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Quantitative Trading: Algorithms, Analytics, Data, Models, Optimization de Xin Guo

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

Autor Xin Guo, Tze Leung Lai, Howard Shek, Samuel Po-Shing Wong
en Limba Engleză Paperback – 10 dec 2019
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.
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Specificații

ISBN-13: 9780367871819
ISBN-10: 0367871815
Pagini: 380
Dimensiuni: 156 x 234 mm
Greutate: 0.45 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC

Cuprins

 


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


Active Por

Notă biografică

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

Recenzii

"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 

Descriere

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 cove