The book begins with an introduction to the language of statistics and then covers descriptive statistics and inferential statistics. Throughout, the author offers readers:- Difficulty Rating Index for each chapter′s material- Tips for doing and thinking about a statistical technique- Top tens for everything from the best ways to create a graph to the most effective techniques for data collection- Steps that break techniques down into a clear sequence of procedures- SPSS tips for executing each major statistical technique- Practice exercises at the end of each chapter, followed by worked out solutions.The book concludes with a statistical software sampler and a description of the best Internet sites for statistical information and data resources. Readers also have access to a website for downloading data that they can use to practice additional exercises from the book. Students and researchers will appreciate the book′s unhurried pace and thorough, friendly presentation.

    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 novel approach taken here banishes determinants to the end of the book and focuses on the central goal of linear algebra: understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space (or an odd-dimensional real vector space) has an eigenvalue. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition includes a new section on orthogonal projections and minimization problems. The sections on self-adjoint operators, normal operators, and the spectral theorem have been rewritten. New examples and new exercises have been added, several proofs have been simplified, and hundreds of minor improvements have been made throughout the text.

    This book starts from the ground up to provide detailed explanations of both these techniques: - An introduction to the different types of execution is followed by a review of market microstructure theory. Throughout the book examples from empirical studies bridge the gap between the theory and practice of trading. - Orders are the fundamental building blocks for any strategy. Market, limit, stop, hidden, iceberg, peg, routed and immediate-or-cancel orders are all described with illustrated examples. - Trading algorithms are explained and compared using charts to show potential trading patterns. TWAP, VWAP, Percent of Volume, Minimal Impact, Implementation Shortfall, Adaptive Shortfall, Market On Close and Pairs trading algorithms are all covered, together with common variations. - Transaction costs can have a significant effect on investment returns. An in-depth example shows how these may be broken down into constituents such as market impact, timing risk, spread and opportunity cost and other fees. - Coverage includes all the major asset classes, from equities to fixed income, foreign exchange and derivatives. Detailed overviews for each of the world's major markets are provided in the appendices. - Order placement and execution tactics are covered in more detail, as well as potential enhancements (such as short-term forecasts), for those interested in the specifics of implementing these strategies. - Cutting edge applications such as portfolio and multi-asset trading are also considered, as are handling news and data mining/artificial intelligence.

    If you are interested in the theory or practice of valuation, you should have Damodaran on Valuation on your bookshelf. You can bet that I do. -- Michael J. Mauboussin, Chief Investment Strategist, Legg Mason Capital Management and author of More Than You Know: Finding Financial Wisdom in Unconventional Places In order to be a successful CEO, corporate strategist, or analyst, understanding the valuation process is a necessity. The second edition of Damodaran on Valuation stands out as the most reliable book for answering many of today's critical valuation questions. Completely revised and updated, this edition is the ideal book on valuation for CEOs and corporate strategists. You'll gain an understanding of the vitality of today's valuation models and develop the acumen needed for the most complex and subtle valuation scenarios you will face.

    This proven and accessible text speaks to beginning engineering and math students through a wealth of pedagogical aids, including an abundance of examples, explanations, "Remarks" boxes, definitions, and group projects. Using a straightforward, readable, and helpful style, this book provides a thorough treatment of boundary-value problems and partial differential equations.

    The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo MCMC. Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

    It introduces first year Ph.D. students to standard graduate econometrics material from a modern perspective. It covers all the standard material necessary for understanding the principal techniques of econometrics from ordinary least squares through cointegration. The book is also distinctive in developing both time-series and cross-section analysis fully, giving the reader a unified framework for understanding and integrating results.Econometrics has many useful features and covers all the important topics in econometrics in a succinct manner. All the estimation techniques that could possibly be taught in a first-year graduate course, except maximum likelihood, are treated as special cases of GMM (generalized methods of moments). Maximum likelihood estimators for a variety of models (such as probit and tobit) are collected in a separate chapter. This arrangement enables students to learn various estimation techniques in an efficient manner. Eight of the ten chapters include a serious empirical application drawn from labor economics, industrial organization, domestic and international finance, and macroeconomics. These empirical exercises at the end of each chapter provide students a hands-on experience applying the techniques covered in the chapter. The exposition is rigorous yet accessible to students who have a working knowledge of very basic linear algebra and probability theory. All the results are stated as propositions, so that students can see the points of the discussion and also the conditions under which those results hold. Most propositions are proved in the text.For those who intend to write a thesis on applied topics, the empirical applications of the book are a good way to learn how to conduct empirical research. For the theoretically inclined, the no-compromise treatment of the basic techniques is a good preparation for more advanced theory courses.

