In the complex world of quantitative trading, the "Equity Long Short" strategy stands out as one of the most popular, particularly among hedge funds. This article looks at this strategy, how it works, and its application in algorithmic trading. If you're looking to develop your algorithmic trading skills, this is a key element to understand.
What is the Equity Long-Short strategy?
Equity Long Short is an investment strategy that involves taking long positions in stocks considered undervalued (i.e. likely to rise) and short positions in stocks considered overvalued (i.e. likely to fall). The aim is to profit from both rising long stocks and falling short stocks.
How does this strategy work?
The implementation of this strategy begins with the identification of an investment universe. Once this universe has been established, assets are classified according to a given variable, usually based on market effects such as momentum, volatility or other predictive data. The trader will then buy undervalued assets and sell short overvalued ones.
Key steps in the strategy:
- Identifying the investment universe: First and foremost, investors need to define their investment universe, i.e. the set of assets they intend to include in their portfolio.
- Asset ranking: Once the universe has been established, assets are ranked according to a specific variable, often based on past performance.
- Asset selection: Based on this variable, undervalued assets are bought (long position) while overvalued assets are sold short (short position).
Take your algo trading strategies to the next level
Use our strategy database to develop quantitative strategies faster.
✔️ Research papers
✔️ Trading rules
✔️ Performance metrics
✔️ Python code
Market neutral vs. biased strategies
The Equity Long Short strategy can be "market neutral", i.e. the total value of long positions is equal to that of short positions. This minimizes market exposure. However, some traders prefer to have a skewed exposure, such as the "130/30" strategy, where long exposure is 130% and short exposure is 30%.
Relevance to algo trading
With the rise of algo trading, strategies like Equity Long Short have become essential. Algorithmic traders can program models to automatically identify long and short opportunities, optimizing their returns.
Like any investment strategy, Equity Long Short involves risks. If long positions do not appreciate as expected, or if short positions increase in value, the strategy can lead to losses. Moreover, managing market exposure is crucial to minimizing risk.
Concrete example: Pair Trading
A variant of Equity Long Short is "Pair Trading", which involves offsetting a long position in a stock with a short position in another stock in the same sector. For example, a trader could buy Microsoft while shorting Intel. The aim would be to profit from Microsoft's rise and Intel's fall.
The Equity Long-Short strategy is a sophisticated approach to trading, offering profit opportunities in a variety of market scenarios. For those adept at algo trading, understanding this strategy and its nuances is essential to maximizing returns and minimizing risk. As always, a balanced approach and prudent risk management are the keys to success.
💡 Read more:
- Trading strategies papers with code on Equities, Cryptocurrencies, Commodities, Currencies, Bonds, Options
- A curated list of awesome libraries, packages, strategies, books, blogs, and tutorials for systematic trading
- A bunch of datasets for quantitative trading
- A website to help you become a quant trader and achieve financial independence