunden zum Abschließen

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 (Gesamt 92 min), 2 Lektüren, 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 Lektüren
PDFs of Lecture Slides10m
Excel Files for Big Data and Prediction Models10m
1 praktische Übung
Big Data and Prediction Models20m
Woche
4
2 Stunden zum Abschließen

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 (Gesamt 96 min), 2 Lektüren, 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 Lektüren
PDF of Lecture Slides10m
Expected Economic Value Spreadsheet10m
1 praktische Übung
Linking Non-financial Metrics to Financial Performance20m
4.5
312 BewertungenChevron Right

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nahm einen neuen Beruf nach Abschluss dieser Kurse auf

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ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs

Top-Bewertungen

von 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.

von 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.

Dozenten

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Brian J Bushee

The Geoffrey T. Boisi Professor
Accounting
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Christopher D. Ittner

EY Professor of Accounting
Accounting

Über 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. ...

Über die Spezialisierung Unternehmensanalytik

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....
Unternehmensanalytik

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