Finding Alphas

A Quantitative Approach to Building Trading Strategies

Igor Tulchinsky

• Publisher: Wiley
• Year: 2019
• Print ISBN: 9781119571216
• eBook ISBN: 9781119571261
• Edition: 2
• Topic: Business
• Subtopic: Finance
• Index: Business

Discover the ins and outs of designing predictive trading models

Drawing on the expertise of WorldQuant's global network, this new edition of Finding Alphas: A Quantitative Approach to Building Trading Strategies contains significant changes and updates to the original material, with new and updated data and examples.

Nine chapters have been added about alphas – models used to make predictions regarding the prices of financial instruments. The new chapters cover topics including alpha correlation, controlling biases, exchange-traded funds, event-driven investing, index alphas, intraday data in alpha research, intraday trading, machine learning, and the triple axis plan for identifying alphas.

• Provides more references to the academic literature

• Includes new, high-quality material

• Organizes content in a practical and easy-to-follow manner

• Adds new alpha examples with formulas and explanations

If you're looking for the latest information on building trading strategies from a quantitative approach, this book has you covered.

PART I
Introduction

1
Introduction to Alpha Design

By Igor Tulchinsky

What is an alpha? Throughout this book, you'll read different descriptions or definitions of an alpha. Alpha, of course, is the first letter of the Greek alphabet – as in “the alpha and the omega,” the beginning and the end – and it lurks inside the word “alphabet.” Over the centuries, it has attached itself to a variety of scientific terms. The financial use of the word “alpha” goes back to 1968, when Michael Jensen, then a young PhD economics candidate at the University of Chicago, coined the phrase “Jensen's alpha” in a paper he published in The Journal of Finance. Jensen's alpha measured the risk-adjusted returns of a portfolio and determined whether it was performing better or worse than the expected market. Eventually, Jensen's alpha evolved into a measure of investment performance known simply as alpha, and it is most commonly used to describe returns that exceed the market or a benchmark index.

Since then, the term “alpha” has been widely adopted throughout the investing world, particularly by hedge funds, to refer to the unique “edge” that they claim can generate returns that beat the market. At WorldQuant, however, we use the term a little differently. We design and develop “alphas” – individual trading signals that seek to add value to a portfolio.

Fundamentally, an alpha is an idea about how the market works. There are an infinite number of ideas or hypotheses or rules that can be extrapolated, and the number of possibilities is constantly growing with the rapid increase in new data and market knowledge. Each of these ideas could be an alpha, but many are not. An alpha is an automated predictive model that describes, or decodes, some market relation. We design alphas as algorithms, a combination of mathematical expressions, computer source code, and configuration parameters. An alpha contains rules for converting input data to positions or trades to be executed in the financial securities markets. We develop, test, and trade alphas in large numbers because even if markets are operating efficiently, something has to drive prices toward equilibrium, and that means opportunity should always exist. To use a common metaphor, an alpha is an attempt to capture a signal in an always noisy market.

DESIGNING ALPHAS BASED ON DATA

We design alphas based on data, which we are constantly seeking to augment and diversify. Securities prices generally change in response to some event; that event should be reflected in the data. If the data never changes, then there is no alpha. Changes in the data convey information. A change in information should in turn produce a change in the alpha. These changes may be expressed in a variety of alpha expressions. Table 1.1 shows a few simple examples.

Table 1.1 Expressions of changes

A simple difference, A – B	Example: today's_price – yesterday's_price
A ratio, A/B	Example: today's_price/yesterday's_price
An expression	Example: 1/today's price. Increase position when price is low

Alpha design is really just the intelligent search for price information conveyed by possible changes in the data, whether you think of them as patterns, signals, or a code. The mathematical expression of an alpha should embody a hypothesis or a prediction. Again, just a few examples are shown in Table 1.2.

Table 1.2 Expressions and their hypotheses

Expression	Hypothesis
1/price	Invest more if price is low
Price-delay (price,3)	Price moves in the direction of 3-day change
Price	High-priced stocks go higher
Correlation (price,delay(price,1))	Stocks that trend, outperform
(price/delay(price,3)) * rank(volume)	Trending stocks with increasing volume outperform

DEFINING QUALITY IN ALPHAS

Alphas produce returns, which vary over time; like individual stocks, an alpha's aggregate returns rise and fall. The ratio of an alpha's daily return to daily volatility is called the information ratio. This ratio measures the strength and steadiness of the signal, and shows if a strategy is working – whether the signal is robust or weak, whether it is likely to be a true signal or largely noise. We have developed a number of criteria to define the quality of an alpha, though until an alpha is extensively tested, put into production, and observed out of sample, it's difficult to know how good it really is. Nonetheless, here are some traits of quality alphas:

• The idea and expression are simple.
• The expression/code is elegant.
• It has a good in-sample Sharpe ratio.
• It is not sensitive to small changes in data or parameters.
• It works in multiple universes.
• It works in different regions.

