Accounting Analytics

Go to Course: https://www.coursera.org/learn/accounting-analytics

Introduction

### Course Review: Accounting Analytics on Coursera In today’s data-driven world, the ability to analyze financial and non-financial metrics is more critical than ever. Aiming to bridge this gap, Coursera offers an exceptional course titled **Accounting Analytics**, taught by esteemed accounting professors from the Wharton School. This course offers an insightful exploration of how financial statement data correlates with performance indicators, enabling learners to craft informed forecasts and analyses that drive business growth. #### Course Overview **Accounting Analytics** delves into the pivotal relationship between financial and operational metrics, emphasizing how data can be harnessed to evaluate performance, anticipate future trends, and gain strategic insights. Whether you're a finance professional seeking to refine your analytical skills or a newcomer aspiring to delve into accounting analytics, this course serves as an indispensable resource. #### Detailed Syllabus Breakdown 1. **Ratios and Forecasting**: In the first module, participants engage with ratio analysis, focusing on how to interpret a company’s financial statements. The skill of conducting a ratio analysis becomes crucial as students learn to apply frameworks such as the DuPont analysis, profitability, turnover, and liquidity ratios. By the end of this week, you'll not only be adept at identifying potential red flags but also capable of projecting future financial statements. 2. **Earnings Management**: The second week tackles the pressing issue of earnings management—an intentional manipulation of financial statements by management. The module unveils the motives and methods behind this practice, presenting telltale signs of financial deception. Understanding these concepts empowers students to critically evaluate financial reports, making them better informed and more diligent in their assessments. 3. **Big Data and Prediction Models**: As we venture deeper into big data, the focus shifts toward utilizing predictive modeling techniques to unveil hidden patterns and spot earnings management. This module introduces innovative tools such as Discretionary Accruals and Expenditure Models, alongside Fraud Prediction Models and Benford's Law. By the end of this week, you'll possess a robust toolkit for identifying financial discrepancies and anomalies. 4. **Linking Non-financial Metrics to Financial Performance**: The capstone module addresses an often-overlooked aspect of accounting analytics: the connection between non-financial activities and their financial impacts. This portion of the course equips learners with methodologies to evaluate performance metrics that do not share common denominators, fostering a comprehensive understanding of how these measures can influence long-term financial success. #### Why You Should Take This Course - **Expert Instruction**: You will be learning from world-renowned accounting professors who bring a wealth of knowledge and practical insights to the course. - **Practical Applications**: Each module is designed with real-world applications, ensuring that you can apply your learning directly to your career, whether in finance, accounting, or management. - **Comprehensive Curriculum**: The detailed content, spanning from basic ratio analysis to complex predictive modeling, caters to a wide range of learners—from beginners to industry veterans. - **Interactive Learning**: The course includes optional video resources, assignments, and discussions to stimulate engagement and help reinforce your understanding of the material. #### Conclusion The **Accounting Analytics** course on Coursera stands out as a valuable educational opportunity for anyone looking to enhance their understanding of financial data analytics. By systematically covering essential topics, from forecasting and earnings management to linking non-financial metrics with financial outcomes, this course equips participants with vital skills for today's accounting and financial landscape. I highly recommend enrolling in this course, as it will undoubtedly bolster your analytical capabilities and enhance your effectiveness as a finance professional. Embrace the challenge and explore the profound implications of accounting analytics today!

Syllabus

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.

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!

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.

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.