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The Science of Algorithmic Trading and Portfolio Management

The Science of Algorithmic Trading and Portfolio Management
A Book

by Robert Kissell

  • Publisher : Academic Press
  • Release : 2013-10-01
  • Pages : 496
  • ISBN : 0124016936
  • Language : En, Es, Fr & De
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The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. Helps readers design systems to manage algorithmic risk and dark pool uncertainty. Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives.

Algorithmic Trading Methods

Algorithmic Trading Methods
Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques

by Robert L. Kissell

  • Publisher : Academic Press
  • Release : 2020-09-08
  • Pages : 612
  • ISBN : 0128156317
  • Language : En, Es, Fr & De
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Algorithmic Trading Methods: Applications using Advanced Statistics, Optimization, and Machine Learning Techniques, Second Edition, is a sequel to The Science of Algorithmic Trading and Portfolio Management. This edition includes new chapters on algorithmic trading, advanced trading analytics, regression analysis, optimization, and advanced statistical methods. Increasing its focus on trading strategies and models, this edition includes new insights into the ever-changing financial environment, pre-trade and post-trade analysis, liquidation cost & risk analysis, and compliance and regulatory reporting requirements. Highlighting new investment techniques, this book includes material to assist in the best execution process, model validation, quality and assurance testing, limit order modeling, and smart order routing analysis. Includes advanced modeling techniques using machine learning, predictive analytics, and neural networks. The text provides readers with a suite of transaction cost analysis functions packaged as a TCA library. These programming tools are accessible via numerous software applications and programming languages. Provides insight into all necessary components of algorithmic trading including: transaction cost analysis, market impact estimation, risk modeling and optimization, and advanced examination of trading algorithms and corresponding data requirements. Increased coverage of essential mathematics, probability and statistics, machine learning, predictive analytics, and neural networks, and applications to trading and finance. Advanced multiperiod trade schedule optimization and portfolio construction techniques. Techniques to decode broker-dealer and third-party vendor models. Methods to incorporate TCA into proprietary alpha models and portfolio optimizers. TCA library for numerous software applications and programming languages including: MATLAB, Excel Add-In, Python, Java, C/C++, .Net, Hadoop, and as standalone .EXE and .COM applications.

An Introduction to Algorithmic Finance, Algorithmic Trading and Blockchain

An Introduction to Algorithmic Finance, Algorithmic Trading and Blockchain
A Book

by Satya Chakravarty,Palash Sarkar

  • Publisher : Emerald Group Publishing
  • Release : 2020-08-20
  • Pages : 208
  • ISBN : 1789738938
  • Language : En, Es, Fr & De
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The purpose of the book is to provide a broad-based accessible introduction to three of the presently most important areas of computational finance, namely, option pricing, algorithmic trading and blockchain. This will provide a basic understanding required for a career in the finance industry and for doing more specialised courses in finance.

Python for Algorithmic Trading

Python for Algorithmic Trading
A Book

by Yves Hilpisch

  • Publisher : O'Reilly Media
  • Release : 2020-11-12
  • Pages : 380
  • ISBN : 1492053325
  • Language : En, Es, Fr & De
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Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms

Student-Managed Investment Funds

Student-Managed Investment Funds
Organization, Policy, and Portfolio Management

by Brian Bruce

  • Publisher : Academic Press
  • Release : 2020-07-29
  • Pages : 602
  • ISBN : 0128178671
  • Language : En, Es, Fr & De
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Student-Managed Investment Funds: Organization, Policy, and Portfolio Management, Second Edition, helps students work within a structured investment management organization, whatever that organizational structure might be. It aids them in developing an appreciation for day-to-day fund operations (e.g., how to get portfolio trade ideas approved, how to execute trades, how to reconcile investment performance), and it addresses the management of the portfolio and the valuation/selection process for discriminating between securities. No other book covers the "operational" related issues in SMIFs, like organizations, tools, data, presentation, and performance evaluation. With examples of investment policy statements, presentation slides, and organizational structures from other schools, Student-Managed Investment Funds can be used globally by students, instructors, and administrators alike. Addresses the basics of valuation as well as issues related to maintaining compliance, philosophy, performance measurement, and evaluation Provides explanations and examples about organizing a student-managed fund Reviews fundamental stock valuation approaches like multi-stage DDM, FCF, and price multiples

