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In this article, we’re going to dive deeper into what stochastic modelling is, which areas it is most used in, and how it is used to determine various market scenarios and their likelihoods.
What are financial models and why do people make them?
Financial models are essentially blueprints, built using spreadsheets (often Excel) to forecast a company’s financial performance. Models use numbers and calculations based on certain assumptions to make a prediction of the future performance of a company, stock, or financial derivative.
During an acquisition, both buyer and seller use financial models to estimate the target company’s value and determine a fair acquisition price.
In some cases, financial models can also be used to assess potential risks a business might face by simulating different adverse situations, like a recession or a change in interest rates.
What is stochastic modelling in finance?
For any model to be stochastic, it has to have uncertainty. Luckily for us, financial markets are anything but certain.
The term stochastic originates from the Greek word stokhos, meaning ‘chance’ or ‘guess.’
Regular financial models and stochastic models differ in how they account for uncertainty. A typical financial model relies on historical data and analysts’ estimates to project future financial statements. They involve formulas and calculations but don’t incorporate randomness.
Most times, a regular model will give you one specific answer for a metric like future profit or loss. This estimate is based on the assumptions and data used in the model.
Stochastic models, on the other hand, generate a range of possible future financial scenarios with their associated probabilities.
Imagine predicting the weather. A regular model might say “sunny with a high of 75 degrees.” A stochastic model would say there’s a 70% chance of sunshine with a high between 70 and 80 degrees, a 20% chance of scattered clouds with a high of 65-70 degrees, and a 10% chance of rain with a high of 60-65 degrees.
Differences at a glance
Here are the main differences between a regular and stochastic model:
| Feature | Regular Model | Stochastic Model |
| Uncertainty | Not explicitly considered | Embraces randomness |
| Output | Single point estimate (e.g., $1 million profit) | Range of possible outcomes with probabilities (e.g., 70% chance of $0.8-1.2 million profit) |
| Complexity | Simpler, easier to build | More complex, data-driven |
| Risk Assessment | Limited | Enhanced |
| Use Cases | Basic forecasting, internal planning | Valuing complex instruments, portfolio risk management |
| Best For | Clarity, ease of use | Detailed analysis, nuanced risk understanding |
The main applications of stochastic modelling with examples
Here are the main applications of stochastic modelling across a range of industries:
Finance
- Portfolio optimisation: Stochastic modelling could help in simulating various market scenarios to build diversified portfolios that balance risk and return – estimating the probability of a stock market crash to adjust asset allocation, for instance.
- Pricing options: Could be used with complex models like Black-Scholes to value options.
Supply Chain Management
- Inventory control: Involves modelling demand fluctuations and lead times to determine inventory levels, avoiding stock outs or excess inventory.
- Production planning: Could also be used to predict variations in demand to optimise production schedules and minimise costs (e.g., estimating the likelihood of increased demand for a new product launch to adjust production capacity).
Insurance
- Actuarial sciences: This division of insurance is responsible for using models to set insurance premiums and assess financial solvency – for instance, they could be used to model the probability of car accidents for different age groups, etc.
- Risk management: This is also a big one. Evaluating the likelihood of catastrophic events like natural disasters is a prime use of stochastic models. These aim to determine these disasters’ financial impact on the company’s financial health, especially if they could trigger several valid claims simultaneously.
Frequently Asked Questions
While stochastic models can be helpful for portfolio risk management, they’re generally not designed for picking individual stocks. They excel at analysing broad market trends and potential risks, not predicting the specific performance of a single company.
It depends on the client’s risk tolerance and investment goals. For conservative investors with a low-risk appetite, a regular model might suffice. However, for clients with a higher risk tolerance or complex portfolios, stochastic models can provide a more comprehensive picture of potential risks and returns.
Yes, to a certain extent. Stochastic models can be used to simulate various economic scenarios (e.g., changes in interest rates, fluctuations in customer demand) and their impact on your business finances.
Accuracy depends on the quality of data used and the underlying assumptions, just like any other model. Regular models can be more accurate for short-term forecasting with well-established historical trends. Stochastic models, while providing a wider range of possibilities, may be less precise for specific point estimates.
It’s crucial to remember that stochastic models are not crystal balls. They provide a framework for analysing probabilities, not guarantees.
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