Regression analysis in financial modelling is used to quantify the relationship between variables and forecast the future behaviour of this relationship. It fits into any setting where two or more variables might or might not be correlated.
Regression analysis helps in better strategic planning for a company’s financial future. For instance, regression analysis enables better predictions and risk management in investment decisions based on economic indicators and stock prices.
Here’s a look into the fundamentals of regression analysis and its applications in financial analysis and modelling.
What Is Regression Analysis?
A statistical method, regression analysis determines the relationship between a dependent variable and two or more independent variables.
The dependent variable is what you try to predict; it is the variable being measured and tested. In many cases, the dependent variable is also known as the ‘response variable’.
Independent variables are the ones which can be changed. and directly impact the dependent variables.
The three primary types of regression analysis methods are simple linear, nonlinear, and multiple linear. Linear regression models are the most commonly used in financial analysis and modelling. However, when working with complex data, nonlinear models are more useful because the variables impact each other nonlinearly.
Applications in Financial Analysis and Modelling
Regression analysis is closely associated with the capital asset pricing model (CAPM). This model determines whether an investment in a stock is profitable or whether the return on an asset is worth the investment. While this is one specific example, regression analysis is widely used in several other aspects of financial analysis and modelling.
Let’s look at some more applications:
-
Interest rate sensitivity analysis
Regression analysis is extensively applied in the fixed-income market. The statistical technique comes in handy while assessing the sensitivity of bond rates with alterations in the interest rate, including convexity and duration.
-
Credit risk assessment
Investors use regression analysis to assess credit risk. Regression analysis helps assess the credit risk associated with fixed-income instruments such as corporate bonds.
-
Stock price prediction
Financial analysts can predict future stock prices with regression models and analysis. This requires historical price data and other relevant variables, such as market indicators, company-specific information and data, and trading volume.
-
Portfolio management, risk assessment and asset allocation
Portfolio Managers use regression analysis to determine the optimal combination of assets, including bonds, stocks and commodities, to reach specific investment objectives. They analyse historical data to estimate the return and risk profiles of various portfolios.
-
Economic and market forecasting
Analysts can use regression analysis to forecast trends, such as stock market returns and prices, interest rates, and exchange rates. The models can also analyse the impact of different economic variables on financial markets.
-
Econometric analysis
Regression analysis is a fundamental tool for understanding the relationship between macroeconomic indicators and various financial variables. Such econometric analysis drives informed financial and investment decisions.
-
Credit scoring
Regression analysis is frequently used to create credit scoring models. These models help assess a borrower’s creditworthiness based on credit history, debt, and income.
How to Run a Regression Analysis?
Here’s a quick rundown of the general steps involved in running a regression analysis:
Step 1: Form your hypothesis
Select your variables, gather relevant data and form a hypothesis about their relationship.
Step 2: Chart your data
MS Excel is the go-to spreadsheet software for creating charts and visualising the correlation between the variables.
Step 3: Analyse the results
Examining the chart will reveal the historical relationship between the two data sets and help you predict the model’s future behaviour.
Regression Analysis Tools
Microsoft Excel
The SLOPE and FORECAST functions in Excel are most commonly used; while the SLOPE function returns the slope of the linear regression line, the FORECAST function uses existing values to predict a future value.
For instance, the SLOPE function can calculate a stock’s sensitivity to market movements (beta coefficient). Likewise, the FORECAST function is useful when determining how changes in specific business drivers will affect future expenses or revenue.
R and Python
R and Python are powerful programming languages that have gained popularity for running regression analysis. You can use relevant packages and functions in Python or R to fit your model to the training set.
For example, Python’s LinearRegression class and the lm() function in R can be used to fit a linear regression model. Henceforth, you evaluate your model’s validity using various metrics and tests, then interpret the results and visualise your data and model.
Conclusion
Businesses are implementing regression analysis in financial analysis and modelling to find critical trends and valuable insights from a huge volume of data. The results of this analysis shape strategic business decisions impacting an organisation’s current and future financial health. It also helps boost performance and business efficiency significantly.
The WallStreet School offers an online course and classroom bootcamp coaching in financial modelling and valuations. Enrol today to develop an in-depth knowledge of financial modelling and valuation and get job-ready with complete placement assistance.
Browse through The WallStreet School or contact us via phone (+91-9953729651) or email for more information.
FAQs
- What are the major uses of regression analysis?
The major uses of regression analysis are:
- Trend forecasting
- Forecasting an effect
- Ascertaining the strength of predictors
- What is the formula of regression?
The equation of a linear regression line is y=a+bx.
Here, y is the dependent variable, and x is the explanatory variable. ‘a’ is the intercept (the value of y when x=0), and b is the slope of the line.
- Are there limitations of regression analysis?
Yes, regression analysis has limitations that hinder its applicability and accuracy for some data sets. Regression analysis is sensitive to multicollinearity, which can impact the precision and stability of coefficients.
