Five essential models for data scientists in finance

If you’ve worked with data before, you’re probably familiar with foundational models like logistic and linear regression. These foundational models are important techniques for understanding baselines and relationships within data. As you grow your skills, however, you’ll find there’s a whole range of other interesting modeling methods you can learn and apply to your work.

One of the great things about the data science community is sharing knowledge, and our weekly Data Science Hangout is a space for data science leaders to do just that. We get to hear directly from finance experts; for example, Brad Zielke at Target shared insights on bridging the gap between complex technical work and non-technical business stakeholders, and Yu Cao at Exeter Finance talked about the challenges of including macroeconomic factors when forecasting.

Sometimes, our Hangout conversations go deep into specific data science techniques, with attendees asking about their application in finance. We’ve put together five types of models that have come up in these discussions, along with an example use case and code snippet.

1. Monte Carlo simulation
2. Sensitivity analysis
3. Time series analysis
4. Bayesian model
5. Natural language processing

Monte Carlo simulation

What it is

Monte Carlo simulation is a computational technique that uses random sampling and statistical modeling to simulate a range of possible outcomes. Running numerous simulations with different random inputs provides a probability distribution of potential results. This provides a more realistic view of potential outcomes.

Example use case

• Risk management: Since financial markets and economic factors are inherently uncertain, instead of using single-point estimates for variables like interest rates or stock prices, Monte Carlo simulations use probability distributions (e.g., normal, log-normal, uniform) to represent the range of possible values and their likelihood.

Matt McDonald from KBRA spoke a bit about this during his Data Science Hangout. You can also check out KBRA’s customer spotlight.

Code snippet

There are several Python packages that can help you perform Monte Carlo simulations, such as monaco and pandas-montecarlo (for pandas DataFrames). Sometimes, it’s easier to write your own function with pandas and numpy. Below is Python code that defines a monte_carlo_simulationfunction that simulates the potential growth of an investment over a specified number of years using a Monte Carlo method. The function takes the initial investment, the investment period in years, the number of simulations to run, the expected annual return, and the annual volatility as inputs. It then calculates the portfolio value at the end of each year for each simulation as the output.

It can be easier to analyze Monte Carlo simulations visually, such as in this Shiny app.