While a traditional investor reads financial statements and tries to understand the vision of the management in a company, a quantitative investor (a quant) cares exclusively about numbers. They build advanced computer programs that chew through enormous amounts of data to find patterns that the human eye simply cannot see.
"When you remove all fear and all doubt, the way is clear for miracles to happen."
— Florence Scovel Shinn
Quantitative investing is precisely about removing human emotions like fear, greed, and doubt from the equation. When a computer makes decisions based on pure mathematics and statistics, the psychological mistakes we humans so often make on the stock market are eliminated.
How does it work?
A quantitative strategy always starts with a hypothesis, for example: Companies that have fallen in price three days in a row, but have a high dividend, tend to rise on the fourth day. The computer then tests this rule against decades of historical stock market data to see if it actually works (this is called backtesting). If the model proves to be profitable, the robot is set to trade automatically in the current market.
Robots rule the market: Today, the image of stressed stockbrokers shouting at each other on the trading floor has long since been replaced by quiet server parks. It is estimated that well over 70 percent of all stock trading in the US is now executed by computers and algorithms.
Pros and cons
Advantages
- Eliminates human emotions and psychological misjudgments.
- Computers can analyze thousands of stocks worldwide in fractions of a second.
- You can test exactly how the strategy would have performed during previous financial crises before risking real money.
Disadvantages
- Historical returns are no guarantee for the future. A model that worked in the 1990s can lose a lot of money today.
- Requires enormous knowledge in mathematics, statistics, and programming to develop from scratch.
- If something completely unforeseen happens in the world (something that is not in the data), the algorithms can make catastrophic mistakes.