Explore a variety of Excel features, functions, and productivity tips for various aspects of financial modeling Key Features Explore Excel's financial functions and pivot tables with this updated second edition Build an integrated financial model with Excel for Microsoft 365 from scratch Perform financial analysis with the help of real-world use cases Book DescriptionFinancial modeling is a core skill required by anyone who wants to build a career in finance. Hands-On Financial Modeling with Excel for Microsoft 365 explores financial modeling terminologies with the help of Excel. Starting with the key concepts of Excel, such as formulas and functions, this updated second edition will help you to learn all about referencing frameworks and other advanced components for building financial models. As you proceed, you'll explore the advantages of Power Query, learn how to prepare a 3-statement model, inspect your financial projects, build assumptions, and analyze historical data to develop data-driven models and functional growth drivers. Next, you'll learn how to deal with iterations and provide graphical representations of ratios, before covering best practices for effective model testing. Later, you'll discover how to build a model to extract a statement of comprehensive income and financial position, and understand capital budgeting with the help of end-to-end case studies. By the end of this financial modeling Excel book, you'll have examined data from various use cases and have developed the skills you need to build financial models to extract the information required to make informed business decisions. What you will learn Identify the growth drivers derived from processing historical data in Excel Use discounted cash flow (DCF) for efficient investment analysis Prepare detailed asset and debt schedule models in Excel Calculate profitability ratios using various profit parameters Obtain and transform data using Power Query Dive into capital budgeting techniques Apply a Monte Carlo simulation to derive key assumptions for your financial model Build a financial model by projecting balance sheets and profit and loss Who this book is forThis book is for data professionals, analysts, traders, business owners, and students who want to develop and implement in-demand financial modeling skills in their finance, analysis, trading, and valuation work. Even if you don't have any experience in data and statistics, this book will help you get started with building financial models. Working knowledge of Excel is a prerequisite.

The trusted series of workbooks by Philip H. Pollock III and Barry C. Edwards continues with A Microsoft Excel (R)Companion to Political Analysis. In this new guide, students dive headfirst into actual political data working with the ubiquitous Excel software. Students learn by doing with new guided examples, annotated screenshots, step-by-step instructions, and exercises that reflect current scholarly debates in varied subfields of political science, including American politics, comparative politics, law and courts, and international relations. Chapters cover all major topics in political data analysis, from descriptive statistics through logistic regression, all with worked examples and exercises in Excel. No matter their professional goals, students can gain a leg up for their future careers by developing a working knowledge of statistics using Excel. By encouraging students to build on their existing familiarity with the Excel program, instructors can flatten the statistics learning curve and take some of the intimidation out of the learning process. Gain lost time usually spent troubleshooting software to provide students with a smooth transition into political analysis.

The prediction of the valuation of the "quality" of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in the relevant economic literature. While there are a plethora of machine learning methods and algorithms that have been implemented in recent years in the field of economics that aim at creating predictive models for detecting business failure, only a small amount of literature is provided towards the prediction of the "actual" financial performance of the business activity. Machine Learning Applications for Accounting Disclosure and Fraud Detection is a crucial reference work that uses machine learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behavior and fraud detection in the corporate environment. The book applies machine learning models to identify "quality" characteristics in corporate accounting disclosure, proposing specific tools for detecting core business fraud characteristics. Covering topics that include data mining; fraud governance, detection, and prevention; and internal auditing, this book is essential for accountants, auditors, managers, fraud detection experts, forensic accountants, financial accountants, IT specialists, corporate finance experts, business analysts, academicians, researchers, and students.

You’re beyond the basics—so dive in and really put your spreadsheet skills to work! This supremely organized reference is packed with hundreds of timesaving solutions, troubleshooting tips, and workarounds. It’s all muscle and no fluff. Learn how the experts tackle Excel—and challenge yourself to new levels of mastery. Includes companion eBook and sample files.

Topics include:

•Customizing the Excel workspace

•Best practices for designing and managing worksheets

•Creating formulas and functions

•Performing statistical, what-if, and other data analysis

•Core to advanced charting techniques

•Using graphics and sparklines

•Managing databases and tables

•Automating Excel with macros and custom functions

•Collaborating in Excel online, in the cloud, and more

•Extending Excel