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Author: Marc L. Lipson Publisher: ISBN: Category : Languages : en Pages : 8
Book Description
This note describes the most common financial ratios and how they provide insight into firm performance. The emphasis is on how ratios summarize operating behaviors and results in ways that facilitate interpretation and highlight decisions. There are three broad categories of ratios covered in the note: profitability, operating efficiency, and leverage (the use of debt financing). The note defines the ratios, explains how they provide insight, and explores complications related to their use. The concepts in this note are applied to the firm Morgan Industries, a setting that has been integrated across all the Financial Analytics Toolkit series of technical notes.ExcerptUVA-F-1897Rev. Dec. 6, 2019Financial Analytics Toolkit: Ratio AnalysisRatio analysis provides insight into the performance of a firm, and ratios are often used in forecasting expected future performance. They summarize operating behaviors and results in ways that facilitate interpretation and highlight decisions. There are three broad categories of ratios: profitability, operating efficiency, and leverage (the use of debt financing). Most applications use line items directly from financial statements, although a few also use a firm's stock price. Many ratios link income statement amounts to balance sheet amounts. When doing so, one typically uses the end-of-year balance sheet amount associated with a given year's income statement amount.Profitability RatiosProfitability ratios evaluate the degree to which operations are providing an acceptable return on investment (ROI). The return can be evaluated relative to all investments (assets) or just to the investment made by shareholders (equity). A common framework for discussing firm profitability is the so-called DuPont decomposition. The elements of this framework are shown in Table 1.
Author: Marc L. Lipson Publisher: ISBN: Category : Languages : en Pages : 8
Book Description
This note describes the most common financial ratios and how they provide insight into firm performance. The emphasis is on how ratios summarize operating behaviors and results in ways that facilitate interpretation and highlight decisions. There are three broad categories of ratios covered in the note: profitability, operating efficiency, and leverage (the use of debt financing). The note defines the ratios, explains how they provide insight, and explores complications related to their use. The concepts in this note are applied to the firm Morgan Industries, a setting that has been integrated across all the Financial Analytics Toolkit series of technical notes.ExcerptUVA-F-1897Rev. Dec. 6, 2019Financial Analytics Toolkit: Ratio AnalysisRatio analysis provides insight into the performance of a firm, and ratios are often used in forecasting expected future performance. They summarize operating behaviors and results in ways that facilitate interpretation and highlight decisions. There are three broad categories of ratios: profitability, operating efficiency, and leverage (the use of debt financing). Most applications use line items directly from financial statements, although a few also use a firm's stock price. Many ratios link income statement amounts to balance sheet amounts. When doing so, one typically uses the end-of-year balance sheet amount associated with a given year's income statement amount.Profitability RatiosProfitability ratios evaluate the degree to which operations are providing an acceptable return on investment (ROI). The return can be evaluated relative to all investments (assets) or just to the investment made by shareholders (equity). A common framework for discussing firm profitability is the so-called DuPont decomposition. The elements of this framework are shown in Table 1.
Author: Marc L. Lipson Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Managers need to be comfortable with financial statement forecasting. Forecasts allow managers to plan properly by weighing consequences and preparing for outcomes. A less-recognized but equally important benefit is that the act of forecasting forces managers to make explicit the many links that exist between decisions, possibly drawing attention to constraints that otherwise might have been overlooked. This is particularly important since financial statements integrate the financial and operating decisions of a firm. This note focuses on forecasting in the context of decision-making. Aspects of forecasting explored include: revenue growth estimates, sources of error, assumptions, typical ratios, and the T-account approach. It also includes a detailed example of a typical financial forecast. The concepts are applied to the hypothetical firm Morgan Industries, a setting that has been integrated across all the Financial Analytics Toolkit series of technical notes.While this note focuses on financial statements, the forecasting techniques described herein are readily transferred to related contexts, such as building cash-flow forecasts and using ratios to evaluate financial performance.ExcerptUVA-F-1928Dec. 4, 2019Financial Analytics Toolkit: Financial Statement ForecastingManagers need to be comfortable with financial statement forecasting. Forecasts allow managers to plan properly by weighing consequences and preparing for outcomes. A less-recognized but equally important benefit is that the act of forecasting forces managers to make explicit the many links that exist between decisions, possibly drawing attention to constraints that otherwise might have been overlooked. This is particularly important since financial statements integrate the financial and operating decisions of a firm.While this note focuses on financial statements, the forecasting techniques described herein are readily transferred to related contexts, such as building cash-flow forecasts and using ratios to evaluate financial performance. Furthermore, while forecasts can be generated for many reasons, this note will focus on the use of forecasting in the context of decision-making. Thus the two most important criteria for forecasts are that they be unbiased (neither optimistic nor pessimistic) and consistent (rationally constructed from all the available information). The need to be unbiased follows directly from the role of financial statements in decision-making: biased forecasts lead to biased decisions. Consistency is the ultimate measure of forecast quality.Growth Estimates and Consistency.
