Download or read book Your Essential Guide to Quantitative Hedge Fund Investing written by Marat Molyboga and published by CRC Press. This book was released on 2023-07-18 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your Essential Guide to Quantitative Hedge Fund Investing provides a conceptual framework for understanding effective hedge fund investment strategies. The book offers a mathematically rigorous exploration of different topics, framed in an easy to digest set of examples and analogies, including stories from some legendary hedge fund investors. Readers will be guided from the historical to the cutting edge, while building a framework of understanding that encompasses it all. Features Filled with novel examples and analogies from within and beyond the world of finance Suitable for practitioners and graduate-level students with a passion for understanding the complexities that lie behind the raw mechanics of quantitative hedge fund investment A unique insight from an author with experience of both the practical and academic spheres.

Download or read book Statistical Portfolio Estimation written by Masanobu Taniguchi and published by CRC Press. This book was released on 2017-09-01 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: The composition of portfolios is one of the most fundamental and important methods in financial engineering, used to control the risk of investments. This book provides a comprehensive overview of statistical inference for portfolios and their various applications. A variety of asset processes are introduced, including non-Gaussian stationary processes, nonlinear processes, non-stationary processes, and the book provides a framework for statistical inference using local asymptotic normality (LAN). The approach is generalized for portfolio estimation, so that many important problems can be covered. This book can primarily be used as a reference by researchers from statistics, mathematics, finance, econometrics, and genomics. It can also be used as a textbook by senior undergraduate and graduate students in these fields.

Download or read book Intelligent and Fuzzy Systems written by Cengiz Kahraman and published by Springer Nature. This book was released on 2023-08-16 with total page 833 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book consists of the papers accepted after a careful review process at an international scientific meeting where the latest developments on intelligent and fuzzy systems are presented and discussed. The latest developments in both the theoretical and practical fields of the new fuzzy set extensions have been prepared by expert researchers. Contributed by participants from more than 40 different countries, this book is also a useful resource in terms of showing the levels that fuzzy and intelligent systems have reached in various countries of the world. The intended readers are intelligent and fuzzy systems researchers, lecturers, M.Sc., and Ph.D. students studying fuzzy sets and artificial intelligence. The book covers fuzzy logic theory and applications, heuristics, and metaheuristics from optimization to machine learning, from quality management to risk management, making the book an excellent source for researchers.

Download or read book Approximation and Optimization written by Ioannis C. Demetriou and published by Springer. This book was released on 2019-05-10 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the development of approximation-related algorithms and their relevant applications. Individual contributions are written by leading experts and reflect emerging directions and connections in data approximation and optimization. Chapters discuss state of the art topics with highly relevant applications throughout science, engineering, technology and social sciences. Academics, researchers, data science practitioners, business analysts, social sciences investigators and graduate students will find the number of illustrations, applications, and examples provided useful. This volume is based on the conference Approximation and Optimization: Algorithms, Complexity, and Applications, which was held in the National and Kapodistrian University of Athens, Greece, June 29–30, 2017. The mix of survey and research content includes topics in approximations to discrete noisy data; binary sequences; design of networks and energy systems; fuzzy control; large scale optimization; noisy data; data-dependent approximation; networked control systems; machine learning ; optimal design; no free lunch theorem; non-linearly constrained optimization; spectroscopy.

Download or read book Discrete Time Series Processes and Applications in Finance written by Gilles Zumbach and published by Springer Science & Business Media. This book was released on 2012-09-26 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book surveys empirical properties of financial time series, discusses their mathematical basis, and describes uses in risk evaluation, option pricing or portfolio construction. The author introduces and assesses a range of processes against the benchmark.

