1st Edition

Quantitative Trading Algorithms, Analytics, Data, Models, Optimization

    380 Pages
    by Chapman & Hall

    379 Pages
    by Chapman & Hall

    379 Pages 30 Color Illustrations
    by Chapman & Hall

    The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part covers market impact models, network models, multi-asset trading, machine learning techniques, and nonlinear filtering. The third part discusses electronic market making, liquidity, systemic risk, recent developments and debates on the subject.



    Evolution of trading infrastructure

    Quantitative strategies and time-scales

    Statistical arbitrage and debates about EMH

    Quantitative funds, mutual funds, hedge funds

    Data, analytics, models, optimization, algorithms

    Interdisciplinary nature of the subject and how the book can be used

    Supplements and problems

    Statistical Models and Methods for Quantitative Trading

    Stylized facts on stock price data

    Time series of low-frequency returns

    Discrete price changes in high-frequency data

    Brownian motion at the Paris Exchange and random walk down Wall Street

    MPT as a \walking shoe" down Wall Street

    Statistical underpinnings of MPT

    Multifactor pricing models

    Bayes, shrinkage, and Black-Litterman estimators

    Bootstrapping and the resampled frontier

    A new approach incorporating parameter uncertainty

    Solution of the optimization problem

    Computation of the optimal weight vector

    Bootstrap estimate of performance and NPEB

    From random walks to martingales that match stylized facts

    From Gaussian to Paretian random walks

    Random walks with optional sampling times

    From random walks to ARIMA, GARCH

    Neo-MPT involving martingale regression models

    Incorporating time series e_ects in NPEB

    Optimizing information ratios along e_cient frontier

    An empirical study of neo-MPT

    Statistical arbitrage and strategies beyond EMH

    Technical rules and the statistical background

    Time series, momentum, and pairs trading strategies

    Contrarian strategies, behavioral _nance, and investors' cognitive biases

    From value investing to global macro strategies

    In-sample and out-of-sample evaluation

    Supplements and problems

    Active Por


    Xin Guo is the Coleman Fung Chair Professor of Financial Modeling in the department of Industrial Engineering and Operations Research, UC Berkeley. She founded the Berkeley Risk Analysis and Data Analytics Research (RADAR) Lab and holds a courtesy appointment with the Lawrence Berkeley National Lab. Prior to UC Berkeley, she was a Research Staff Member at the IBM T. J. Watson Research Center and an Associate Professor at Cornell University. Her main research interests are stochastic control, stochastic processes and applications. In addition to high frequency trading modeling and analysis, her recent research includes singular controls, impulse controls, non-linear expectations, mean-field games, and filtration enlargement with application to credit risk.

    Tze Leung Lai is a Professor of Statistics and, by courtesy, of Health Research and Policy in the School of Medicine and of the Institute for Computational & Mathematical Engineering (ICME) in the School of Engineering at Stanford University. He is Director of the Financial and Risk Modeling Institute, Co-Director of the Biostatistics Core of the Stanford Cancer Institute, and Co-Director of the Center for Innovative Study Design at the Stanford School of Medicine. He has held regular and visiting faculty appointments at Columbia University, UC Berkeley, and Nankai University, and holds advisory positions with the University of Hong Kong, Peking University, and Tsinghua University.

    Howard Shek is a senior researcher at Tower Research Capital, where he has built and led the Core Research team with a mandate that covers the wide spectrum of research topics in automated trading. He has over 15 years of quantitative research and trading experience in fixed-income arbitrage, market microstructure, volatility estimation, option pricing, and portfolio theory, and has held senior trading and research positions at Merrill Lynch and J. P. Morgan, focus

    "All in all, it is certainly a welcome addition to the nascent literature on this intriguing subject and recommended reading for those interested in quantitative trading strategies—academics, practitioners, and students alike."
    ~The American Statistician, Mikko S. Pakkanen