    Valuation provides up-to-date insights and practical advice on how to create, manage, and measure an organization's value. Along with all-new case studies that illustrate how valuation techniques and principles are applied in real-world situations, this comprehensive guide has been updated to reflect the events of the Internet bubble and its effect on stock markets, new developments in academic finance, changes in accounting rules (both U. S. and IFRS), and an enhanced global perspective. This edition contains the solid framework that managers at all levels, investors, and students have come to trust.

    The author has a direct style. His book presents detailed and intensive explanations. Many diagrams and key examples are used to aid understanding, as well as the application of calculus to physics and engineering and economics. The text is well organized, and it covers single variable and multivariable calculus in depth. An instructor's manual and student guide are available online at http: //ocw.mit.edu/ans7870/resources/Strang/....

    It proceeds from familiar concepts to the unfamiliar, from the concrete to the abstract. Readers consistently praise this outstanding text for its expository style and clarity of presentation. The applications version features a wide variety of interesting, contemporary applications. Clear, accessible, step-by-step explanations make the material crystal clear. Established the intricate thread of relationships between systems of equations, matrices, determinants, vectors, linear transformations and eigenvalues.

    This is the secret that lets tiny quantitative edges create hedge fund billionaires, and defines the powerful modern global derivatives economy. The same practical techniques are still used today by risk-takers in finance as well as many other fields. "Red-Blooded Risk" examines this approach and offers valuable advice for the calculated risk-takers who need precise quantitative guidance that will help separate them from the rest of the pack. While most commentators say that the last financial crisis proved it's time to follow risk-minimizing techniques, they're wrong. The only way to succeed at anything is to manage true risk, which includes the chance of loss. "Red-Blooded Risk" presents specific, actionable strategies that will allow you to be a practical risk-taker in even the most dynamic markets.Contains a secret history of Wall Street, the parts all the other books leave outIncludes an intellectually rigorous narrative addressing what it takes to really make it in any risky activity, on or off Wall StreetAddresses essential issues ranging from the way you think about chance to economics, politics, finance, and lifeWritten by Aaron Brown, one of the most calculated and successful risk takers in the world of finance, who was an active participant in the creation of modern risk management and had a front-row seat to the last meltdownWritten in an engaging but rigorous style, with no equationsContains illustrations and graphic narrative by renowned manga artist Eric KimThere are people who disapprove of every risk before the fact, but never stop anyone from doing anything dangerous because they want to take credit for any success. The recent financial crisis has swelled their ranks, but in learning how to break free of these people, you'll discover how taking on the right risk can open the door to the most profitable opportunities.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    Leading financial economist Lasse Heje Pedersen combines the latest research with real-world examples and interviews with top hedge fund managers to show how certain trading strategies make money--and why they sometimes don't.Pedersen views markets as neither perfectly efficient nor completely inefficient. Rather, they are inefficient enough that money managers can be compensated for their costs through the profits of their trading strategies and efficient enough that the profits after costs do not encourage additional active investing. Understanding how to trade in this efficiently inefficient market provides a new, engaging way to learn finance. Pedersen analyzes how the market price of stocks and bonds can differ from the model price, leading to new perspectives on the relationship between trading results and finance theory. He explores several different areas in depth--fundamental tools for investment management, equity strategies, macro strategies, and arbitrage strategies--and he looks at such diverse topics as portfolio choice, risk management, equity valuation, and yield curve logic. The book's strategies are illuminated further by interviews with leading hedge fund managers: Lee Ainslie, Cliff Asness, Jim Chanos, Ken Griffin, David Harding, John Paulson, Myron Scholes, and George Soros.Efficiently Inefficient effectively demonstrates how financial markets really work.Free problem sets are available online at http: //www.lhpedersen.com

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.