A Quantitative Exploration of Investments -- So You Can Be a Better Analyst! Quantitative analysts and financial engineers often skip taking an investments course. Many would-be analysts take a less quantitative investments course. This omission robs them of the fundamental knowledge needed to create better, more profitable models. A Quantitative Primer on Investments with R fills that gap by taking a quantitative approach to investments and analyzing real data using R, the open source statistical computing language. This illuminates the commonalities among investment theories and builds intuition. This text collects the author's two decades of experience in finance -- from positions at Goldman Sachs, Morgan Stanley's Equity Trading Lab, and hedge fund Long-Term Capital Management to the quantitative background of a PhD in statistics, teaching at some of the world's top universities, and presenting research at central banks, regulatory agencies, and trading firms. The explanations, questions, and exercises have been tested over a decade and enabled many students to enter the world of quantitative finance and succeed.

A must-read book on the quantitative value investment strategy Warren Buffett and Ed Thorp represent two spectrums of investing: one value driven, one quantitative. Where they align is in their belief that the market is beatable. This book seeks to take the best aspects of value investing and quantitative investing as disciplines and apply them to a completely unique approach to stock selection. Such an approach has several advantages over pure value or pure quantitative investing. This new investing strategy framed by the book is known as quantitative value, a superior, market-beating method to investing in stocks. Quantitative Value provides practical insights into an investment strategy that links the fundamental value investing philosophy of Warren Buffett with the quantitative value approach of Ed Thorp. It skillfully combines the best of Buffett and Ed Thorp—weaving their investment philosophies into a winning, market-beating investment strategy. First book to outline quantitative value strategies as they are practiced by actual market practitioners of the discipline Melds the probabilities and statistics used by quants such as Ed Thorp with the fundamental approaches to value investing as practiced by Warren Buffett and other leading value investors A companion Website contains supplementary material that allows you to learn in a hands-on fashion long after closing the book If you're looking to make the most of your time in today's markets, look no further than Quantitative Value.

R is a free, open source programming language that's become a popular standard for financial and economic analysis. Quantitative Investment Portfolio Analytics In R is your guide to getting started with modeling portfolio risk and return in R. Even if you have no experience with the software, you'll be fluent in R at a basic level after reading this short primer. The chapters provide step-by-step instructions for tapping into R's powerful capabilities for portfolio analytics.

This book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance.If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is assumed, but familiarity with R is not required. With a focus on using R to solve a wide range of issues, this book provides useful content for both the R beginner and more experience users.

This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R.

The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic principles of all three main components of CF, with hands-on examples for programming models in R. Thus, the first chapter gives an introduction to the Principles of Corporate Finance: the markets of stock and options, valuation and economic theory, framed within Computation and Information Theory (e.g. the famous Efficient Market Hypothesis is stated in terms of computational complexity, a new perspective). Chapters 2 and 3 give the necessary tools of Statistics for analyzing financial time series, it also goes in depth into the concepts of correlation, causality and clustering. Chapters 4 and 5 review the most important discrete and continuous models for financial time series. Each model is provided with an example program in R. Chapter 6 covers the essentials of Technical Analysis (TA) and Fundamental Analysis. This chapter is suitable for people outside academics and into the world of financial investments, as a primer in the methods of charting and analysis of value for stocks, as it is done in the financial industry. Moreover, a mathematical foundation to the seemly ad-hoc methods of TA is given, and this is new in a presentation of TA. Chapter 7 reviews the most important heuristics for optimization: simulated annealing, genetic programming, and ant colonies (swarm intelligence) which is material to feed the computer savvy readers. Chapter 8 gives the basic principles of portfolio management, through the mean-variance model, and optimization under different constraints which is a topic of current research in computation, due to its complexity. One important aspect of this chapter is that it teaches how to use the powerful tools for portfolio analysis from the RMetrics R-package. Chapter 9 is a natural continuation of chapter 8 into the new area of research of online portfolio selection. The basic model of the universal portfolio of Cover and approximate methods to compute are also described.

Praise for How I Became a Quant "Led by two top-notch quants, Richard R. Lindsey and Barry Schachter, How I Became a Quant details the quirky world of quantitative analysis through stories told by some of today's most successful quants. For anyone who might have thought otherwise, there are engaging personalities behind all that number crunching!" --Ira Kawaller, Kawaller & Co. and the Kawaller Fund "A fun and fascinating read. This book tells the story of how academics, physicists, mathematicians, and other scientists became professional investors managing billions." --David A. Krell, President and CEO, International Securities Exchange "How I Became a Quant should be must reading for all students with a quantitative aptitude. It provides fascinating examples of the dynamic career opportunities potentially open to anyone with the skills and passion for quantitative analysis." --Roy D. Henriksson, Chief Investment Officer, Advanced Portfolio Management "Quants"--those who design and implement mathematical models for the pricing of derivatives, assessment of risk, or prediction of market movements--are the backbone of today's investment industry. As the greater volatility of current financial markets has driven investors to seek shelter from increasing uncertainty, the quant revolution has given people the opportunity to avoid unwanted financial risk by literally trading it away, or more specifically, paying someone else to take on the unwanted risk. How I Became a Quant reveals the faces behind the quant revolution, offering you?the?chance to learn firsthand what it's like to be a?quant today. In this fascinating collection of Wall Street war stories, more than two dozen quants detail their roots, roles, and contributions, explaining what they do and how they do it, as well as outlining the sometimes unexpected paths they have followed from the halls of academia to the front lines of an investment revolution.