Download or read book Fundamental Statistical Inference written by Marc S. Paolella and published by John Wiley & Sons. This book was released on 2018-09-04 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on approach to statistical inference that addresses the latest developments in this ever-growing field This clear and accessible book for beginning graduate students offers a practical and detailed approach to the field of statistical inference, providing complete derivations of results, discussions, and MATLAB programs for computation. It emphasizes details of the relevance of the material, intuition, and discussions with a view towards very modern statistical inference. In addition to classic subjects associated with mathematical statistics, topics include an intuitive presentation of the (single and double) bootstrap for confidence interval calculations, shrinkage estimation, tail (maximal moment) estimation, and a variety of methods of point estimation besides maximum likelihood, including use of characteristic functions, and indirect inference. Practical examples of all methods are given. Estimation issues associated with the discrete mixtures of normal distribution, and their solutions, are developed in detail. Much emphasis throughout is on non-Gaussian distributions, including details on working with the stable Paretian distribution and fast calculation of the noncentral Student's t. An entire chapter is dedicated to optimization, including development of Hessian-based methods, as well as heuristic/genetic algorithms that do not require continuity, with MATLAB codes provided. The book includes both theory and nontechnical discussions, along with a substantial reference to the literature, with an emphasis on alternative, more modern approaches. The recent literature on the misuse of hypothesis testing and p-values for model selection is discussed, and emphasis is given to alternative model selection methods, though hypothesis testing of distributional assumptions is covered in detail, notably for the normal distribution. Presented in three parts—Essential Concepts in Statistics; Further Fundamental Concepts in Statistics; and Additional Topics—Fundamental Statistical Inference: A Computational Approach offers comprehensive chapters on: Introducing Point and Interval Estimation; Goodness of Fit and Hypothesis Testing; Likelihood; Numerical Optimization; Methods of Point Estimation; Q-Q Plots and Distribution Testing; Unbiased Point Estimation and Bias Reduction; Analytic Interval Estimation; Inference in a Heavy-Tailed Context; The Method of Indirect Inference; and, as an appendix, A Review of Fundamental Concepts in Probability Theory, the latter to keep the book self-contained, and giving material on some advanced subjects such as saddlepoint approximations, expected shortfall in finance, calculation with the stable Paretian distribution, and convergence theorems and proofs.

Download or read book Encyclopedia of Financial Models written by Frank J. Fabozzi and published by John Wiley & Sons. This book was released on 2012-10-15 with total page 3180 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential reference dedicated to a wide array of financial models, issues in financial modeling, and mathematical and statistical tools for financial modeling The need for serious coverage of financial modeling has never been greater, especially with the size, diversity, and efficiency of modern capital markets. With this in mind, the Encyclopedia of Financial Models, 3 Volume Set has been created to help a broad spectrum of individuals—ranging from finance professionals to academics and students—understand financial modeling and make use of the various models currently available. Incorporating timely research and in-depth analysis, the Encyclopedia of Financial Models is an informative 3-Volume Set that covers both established and cutting-edge models and discusses their real-world applications. Edited by Frank Fabozzi, this set includes contributions from global financial experts as well as academics with extensive consulting experience in this field. Organized alphabetically by category, this reliable resource consists of three separate volumes and 127 entries—touching on everything from asset pricing and bond valuation models to trading cost models and volatility—and provides readers with a balanced understanding of today's dynamic world of financial modeling. Frank Fabozzi follows up his successful Handbook of Finance with another major reference work, The Encyclopedia of Financial Models Covers the two major topical areas: asset valuation for cash and derivative instruments, and portfolio modeling Fabozzi explores the critical background tools from mathematics, probability theory, statistics, and operations research needed to understand these complex models Organized alphabetically by category, this book gives readers easy and quick access to specific topics sorted by an applicable category among them Asset Allocation, Credit Risk Modeling, Statistical Tools 3 Volumes onlinelibrary.wiley.com Financial models have become increasingly commonplace, as well as complex. They are essential in a wide range of financial endeavors, and this 3-Volume Set will help put them in perspective.

Download or read book Classification Analysis of DNA Microarrays written by Leif E. Peterson and published by John Wiley & Sons. This book was released on 2013-06-24 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wiley Series in Bioinformatics: Computational Techniques and Engineering Yi Pan and Albert Y. Zomaya, Series Editors Wide coverage of traditional unsupervised and supervised methods and newer contemporary approaches that help researchers handle the rapid growth of classification methods in DNA microarray studies Proliferating classification methods in DNA microarray studies have resulted in a body of information scattered throughout literature, conference proceedings, and elsewhere. This book unites many of these classification methods in a single volume. In addition to traditional statistical methods, it covers newer machine-learning approaches such as fuzzy methods, artificial neural networks, evolutionary-based genetic algorithms, support vector machines, swarm intelligence involving particle swarm optimization, and more. Classification Analysis of DNA Microarrays provides highly detailed pseudo-code and rich, graphical programming features, plus ready-to-run source code. Along with primary methods that include traditional and contemporary classification, it offers supplementary tools and data preparation routines for standardization and fuzzification; dimensional reduction via crisp and fuzzy c-means, PCA, and non-linear manifold learning; and computational linguistics via text analytics and n-gram analysis, recursive feature extraction during ANN, kernel-based methods, ensemble classifier fusion. This powerful new resource: Provides information on the use of classification analysis for DNA microarrays used for large-scale high-throughput transcriptional studies Serves as a historical repository of general use supervised classification methods as well as newer contemporary methods Brings the reader quickly up to speed on the various classification methods by implementing the programming pseudo-code and source code provided in the book Describes implementation methods that help shorten discovery times Classification Analysis of DNA Microarrays is useful for professionals and graduate students in computer science, bioinformatics, biostatistics, systems biology, and many related fields.

