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英語

字幕:英語, モンゴル語

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AccountingAnalyticsEarnings ManagementFinance

次における5の4コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

約14時間で修了

推奨:4 weeks, 3 -5 hours per week...

英語

字幕:英語, モンゴル語

シラバス - 本コースの学習内容

1
2時間で修了

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件のビデオ (合計101分), 2 readings, 1 quiz
9件のビデオ
Review of Financial Statements (Optional) 1.111 分
Sources for Financial Statement Information (Optional) 1.26 分
Ratio Analysis: Case Overview 1.37 分
Ratio Analysis: Dupont Analysis 1.413 分
Ratio Analysis: Profitability and Turnover Ratios 1.518 分
Ratio Analysis: Liquidity Ratios 1.610 分
Forecasting 1.715 分
Accounting-based Valuation (Optional) 1.815 分
2件の学習用教材
PDF of Lecture Slides10 分
Excel Files for Ratio Analysis10 分
1の練習問題
Ratio Analysis and Forecasting Quiz20 分
2
2時間で修了

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件のビデオ (合計98分), 2 readings, 1 quiz
6件のビデオ
Overview of Earnings Management 2.115 分
Revenue Recognition Red Flags: Revenue Before Cash Collection 2.218 分
Revenue Recognition Red Flags: Revenue After Cash Collection 2.317 分
Expense Recognition Red Flags: Capitalizing vs. Expensing 2.419 分
Expense Recognition Red Flags: Reserve Accounts and Write-Offs 2.523 分
2件の学習用教材
PDFs of Lecture Slides10 分
Excel Files for Earnings Management10 分
1の練習問題
Earnings Management20 分
3
2時間で修了

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件のビデオ (合計92分), 2 readings, 1 quiz
7件のビデオ
Discretionary Accruals: Model 3.119 分
Discretionary Accruals: Cases 3.213 分
Discretionary Expenditures: Models 3.311 分
Discretionary Expenditures: Refinements and Cases 3.414 分
Fraud Prediction Models 3.513 分
Benford's Law 3.615 分
2件の学習用教材
PDFs of Lecture Slides10 分
Excel Files for Big Data and Prediction Models10 分
1の練習問題
Big Data and Prediction Models20 分
4
2時間で修了

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件のビデオ (合計96分), 2 readings, 1 quiz
8件のビデオ
Linking Non-financial Metrics to Financial Performance: Overview 4.114 分
Steps to Linking Non-financial Metrics to Financial Performance 4.216 分
Setting Targets 4.313 分
Comprehensive Examples 4.412 分
Incorporating Analysis Results in Financial Models 4.514 分
Using Analytics to Choose Action Plans 4.68 分
Organizational Issues 4.714 分
2件の学習用教材
PDF of Lecture Slides10 分
Expected Economic Value Spreadsheet10 分
1の練習問題
Linking Non-financial Metrics to Financial Performance20 分
4.5
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Accounting Analytics からの人気レビュー

by 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.

by 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.

講師

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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

ペンシルベニア大学(University of Pennsylvania)について

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. ...

ビジネス分析の専門講座について

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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