Quantitative trading, often referred to as quant trading, is a complex, math-based way to invest. It's more the domain of hedge funds and large financial institutions than retail investors, but it's technically possible for anyone to implement quantitative trading strategies. And even if you have no intention of doing so, it's still good to know how quant trading works and how it affects financial markets.
Understanding quant trading
Understanding quantitative trading
Quantitative trading is a type of trading based on quantitative analysis. It uses mathematical formulas and statistical data to analyze the stock market and make predictions about when a stock's share price will rise and fall. Trading decisions are based on the patterns found in that information, normally by an automated trading system that can analyze massive amounts of data and make trades in milliseconds.
Quantitative traders, also known as quants, start with a trading model that they test and refine using historical stock market data. Once they've found a model that works with historical data, then they'll use it for real stock market trades. Most quants work for investment banks and hedge funds. The financial institutions that use quantitative trading play a crucial role in the stock market, since the amount of trades they execute add liquidity and help lower bid-ask spreads.
Pros and cons
Pros and cons of quantitative trading
Investors can process a limited amount of information. We only have so much time and mental energy available, and we can't constantly monitor market data. Because quantitative trading uses computer programs, trading decisions are based on far more data than a single investor could review.
Quantitative trading also takes the emotion out of investing. As most investors know, the emotional side of investing is one of the most challenging parts. It's easy to fall into psychological traps, such as the sunk cost fallacy or confirmation bias, that lead to subpar decisions. The computer systems and mathematical models used in quantitative trading only look at the data.
But success is far from guaranteed with quantitative trading. Just because a model works with historical data doesn't mean it will work in the present day. Even if it's initially successful, a model could stop working when market conditions change. The stock market is unpredictable, and looking for quality businesses is generally better than looking for patterns and technical indicators that signal when to buy and sell. Quant trading also often involves short-term, high-volume trading, not the long-term, buy-and-hold approach that The Motley Fool recommends.
Quant trading for retail investors
Quant trading and the everyday investor
As technology has improved, quantitative trading has become more accessible to retail investors. Robo-advisors are a basic form of quantitative trading, as they're essentially data-based investing programs. There are also platforms investors can use to build their own quant trading models, including QuantConnect, StrategyQuant, and QuantRocket.
However, retail investors don't have anywhere near the same level of resources as quantitative trading firms. Quant trading on a professional level involves a team of quants, high-speed trading computers, and typically billions of dollars in capital.
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Quant trading pioneer
A pioneer in quantitative trading
Jim Simons was one of the first investors to employ quantitative trading. Born in 1938, he earned a doctorate in mathematics and became a mathematician and professor. With his knowledge of math and pattern recognition, he decided to start a new career in finance and founded a hedge fund, Monometrics, in 1978. The name was changed to Renaissance Technologies in 1982.
Renaissance Technologies has used quantitative trading to great success, particularly with its Medallion Fund, which delivered a remarkable 66% average annual return from 1988 through 2018. It has about 300 employees, including 90 with doctorates in mathematics, physics, computer science, and related fields, and 52,000 computer cores -- a perfect example of the resources involved with institutional quant trading.
