A good way to think of finance is that it is a discipline that attempts to identify the value of money at different points in time.
The starting point is that a dollar right now is worth a dollar right now, but how much is that same dollar worth in a year? Well, to answer that question we would need to assess the time value of money and determine the difference between that dollar’s value today and tomorrow, accounting for the fact that a dollar today is worth more than a dollar in the future.
“More” is vague, and in reality the difference in value can be quite signifiant. This is of crucial importance for any economic decision, thus finance is the underpinning of every single economic transaction made, and the discipline is involved in finding the “true” difference in a dollar’s value today and in the future before that difference manifests itself.
No one can know the future with certainty, which is why finance’s answers are always varying, complex, and provisional. And it typically means that we need to develop tools to help us identify the risk that our future dollar’s value will be different than what we think it is today.
For this reason, finance at its core is about not just money and time, but risk as well.
That risk comes from many sources that are external to the transaction, so it is the financier’s job to find those sources and quantify them. This process is known as “due diligence” and is complicated, and sometimes impossible. When that is the case, a statistical modeling is typically necessary to try to estimate all of those external factors without knowing them.
This is why finance is a math-heavy discipline, and the math mostly used is some kind of statistical analysis that can help the analyst infer future movements.
The question will determine the kind of statistical tool used. Stocks and portfolio returns typically are analyzed using a simple random walk, where the price at a future moment is known as a combination of the price at the current time and an error term. This gives us a pretty good sense of how far stocks are from their long-term trend.
More complicated questions like the pricing of options require a logarithmic approach, which is why Black-Scholes modeling focuses on the notion of drift that is taken from Brownian motion. From this perspective, the math becomes harder ultimately because there are a lot more elements to keep track of, but it still ends up being pretty easy for any one analyst to do.
Things get more complicated from there when we talk about risk management and financial forecasting, particularly when it comes to things like interest rates, currency valuations, and credit ratings. These involve a lot of factors that are unpredictable, so GARCH models, which try to find clusters of sudden unexpected change, and Markov Chains and Levy processes, which try to control for extreme factors through targeted statistical analysis.
Whether you find these tools intimidating or not will likely depend upon your academic background, but a common refrain one hears on Wall Street is that the math is too easy. This may sound surprising, but at the end of the day much of these mathematical tools are easily produced and replicated, and more sophisticated tools are often unnecessary. While there are exceptions, like quant engineers at a prop trading shop or a market maker, these are a very small part of the overall financial sector.
Thus, many on Wall Street provide value by being creative, or being affable, or typically a mixture of the two. These statistical tools can help you do your job well, but they are also quite easy to learn since the tools most financiers use are limited.
But not everyone on Wall Street can explain these tools to a boss, a client, or an investor, and it is ultimately the ability to communicate the power of financial tools to these entities that makes one successful on Wall Street. Furthermore, knowing what tools are not typically used for a particular problem and introducing them in a novel way to discover something new is a skill that is highly valued and rarely found on Wall Street; this demands some math, for sure, but it demands creativity even more.
So, does finance use statistics? Sure. Do you need to be a statistics expert to succeed in finance? Absolutely not, and on the contrary you might be surprised to discover people who leave finance because it isn’t math-heavy enough. Because the real winners in finance ultimately are people who understand the real world and are very good at interacting with it.