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Quantitative traders can employ a vast number of strategies, from the simple to the incredibly complex. Here are six common examples you might encounter:
Many quant strategies fall under the general umbrella of mean reversion. Mean reversion is a financial theory that posits that prices and returns have a long-term trend. Any deviations should, eventually, revert to that trend.
Quants will write code that finds markets with a long-standing mean and highlight when it diverges from it. If it diverges up, the system will calculate the probability of a profitable short trade. If it diverges down, it will do the same for a long position.
Mean reversion doesn’t have to apply to the price of a single market. Two correlated assets, for example, may have a spread with a long-term trend.
Another broad category of quant strategy is trend following, often called momentum trading. Trend following is one of the most straightforward strategies, seeking only to identify a significant market movement as it starts and ride it until it ends.
There are lots of different methods to spot an emerging trend using quantitative analysis. You could, for instance, monitor sentiment among traders at major firms to build a model that predicts when institutional investors are likely to heavily buy or sell a stock. Alternatively, you could find a pattern between volatility breakouts and new trends.
Statistical arbitrage builds on the theory of mean reversion. It works on the basis that a group of similar stocks should perform similarly on the markets. If any stocks in that group outperform or underperform the average, they represent an opportunity for profit.
A statistical arbitrage strategy will find a group of stocks with similar characteristics. Shares in US car companies, for example, all trade on the same exchange, in the same sector and are subject to the same market conditions. The model would then calculate an average ‘fair price’ each stock.
You would then short any companies in the group that outperform this fair price, and buy any that underperform it. When the stocks revert to the mean price, both positions are closed for a profit.
Pure statistical arbitrage comes with a fair degree of risk: chiefly that it ignores the factors that can apply to an individual asset but not affect the rest of the group. These can result in long-term deviations that don’t revert to the mean for an extended time. To negate this risk, many quant traders use HFT algorithms to exploit extremely short-term market inefficiencies instead of wide divergences.
This strategy involves building a model that can identify when a large institutional firm is going to make a large trade, so you can trade against them. It’s also sometimes known as high-tech front running.
Nowadays, almost all institutional trading is done via algorithms. Firms want to make large orders without affecting the market price of the assets they are buying or selling, so they route their orders to multiple exchanges – as well as different brokers, dark pools and crossing networks – in a staggered pattern to disguise their intentions.
If you build a model that can ‘break the code’, you can get ahead of the trade. So algorithmic pattern recognition attempts to recognise and isolate the custom execution patterns of institutional investors.
For instance, if your model flags that a large firm is attempting to buy a significant amount of Coca-Cola stock, you could buy the stock ahead of them then sell it back at a higher price.
Like statistical arbitrage, algorithmic pattern recognition is often used by firms with access to powerful HFT systems. These are required to open and close positions ahead of an institutional investor.
Behavioural bias recognition is a relatively new type of strategy that exploits the psychological quirks of retail investors.
These are well known and documented. For example, the loss-aversion bias leads retail investors to cut winning positions and add to losing ones. Why? Because the urge to avoid realising a loss – and therefore accept the regret that comes with it – is stronger than to let a profit run.
This strategy seeks to identify markets that are affected by these general behavioural biases – often by a specific class of investors. You can then trade against the irrational behaviour as a source of return.
Like many quant strategies, behavioural bias recognition seeks to exploit market inefficiency in return for profit. But unlike mean reversion, which works off the theory that inefficiencies will eventually rectify themselves, behavioural finance involves predicting when they might arise and trading accordingly.
When a new stock is added to an index, the ETFs representing that index often have to buy that stock as well. If ABC Limited were to join the FTSE 100, for example, then numerous ETFs that track the FTSE 100 would have to buy ABC Limited shares.
By understanding the rules of index additions and subtractions and utilising ultra-fast execution systems, quant funds can capitalise on this rule and trade ahead of the forced buying. For instance, by buying ABC Limited stock ahead of the ETF managers and selling it back to them for a higher price.