Quantitative Trading : Algorithms, Analytics, Data, Models, Optimization book cover
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Quantitative Trading
Algorithms, Analytics, Data, Models, Optimization




ISBN 9780367871819
Published December 10, 2019 by Chapman and Hall/CRC
379 Pages

 
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Book Description

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.

Table of Contents

 



Introduction



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



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Author(s)

Biography

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