# The Monte Carlo Method & Its Uses in Finance

By Colin Dodds · September 14, 2023 · 8 minute read

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A Monte Carlo simulation is a mathematical technique used by investors and others to estimate the probability of different outcomes given a situation where multiple variables may come into play.

Monte Carlo simulations are used in such a wide range of industries — e.g., physics, engineering, meteorology, finance, and more — that the term doesn’t refer to a single formula, but rather a type of multivariate modeling technique. Multivariate modeling is a statistical method that uses multiple variables to forecast outcomes. A Monte Carlo simulation is an example of this type of calculation, which provides a range of potential outcomes using a probability distribution.

## What Is the Monte Carlo Method?

A Monte Carlo simulation calculates a probability distribution for any variable that has inherent uncertainty. It then recalculates the results thousands of times over, each time using a different set of random numbers pertaining to each variable, to produce a vast array of outcomes that are then averaged together. In this way, a Monte Carlo analysis enables researchers from many industries to run multiple trials, and thus to define the potential outcome or risk of an event or a decision.

Applying mathematics to investment or business scenarios is difficult precisely because there are so many random variables involved in any single decision or any single investment or portfolio of investments. That’s why a Monte Carlo analysis can be more informative compared with predictive models that use fixed inputs.

The ability to apply mathematics to situations where many elements are probable, and then rank the likelihood of possible outcomes in order to gauge the potential for risk, is a chief advantage of Monte Carlo simulations.

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## Monte Carlo Method History

Using simulations to solve problems dates back to the 19th century, and perhaps even earlier, when simulations were an experimental way to test theories, analyze data, or support scientific intuition using statistics. But these simulations typically dealt with established deterministic problems. A modern Monte Carlo analysis, however, inverts that structure by using probabilities to solve the problem.

One of the first known uses of a modern Monte Carlo simulation dates back to the 1930s, when physicist Enrico Fermi experimented with an early form of the method to understand the diffusion of neutrons.

Physicists Stanislaw Ulam and John von Neumann are credited with developing and refining the current Monte Carlo method while working at the Los Alamos National Laboratory on nuclear weapons in the 1940s. Of course, the technique needed a code name, and Monte Carlo was chosen because the element of chance also drives the games at a casino (the Monte Carlo region of Monaco is well-known as a gambling hub).

Soon, the simulation method gained traction in the fields of physics, chemistry, and operations research, thanks to its adoption by the Rand Corporation and the U.S. Air Force. From there, it spread to many of the natural sciences, and eventually found its way to finance.

## How the Monte Carlo Method is Used in Finance

In terms of practicality in the financial space, the Monte Carlo method has numerous potential uses.

For instance, money managers might use a Monte Carlo analysis to estimate risk levels for different investments when constructing a portfolio. Corporate finance managers might use a Monte Carlo simulation to assess the impact of variables like future sales, commodities prices, interest rates, currency fluctuations, and so on. Brokers might use a Monte Carlo analysis to calculate the risks of stock options.

## Monte Carlo Simulation Method

The Monte Carlo simulation works by constructing a model of possible outcomes based on an estimated range of possible conditions. It does this by creating a curve of different variables for each unknown variable, and inserting random numbers between the minimum and maximum value for each variable, and running the calculation over and over again.

A Monte Carlo experiment will run the calculation thousands upon thousands of times. Along the way, it will produce a large number of possible outcomes.

But even for a simple investment, there are a host of factors that will affect its outcome. There are interest rates, regulations, market swings, as well as factors innate to that investment, such as the sales and revenue of the underlying business, or its competitive landscape, or disruptive technology, and so on.

And as an investor seeks to peer further into the future, more possible variables emerge. Using a Monte Carlo simulation to understand those potential investment risks requires using a growing number of inputs as the time horizon grows longer.

After an investor runs a Monte Carlo simulation, the calculation will deliver a range of possible outcomes, with a probability score assigned to each outcome. By weighing the probability scores of different outcomes, an investor can proceed with a better sense of the risks and possible rewards of a given investment decision.

