Statistical Arbitrage And Mean Reversion Strategies
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| Author | : Andrew Pole |
| Publisher | : John Wiley & Sons |
| Total Pages | : 230 |
| Release | : 2011-07-07 |
| Genre | : Business & Economics |
| ISBN | : 1118160738 |
While statistical arbitrage has faced some tough times?as markets experienced dramatic changes in dynamics beginning in 2000?new developments in algorithmic trading have allowed it to rise from the ashes of that fire. Based on the results of author Andrew Pole?s own research and experience running a statistical arbitrage hedge fund for eight years?in partnership with a group whose own history stretches back to the dawn of what was first called pairs trading?this unique guide provides detailed insights into the nuances of a proven investment strategy. Filled with in-depth insights and expert advice, Statistical Arbitrage contains comprehensive analysis that will appeal to both investors looking for an overview of this discipline, as well as quants looking for critical insights into modeling, risk management, and implementation of the strategy.
| Author | : Tim Leung (Professor of industrial engineering) |
| Publisher | : World Scientific |
| Total Pages | : 221 |
| Release | : 2015-11-26 |
| Genre | : Business & Economics |
| ISBN | : 9814725927 |
"Optimal Mean Reversion Trading: Mathematical Analysis and Practical Applications provides a systematic study to the practical problem of optimal trading in the presence of mean-reverting price dynamics. It is self-contained and organized in its presentation, and provides rigorous mathematical analysis as well as computational methods for trading ETFs, options, futures on commodities or volatility indices, and credit risk derivatives. This book offers a unique financial engineering approach that combines novel analytical methodologies and applications to a wide array of real-world examples. It extracts the mathematical problems from various trading approaches and scenarios, but also addresses the practical aspects of trading problems, such as model estimation, risk premium, risk constraints, and transaction costs. The explanations in the book are detailed enough to capture the interest of the curious student or researcher, and complete enough to give the necessary background material for further exploration into the subject and related literature. This book will be a useful tool for anyone interested in financial engineering, particularly algorithmic trading and commodity trading, and would like to understand the mathematically optimal strategies in different market environments."--
| Author | : Ganapathy Vidyamurthy |
| Publisher | : John Wiley & Sons |
| Total Pages | : 295 |
| Release | : 2011-02-02 |
| Genre | : Business & Economics |
| ISBN | : 111804570X |
The first in-depth analysis of pairs trading Pairs trading is a market-neutral strategy in its most simple form. The strategy involves being long (or bullish) one asset and short (or bearish) another. If properly performed, the investor will gain if the market rises or falls. Pairs Trading reveals the secrets of this rigorous quantitative analysis program to provide individuals and investment houses with the tools they need to successfully implement and profit from this proven trading methodology. Pairs Trading contains specific and tested formulas for identifying and investing in pairs, and answers important questions such as what ratio should be used to construct the pairs properly. Ganapathy Vidyamurthy (Stamford, CT) is currently a quantitative software analyst and developer at a major New York City hedge fund.
| Author | : Ernie Chan |
| Publisher | : John Wiley & Sons |
| Total Pages | : 230 |
| Release | : 2013-05-28 |
| Genre | : Business & Economics |
| ISBN | : 1118460146 |
Praise for Algorithmic TRADING “Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers.” —DAREN SMITH, CFA, CAIA, FSA, Managing Director, Manager Selection & Portfolio Construction, University of Toronto Asset Management “Using an excellent selection of mean reversion and momentum strategies, Ernie explains the rationale behind each one, shows how to test it, how to improve it, and discusses implementation issues. His book is a careful, detailed exposition of the scientific method applied to strategy development. For serious retail traders, I know of no other book that provides this range of examples and level of detail. His discussions of how regime changes affect strategies, and of risk management, are invaluable bonuses.” —ROGER HUNTER, Mathematician and Algorithmic Trader
| Author | : Ali N. Akansu |
| Publisher | : John Wiley & Sons |
| Total Pages | : 312 |
| Release | : 2016-04-21 |
| Genre | : Technology & Engineering |
| ISBN | : 1118745639 |
The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.
| Author | : Jan Becker |
| Publisher | : GRIN Verlag |
| Total Pages | : 59 |
| Release | : 2012-06-01 |
| Genre | : Business & Economics |
| ISBN | : 3656200971 |
Bachelor Thesis from the year 2010 in the subject Business economics - Investment and Finance, University of Frankfurt (Main), language: English, abstract: In the following bachelor’s thesis I am going to present a short survey of the hedge fund industry, its regulation and the existent hedge fund strategies. Especially statistical arbitrage is explained in further detail and major performance measurement ratios are presented. In the second part, I am going to introduce a semi-variance model for statistical arbitrage. The model is compared to the standard Garch model, which is so often used in daily option trading, derivate pricing and risk management. Because investment returns are not equally distributed over time, sources for statistical arbitrage occur. The semi-variance model takes skewness into account and provides higher returns at lower volatility than the Garch model. The concept is aimed to be a synopsis of mean reversion and chart pattern detection. The computer model is generated with respect to Brownian motion and technical analysis and provide significant returns to the investment. As market efficiency hypothesis states the impossibility of arbitrage opportunities over the long run, on the other hand market anomalies significantly outstand. Connecting both elements creates a profitable trading system. The combination of both approaches delivers a sensible hedge fund concept. The out-ofsample backtest verifies out-performance and implies the need for further research in the area of higher moment CAPM and additional market timing strategies as sources of statistical arbitrage.