Author: Marc L. Lipson Publisher: ISBN: Category : Languages : en Pages : 7
Book Description
This note reviews the basics of projecting cash flows for a typical operating decision. To find the economic consequences of any decision, one needs to project the cash flow effects of that decision and discount those at the appropriate hurdle rate. The focus on cash flows arises because any evaluation of economic impact must recognize opportunity costs--the other uses to which one might allocate resources available to a firm. This note discusses two typical ways to organize operating information to calculate cash flow, typically referred to as free cash flow in this context. The first estimates the cash consequences related to various elements of a decision. The second starts with a typical accounting estimate of operating income before taxes and then makes adjustments. The concepts in this note are applied to the firm Morgan Industries, a setting that has been integrated across all the Financial Analytics Toolkit series of technical notes.ExcerptUVA-F-1896Rev. Dec. 6, 2019Financial Analytics Toolkit: Cash Flow ProjectionsTo find the economic consequences of any decision, one needs to project the cash flow effects of that decision and discount those at the appropriate hurdle rate. This note reviews the basics of projecting cash flows for a typical operating decision.The focus on cash flows arises because any evaluation of economic impact must recognize opportunity costs--the other uses to which one might allocate resources available to a firm. Since a cash flow received today can be invested, it matters whether a decision generates a cash flow today versus a cash flow in the future. The central challenge with cash flow projections, therefore, is to adjust correctly for items that affect the timing of cash flows: accrual accounting impacts on reported results and tax effects associated with depreciation and amortization.There are two typical ways to organize operating information to calculate cash flow. The first estimates the cash consequences related to various elements of a decision. We will refer to this approach as a calculation of cash flow by parts. The second starts with a typical accounting estimate of operating income before taxes and then makes adjustments. We will refer to this approach as a calculation of free cash flow. In either situation, the resulting cash flow can be referred to as "free cash flow," though this name is more commonly associated with the second approach.
Author: Erich A. Helfert Publisher: McGraw Hill Professional ISBN: 0071395415 Category : Business & Economics Languages : en Pages : 510
Book Description
Praise for Financial Analysis Tools and Techniques: "Bona fide treasury for executives, managers, entrepreneurs. Have long used this great work in corporate & university programs. Uniquely makes the arcane clear."Allen B. Barnes, Provost, IBM Advanced Business Institute "A candidate for every consultant-to-management's bookshelf. Its beauty lies in the dynamic model of the business system and its management decision framework."Stanley Press CMC, Book review in C2M Consulting to Management Journal "Goes a long way to remove the mystery from business finance. Approach allows managers from all areas to understand how their decisions impact shareholder value."Stephen E. Frank, Chairman and Chief Executive Officer, Southern California Edison "Helfert has rare ability to make financial concepts understandable to those lacking financial background. His finance seminars exceeded our high expectations."L. Pendleton Siegel, Chairman and Chief Executive Officer, Potlatch Corporation "Commend the clarity, organization and currency of contents. There is no other book available that does the task in such an understandable and accessible way."Dr. Thomas F. Hawk, Frostburg State University "Helfert's excellent overviews and simplified models effectively broadened our managers' understanding of their fiscal responsibility to HP and our shareholders."Robert P. Wayman, Executive Vice President, Chief Financial Officer, Hewlett-Packard Company "The book has become a classic, and Helfert has been of substantial help to my company in teaching our people how to think about the numbers which drive it."Robert J. Saldich, President and Chief Executive Officer, Raychem Corporation "Helfert has contributed to the development of financial skills of TRW managers through his book, case studies and presentations, and highly rated instruction."Peter S. Hellman, President and Chief Operating Officer, TRW Inc. "Helfert has the ability to make financial concepts understandable, and his credibility and content added