This book provides computational tools that readers can use to flourish in the retirement income industry. Each chapter describes recipe-like algorithms and explains how to implement them via simple scripts in the freely available R coding language. Students can use those skills to generate quantitative answers to the most common questions in retirement income planning, as well as to develop a deeper understanding of the finance and economics underlying the field itself. The book will be an excellent asset for experienced students who are interested in advanced wealth management, and specifically within courses that focus on holistic modeling of the retirement income process. The material will also be useful to current and future wealth management professionals within the financial services industry. Readers should have a solid understanding of financial principles, as well as a rudimentary background in economics and accounting.

This open access book introduces the modern tontine and its applications in retirement and decumulation. Personal financial management in the later stages of life presents unique challenges, and renowned retirement planning expert Dr. Milevsky proposes the modern tontine as a solution. With the goal of guiding professionals and retirees in more efficient decumulation, the book demonstrates how to build a modern tontine. It is technically oriented, employing a cookbook format, featuring R code, and examining retirement planning through a statistical lens. This how-to guide, which is a sequel to his 2020 book “Retirement Income Recipes in R”, will be invaluable for retirement planning professionals and advisors, as well as for PhD scholars in retirement planning, quantitative finance, and related fields. This book is open access.

The 800 years of scientific breakthroughs that will help salvage your retirement plans Physics, Chemistry, Astronomy, Biology; every field has its intellectual giants who made breakthrough discoveries that changed the course of history. What about the topic of retirement planning? Is it a science? Or is retirement income planning just a collection of rules-of-thumb, financial products and sales pitches? In The 7 Most Important Equations for Your Retirement...And the Stories Behind Them Moshe Milevsky argues that twenty first century retirement income planning is indeed a science and has its foundations in the work of great sages who made conceptual and controversial breakthroughs over the last eight centuries. In the book Milevsky highlights the work of seven scholars—summarized by seven equations—who shaped all modern retirement calculations. He tells the stories of Leonardo Fibonnaci the Italian businessman; Benjamin Gompertz the gentleman actuary; Edmund Halley the astronomer; Irving Fisher the stock jock; Paul Samuelson the economic guru; Solomon Heubner the insurance and marketing visionary, and Andrey Kolmogorov the Russian mathematical genius—all giants in their respective fields who collectively laid the foundations for modern retirement income planning. With baby boomers starting to hit retirement age, planning for retirement income has become a hot topic across the country Author Moshe Milevsky is an internationally-respected financial expert with the knowledge you need to assess whether you are ready to retire or not Presents an entertaining, informative narrative approach to financial planning Understanding the ideas behind these seven foundation equations—which Moshe Milevsky explains in a manner that everyone can appreciate—will help baby boomers better prepare for retirement. This is a book unlike anything you have ever read on retirement planning. Think Suze Orman meets Stephen Hawking. If you ever wondered what the point of all that high school mathematics was, Moshe Milevsky's answer is: So that you can figure out how to retire...while you can still enjoy your money.

Learn the ins and outs of the social security system in this accessible, easy-to-navigate guide with refreshed, updated information in Social Security 101, 2nd Edition. Too often, writing about social security turns the noteworthy details of the benefits into boring details about regulations or biased political arguments that would put even a diehard bureaucrat to sleep. Social Security 101, 2nd Edition, cuts out the tedious explanations and instead provides a hands-on lesson that keeps you engaged as you learn all you need to know about the federal program that’s been around since the Great Depression. From the history of social security to its likely role in the future, this primer is packed with hundreds of entertaining tidbits and concepts that will keep you on track as you learn how to maximize your benefits. Whether you want to learn about calculating your retirement age or estimating your projected payments, Social Security 101 has all the answers—even the ones you didn’t know you were looking for.

This book offers an application-oriented guide to random forests: a statistical learning method extensively used in many fields of application, thanks to its excellent predictive performance, but also to its flexibility, which places few restrictions on the nature of the data used. Indeed, random forests can be adapted to both supervised classification problems and regression problems. In addition, they allow us to consider qualitative and quantitative explanatory variables together, without pre-processing. Moreover, they can be used to process standard data for which the number of observations is higher than the number of variables, while also performing very well in the high dimensional case, where the number of variables is quite large in comparison to the number of observations. Consequently, they are now among the preferred methods in the toolbox of statisticians and data scientists. The book is primarily intended for students in academic fields such as statistical education, but also for practitioners in statistics and machine learning. A scientific undergraduate degree is quite sufficient to take full advantage of the concepts, methods, and tools discussed. In terms of computer science skills, little background knowledge is required, though an introduction to the R language is recommended. Random forests are part of the family of tree-based methods; accordingly, after an introductory chapter, Chapter 2 presents CART trees. The next three chapters are devoted to random forests. They focus on their presentation (Chapter 3), on the variable importance tool (Chapter 4), and on the variable selection problem (Chapter 5), respectively. After discussing the concepts and methods, we illustrate their implementation on a running example. Then, various complements are provided before examining additional examples. Throughout the book, each result is given together with the code (in R) that can be used to reproduce it. Thus, the book offers readers essential information and concepts, together with examples and the software tools needed to analyse data using random forests.