Download or read book FUNDAMENTAL MODELS IN FINANCIAL THEORY written by Doron Peleg and published by Web PR Is US. This book was released on 2014-04-11 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an innovative, integrated, and methodical approach to understanding complex financial models, integrating topics usually presented separately into a comprehensive whole. The book brings together financial models and high-level mathematics, reviewing the mathematical background necessary for understanding these models organically and in context. It begins with underlying assumptions and progresses logically through increasingly complex models to operative conclusions. Readers who have mastered the material will gain the tools needed to put theory into practice and incorporate financial models into real-life investment, financial, and business scenarios. Modern finance’s most bothersome shortcoming is that the two basic models for building an optimal investment portfolio, Markowitz’s mean-variance model and Sharpe and Treynor’s Capital Asset Pricing Model (CAPM), fall short when we try to apply them using Excel Solver. This book explores these two models in detail, and for the first time in a textbook the Black-Litterman model for building an optimal portfolio constructed from a small number of assets (developed at Goldman Sachs) is thoroughly presented. The model’s integration of personal views and its application using Excel templates are demonstrated. The book also offers innovative presentations of the Modigliani–Miller model and the Consumption-Based Capital Asset Pricing Model (CCAPM). Problems at the end of each chapter invite the reader to put the models into immediate use. Fundamental Models in Financial Theory is suitable for classroom use or as a reference for finance practitioners.

Download or read book Empirical Finance for Finance and Banking written by Robert Sollis and published by John Wiley & Sons. This book was released on 2012-02-06 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Empirical Finance for Finance and Banking provides the student with a relatively non-technical guide to some of the key topics in finance where empirical methods play an important role Written for students taking Master’s degrees in finance and banking, it is also suitable for students and researchers in other areas, including economics. The first three introductory chapters outline the structure of the book and review econometric and statistical techniques, while the remaining chapters discuss various topics, including: portfolio theory and asset allocation, asset pricing and factor models, market efficiency, modelling and forecasting exchange and interest rates and Value at Risk. Understanding these topics and the methods covered will be helpful for students interested in working as analysts and researchers in financial institutions.

Download or read book Strategic and Tactical Asset Allocation written by Henrik Lumholdt and published by Springer. This book was released on 2018-07-21 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers each step in the asset allocation process, addressing as many of the relevant questions as possible along the way. How can we formulate expectations about long-term returns? How relevant are valuations? What are the challenges to optimizing the portfolio? Can factor investing add value and, if so, how can it be implemented? Which are the key performance drivers for each asset class, and what determines how they are correlated? How can we apply insights about the business cycle to tactical asset allocation? The book is aimed at finance professionals and others looking for a coherent framework for decision-making in asset allocation, both at the strategic and tactical level. It stresses analysis rather than pre-conceived ideas about investments, and it draws on both empirical research and practical experience to give the reader as strong a background as possible.

Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2022-03-01 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Download or read book Handbook of Financial Econometrics written by Yacine Ait-Sahalia and published by Elsevier. This book was released on 2009-10-19 with total page 809 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of original articles—8 years in the making—shines a bright light on recent advances in financial econometrics. From a survey of mathematical and statistical tools for understanding nonlinear Markov processes to an exploration of the time-series evolution of the risk-return tradeoff for stock market investment, noted scholars Yacine Aït-Sahalia and Lars Peter Hansen benchmark the current state of knowledge while contributors build a framework for its growth. Whether in the presence of statistical uncertainty or the proven advantages and limitations of value at risk models, readers will discover that they can set few constraints on the value of this long-awaited volume. - Presents a broad survey of current research—from local characterizations of the Markov process dynamics to financial market trading activity - Contributors include Nobel Laureate Robert Engle and leading econometricians - Offers a clarity of method and explanation unavailable in other financial econometrics collections

