Accounting Analytics

所在平台: Coursera

课程主页: https://www.coursera.org/learn/accounting-analytics

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课程简介

课程名称：会计分析（Accounting Analytics） 课程概述： 会计分析课程探讨如何将财务报表数据与非财务指标关联，以分析其对财务绩效的影响。在沃顿商学院的著名会计教授授课下，学员将学习如何利用数据评估影响财务绩效的因素，并预测未来的财务情景。课程将深入探讨会计数据如何提供对消费者行为预测、公司战略、风险管理和优化等业务领域的洞见。完成课程后，学员将理解财务数据和非财务数据的互动关系，如何预测事件、优化运营及制定战略。该课程旨在帮助你在会计分析的日益重要的角色中做出更明智的商业决策，并将所学应用于自身的商业决策和战略制定中。 课程大纲： 1. **比率与预测**： 本周主题为比率分析和预测。通过对一家公司的比率分析，学员将评估其战略与商业模式，并进行杜邦分析。分析包括盈利能力、周转率和流动比率等，最终利用这些比率预测未来的财务报表。 2. **盈利管理**： 本周将探讨“盈利管理”，即故意修饰财务报表使其看起来更好的做法。课程将覆盖盈利管理的手段、动机和机会，并识别两种收入操控的警示信号，以帮助学员识别和理解财务报表中潜在的操控行为。 3. **大数据与预测模型**： 本周使用大数据方法检测盈利管理，应用预测模型推测如果没有管理者操控，财务报表的表现。介绍不同的预测模型，如自由裁量应计模型和诈骗预测模型，帮助学员识别可能操控的财务报表。 4. **将非财务指标与财务绩效关联**： 本模块探讨如何将非财务指标与财务绩效关联，分析非财务因素如何影响财务绩效。学员将学习如何识别和评估关键的非财务驱动因素，设定理想的绩效目标，并通过案例理解会计分析如何促进非财务投资的未来收益。 通过这些模块的学习，学员将获得相关工具和知识，以便更好地应用于真实的商业环境中。

课程大纲

Name:Ratios and Forecasting

Description: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.

Name:Earnings Management

Description: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!

Name:Big Data and Prediction Models

Description: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.

Name:Linking Non-financial Metrics to Financial Performance

Description: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.

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