Math and the Market

Most adults know that stocks, simply put, are investments in companies in the profit said companies make. Investors are the people who buy stocks in a company and they typically do so to earn returns, or money back, on their investments. People often use them to build wealth and some people trade stocks all day long for a living. When you buy stock in a company, you’re buying a piece of ownership in it, known as a share. Each share of a company costs money and they can range from a single penny to $305,085, which is what Warren Buffett’s Berkshire Hathaway stock was at as of April 2019. There are then two ways investors/traders make money:

1. The stock’s price goes up or appreciates. Just like when you buy a home, you’re ideally investing in an asset that appreciates so that when you go to sell it, you make money, some stock appreciates. The time over which is appreciates is very variable, which is why keeping your eyes on the stocks you have is always a good idea. The notion of “buy low, sell high” is deal for making money this way.
2. The stock pays dividends. Dividends are payments that are made quarterly to all people who hold shares in the company. It’s based on if the company is doing well, making profit and meeting their financial goals or beyond. Not every company pays dividends and sometimes in order to qualify for dividends, you have to own a certain number of shares in a company. 

With that little lesson in how investment in stocks works, let’s move on.

Years ago, day traders were thought of as being experienced Wall Street professionals who worked their way up in a frantic work environment by being cunning and ruthless. However, with technology breaking into trading space over the last decade or so, many people can trade all day long with the touch of a few buttons on their phones. The industry still has the same principles, but it’s been quite disrupted by young professionals with phones and math and statistical prowess.

In the past, there’s been a perception that using too much math is not ethical; that those who used math were gaming the system and the way stocks were supposed to be traded. When portfolios (packages of stocks managed by one person) were managed too perfectly by a trader, other traders would almost feel like they were cheating. However, the New York Stock Exchange executes electronic trades in less than 1 second and manual trades in 10 seconds or less. The rate of trades is so fast that even Einstein wouldn’t be able to keep tabs on every day trade made daily! 

This is where statistics come in.

Math cannot guarantee or flawlessly assist in execute trades. To be more realistic, math in day trading is meant to stack probability in the trader’s favor. No statistical model can 100% accurately predict the future. If so, March Madness would become a lot more boring (odds of picking a perfect NCAA tournament bracket for March Madness are 1 in 9.2 quintillion!). When using math to analyze things that happen in the real world, not just hypothetical situations, like much of the math done in school, you can’t always guarantee the parameters within which your mathematical model will go. In other words, lots happens in life that is unpredictable and the stock market is often one of the most real reflections of economics and life. With so much shifting from day to day and week to week in economics, even the most advanced statistical model that is used to predict the market relies on assumption that things will act as they have based on what’s been analyzed. History is a HUGE part of the probability models and algorithms these young mathematicians use to forecast market swings but, as Warren Buffet has said, “If past history was all that is needed to play the game of money, the richest people would be librarians.”  

So why even use modeling if it can really go wrong when the market doesn’t act as predicted? Why? Because good statistical modeling can help you predict risks. We have yet to see the next Warren Buffett when it comes to the new young group of math-forward Wall Street traders, which is proof that math certainly can’t beat the markets, but it can certainly improve the chances of success for traders. Most of the young new math enthusiasts are playing the markets with huge numbers of investments. While no statistical model can entirely predict a good strategy, the larger the numbers, the more likely a good model is able to reveal trends and what will likely happen next. Managing a large portfolio with math can then be least likely to lose, thus is can shield a bit of risk. 

By using in-depth statistical analysis to detect and act on probabilities the new wave of Wall Street young-ins are using math more than ever. It makes you wonder if at some point in the future, the models will be great enough to solve the market. Just another way math is incredibly helpful in the world!