Multi-Asset Risk Modeling

Multi-Asset Risk Modeling
Techniques for a Global Economy in an Electronic and Algorithmic Trading Era

by Morton Glantz,Robert Kissell

  • Publisher : Academic Press
  • Release : 2013-12-03
  • Pages : 544
  • ISBN : 0124016944
  • Language : En, Es, Fr & De
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Multi-Asset Risk Modeling describes, in a single volume, the latest and most advanced risk modeling techniques for equities, debt, fixed income, futures and derivatives, commodities, and foreign exchange, as well as advanced algorithmic and electronic risk management. Beginning with the fundamentals of risk mathematics and quantitative risk analysis, the book moves on to discuss the laws in standard models that contributed to the 2008 financial crisis and talks about current and future banking regulation. Importantly, it also explores algorithmic trading, which currently receives sparse attention in the literature. By giving coherent recommendations about which statistical models to use for which asset class, this book makes a real contribution to the sciences of portfolio management and risk management. Covers all asset classes Provides mathematical theoretical explanations of risk as well as practical examples with empirical data Includes sections on equity risk modeling, futures and derivatives, credit markets, foreign exchange, and commodities

Symposium proceedings - XV International symposium Symorg 2016

Symposium proceedings - XV International symposium Symorg 2016
Reshaping the Future Through Sustainable Business Development and Entepreneurship

by Ondrej Jaško,Sanja Marinković

  • Publisher : University of Belgrade, Faculty of Organizational Sciences
  • Release : 2016-06-03
  • Pages : 1520
  • ISBN : 8676803269
  • Language : En, Es, Fr & De
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PRICAI 2019: Trends in Artificial Intelligence

PRICAI 2019: Trends in Artificial Intelligence
16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019, Proceedings, Part III

by Abhaya C. Nayak,Alok Sharma

  • Publisher : Springer Nature
  • Release : 2019-08-22
  • Pages : 761
  • ISBN : 3030298949
  • Language : En, Es, Fr & De
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This three-volume set LNAI 11670, LNAI 11671, and LNAI 11672 constitutes the thoroughly refereed proceedings of the 16th Pacific Rim Conference on Artificial Intelligence, PRICAI 2019, held in Cuvu, Yanuca Island, Fiji, in August 2019. The 111 full papers and 13 short papers presented in these volumes were carefully reviewed and selected from 265 submissions. PRICAI covers a wide range of topics such as AI theories, technologies and their applications in the areas of social and economic importance for countries in the Pacific Rim.

Optimal Sports Math, Statistics, and Fantasy

Optimal Sports Math, Statistics, and Fantasy
A Book

by Robert L. Kissell,James Poserina

  • Publisher : Academic Press
  • Release : 2017-04-06
  • Pages : 352
  • ISBN : 0128052937
  • Language : En, Es, Fr & De
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Optimal Sports Math, Statistics, and Fantasy provides the sports community—students, professionals, and casual sports fans—with the essential mathematics and statistics required to objectively analyze sports teams, evaluate player performance, and predict game outcomes. These techniques can also be applied to fantasy sports competitions. Readers will learn how to: Accurately rank sports teams Compute winning probability Calculate expected victory margin Determine the set of factors that are most predictive of team and player performance Optimal Sports Math, Statistics, and Fantasy also illustrates modeling techniques that can be used to decode and demystify the mysterious computer ranking schemes that are often employed by post-season tournament selection committees in college and professional sports. These methods offer readers a verifiable and unbiased approach to evaluate and rank teams, and the proper statistical procedures to test and evaluate the accuracy of different models. Optimal Sports Math, Statistics, and Fantasy delivers a proven best-in-class quantitative modeling framework with numerous applications throughout the sports world. Statistical approaches to predict winning team, probabilities, and victory margin Procedures to evaluate the accuracy of different models Detailed analysis of how mathematics and statistics are used in a variety of different sports Advanced mathematical applications that can be applied to fantasy sports, player evaluation, salary negotiation, team selection, and Hall of Fame determination

