Few interesting books coming out
in this area:
Chasing the Same Signals: How Black-Box Trading Influences Stock Markets from Wall Street to Shanghai
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"The worst stock market crash since Black Monday during October of 1987 occurred during the first week of August of 2007. But nobody noticed. On the morning of August 6th 2007, investment professionals were baffled with unprecedented stock patterns. Mining sector stocks were up +18% but manufacturing stocks were down -14%. It was an extreme sector skew yet the S&P index was unchanged at +0.5% on the day. The next few days would continue with excessive volatility. MBI Insurance, a stock that had rarely attracted speculation would finish up +15% on Aug 6th, followed by another +7% on Aug 7th, and then finish down -22% over the subsequent two days. The brief rally in MBI was short lived. Only weeks later would investors begin to have insights on the dispersion patterns. Prominent hedge funds that had never had a negative annual performance began disclosing excessive trading loses with many notable firms reporting several hundred millions were lost - in a single day. Hedge funds were hemorrhaging in excess of 30% of their assets when the S&P index was unchanged. The market dispersion was the side effects of the synchronous unwind ignited by the hordes of 'computerized' strategies that were caught off guard when history didn't repeat. It was the industry's first world wide panic - by machines. Over the past decade, computerized (or black-box) trading has had a coming of age. Black-box firms use mathematical formulas to buy and sell stocks. The industry attracts the likes of mathematicians, astrophysics and robot scientists. They describe their investment strategy as a marriage of economics and science. Their proliferation has been on the back of success, black-box firms have been among the best performing funds over the past decade, the marquee firms have generated double-digit performance with few if any months of negative returns. Through their coming of age, these obscure mathematicians have joined the ranks of traditional buy-n-hold investors in their influence of market valuations. A rally into the market close is just as likely the byproduct of a technical signal as an earnings revision. They are speculated to represent a one third of all market volume albeit their influence to the day-to-day gyrations goes largely unnoticed. CNBC rarely comments on the sentiments of computerized investors. Conventional wisdom suggests that markets are efficient, random walks and that stock prices rise and fall with the fundamentals of the company. How then have black-box traders prospered and how do they exploit market inefficiencies? Are their strategies on their last legs or will they adapt to the new landscape amidst the global financial crisis? Chasing the Same Signals is a unique chronicle of the black-box industry's rise to prominence and their influence on the market place. This is not a story about what signals they chase, but rather a story on how they chase and compete for the same signals."
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Chapter 1 Introduction.
DoCoMo Man.
Rules Based Trading.
Our Economic Barometer.
Chapter 2 The Black Box Investment Philosophy.
The Black Box Community.
The Thought Process.
The Investment Objective.
Chapter 3 An Adaptive Industry.
Period of Soul Searching.
Behavioral Economics.
Natural Selection.
Chapter 4 Finding the Footprint.
Momentum Trading.
Demographics of Liquidity.
The Shortfall Movement.
Chapter 5 Disciples of Risk Factors.
Market Neutral Strategies.
Quantitative Stock Selection.
A Leveraged Life.
Chapter 6 The Game of High Frequency.
Pining the Book.
Liquidity Providers.
Automated Specialists.
Chapter 7 Era of Execution Strategies.
The Russell Rebalance.
Sourcing Liquidity.
Risk Consolidation.
Chapter 8 Globalization of Equity Markets.
The Microstructure Layer.
Dispersion Opportunities.
Market Structure.
Chapter 9 A Future of Adaptation.
The Man Machine Interface.
Adaptive Market Place.
Outlook for the Species.
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Narang, Rishi K
Inside the Black Box
A straightforward look at quantitative trading
Investors, from high-net-worth individuals to pension funds, have never been more interested in quantitative trading-mainly due to the impressive returns they usually generate. And yet, few actually understand what goes on inside these black box trading strategies. That's why expert fund manager Rishi Narang has created Inside the Black Box. In non-mathematical terms-and supplemented by anecdotes and real-world stories-this guide explains how quantitative trading strategies actually work. Written in a straightforward and accessible style, this book also skillfully explains how quant strategies fit into a portfolio, why they are valuable, and how to evaluate a quant manager. Some of the questions covered throughout these pages include: How do quants capture alpha? What is the difference between theory-driven systems vs. data-mining strategies? How do quants model risk?
* Demystifies quantitative trading and how it works
* Provides key information that investors need to evaluate the best quantitative trades
* Explains the essential elements of this discipline without heavy-handed mathematical jargon
For anyone looking to gain a better understanding of quantitative and algorithmic trading, Inside the Black Box is a highly recommended read.
Foreword.
Acknowledgments.
Chapter 1: Why Does Quant Trading Matter?
The Measurement and Mismeasurement of Risk.
Summary.
What is a Quant?
Summary.
Chapter 3: How Do Quants Make Money?
Types of Alpha Models: Theory-driven and Data-driven.
Data-Driven Alpha Models.
Blending Alpha Models.
Chapter 4: Risk Models.
Limiting the Types of Risk.
Chapter 5: Transaction Cost Models.
Types of Transaction Cost Models.
Chapter 6: Portfolio Construction Models.
Portfolio Optimizers.
How Quants Choose a Portfolio Construction Model.
Chapter 7: Execution.
High Frequency Trading: Blurring the Line between Alpha and Execution.
Summary.
The Importance of Data.
Sources of Data..
Storing Data.
Chapter 9: Research.
Idea Generation.
Summary.
Chapter 10: Risks Inherent to Quant Strategies.
Regime Change Risk.
Contagion, or Common Investor, Risk.
Summary.
Setting the Record Straight.
Quants Cause More Market Volatility by Underestimating Risk.
Quants are All the Same.
Quants are Guilty of Data Mining.
Chapter 12: Evaluating Quants and Quant Strategies.
Evaluating a Quantitative Trading Strategy.
The Edge.
How Quants Fit into a Portfolio.
Chapter 13: Looking to the Future of Quant Trading.
About the Author.
