Quantitative Trading : Algorithms, Analytics, Data, Models, Optimization book cover
1st Edition

Quantitative Trading
Algorithms, Analytics, Data, Models, Optimization

ISBN 9781315371580
Published January 6, 2017 by Chapman & Hall
379 Pages 30 Color Illustrations

FREE Standard Shipping

What are VitalSource eBooks?

Prices & shipping based on shipping country


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



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

View More



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