Book picks similar to
The Concepts and Practice of Mathematical Finance by Mark S. Joshi
finance
quant
mathematics
math
Investing in One Lesson
Mark Skousen - 2007
In Investing In One Lesson, investment guru Mark Skousen clearly and convincingly reveals the reasons for the seemingly perverse, unpredictable nature of the stock market. Drawing upon his decades of experience as an investment advisor, writer, and professor, Dr. Skousen explains in one spirited, easy-to-follow lesson why stock prices fluctuate with such apparent irrationality. Lifting back the veil of perplexity and confusion that surrounds the workings of the stock market, Dr. Skousen explains:*Why good news for the economy is often bad news for the stock market*Why stocks of old, established companies in shrinking industries tend to be a better investment than shares in rapidly growing firms in cutting-edge fields*Why stock prices can suddenly skyrocket or collapse--regardless of market fundamentals*Why initial public offerings often enrich insiders at the expense of the majority of investors*How Wall Street is like a giant casino--and how it isn'tThe perfect investment primer, Investing In One Lesson provides an introduction to everything from day trading to contrary investing to chart-based techniques. Dr. Skousen's book concludes with a comprehensive but simple investment strategy to maximize your returns without having to dedicate countless hours to researching the market. Dr. Skousen packs his book with entertaining personal and professional anecdotes illustrating his central point--that the business of investing is not the same as investing in a business. He offers investors a wide-ranging but accessible course on investing history, psychology, and strategy--all in one lesson.
Value Investing: From Graham to Buffett and Beyond
Bruce C. Greenwald - 2001
Some of the savviest people on Wall Street have taken his Columbia Business School executive education course on the subject. Now this dynamic and popular teacher, with some colleagues, reveals the fundamental principles of value investing, the one investment technique that has proven itself consistently over time. After covering general techniques of value investing, the book proceeds to illustrate their applications through profiles of Warren Buffett, Michael Price, Mario Gabellio, and other successful value investors. A number of case studies highlight the techniques in practice. Bruce C. N. Greenwald (New York, NY) is the Robert Heilbrunn Professor of Finance and Asset Management at Columbia University. Judd Kahn, PhD (New York, NY), is a member of Morningside Value Investors. Paul D. Sonkin (New York, NY) is the investment manager of the Hummingbird Value Fund. Michael van Biema (New York, NY) is an Assistant Professor at the Graduate School of Business, Columbia University.
A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature
Tom Siegfried - 2006
Today Nash's beautiful math has become a universal language for research in the social sciences and has infiltrated the realms of evolutionary biology, neuroscience, and even quantum physics. John Nash won the 1994 Nobel Prize in economics for pioneering research published in the 1950s on a new branch of mathematics known as game theory. At the time of Nash's early work, game theory was briefly popular among some mathematicians and Cold War analysts. But it remained obscure until the 1970s when evolutionary biologists began applying it to their work. In the 1980s economists began to embrace game theory. Since then it has found an ever expanding repertoire of applications among a wide range of scientific disciplines. Today neuroscientists peer into game players' brains, anthropologists play games with people from primitive cultures, biologists use games to explain the evolution of human language, and mathematicians exploit games to better understand social networks. A common thread connecting much of this research is its relevance to the ancient quest for a science of human social behavior, or a Code of Nature, in the spirit of the fictional science of psychohistory described in the famous Foundation novels by the late Isaac Asimov. In A Beautiful Math, acclaimed science writer Tom Siegfried describes how game theory links the life sciences, social sciences, and physical sciences in a way that may bring Asimov's dream closer to reality.
Statistics in Plain English
Timothy C. Urdan - 2001
Each self-contained chapter consists of three sections. The first describes the statistic, including how it is used and what information it provides. The second section reviews how it works, how to calculate the formula, the strengths and weaknesses of the technique, and the conditions needed for its use. The final section provides examples that use and interpret the statistic. A glossary of terms and symbols is also included.New features in the second edition include:an interactive CD with PowerPoint presentations and problems for each chapter including an overview of the problem's solution; new chapters on basic research concepts including sampling, definitions of different types of variables, and basic research designs and one on nonparametric statistics; more graphs and more precise descriptions of each statistic; and a discussion of confidence intervals.This brief paperback is an ideal supplement for statistics, research methods, courses that use statistics, or as a reference tool to refresh one's memory about key concepts. The actual research examples are from psychology, education, and other social and behavioral sciences.Materials formerly available with this book on CD-ROM are now available for download from our website www.psypress.com. Go to the book's page and look for the 'Download' link in the right-hand column.
How to read and do proofs
Daniel Solow - 1982
Shows how any proof can be understood as a sequence of techniques. Covers the full range of techniques used in proofs, such as the contrapositive, induction, and proof by contradiction. Explains how to identify which techniques are used and how they are applied in the specific problem. Illustrates how to read written proofs with many step-by-step examples. Includes new, expanded appendices related to discrete mathematics, linear algebra, modern algebra and real analysis.
Structure and Interpretation of Computer Programs
Harold Abelson - 1984
This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.
Advanced Macroeconomics
David Romer - 1995
A series of formal models are used to present and analyze important macroeconomic theories. The theories are supplemented by examples of relevant empirical work, which illustrate the ways that theories can be applied and tested. This well-respected and well-known text is unique in the marketplace.
