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Regression Analysis in Financial Modelling

Updated on June 6, 2024
Article byPallabi Banerjee
Edited byAshish Kumar Srivastav
Reviewed byDheeraj Vaidya, CFA, FRM

Introduction

Regression Analysis in financial modelling is an important branch of study because it explains how one variable can be used to explain the effects or changes in another variable.  It allows for the testing of situations or relationships between the two by evaluating the strength of the relationship between them.

Regression Analysis in Financial Modelling

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The variables in this analysis are one or more than one independent and one dependent variable. Thus, there can be simple linear, non-linear, or multiple linear regression analysis that is used in financial analysis and modeling. Through this method, we can easily identify those factors that matter the most in our study and the extent of their influence on the models that we create. To learn more about financial modeling, the best financial modeling courses will offer good insight.

Key Takeaways

  • Regression Analysis in financial modeling is a method to explain the relationship between one or more independent variables and one variable that is dependent on it.
  • The analysis can show a relationship that can be linear, non-linear, or multiple linear, in case the independent variables are more than one.
  • The outcome will depend on the strength of the relationship and is treated as a very powerful tool in the field of statistics that is widely used in the financial market.

Applications In Financial Modeling

Since the main aim of financial modeling is to predict or forecast future opportunities and events that will impact and help in making business decisions, by using historical and current financial data, regression analysis in financial modeling can provide valuable insights because it follows the method of deriving the effect of changes in independent variables on dependent ones.

We can point out various cases where this concept of regression analysis in financial modeling is successfully implemented, some of which are given below:

  • Credit risk assessment – Since the analysis involves the relationship between two or more variables and identifying the strength of their relationship, a modeler can use it to assess the credit risk of the company. Let us elaborate on it a bit. A great method of raising funds for corporates is through bonds, which are considered a fixed-income instrument. It is a cheap and easy method as compared to bank loans. However, by using financial modeling, companies can also assess their creditworthiness in the market. Investors can use this data to relate the payment obligations on bonds issued and the chances of default by the issuer by taking the help of regression analysis.
  • Sensitivity of interest rates – The financial market widely speaks about this application of regression analysis while doing the financial modeling of a business. Here, too, we can refer to fixed-income instruments like bonds that corporations issue for long-term financing. If you happen to notice the changes that the bond market faces when there are interest rate fluctuations, you will see that when interest rates rise, the prices of the existing bonds fall due to the sole reason that the newly issued bonds will earn better returns now. The opposite will happen in case of a fall in interest rates. During regression analysis in financial modeling, analysts can relate the prices of corporate bonds with the market rates using regression to understand how and up to what extent the rates can affect the bond prices and how this, in turn, will affect the company’s financials.
  •  Stock prices – You can also successfully use regression analysis to predict future movement of stock prices by using past stock price data along with company and sector performance, the various technical indicators and analysis of fundamental data, assessment of trading volumes, etc.
  • Portfolio management – Risk analysts, portfolio managers, and even investors often use regression to evaluate the current and future effect of the company’s assets on its performance and growth. In this context, financial models can provide the data required to compare and identify the relationship.
  • Forecast market conditions – Typically, what do you aim for when you design a financial model for your company or client company? Is it not about finding out how the current situation of the company will work out in the days to come with reference to market demand and supply, customer taste and preference, business revenue and cost levels, etc? Therefore, it is not hard to understand that regression is very useful here because, once again, we come across a situation where we analyze one or more independent variables to evaluate their effect on dependent variables.

Regression Analysis Tools Used In Financial Modeling

Regression analysis in financial modeling uses many tools that successfully derive an outcome used to make relevant decisions. Some of the tools are as mentioned below:

  • Excel – This remains the most commonly used tool for the analysis. Within Excel, the SLOPE function and the FORECAST functions are widely used. Within the linear regression line, the SLOPE function derives the slope value, and the FORECAST function gets the future values from the current ones.
  • Python and R – These are two programming languages that have gained wide popularity over the years. Both offer different functions and packages.

