Stochastic Modeling: Definition, Advantage, and Who Uses It

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Will Kenton
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Will Kenton is an expert on the economy and investing laws and regulations. He previously held senior editorial roles at Investopedia and Kapitall Wire and holds a MA in Economics from The New School for Social Research and Doctor of Philosophy in English literature from NYU.
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Updated October 20, 2021
Reviewed by Margaret James
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Suzanne Kvilhaug
Suzanne Kvilhaug
Fact checked by Suzanne Kvilhaug
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Suzanne is a content marketer, writer, and fact-checker. She holds a Bachelor of Science in Finance degree from Bridgewater State University and helps develop content strategies for financial brands.
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Stochastic Modeling

Dennis Madamba / Investopedia

What Is Stochastic Modeling?

Stochastic modeling is a form of financial model that is used to help make investment decisions. This type of modeling forecasts the probability of various outcomes under different conditions, using random variables.

Stochastic modeling presents data and predicts outcomes that account for certain levels of unpredictability or randomness. Companies in many industries can employ stochastic modeling to improve their business practices and increase profitability. In the financial services sector, planners, analysts, and portfolio managers use stochastic modeling to manage their assets and liabilities and optimize their portfolios.

Key Takeaways

  • Stochastic modeling forecasts the probability of various outcomes under different conditions, using random variables.
  • Stochastic modeling presents data and predicts outcomes that account for certain levels of unpredictability or randomness.
  • In the financial services sector, planners, analysts, and portfolio managers use stochastic modeling to manage their assets and liabilities and optimize their portfolios.
  • The opposite of stochastic modeling is deterministic modeling, which gives you the same exact results every time for a particular set of inputs.
  • The Monte Carlo simulation is one example of a stochastic model; it can simulate how a portfolio may perform based on the probability distributions of individual stock returns.

Understanding Stochastic Modeling: Constant vs. Changeable

To understand the concept of stochastic modeling, it helps to compare it to its opposite: deterministic modeling.

Deterministic Modeling Produces Constant Results

Deterministic modeling gives you the same exact results for a particular set of inputs, no matter how many times you recalculate the model. Here, the mathematical properties are known. None of them is random, and there is only one set of specific values and only one answer or solution to a problem. With a deterministic model, the uncertain factors are external to the model.

Stochastic Modeling Produces Changeable Results

Stochastic modeling, on the other hand, is inherently random, and the uncertain factors are built into the model. The model produces many answers, estimations, and outcomes—like adding variables to a complex math problem—to see their different effects on the solution. The same process is then repeated many times under various scenarios.

Who Uses Stochastic Modeling?

Stochastic modeling is used in a variety of industries around the world. The insurance industry, for example, relies heavily on stochastic modeling to predict how company balance sheets will look at a given point in the future. Other sectors, industries, and disciplines that depend on stochastic modeling include stock investing, statistics, linguistics, biology, and quantum physics.

A stochastic model incorporates random variables to produce many different outcomes under diverse conditions.

An Example of Stochastic Modeling in Financial Services

Stochastic investment models attempt to forecast the variations of prices, returns on assets (ROA), and asset classes—such as bonds and stocks—over time. The Monte Carlo simulation is one example of a stochastic model; it can simulate how a portfolio may perform based on the probability distributions of individual stock returns.

Stochastic investment models can be either single-asset or multi-asset models, and may be used for financial planning, to optimize asset liability management (ALM) or asset allocation; they are also used for actuarial work.

A Pivotal Tool in Financial Decision Making

The significance of stochastic modeling in finance is extensive and far-reaching. When choosing investment vehicles, it is critical to be able to view a variety of outcomes under multiple factors and conditions. In some industries, a company’s success or demise may even hinge on it.

In the ever-changing world of investing, new variables can come into play anytime and could affect a stock picker’s decisions enormously. Hence, finance professionals often run stochastic models hundreds or even thousands of times, which proffers numerous potential solutions to help target decision making.

What Is the Difference Between Stochastic and Deterministic Models?

Unlike deterministic models that produce the same exact results for a particular set of inputs, stochastic models are the opposite. The model presents data and predicts outcomes that account for certain levels of unpredictability or randomness.

What Does a Lot of Variation Mean in a Stochastic Model?

Stochastic models are all about calculating and predicting an outcome based on volatility and variability. The more variation in a stochastic model is reflected in the number of input variables.

What Is an Example of a Stochastic Event?

The Monte Carlo simulation is one example of a stochastic model. It can simulate how a portfolio may perform based on the probability distributions of individual stock returns.

What Is the Difference Between Stochastic and Probabilistic?

They are generally considered synonyms of each other. Stochastic can be thought of as a random event, whereas probabilistic is derived from probability.

The Bottom Line

Stochastic modeling is used to help make investment decisions. This form of financial model forecasts the probability of various outcomes under different conditions, using random variables.