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Actex Soa Exam Srm
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Book Synopsis Regression Modeling with Actuarial and Financial Applications by : Edward W. Frees
Download or read book Regression Modeling with Actuarial and Financial Applications written by Edward W. Frees and published by Cambridge University Press. This book was released on 2010 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
Book Synopsis ACTEX SOA Exam SRM by : Runhuan Feng
Download or read book ACTEX SOA Exam SRM written by Runhuan Feng and published by . This book was released on 2018 with total page 673 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A/S/M SOA Exam IFM by : Abraham Weishaus
Download or read book A/S/M SOA Exam IFM written by Abraham Weishaus and published by . This book was released on 2018 with total page 747 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis ACTEX Study Manual for SOA Exam P by : Samuel A. Broverman
Download or read book ACTEX Study Manual for SOA Exam P written by Samuel A. Broverman and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study guide is designed to help in the preparation for the Society of Actuaries Exam P. The study manual is divided into two main parts. It will be most effective for those who have had courses in college calculus at least to the sophomore level and courses in probability to the sophomore or junior level.
Book Synopsis Actex Study Manual by : Samuel A. Broverman
Download or read book Actex Study Manual written by Samuel A. Broverman and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis ACTEX MLC Study Manual by : Johny Li
Download or read book ACTEX MLC Study Manual written by Johny Li and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book SOA Exam FM written by Harold Cherry and published by . This book was released on 2017 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The 12th edition of the manual has the following features: •The manual has been revised and updated to conform to the new syllabus for the June 2017 and subsequent exams. •The concepts of financial mathematics are explained in plain English, in a manner that appeals to your intuition and common sense. •The manual shows you tricks and shortcuts for various types of problems, warns you about common traps that students fall into, and tells you how to avoid them. •Over 1,000 problems with detailed solutions, about half of them from prior SOA/CAS exams and half that are original to the manual. •After each topic there are examples called “Stepping Stones” that are designed to tell you whether you have understood what you have just read, and to serve as a bridge to more difficult exam-level problems. •There is a summary of the key concepts and formulas after each topic. •There are 9 sets of Calculator Notes that give you detailed instructions for using the BA II Plus calculator. •Six original full-length (35 questions) practice exams, with complete solutions are included. •Over 600 pages in all."--Résumé de l'éditeur.
Book Synopsis A/S/M SOA Exam SRM by : Abraham Weishaus
Download or read book A/S/M SOA Exam SRM written by Abraham Weishaus and published by . This book was released on 2018 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Financial Data Analytics with Machine Learning, Optimization and Statistics by : Sam Chen
Download or read book Financial Data Analytics with Machine Learning, Optimization and Statistics written by Sam Chen and published by John Wiley & Sons. This book was released on 2024-10-21 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Computational Actuarial Science with R by : Arthur Charpentier
Download or read book Computational Actuarial Science with R written by Arthur Charpentier and published by CRC Press. This book was released on 2014-08-26 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Hands-On Approach to Understanding and Using Actuarial ModelsComputational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/
Download or read book bookdown written by Yihui Xie and published by CRC Press. This book was released on 2016-12-12 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats, including LaTeX/PDF, HTML, EPUB, and Word, thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book, but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers, reports, dissertations, course handouts, study notes, and even novels. You do not have to use R, either. Other choices of computing languages include Python, C, C++, SQL, Bash, Stan, JavaScript, and so on, although R is best supported. You can also leave out computing, for example, to write a fiction. This book itself is an example of publishing with bookdown and R Markdown, and its source is fully available on GitHub.
