Understanding: Linear vs Non Linear Regression

Trading Education

Apr 9, 2024

Understanding: Linear vs Non Linear Regression


Across diverse fields ranging from finance to epidemiology, regression empowers analysts and researchers to discern patterns, predict outcomes, and make informed decisions. In this exploration, we delve into the intricacies of regression analysis, elucidating its fundamental principles, applications, and real-life examples.

What is Regression?

Regression analysis, a statistical method employed across finance, investing, and various disciplines, serves as a powerful tool in uncovering relationships between variables. At its core, regression seeks to discern the strength and nature of the connection between a dependent variable, typically denoted as Y, and a series of other variables termed independent variables. Within the realm of regression, two primary forms exist: simple linear regression and nonlinear regression. The former, epitomized by the familiar equation y = mx + b, establishes a linear relationship between two variables through a line of best fit, while the latter ventures beyond linearity, encompassing curved relationships between variables. 

In the pursuit of modeling relationships between variables, the overarching objective of regression is to minimize the sum of squares, a measure that gauges the deviation of observed Y values from the predicted values derived from the regression model. Nonlinear regression, distinguished by its utilization of various mathematical functions like logarithmic, trigonometric, and exponential functions, among others, offers a more flexible framework to capture the complexities of real-world data. 

Regression Utility

A common real-life example of linear regression is in the field of finance, where it is used to model the relationship between a company's stock price and various fundamental factors such as earnings per share, revenue, and market capitalization. By fitting a linear regression model to historical data, analysts can estimate how changes in these fundamental factors impact the stock price, allowing for better investment decision-making. 

On the other hand, nonlinear regression finds application in scenarios where the relationship between variables is not adequately captured by a straight line. For instance, in population growth modeling, nonlinear regression is often employed to predict how population size changes over time. By fitting a nonlinear model to historical population data, demographers can forecast future population trends, essential for urban planning, resource allocation, and policy formulation.

Regression within Trading

Regression's applicability in trading and finance is multifaceted. Linear regression, the more conventional form, finds extensive use in asset valuation and discerning correlations between factors such as commodity prices and stock values. Beyond its foundational role in asset valuation, regression analysis assists in predicting sales figures based on diverse conditions like economic indicators, weather patterns, and historical sales data. 

Moreover, regression's utility extends into the realm of econometrics, where it serves as a cornerstone for analyzing economic data. By scrutinizing associations between variables such as income and consumption, economists leverage regression to assess the statistical significance of relationships and gauge the strength of connections within economic models. 

In practice, regression underpins widely-used financial models like the Capital Asset Pricing Model (CAPM), wherein stock returns are regressed against market returns to derive a beta coefficient, indicative of the stock's risk relative to the market. Furthermore, regression enables the incorporation of multiple explanatory variables, as evident in the Fama-French factors, enhancing the robustness and explanatory power of financial models. 


In essence, regression analysis serves as an indispensable cornerstone within the arsenal of tools wielded by both finance professionals and researchers. It provides a structured and rigorous methodology for unraveling intricate relationships buried within datasets, thereby facilitating the prediction of outcomes and furnishing invaluable insights to guide decision-making across a diverse array of domains.