Download or read book Investment Risk and Uncertainty written by Steven P. Greiner and published by John Wiley & Sons. This book was released on 2013-03-14 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: Valuable insights on the major methods used in today's asset and risk management arena Risk management has moved to the forefront of asset management since the credit crisis. However, most coverage of this subject is overly complicated, misunderstood, and extremely hard to apply. That's why Steven Greiner—a financial professional with over twenty years of quantitative and modeling experience—has written Investment Risk and Uncertainty. With this book, he skillfully reduces the complexity of risk management methodologies applied across many asset classes through practical examples of when to use what. Along the way, Greiner explores how particular methods can lower risk and mitigate losses. He also discusses how to stress test your portfolio and remove the exposure to regular risks and those from "Black Swan" events. More than just an explanation of specific risk issues, this reliable resource provides practical "off-the-shelf" applications that will allow the intelligent investor to understand their risks, their sources, and how to hedge those risks. Covers modern methods applied in risk management for many different asset classes Details the risk measurements of truly multi-asset class portfolios, while bridging the gap for managers in various disciplines—from equity and fixed income investors to currency and commodity investors Examines risk management algorithms for multi-asset class managers as well as risk managers, addressing new compliance issues and how to meet them The theory of risk management is hardly ever spelled out in practical applications that portfolio managers, pension fund advisors, and consultants can make use of. This book fills that void and will put you in a better position to confidently face the investment risks and uncertainties found in today's dynamic markets.

Download or read book Quantitative Finance with Python written by Chris Kelliher and published by CRC Press. This book was released on 2022-05-19 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors. Features Useful as both a teaching resource and as a practical tool for professional investors. Ideal textbook for first year graduate students in quantitative finance programs, such as those in master’s programs in Mathematical Finance, Quant Finance or Financial Engineering. Includes a perspective on the future of quant finance techniques, and in particular covers some introductory concepts of Machine Learning. Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432 and on https://github.com/lingyixu/Quant-Finance-With-Python-Code.

Download or read book The Theory and Practice of Investment Management written by Frank J. Fabozzi and published by John Wiley & Sons. This book was released on 2011-04-18 with total page 708 pages. Available in PDF, EPUB and Kindle. Book excerpt: An updated guide to the theory and practice of investment management Many books focus on the theory of investment management and leave the details of the implementation of the theory up to you. This book illustrates how theory is applied in practice while stressing the importance of the portfolio construction process. The Second Edition of The Theory and Practice of Investment Management is the ultimate guide to understanding the various aspects of investment management and investment vehicles. Tying together theoretical advances in investment management with actual practical applications, this book gives you a unique opportunity to use proven investment management techniques to protect and grow a portfolio under many different circumstances. Contains new material on the latest tools and strategies for both equity and fixed income portfolio management Includes key take-aways as well as study questions at the conclusion of each chapter A timely updated guide to an important topic in today's investment world This comprehensive investment management resource combines real-world financial knowledge with investment management theory to provide you with the practical guidance needed to succeed within the investment management arena.

Download or read book Introduction to Financial Forecasting in Investment Analysis written by John B. Guerard, Jr. and published by Springer Science & Business Media. This book was released on 2013-01-04 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting—the art and science of predicting future outcomes—has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions. With an emphasis on "earnings per share" (eps), the author presents a data-oriented text on financial forecasting, understanding financial data, assessing firm financial strategies (such as share buybacks and R&D spending), creating efficient portfolios, and hedging stock portfolios with financial futures. The opening chapters explain how to understand economic fluctuations and how the stock market leads the general economic trend; introduce the concept of portfolio construction and how movements in the economy influence stock price movements; and introduce the reader to the forecasting process, including exponential smoothing and time series model estimations. Subsequent chapters examine the composite index of leading economic indicators (LEI); review financial statement analysis and mean-variance efficient portfolios; and assess the effectiveness of analysts’ earnings forecasts. Using data from such firms as Intel, General Electric, and Hitachi, Guerard demonstrates how forecasting tools can be applied to understand the business cycle, evaluate market risk, and demonstrate the impact of global stock selection modeling and portfolio construction.