Last updated on June 19th, 2025 at 09:38 am
Reading Time: 4 minutesEver wondered why financial forecasts sometimes miss the mark, even with mountains of historical data behind them? Or why two companies in the same sector can post completely different growth numbers, despite operating under similar economic conditions?
If those questions sound familiar, you’re already thinking like a financial analyst.
That’s exactly what multiple regression analysis helps you understand. That’s where multiple regression analysis comes in. When I first started using it in my financial models, it completely changed the way I looked at numbers. Suddenly, patterns made more sense, outliers became easier to explain, and I wasn’t just making educated guesses. I was building smarter forecasts.
And if you’re planning to go deeper into this area, enrolling in a CFA course can sharpen your understanding further.
So how does what is multiple regression analysis fit into real-world financial modelling? Why is it such a game-changer? Let’s back down a bit and start with the basics.
What is Multiple Regression Analysis?
Multiple regression analysis is a statistical method used to figure out how several independent variables (inputs) influence one dependent variable (output). Unlike simple regression, which focuses on just one factor, multiple regression looks at how a bunch of variables work together to affect an outcome.
Think about analyzing stock prices. You know it’s not just one thing that drives performance. Interest rates, earnings, market sentiment, all of these play a role. Multiple regression helps you measure the impact of each of them, together.
Where It Fits in Financial Modelling:
You’ll see multiple regression popping up all over financial modelling, especially in areas like:
- Stock valuation: Estimating returns based on market and company-specific metrics.
- Credit risk assessment: Predicting default probability using borrower characteristics.
- Revenue forecasting: Accounting for multiple business drivers to get future numbers.
- Investment portfolio optimisation: Identifying which factors affect returns on investments.
If you’re just getting started, brushing up on linear regression first is a smart move. There’s a good explainer video out there that walks through the basics—it’s worth the quick watch.
Multiple Regression Analysis Formula
The general formula for multiple regression analysis looks like:
Y = β0+ β1X1+ β2X2 + … + βnXn +
Here’s a breakdown of the formula:
- Y = Dependent variable (e.g., stock price, revenue)
- β0 = Intercept (constant term)
- β1, β2,…βn = Regression coefficients (showing impact of each independent variable)
- X1, X2,…Xn = Independent variables (factors affecting Y)
- ϵ = Error term (unexplained variance)
When you plug in your data and run the model, you can see how each factor contributes to the final outcome and make data-driven predictions.
Multiple Regression Analysis Example
Let’s say you’re an investment analyst analysing a company’s stock price. You believe three variables affect stock performance: earnings per share (EPS), interest rates, and market sentiment.
Your regression equation might look like this:
Stock Price = β0 + β1(EPS) + β2(Interest Rates) + β3(Market Sentiment) + ϵ
By running this regression on historical data, you can determine how each factor influences stock price fluctuations and make informed investment decisions.
| Variable | Type | Example Value |
| Stock Price (Y) | Dependent | $100 |
| EPS (X1) | Independent | $5.00 |
| Interest Rates (X2) | Independent | 2.50% |
| Market Sentiment (X3) | Independent | 75% (positive) |
Once you run the model, you can see how sensitive the stock price is to changes in each variable, and you can use those insights to make smarter forecasts.
How to Apply Multiple Regression Analysis in Financial Modeling
Understanding the multiple regression analysis formula is one thing, but putting it to work is another. Here’s how to actually use multiple regression in your financial models:
1. Pick the Right Variables
- Start with a clear question: What are the main factors influencing your outcome?
- Make sure your inputs are grounded in financial logic, not just data availability.
2. Collect Data Clean It
- Use historical financial data from reliable sources.
- Remove outliers and missing values to ensure accuracy.
3. Run the Regression Model
- Use statistical tools like Excel, Python, or R for computation.
- Once you’ve set it up, check things like R-squared values and p-values to ensure your results hold water.
4. Interpret the Results and Make Predictions
- If your variables are statistically significant, great; you can use them for forecasting.
- Remember, markets change. Hence, adjust models periodically to incorporate new data and improve accuracy.
Advantages and Limitations of Multiple Regression Analysis
| Advantages | Limitations |
| Helps you make evidence-based financial decisions | Requires large, clean datasets for accurate results |
| Accounts for multiple variables influencing an outcome | Can suffer from multicollinearity (high correlation between independent variables) |
| Improves accuracy of forecasts and analysis in investment and risk analysis | May miss factors like omitted not included in the model or errors in data impacting results |
No model is perfect. But when used correctly, multiple regression analysis can be an incredibly powerful tool. Just make sure you’re not blindly trusting the numbers without context.
Additional Resources on Multiple Regression Analysis
This post is just a primer. If you’re serious about mastering multiple regression analysis, here are some additional resources I recommend checking out:
And if you’re more of a visual learner, these videos are great too:
Conclusion
Financial modeling is no longer just about spreadsheets. It is about connecting the dots to make data-backed decisions that drive investment and business strategy. Whether you’re in investment banking, risk management, or corporate finance, mastering what is multiple regression analysis can give you a head start.
If you’re serious about refining your expertise, enrolling in the Chartered Financial Analyst (CFA) programme is one of the best ways to gain deep insights into financial modelling, quantitative analysis, and risk assessment.
FAQs
- What’s the main use of multiple regression analysis in finance?
It’s used to model relationships between financial outcomes and multiple influencing factors—like predicting stock performance or evaluating credit risk.
- What is a multiple regression analysis example in finance?
A common example is predicting stock prices based on earnings per share, interest rates, and market sentiment.
- How do I interpret the coefficients in a multiple regression analysis?
Each coefficient shows how a unit change in that variable affects the dependent variable—assuming everything else stays the same.
- What software can I use for multiple regression analysis?
Excel works well for small datasets. For more complex models, Python (with pandas, statsmodels) or R is great. Some analysts also use platforms like Bloomberg Terminal.
- How does multiple regression differ from simple regression?
Simple regression examines one independent variable, while multiple regression analyses two or more variables affecting the outcome. The latter gives you a more complete picture.
- Is multiple regression analysis part of the CFA curriculum?
Yes, the CFA course covers multiple regression analysis extensively, particularly in the quantitative analysis and financial modelling sections.