Gewei Ye - High Frequency Trading Models

Copyright 2011 by Gewei Ye. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Ye, Gewei, 1971
High-frequency trading models / Gewei Ye.
p. cm. (Wiley trading series)
Includes bibliographical references and index.
ISBN 978-0-470-63373-1 (cloth)
1. Investment analysis. 2. SpeculationMathematical models. 3. Portfolio managementMathematical models. 4. Financial engineering. I. Title.
HG4529.Y42 2011
332.64501dc22
2010024731

To my parents, Lei, Jessica, and friends

Preface

Lets start the book by explaining the title: High-Frequency Trading Models . First, there are three types of models of high-frequency trading: revenue models, theoretical (including behavioral, quantitative, and financial) models, and computer models. Revenue models are strategies, means, and ways to generate revenue and profit for a financial institution. Theoretical models are foundations for building computer models for high-frequency trading operations. Computer models refer to the computer algorithms (algos for short) that program the theoretical models and trading ideas. To summarize, the computer algos automate the trading ideas and the theoretical models with computer programming languages and technology infrastructure so that the revenue models of financial institutions may be materialized in a systematic way.

Thus, the high-frequency trading models may expand to the following: (1) existing revenue models; (2) new revenue modes, for example, high-frequency trading in derivatives markets; (3) theoretical (behavioral, financial, and quantitative) models for building unique investment strategies for high-frequency trading; and (4) computer algos for high-frequency trading and portfolio management. These four topics make up the central themes of the book.

Second, the high-frequency trading models belong to investment research and practice that are part of investment management. Investment management provides professional asset and portfolio management for financial institutions or private investors. As part of the functions of a financial institution, investment management provides the people, resources, and objectives to conduct high-frequency trading operations with the high-frequency trading models as the tools or goals. The advent of high-frequency trading may impact the investment management industry profoundly if not revolutionarily. As a result, investment management, including portfolio management, will benefit from computer algos e.g., Sentiment Asset Pricing Engine (SAPE) designed for high-frequency trading operations.

To elaborate, SAPE is a unique set of computer algos that are built on top of several Nobel models such as modern portfolio theory (MPT) and the capital asset pricing model (CAPM), the Black-Scholes option pricing model, and the autoregressive conditional heteroskedastic (ARCH) model, by engaging a human behavioral factor, namely, traders sentiment. Though the Nobel models have considered important elements such as risk and return, future-dated option pricing, and volatility clustering, traders sentiment can also affect stock prices. As the Nobel models did not consider behavioral factors in asset pricing, SAPE fills in the gaps by adding traders real-time sentiment to the equation. SAPE estimates future prices of individual assets by aggregating traders real-time sentiment. Compared to the Nobel models that provide theories and formulas, SAPE provides an end-to-end solution to portfolio management, including a new theory on behavioral investing, a new formula on estimating future prices of individual assets, and a new computer system for real-time future asset pricing, asset allocation, and market timing.

At a higher level of abstraction, SAPE for portfolio management, with a collection of computer algos for high-frequency trading, represents technology as the driver of financial innovation and risk management. SAPE algos reflect the principle that technology may have a profound impact on new financial instruments and applications. Similar to the probability theory driving inventions in portfolio management, insurance, and risk management, the advent of high-frequency trading, with an emphasis on information technology, may give rise to more liquidity in securities (especially derivatives) markets, and to inventions in investment management and risk management.

High-frequency trading has swept Wall Street with the stunning profit generated by top tier investment banks. In the meantime, high-frequency trading has been mentioned repeatedly in headline news such as congressional hearings on the practices of Goldman Sachs, and the record-high market volatility on May 6, 2010.

Many financial institutions, regulators, and financial professionals would like to know how high-frequency trading works, how it profits, and what is needed to build the algorithms with technologies available in the public domain. There is a lot of demand in the financial services and regulatory community for an in-depth book on this topic.

The audience for this book includes traders, regulators, portfolio managers, financial engineers, IT professionals, graduate or senior undergraduate students in finance, investment analysts, financial advisors, investment bankers, hedge fund managers, and financial institutions.

This book may be instrumental to the effort of reforming domestic or global financial systems and improving financial regulations. Imagine if financial regulators could develop a new high-frequency trading monitoring system based on the theoretical models and computer algos of this book. The monitoring system might automatically detect the preconditions of market anomalies and prevent the occurrence of undesirable anomalies. It would be especially useful for financial regulators to use computer algos to monitor and regulate trading. As a result, abnormal market behaviors like the one on May 6, 2010, could be anticipated.