Environmental, Social, and Governance (ESG) Investing

Environmental, Social, and Governance (ESG) Investing
A Balanced Analysis of the Theory and Practice of a Sustainable Portfolio

by John Hill

  • Publisher : Academic Press
  • Release : 2020-01-30
  • Pages : 370
  • ISBN : 0128186933
  • Language : En, Es, Fr & De
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Environmental, Social, and Governance (ESG) Investing: A Balanced Analysis of the Theory and Practice of a Sustainable Portfolio presents a balanced, thorough analysis of ESG factors as they are incorporated into the investment process. An estimated 25% of all new investments are in ESG funds, with a global total of $23 trillion and the U.S. accounting for almost $9 trillion. Many advocate the sustainability goals promoted by ESG, while others prefer to maximize returns and spend their earnings on social causes. The core problem facing those who want to promote sustainability goals is to define sustainability investing and measure its returns. This book examines theories and their practical implications, illuminating issues that other books leave in the shadows. Provides a dispassionate examination of ESG investing Presents the historical arguments for maximizing returns and competing theories to support an ESG approach Reviews case studies of empirical evidence about relative returns of both traditional and ESG investment approaches

Digital Designs for Money, Markets, and Social Dilemmas

Digital Designs for Money, Markets, and Social Dilemmas
A Book

by Yuji Aruka

  • Publisher : Springer Nature
  • Release : 2022
  • Pages : 129
  • ISBN : 9811909377
  • Language : En, Es, Fr & De
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Hands-On Machine Learning for Algorithmic Trading

Hands-On Machine Learning for Algorithmic Trading
Design and implement investment strategies based on smart algorithms that learn from data using Python

by Stefan Jansen

  • Publisher : Packt Publishing Ltd
  • Release : 2018-12-31
  • Pages : 684
  • ISBN : 1789342716
  • Language : En, Es, Fr & De
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With the help of this book, you'll build smart algorithmic models using machine learning algorithms covering tasks such as time series forecasting, backtesting, trade predictions, and more using easy-to-follow examples. By the end, you'll be able to adopt algorithmic trading in your own business and implement intelligent investigative strategies.

Machine Learning and Data Science Blueprints for Finance

Machine Learning and Data Science Blueprints for Finance
A Book

by Hariom Tatsat,Sahil Puri,Brad Lookabaugh

  • Publisher : O'Reilly Media
  • Release : 2020-10-01
  • Pages : 432
  • ISBN : 1492073024
  • Language : En, Es, Fr & De
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Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Long/Short Market Dynamics

Long/Short Market Dynamics
Trading Strategies for Today's Markets

by Clive M. Corcoran

  • Publisher : John Wiley & Sons
  • Release : 2007-02-06
  • Pages : 358
  • ISBN : 0470065311
  • Language : En, Es, Fr & De
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Hedge funds are now the largest volume players in the capital markets. They follow a wide assortment of strategies but their activities have replaced and overshadowed the traditional model of the long only portfolio manager. Many of the traditional technical indicators and commonly accepted trading strategies have become obsolete or ineffective. The focus throughout the book is to describe the principal innovations that have been made within the equity markets over the last several years and that have changed the ground rules for trading activities. By understanding these changes the active trader is far better equipped to profit in today’s more complex and risky markets. Long/Short Market Dynamics includes: A completely new technique, Comparative Quantiles Analysis, for identifying market turning points is introduced. It is based on statistical techniques that can be used to recognize money flow and price/momentum divergences that can provide substantial profit opportunities. Power laws, regime shifts, self-organized criticality, phase transitions, network dynamics, econophysics, algorithmic trading and other ideas from the science of complexity are examined. All are described as concretely as possible and avoiding unnecessary mathematics and formalism. Alpha generation, portfolio construction, hedge ratios, and beta neutral portfolios are illustrated with case studies and worked examples. Episodes of financial contagion are illustrated with a proposed explanation of their origins within underlying market dynamics

Recent Advances and Applications in Alternative Investments

Recent Advances and Applications in Alternative Investments
A Book

by Zopounidis, Constantin,Kenourgios, Dimitris,Dotsis, George

  • Publisher : IGI Global
  • Release : 2020-02-07
  • Pages : 385
  • ISBN : 1799824381
  • Language : En, Es, Fr & De
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In recent years, there has been a swell of investment opportunities in contemporary asset classes that have gained considerable attention, including cryptocurrencies, hedge funds, and private equity. These alternative investments provide the opportunity to enhance the diversification of financial portfolios and harvest risk premiums that traditional assets like stocks and bonds fail to provide. The emergence of these new properties has created the need to further understand the mechanics, risks, and returns of alternative investments. Recent Advances and Applications in Alternative Investments is a pivotal reference source that provides vital research on the emergence and development of complementary asset classes in the field of finance and investment. While highlighting topics such as carbon emission markets, renewable energy, and digital currencies, this publication explores modern investment strategies as well as the latest products and new types of risk. This book is ideally designed for managers, strategists, accountants, financial professionals, economists, brokers, investors, business practitioners, policymakers, researchers, and academicians seeking current research on contemporary developments in investment strategies and alternative assets.