Download or read book Derivative Pricing written by Ambrose Lo and published by CRC Press. This book was released on 2018-07-04 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proliferation of financial derivatives over the past decades, options in particular, has underscored the increasing importance of derivative pricing literacy among students, researchers, and practitioners. Derivative Pricing: A Problem-Based Primer demystifies the essential derivative pricing theory by adopting a mathematically rigorous yet widely accessible pedagogical approach that will appeal to a wide variety of audience. Abandoning the traditional "black-box" approach or theorists’ "pedantic" approach, this textbook provides readers with a solid understanding of the fundamental mechanism of derivative pricing methodologies and their underlying theory through a diversity of illustrative examples. The abundance of exercises and problems makes the book well-suited as a text for advanced undergraduates, beginning graduates as well as a reference for professionals and researchers who need a thorough understanding of not only "how," but also "why" derivative pricing works. It is especially ideal for students who need to prepare for the derivatives portion of the Society of Actuaries Investment and Financial Markets Exam. Features Lucid explanations of the theory and assumptions behind various derivative pricing models. Emphasis on intuitions, mnemonics as well as common fallacies. Interspersed with illustrative examples and end-of-chapter problems that aid a deep understanding of concepts in derivative pricing. Mathematical derivations, while not eschewed, are made maximally accessible. A solutions manual is available for qualified instructors. The Author Ambrose Lo is currently Assistant Professor of Actuarial Science at the Department of Statistics and Actuarial Science at the University of Iowa. He received his Ph.D. in Actuarial Science from the University of Hong Kong in 2014, with dependence structures, risk measures, and optimal reinsurance being his research interests. He is a Fellow of the Society of Actuaries (FSA) and a Chartered Enterprise Risk Analyst (CERA). His research papers have been published in top-tier actuarial journals, such as ASTIN Bulletin: The Journal of the International Actuarial Association, Insurance: Mathematics and Economics, and Scandinavian Actuarial Journal.
Download or read book R for Everyone written by Jared P. Lander and published by Addison-Wesley Professional. This book was released on 2017-06-13 with total page 1456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available.
Book Synopsis Achieving Your Pinnacle: A Career Guide for Actuaries by : Tom Miller
Download or read book Achieving Your Pinnacle: A Career Guide for Actuaries written by Tom Miller and published by Lulu.com. This book was released on 2009-05-12 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tom Miller recognized the need to write this book a few years ago, after reviewing postings on popular discussion pages frequented by actuaries. He was surprised and troubled by the magnitude of misinformation posted on these websites. Clearly actuaries and actuarial students posting this information are only trying to be helpful to one another, but they frequently lack the necessary experience and expertise to offer sound advice. Tom seeks to provide readers of his career guide with valuable insights regarding the actuarial employment market, covering topics such as choice of product specialization, how to conduct effective job searches, switching successfully from insurance to consulting and inside tips on what clients are really looking for when they interview you. Armed with deep knowledge and a unique perspective on the actuarial profession, Tom expects that this book will be a resource that will help you make better career decisions and "Achieve Your Pinnacle."
Book Synopsis Probability for Risk Management by : Matthew J. Hassett
Download or read book Probability for Risk Management written by Matthew J. Hassett and published by ACTEX Publications. This book was released on 2006 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Learning to Look Up by : Isabelle Kooreman
Download or read book Learning to Look Up written by Isabelle Kooreman and published by . This book was released on 2020-03-21 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning to Look Up is a poetry book for everyone who's had to go through the darkness in order to get to the light. It is the product of years of self-discovery and growth. The two parts of the book ("Black and white" and "Living color") represent the different phases of emotional growth. Each part containing pieces that describe emotions and feelings or containing words of encouragement you may need to hear. It is the literary embodiment of a venting session with a friend.
Book Synopsis Probability and Statistics with Applications: A Problem Solving Text by : Leonard Asimow, Ph.D., ASA
Download or read book Probability and Statistics with Applications: A Problem Solving Text written by Leonard Asimow, Ph.D., ASA and published by ACTEX Publications. This book was released on 2015-06-30 with total page 762 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is listed on the Course of Reading for SOA Exam P. Probability and Statistics with Applications is an introductory textbook designed to make the subject accessible to college freshmen and sophomores concurrent with Calc II and III, with a prerequisite of just one smester of calculus. It is organized specifically to meet the needs of students who are preparing for the Society of Actuaries qualifying Examination P and Casualty Actuarial Society's new Exam S. Sample actuarial exam problems are integrated throughout the text along with an abundance of illustrative examples and 870 exercises. The book provides the content to serve as the primary text for a standard two-semester advanced undergraduate course in mathematical probability and statistics. 2nd Edition Highlights Expansion of statistics portion to cover CAS ST and all of the statistics portion of CAS SAbundance of examples and sample exam problems for both Exams SOA P and CAS SCombines best attributes of a solid text and an actuarial exam study manual in one volumeWidely used by college freshmen and sophomores to pass SOA Exam P early in their college careersMay be used concurrently with calculus coursesNew or rewritten sections cover topics such as discrete and continuous mixture distributions, non-homogeneous Poisson processes, conjugate pairs in Bayesian estimation, statistical sufficiency, non-parametric statistics, and other topics also relevant to SOA Exam C.