significantly to the success of our educational effort."Giulio Agostini, Senior Vice President Finance, and Office Administration, 3M Corporation "Helfert's writing and teaching have become a mainstay for us, and his business and strategic sense have been recognized as valuable guides to our process."William H. Clover, Ph.D., Manager of Training, and AMOCO Learning Center Concepts and tools for making sound business decisions Financial Analysis Tools and Techniques, a business-focused revision of Erich Helfert's perennial college bestseller Techniques of Financial Analysis, is a quick, easy read for nonfinancial managers and an excellent refresher and reference for finance professionals. This practical, hands-on guide provides a new introductory chapter that gives context to today's valuation turmoil and helps professionals understand the economic drivers of a business and the importance of cash flow. The book's overriding theme is that any business should be viewed as a dynamic, integrated system of cash flowsone that can be activated and managed by investment decisions. Topics, discussed in clear, comprehensive, and easy-to-understand detail, include: Increasing shareholder value through value-based management (VBM) Interpreting pro forma financial statements
Author: Gharoie Ahangar, Reza Publisher: IGI Global ISBN: 1668483882 Category : Business & Economics Languages : en Pages : 338
Book Description
The relentless growth of data in financial markets has boosted the demand for more advanced analytical tools to facilitate and improve financial planning. The ability to constructively use this data is limited for managers and investors without the proper theoretical support. Within this context, there is an unmet demand for combining analytical finance methods with business analytics topics to inform better investment decisions. Advancement in Business Analytics Tools for Higher Financial Performance explores the financial applications of business analytics tools that can help financial managers and investors to better understand financial theory and improve institutional investment practices. This book explores the value extraction process using more accurate financial data via business analytical tools to help investors and portfolio managers develop more modern financial planning processes. Covering topics such as financial markets, investment analysis, and statistical tools, this book is ideal for accountants, data analysts, researchers, students, business professionals, academicians, and more.
Author: Yongzhao Chen Publisher: John Wiley & Sons ISBN: 1119863376 Category : Business & Economics Languages : en Pages : 823
Book Description
An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.
Author: Gregory S. Nelson Publisher: John Wiley & Sons ISBN: 1119425093 Category : Business & Economics Languages : en Pages : 468
Book Description
An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results.
Author: Renard Siew Publisher: Springer Nature ISBN: 9811982376 Category : Business & Economics Languages : en Pages : 262
Book Description
This book solicits meaningful contributions from key experts and practitioners that have been dealing with the emerging area of sustainability analytics. In doing so, readers would understand the cost, impact and performance of their sustainability initiatives. The book covers current analytical tools (eg: frameworks, standards, ESG indexes) to measure sustainability, and how these tools embed the Sustainable Development Goals (SDGs). In addition to that, a part of the book is also dedicated to the application of sustainability analytics, highlighting key challenges as well as the importance of engagement and communication in shaping the future direction of sustainability assessments. This book will be extremely useful to both researchers and practitioners who are looking for best--in-class practices to create value from their sustainability initiatives.
Author: Marcello Minenna Publisher: ISBN: 9781904339472 Category : Business mathematics Languages : en Pages : 523
Book Description
Are you applying quantitative methods without a full understanding of how they really work? Bridging the gap between mathematical theory and financial practice, A Guide to Quantitative Finance provides you with all the tools and techniques to comprehend and implement the quantitative models adopted in the financial markets.
Author: Marc L. Lipson
Author: Marc L. Lipson
Author: Marc L. Lipson
Author: Marc L. Lipson
Author: Erich A. Helfert
Author: Gharoie Ahangar, Reza
Author: Yongzhao Chen
Author: Gregory S. Nelson
Author: Renard Siew
Author: Marcello Minenna
Author: Jerry A. Viscione