ALPHA CONSTRUCTION, STEP BY STEP

We can broadly define the steps required to construct alphas. Although the devil is in the details, developers need only repeat the following five steps:

• Analyze the variables in the data.
• Get an idea of the price response to the change you want to model.
• Come up with a mathematical expression that translates this change into stock positions.
• Test the expression.
• If the result is favorable, submit the alpha.

CONCLUSION

The chapters that follow delve into many of these topics in much greater detail. These chapters have been written by WorldQuant researchers, portfolio managers, and technologists, who spend their days, and often their nights, in search of alphas. The topics range widely, from the nuts-and-bolts development of alphas, to their extensive backtesting, and related subjects like momentum alphas, the use of futures in trading, institutional research in alpha development, and the impact of news and social media on stock returns. There's also a chapter focused on various aspects of WorldQuant's WebSim platform, our proprietary, internet-enabled simulation platform. WebSim's simulation software engine lets anyone backtest alphas, using a large and expanding array of datasets. Last, in this edition of Finding Alphas, we've added new material on topics such as machine learning, alpha correlation, intraday trading, and exchange-traded funds.

What is an alpha and how do we find them? Turn the page.

2
Perspectives on Alpha Research

By Geoffrey Lauprete

In the field of finance, an alpha is the measure of the excess return of an investment over a suitable benchmark, such as a market or an industry index. Within the quantitative investment management industry, and in this book, the term “alpha” refers to a model used to try to forecast the prices, or returns, of financial instruments relative to a benchmark. More precisely, an alpha is a function that takes, as input, data that is expected to be relevant to the prediction of future prices and outputs values corresponding to the forecasted prices of each instrument in its prediction universe, relative to a benchmark. An alpha can be expressed as an algorithm and implemented in a computer language such as C++, Python, or any number of alternative modern or classical programming languages.

Attempts to forecast markets predate the digital era and the arrival of computers on Wall Street. For example, in his 1688 treatise on economic philosophy, Confusion of Confusions, stock operator and writer Josseph Penso de la Vega described valuation principles for complex derivatives and techniques for speculating on the Amsterdam Stock Exchange. Two hundred years later, in a series of articles, Charles Dow (co-founder of Dow Jones & Co., which publishes The Wall Street Journal) codified some of the basic tenets of charting and technical analysis. His writings provide one of the first recorded instances of a systematic market forecasting technique, but investors had to wait until the 1980s for affordable computing power to arrive on the Wall Street scene and change the modeling paradigm: instead of pencil and paper, the main design tools and their hardware were set to become computers and digital data.

PHDS ON THE STREET

Until the 1960s, all or almost all back-office processes, and stock settlement in particular, were done manually. It took the unprecedented increase in stock trading volumes experienced in the late 1960s (between 1965 and 1968, the daily share volume of the New York Stock Exchange increased from 5 million to 12 million), and the accompanying “traffic jams” in trade processing due to the reliance on pen-and-paper recordkeeping, for the adoption of computers to become a business imperative. By the 1970s, Wall Street had digitized its back offices. Within a few years, computers and programmable devices were ubiquitous on the Street, playing a role in every corner of the financial industry.

The arrival of computing machines on the trading floor of large Wall Street firms allowed previously intractable problems in valuation – the pricing of options and other derivatives, and price forecasting based on databases of digital data – to become practically solvable. But formulating the problems in such a way that the new machines could solve them required a new type of market operator, who historically had not been part of the sales and trading ecosystem: PhDs and other analytically minded individuals, not traditionally Wall Street material, became sought-after contributors to this new and modernized version of the trading floor.

A NEW INDUSTRY

One of the early adopters of computer-based investment methods to exploit syste...

Table of contents

• Cover
• Table of Contents
• Preface
• Preface (to the Original Edition)
• Acknowledgments
• About the WebSim Website
• PART I: Introduction
• PART II: Design and Evaluation
• PART III: Extended Topics
• PART IV: New Horizon – WebSim
• PART V: A Final Word
• References
• Index
• End User License Agreement