The Mathematics of Poker
Bill Chen - 2006
By the mid-1990s the old school grizzled traders had been replaced by a new breed of quantitative analysts, applying mathematics to the "art" of trading and making of it a science. A similar phenomenon is happening in poker. The grizzled "road gamblers" are being replaced by a new generation of players who have challenged many of the assumptions that underlie traditional approaches to the game. One of the most important features of this new approach is a reliance on quantitative analysis and the application of mathematics to the game. This book provides an introduction to quantitative techniques as applied to poker and to a branch of mathematics that is particularly applicable to poker, game theory, in a manner that makes seemingly difficult topics accessible to players without a strong mathematical background.
The Neatest Little Guide to Stock Market Investing
Jason Kelly - 1998
Since the dot.com crash and ensuing bear market, significant changes have come about in the investing world, and The Neatest Little Guide takes this into account. In this revised edition, readers will learn: € Strategies on how to double the Dow with one simple investment and the latest products required for this approach € Methods investors can use to avoid disasters such as Enron and WorldCom € Thoroughly updated reference lists, including new websites, new software, new brokers, and new publications With the right information for investors to keep pace, and rooted in the principles that made it invaluable from the start, The Neatest Little Guide to Stock Market Investing is a resource that no serious investor can be without.
Best Practices for Equity Research Analysts: Essentials for Buy-Side and Sell-Side Analysts
James J. Valentine - 2010
I only wish I had this book by my side throughout my career."" -- Byron R. Wien, Vice Chairman, Blackstone Advisory Partners LP""Given the fast pace and high-pressure nature of the markets, analysts don't have the luxury to make mistakes. James J. Valentine's Best Practices for Equity Research Analysts should be required reading for all new and experienced analysts, particularly those who were not lucky enough to be brought up in the business under a mentor. Valentine can be that mentor."" -- Jami Rubin, Managing Director, Global Investment Research, Goldman Sachs""Jim's book is an excellent window into the world of securities research. Very few works cover the complete life cycle of an analyst and the necessary balance between theory and practice. This is one of them."" -- Juan-Luis Perez, Global Director of Research, Morgan Stanley""Valentine's book doesn t rehash the basics of finance but covers all the nonacademic topics in terms of how the analysts should manage their time, resources, data, and contacts in order to come up with the best stock picks. This book is required reading for beginning analysts and a must-read for all analysts who want to develop an edge."" -- Carl Schweser, Founder of Schweser s Study Program for the CFA Exam""Best Practices for Equity Research Analysts is by far the best written and most comprehensive book that I have read on how to become a top-notch analyst. I shouldn't be surprised; it was written by one of the best analysts that Wall Street has ever seen. Every securities firm should require their analysts to read this book."" -- Eli Salzmann, Portfolio ManagerMost equity research analysts learn their trade on the job by apprenticing under a senior analyst. However, equity analysts who work for senior producers often have little time or incentive to train new hires, and those who do have the time may not have research skills worth emulating.Now, "Best Practices for Equity Research Analysts" offers promising equity research analysts a practical curriculum for mastering their profession. James J. Valentine, a former Morgan Stanley analyst, explains everything today's competitive analyst needs to know, providing practical training materials for buyand sell-side research analysis in the United States and globally.Conveniently organized for use as a learning tool and everyday reference on the job, "Best Practices for Equity Research Analysts" covers the five primary areas of the equity research analyst's role: Identifying and monitoring critical factors Creating and updating financial forecasts Deriving price targets or a range of targets Making stock recommendations Communicating stock ideasExpanding upon material covered in undergraduate courses but written specifically to help you perform in the real world, this authoritative book gives you access to the wisdom and expertise of leading professionals in the field. You'll learn best practices for setting up an information hub, influencing others, identifying the critical factors and information sources for better forecasting, creating a better set of financial forecast scenarios, improving valuation and stock-picking techniques, communicating your message effectively, making ethical decisions, and more.Without "Best Practices for Equity Research Analysts," you're just treading water in the sink-or-swim world of the equity analyst."
Mathematics: The Core Course For A Level (Core Course)
Linda Bostock - 1981
Worked examples and exercises support the text. An ELBS/LPBB edition is available.
A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation
Richard Bookstaber - 2007
The very things done to make markets safer, have, in fact, created a world that is far more dangerous. From the 1987 crash to Citigroup closing the Salomon Arb unit, from staggering losses at UBS to the demise of Long-Term Capital Management, Bookstaber gives readers a front row seat to the management decisions made by some of the most powerful financial figures in the world that led to catastrophe, and describes the impact of his own activities on markets and market crashes. Much of the innovation of the last 30 years has wreaked havoc on the markets and cost trillions of dollars. A Demon of Our Own Design tells the story of man's attempt to manage market risk and what it has wrought. In the process of showing what we have done, Bookstaber shines a light on what the future holds for a world where capital and power have moved from Wall Street institutions to elite and highly leveraged hedge funds.
How Risky Is It, Really?: Why Our Fears Don't Always Match the Facts
David Ropeik - 2010
HOW RISKY IS IT, REALLY?International risk expert David Ropeik takes an in-depth look at our perceptions of risk and explains the hidden factors that make us unnecessarily afraid of relatively small threats and not afraid enough of some really big ones. This read is a comprehensive, accessible, and entertaining mixture of what's been discovered about how and why we fear — too much or too little. It brings into focus the danger of The Perception Gap: when our fears don't match the facts, and we make choices that create additional risks.This book will not decide for you what is really risky and what isn't. That's up to you. HOW RISKY IS IT, REALLY? will tell you how you make those decisions. Understanding how we perceive risk is the first step toward making wiser and healthier choices for ourselves as individuals and for society as a whole.TEST YOUR OWN "RISK RESPONSE" IN DOZENS OF SELF-QUIZZES!
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
Cameron Davidson-Pilon - 2014
However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power.
Bayesian Methods for Hackers
illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.