## Monte Carlo Simulation Steps

Using a Monte Carlo simulation is a complicated process that requires a background in mathematics, though some investors have created Monte-Carlo-like models using spreadsheet software. Some of those homespun programs can be used to try to project possible price trajectories of a given asset.

If you wanted to get an idea of how the Monte Carlo method could be used to estimate potential stock movements, the steps to do so would look something like the following — but note that this is a very simplistic, pared down model.

•   Step 1: Use historical price data of a stock to generate a set of daily returns data

•   Step 2: Use that data set to determine further variables, such as standard deviations and variance

•   Step 3: Define a random input or variable

•   Step 4: Run a simulation (again, this will require software or a program) and analyze the results

In Monte Carlo fashion, the user will repeatedly run the equation an arbitrary number of times, to see how often each outcome occurs. The frequency of each outcome will reflect the likelihood of each outcome.

The results will most likely form a bell curve, with the most likely result in the middle of the curve. But as with any bell curve, those results also indicate that there is an equal chance that the actual result will be either higher or lower than the number in the middle.

## Estimating Risk Using the Monte Carlo Method

The Monte Carlo method can be used to determine the likelihood of certain risks when investing, but there are some important things to take into consideration.

For one, a Monte Carlo simulation is only as good as the data that’s programmed into it. No matter how well the simulation is run, its predictive powers can easily be undone by factors that haven’t been added into the equation. For example, when using a Monte Carlo simulation to decide whether or not to buy a given stock, the model could seem to deliver a clear picture of the risks and rewards of the investment.

In that example, the problems arise if the programmer or investor leaves out one single factor, such as macro trends, the effectiveness of company leadership, cyclical factors, political changes, and so on.

There’s a chance that factor could be the one that completely subverts the simulation. And those variables are potentially without limit.

## Who Uses Monte Carlo Simulations, and How

Nonetheless, large institutional investors might use Monte Carlo simulations as a tool in their projections and decision making. And its use for investors isn’t limited to hedge fund managers and spreadsheet wizards. There are even online Monte Carlo simulators that can help people save for retirement.

Those tools are designed for the average investor to input some basic information like their savings, and years until retirement to help them understand the likelihood that they will be able to reach their financial goals, and whether they will have enough income in retirement. Those calculators use a generic set of parameters for their calculations, with inputs such as interest rates, and a generic portfolio allocation.

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## The Takeaway

A Monte Carlo simulation is a mathematical technique used to estimate possible outcomes of an uncertain event, such as the movement of securities.

The basis of this analysis is that the probability of different outcomes cannot be determined because random variables cannot be predicted. Therefore, a Monte Carlo simulation will constantly repeat random samples to achieve certain results that can be used to gauge the likelihood of various outcomes, and therefore different risk levels associated with different choices.

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## FAQ

### What are the advantages of using the Monte Carlo method compared to other numerical techniques?

Though many other numerical techniques have the same goal as the Monte Carlo method, it may be advantageous in that it tests out numerous random variables and then works to an average, rather than starting from an average — which is not to say that it’ll always provide a superior result than another technique.

### How is randomness or probability incorporated into the Monte Carlo method?

The Monte Carlo method incorporates randomness or probability into the mix by using random numbers and distributions of probability, which could include formulas or data sets associated with random variables.

### Are there any techniques to improve the efficiency or speed of Monte Carlo simulations?

There are potential techniques and strategies to improve upon the base Monte Carlo method model, and they’re all fairly high-level and abstract (remember, it was developed by physicists at Los Alamos!). For the typical investor, it may not be worth looking too far into.

### What are some historical origins and applications of the Monte Carlo method?

The Monte Carlo method’s origins can be traced back to the 1930s and the experiments of physicist Enrico Fermi, and later, others during the 1940s working on nuclear weapon development. It can be used to determine the probability of different outcomes or results that may not easily be predicted.

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