| Author | : Ralph Vince |
| Publisher | : John Wiley & Sons |
| Total Pages | : 404 |
| Release | : 1992-08-04 |
| Genre | : Business & Economics |
| ISBN | : 9780471547389 |
Every futures, options, and stock markets trader operates under a set of highly suspect rules and assumptions. Are you risking your career on yours? Exceptionally clear and easy to use, The Mathematics of Money Management substitutes precise mathematical modeling for the subjective decision-making processes many traders and serious investors depend on. Step-by-step, it unveils powerful strategies for creating and using key money management formulas--based on the rules of probability and modern portfolio theory--that maximizes the potential gains for the level of risk you are assuming. With them, you'll determine the payoffs and consequences of any potential trading decision and obtain the highest potential growth for your specified level of risk. You'll quickly decide: What markets to trade in and at what quantities When to add or subtract funds from an account How to reinvest trading profits for maximum yield The Mathematics of Money Management provides the missing element in modern portfolio theory that weds optimal f to the optimal portfolio.
| Author | : Markos Katsanos |
| Publisher | : John Wiley & Sons |
| Total Pages | : 428 |
| Release | : 2010-03-11 |
| Genre | : Business & Economics |
| ISBN | : 1119995906 |
This book shows traders how to use Intermarket Analysis to forecast future equity, index and commodity price movements. It introduces custom indicators and Intermarket based systems using basic mathematical and statistical principles to help traders develop and design Intermarket trading systems appropriate for long term, intermediate, short term and day trading. The metastock code for all systems is included and the testing method is described thoroughly. All systems are back tested using at least 200 bars of historical data and compared using various profitability and drawdown metrics.
| Author | : Jamie Flux |
| Publisher | : Independently Published |
| Total Pages | : 0 |
| Release | : 2024-08-15 |
| Genre | : Business & Economics |
| ISBN | : |
Discover the secrets of successful statistical arbitrage and mean reversion strategies with this comprehensive guide. Packed with essential knowledge and practical examples, this book is an invaluable resource for traders, analysts, and finance professionals looking to enhance their understanding of quantitative trading. Key Features: - Detailed explanations of statistical arbitrage and mean reversion strategies - Comprehensive coverage of time series analysis, cointegration theory, and autoregressive models - In-depth exploration of popular trading tools such as the Kalman filter, Bollinger Bands, and the Z-Score - Insights into machine learning techniques and dimensionality reduction for feature detection - Real-life examples and case studies with Python code provided for easy implementation Book Description: Statistical Arbitrage and Mean Reversion Strategies introduces you to the fundamentals of statistical arbitrage and mean reversion, covering everything from basic concepts to advanced techniques. Through clear explanations and practical examples, this book breaks down complex theories into easily understandable concepts. Whether you are a novice trader or an experienced professional, you will gain the knowledge needed to successfully apply these strategies in your trading. What You Will Learn: - Understand the foundational principles of statistical arbitrage and mean reversion - Analyze time series data and identify key statistical properties - Implement the Kalman filter for more accurate mean reversion analysis - Construct trading strategies using Bollinger Bands and Z-Scores - Use machine learning models for feature detection and improving trading performance - Manage risk through VaR and CVaR approaches - Validate and optimize your models through backtesting and simulation techniques Who This Book Is For: This book is suitable for traders, analysts, and finance professionals who want to expand their knowledge and skills in the area of statistical arbitrage and mean reversion strategies. It is also suitable for advanced students or researchers interested in quantitative finance. Whether you are new to statistical arbitrage or seeking to refine your strategies, this comprehensive guide provides the tools and insights you need to succeed in today's dynamic market. With its practical approach and real-life examples, this book is an essential companion for anyone looking to enhance their quantitative trading skills.
| Author | : Gregory Zuckerman |
| Publisher | : Penguin |
| Total Pages | : 401 |
| Release | : 2019-11-05 |
| Genre | : Business & Economics |
| ISBN | : 0735217998 |
NEW YORK TIMES BESTSELLER Shortlisted for the Financial Times/McKinsey Business Book of the Year Award The unbelievable story of a secretive mathematician who pioneered the era of the algorithm--and made $23 billion doing it. Jim Simons is the greatest money maker in modern financial history. No other investor--Warren Buffett, Peter Lynch, Ray Dalio, Steve Cohen, or George Soros--can touch his record. Since 1988, Renaissance's signature Medallion fund has generated average annual returns of 66 percent. The firm has earned profits of more than $100 billion; Simons is worth twenty-three billion dollars. Drawing on unprecedented access to Simons and dozens of current and former employees, Zuckerman, a veteran Wall Street Journal investigative reporter, tells the gripping story of how a world-class mathematician and former code breaker mastered the market. Simons pioneered a data-driven, algorithmic approach that's sweeping the world. As Renaissance became a market force, its executives began influencing the world beyond finance. Simons became a major figure in scientific research, education, and liberal politics. Senior executive Robert Mercer is more responsible than anyone else for the Trump presidency, placing Steve Bannon in the campaign and funding Trump's victorious 2016 effort. Mercer also impacted the campaign behind Brexit. The Man Who Solved the Market is a portrait of a modern-day Midas who remade markets in his own image, but failed to anticipate how his success would impact his firm and his country. It's also a story of what Simons's revolution means for the rest of us.
