The stock market is staffed with people with excellent mathematical skills. These staff, who use quantitative analysis to make financial trades are known as ‘Quants’. Quants trade using a purely mathematical approach, using in-depth statistical analysis to detect and act upon probabilities.

### Can math really beat the market?

The answer is **‘Not yet’**. Some economists and modelers think Gaussian functions are the best tool available to the financial markets. Yet the stock market has suffered wild swings over a period of days. A standard Gaussian function appears to do a poor job at explaining steep fluctuations in stock prices since it would predict extreme dips and rises would be extremely rare and even less likely to continue over days, yet they are not rare on the stock markets.

Many people believe that those who do use math in the markets are playing the system to some extent, finding solutions where only a handful of traders will see them and therefore handling their portfolios to near perfection. This is unrealistic, as the market moves too quickly for total accuracy. Electronic trades execute in less than a second, and those issued manually are still usually completed in less than 10 seconds. This translates to constant movement that no mathematical genius can keep up with in a day trading scenario.

### Analyzing market trends

However, math can be useful in analyzing market trends, but this is more to look at the probability of risk, rather than to guarantee a perfect trade. No mathematical model, even by the most careful and brilliant mathematician, can predict the future, but a good model can help to assess and predict risks. For example, if there is concern about a segment of the US economy that deals with steel, you make a model of what that whole market is all about based on the data you have and then you see if we did ‘X’ what would likely happen. Sometimes the model is correct and this gives the trader an advantage.

Any model will have to include an assumption when analysing a real-world situation. It is not always possible to guarantee the parameters within which the quants expectations and mathematical model will go to work. This means that even the most advanced model relies a little on the assumption that things will act as expected.

Mathematicians have compiled some spectacular probability models and algorithms to forecast market swings, but the only way to do so is by using data that’s already available, and currently there’s not a lot of data. So, despite the gradual takeover of Wall Street by “quants,” there has not yet been a brilliant statistician who has made incredible gains on the stock market.

This is not to suggest that Quants are not successful, they are. Quants use math to improve the chances of success as a trader, playing the markets with a large variety of strategies and investments. This means that while no mathematical model can completely ensure a correct strategy, since math starts with assumptions and the real world does not work like that, a good model’s effectiveness can be seen in a broad sense to reveal what is most likely to happen. This advantage, stretched over a multitude of trades on a consistent basis, essentially means that those managing the market with math may, at the very least, be less likely to lose.