Book Image

Algorithmic Short Selling with Python

By : Laurent Bernut
Book Image

Algorithmic Short Selling with Python

By: Laurent Bernut

Overview of this book

If you are in the long/short business, learning how to sell short is not a choice. Short selling is the key to raising assets under management. This book will help you demystify and hone the short selling craft, providing Python source code to construct a robust long/short portfolio. It discusses fundamental and advanced trading concepts from the perspective of a veteran short seller. This book will take you on a journey from an idea (“buy bullish stocks, sell bearish ones”) to becoming part of the elite club of long/short hedge fund algorithmic traders. You’ll explore key concepts such as trading psychology, trading edge, regime definition, signal processing, position sizing, risk management, and asset allocation, one obstacle at a time. Along the way, you’ll will discover simple methods to consistently generate investment ideas, and consider variables that impact returns, volatility, and overall attractiveness of returns. By the end of this book, you’ll not only become familiar with some of the most sophisticated concepts in capital markets, but also have Python source code to construct a long/short product that investors are bound to find attractive.
Table of Contents (17 chapters)
14
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15
Index

Net beta

In early 2005, the Japanese equities market had an epic year. It felt like this time, it was different. The party came to a screeching halt when the Japanese authorities decided to arrest Takafumi Horie, CEO of Livedoor (JT:4753) and symbol of the new Japan. High-flying small caps rediscovered Newtonian physics.

We long high-flying small caps with esoteric business models, and short a few asthmatic "structural shorts" along with index futures. The fund manager quickly responded to the crisis by selling futures. Despite a reasonable +30% net exposure, the ship was taking on water fast. As a self-appointed risk manager, I promptly brought to his attention that we were synthetically exposed on the beta, market cap, exchange, and liquidity sides. With small caps at 1.7 and beyond on the long side, agonizing shorts at 0.8, and futures at 1 on the short side, our net beta was hovering around 0.7. As one investor later pointed out, we had a "beta of 1.5 on the...