Quantitative Portfolio Management

Quantitative Portfolio Management
The Art and Science of Statistical Arbitrage

by Michael Isichenko

  • Publisher : John Wiley & Sons
  • Release : 2021-08-31
  • Pages : 304
  • ISBN : 1119821320
  • Language : En, Es, Fr & De
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Discover foundational and advanced techniques in quantitative equity trading from a veteran insider In Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage, distinguished physicist-turned-quant Dr. Michael Isichenko delivers a systematic review of the quantitative trading of equities, or statistical arbitrage. The book teaches you how to source financial data, learn patterns of asset returns from historical data, generate and combine multiple forecasts, manage risk, build a stock portfolio optimized for risk and trading costs, and execute trades. In this important book, you’ll discover: Machine learning methods of forecasting stock returns in efficient financial markets How to combine multiple forecasts into a single model by using secondary machine learning, dimensionality reduction, and other methods Ways of avoiding the pitfalls of overfitting and the curse of dimensionality, including topics of active research such as “benign overfitting” in machine learning The theoretical and practical aspects of portfolio construction, including multi-factor risk models, multi-period trading costs, and optimal leverage Perfect for investment professionals, like quantitative traders and portfolio managers, Quantitative Portfolio Management will also earn a place in the libraries of data scientists and students in a variety of statistical and quantitative disciplines. It is an indispensable guide for anyone who hopes to improve their understanding of how to apply data science, machine learning, and optimization to the stock market.

Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry

Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
A Book

by Chkoniya, Valentina

  • Publisher : IGI Global
  • Release : 2021-06-25
  • Pages : 653
  • ISBN : 1799869865
  • Language : En, Es, Fr & De
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The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students.

Machine Learning for Algorithmic Trading

Machine Learning for Algorithmic Trading
Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition

by Stefan Jansen

  • Publisher : Packt Publishing Ltd
  • Release : 2020-07-31
  • Pages : 820
  • ISBN : 1839216786
  • Language : En, Es, Fr & De
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Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data Who this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

High-Frequency Trading

High-Frequency Trading
A Practical Guide to Algorithmic Strategies and Trading Systems

by Irene Aldridge

  • Publisher : John Wiley & Sons
  • Release : 2013-04-22
  • Pages : 306
  • ISBN : 1118343506
  • Language : En, Es, Fr & De
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A fully revised second edition of the best guide to high-frequency trading High-frequency trading is a difficult, but profitable, endeavor that can generate stable profits in various market conditions. But solid footing in both the theory and practice of this discipline are essential to success. Whether you're an institutional investor seeking a better understanding of high-frequency operations or an individual investor looking for a new way to trade, this book has what you need to make the most of your time in today's dynamic markets. Building on the success of the original edition, the Second Edition of High-Frequency Trading incorporates the latest research and questions that have come to light since the publication of the first edition. It skillfully covers everything from new portfolio management techniques for high-frequency trading and the latest technological developments enabling HFT to updated risk management strategies and how to safeguard information and order flow in both dark and light markets. Includes numerous quantitative trading strategies and tools for building a high-frequency trading system Address the most essential aspects of high-frequency trading, from formulation of ideas to performance evaluation The book also includes a companion Website where selected sample trading strategies can be downloaded and tested Written by respected industry expert Irene Aldridge While interest in high-frequency trading continues to grow, little has been published to help investors understand and implement this approach—until now. This book has everything you need to gain a firm grip on how high-frequency trading works and what it takes to apply it to your everyday trading endeavors.

The Oxford Handbook of Quantitative Asset Management

The Oxford Handbook of Quantitative Asset Management
A Book

by Bernd Scherer

  • Publisher : Oxford University Press
  • Release : 2012
  • Pages : 501
  • ISBN : 0199553432
  • Language : En, Es, Fr & De
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This book explores the current state of the art in quantitative investment management across seven key areas. Chapters by academics and practitioners working in leading investment management organizations bring together major theoretical and practical aspects of the field.