However, in the world of continuous advancement of technology, different tools have been entering the market and gaining importance because of their automated process that saves time and helps in running regression tests. Given below are some of them:

  • Rainforest QA – This is a cloud-based platform that offers a combination of AI and QA, with automatic maintenance and test creation techniques to ensure accuracy.
  • TestRigor – It is a very fast and low-maintenance software with easy-to-understand instructions.
  • Selenium – It creates strong regression- automation suites and tests that are browser-based and suitable for different environments.
  • IBM Rational Functional Tester – It is also an automated regression and functional testing tool. It can support many applications, like .Net, SAP, Siebel, Java, etc. It has a very simple process that is easy to visualize and edit using easy language and screenshots.
  • Watir – This tool is very suitable if you need to execute a regression test on the browser itself.
  • Leapwork – This is also an AI-powered testing tool that is automatic, very fast, and easy to maintain.
  • Tricentis Testim – This AI-powered tool meant for testing is equally useful in mobile applications and desktops. They can be customized as per requirement with auto-improvement features that require minimum maintenance. It has a high efficiency level that allows it to manage tests and applications easily.
  • Appsurify TestBrain – It is a very fast and cost-saving application that can detect changes and run tests only in those areas. It is also AI-powered.
  • Katalon Studio – It is a beginner-friendly regression testing platform. It does not require the user to learn coding. It has a flexible editor and is used across any type of application, be it mobile, desktop, web, etc., and requires very low or zero maintenance.
  • Micro Focus Unified Functional Testing – This is also an AI-powered regression testing tool that can be used across mobile, desktop, web, etc.

Apart from the above, there are other testing tools available on the market. You should be able to identify which is best for you.

How To Run A Regression Analysis?

Here, we learn about how to run the analysis while doing financial modeling and analysis.

  • Step 1 – The very first step that you should do is to form a hypothesis. For this purpose, you will need to gather enough data that is relevant. A hypothesis is a theory that we design based on a proper understanding of factors and variables related to the study and how they affect the outcome. Thus, to establish a relationship between different variables, it is important to have a hypothesis in place.
  • Step 2 – Next comes the step where you need to use some software or application that will help you to represent your ideas through charts and formulas. This visual representation will help in establishing the correlation between the variables and understanding and forming opinions.
  • Step 3 – Finally, it is important to analyze the outcome. Here, the charts and their explanation give a clear view of the situation and establish a relationship between the variables or two or more data sets that will reveal how the dependent variable derives its outcome from the independent ones. Such analysis will give you an idea about the future trends of the business, its performance, and prospects for growth.

Importance

Regression analysis holds an important place in financial modeling. Let us study why it has gained such wide acceptance over the years.

  • The main purpose of models is to get critical insight into future scenarios and trends that the business may encounter during the process of growth and expansion. For this, we use various valuable financial data like cost, revenue, profits, expenses, assets, liabilities, capital, and so on. But how do we establish the link or effect of the same on the company’s future performance? Regression analysis is a great way to do so.
  • From the above point, we can derive the fact that the regression analysis in financial modeling helps the management make comprehensive and useful business decisions that can boost the performance of both individual employees and the business as a whole. Only a proper analysis will help in tracking the result of strategic process implementation and changes, whether positive or negative, through comparison of different variables.
  • Regression is suitable for both complex and simple data. In the case of simple data, you can use linear analysis, which establishes a linear relationship. However, in the case of complex data, we can go for non-linear analysis, or in the case of multiple independent variables, we can use multiple regression analysis.
  • Another point to note is that since the relationship is between dependent and independent variables, you can always change the independent variable to analyze its effect on the dependent one. This gives us crucial information about the various aspects of the business.

Thus, the above are some points that speak about the importance of the concept of regression analysis in financial modeling.

Frequently Asked Questions (FAQs)

How to choose tools for regression analysis in financial modeling?

To select the correct tool, you should keep in mind the type of problem that you are trying to solve. The functions of the tool should match the requirements. However, the number and type of users are equally important. Keeping in mind the cost factor, if many users across the organization will use the tool, then it should be able to work with speed and accuracy.

What are the main uses of regression analysis in financial modeling?

The analysis has an essential part to play within the model. We use financial models to analyze trends and forecast future performance, as well as evaluate the effect of various strategies that management has implemented for the betterment of operational procedures. The regression analysis should be able to use variables to establish relevant relations between them and provide the outcome mentioned earlier with maximum accuracy.

What are the limitations of regression analysis in financial modeling?

The analysis has some limitations that affect its accuracy and productivity levels. It is sensitive to multicollinearity, which means that if there are multiple independent variables and they are related to each other, then the result of the regression may not be accurate. Therefore, all independent variables should be unrelated. There should also be normally distributed variables.

This has been a guide to Regression Analysis in Financial Modelling. Here, we explain its applications, tools, how to run it, and its importance. You can learn more about accounting from the articles below –

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