What is Regression Analysis?
Regression analysis is a way of using statistical methods to explain the cause of a “Causation”. It can be used to explain why a population changed over time, and identify the change in behavior as something that can be modified. Regression analysis is useful for almost any field which has some type of quantitative data, but it is often most relevant for those fields that have discrete quantitative outcomes such as economics, finance, marketing, data mining etc. Regression analysis in finance is used to examine the patterns in data that may be discovered with a predictive model. It is possible to use regression analysis in financial markets such as stocks, options and futures to identify trends and predict when certain events will happen. Some examples of situations when this method can be applied are:
The market has seen increasing volatility over time;
A company’s stock price has gone up but its earnings are low;
The stock price increased after an acquisition or merger was announced.
This article will explore the basics of regression analysis by explaining what it is, how it works, and what software can be used to carry out this task.
The History of Regression Analysis
Regression analysis was originally developed by a French economist, Simon Lefebvre, in 1948. He observed that the relationship between a dependent variable and a set of explanatory variables was always linear. The idea became popular with the work of J.D. Edwards (Edwards (1954)) and W.F. Gaus (Gaus (1962). Edwards first introduced regression analysis to colleges and universities as an adjunct to calculus classes; he also found it useful for econometric research, especially on agricultural data. Gaus applied it to the field of meteorology. He also developed what is known as “graphic correlation” which allowed him to predict the future weather based on past data.
The first use of regression analysis in economics was done by Milton Friedman (Friedman (1957)) and A.C. Smith (Smith (1960)). They used regression analysis for time series econometrics, discovering that production fluctuates with respect to month-to-month time lags, but not with respect to seasonal factors, nor with respect to longer term shifts in demand patterns. They also found that the business cycle can be analyzed using regression analysis; their work helped to create the “stochastic” method, which attempts to forecast economic variables over time.
The application of regression analysis in the field of financial science was done by Peter Bernstein (Bernstein (1977)) who examined empirical data of stock returns on inflation, interest rates and other macroeconomic variables. He discovered that historical relationships between variables may not always be present in the future. Take for example the relationship between interest rates and large capital gains – these two tended to go together in past data but they may not necessarily continue this pattern into the future.
Which approach should be used?
One important component of regression analysis is that it can be applied to different situations depending on the situation’s specific characteristics. It all depends on the type of data collected, the nature of the relationship between variables, and whether or not you use multiple regression or not. Multiple regression is sometimes referred to as “ordinary” linear regressions because it is simple linear regression with one independent variable.
There are different ways to use regression analysis based on these three factors (one variable, multiple variables allowed; simple linear vs least squares least absolute deviations; use or not use transformations). The method to apply depends on what data you have and what your objective is in using the results. The simplest regression analysis is called ordinary multiple regression. It only uses the one independent variable, but it does not take into account any other variables. Most of the time, this method is used in financial research to estimate the effects of other factors on the dependent variable, for example to see how changes in interest rates affect company profits.
The second method of using regression analysis that you can use is coupled or “weighted” multiple regression. This type assumes that there are two or more explanatory variables that may be affecting the dependent variable. The model will take into account weights based on how significant each explanatory variable is relative to another one. This method tries to fit the data even if some of the variables are not significant for the purpose of the research. This method is mostly used in academia because it allows researchers to be more thorough in their work and they can take into account all variables that they think may affect a particular outcome.
The third type of regression analysis is simultaneous or “non-linear” multiple regression. This type allows you to take into account in your model any number of variables, but when you run this type, it assumes that there are no relationships between these variables when in fact they may be highly correlated with one another.
The simplest type of regression analysis you can do is ordinary linear regression. Linear regression uses ordinary least squares, which means that the formula used to estimate the model is y = mx + e where e is the error term. Linear regression also assumes that there are no correlations between any of the independent variables.
Linear Regression in Finance
Linear Regression in finance uses the relationship between x and y to predict future values of y using past data on x. The purpose of linear regression in financial research is generally to model market prices, stock prices, or other financial variables for future use. Linear regression can be used to estimate future values of financial variables like stock prices, market indexes, commodity prices, and interest rates under different future scenarios. Linear regression is often used to forecast changes in stock prices or indices based on interest rate changes or monetary policy. During this type of analysis, the dependent variable (y) is measured as an index – for example, an index of the S&P 500 stock index would be dependent variable (y). The independent variables include dummies for each time period dt dt dt dt d t d t d t . Linear regression can deal with nonlinear variables . It is quite common to run linear regression analysis of stock prices using linear regression coefficients for time periods varying from long to short, so there are three different types of variables- short, intermediate, and long.
Applications of Linear Regression in Finance
Linear Regression in finance uses the relationship between x and y to predict future values of y using past data on x. The purpose of linear regression in financial research is generally to model market prices, stock prices, or other financial variables for future use. Linear regression can be used to estimate future values of financial variables like stock prices, market indexes, commodity prices, and interest rates under different future scenarios. Linear regression is often used to forecast changes in stock prices or indices based on interest rate changes or monetary policy. During this type of analysis, the dependent variable (y) is measured as an index – for example, an index of the S&P 500 stock index would be dependent variable (y). The independent variables include dummies for each time period t . Linear regression can deal with non-linear variables . It is quite common to run linear regression analysis of stock prices using linear regression coefficients for time periods varying from long to short, so there are three different types of variables- short, intermediate, and long.
The employment of econometric tools like regression analysis have over time proven to be most helpful in the field of finance as it has allowed financial analysts to be able to make near accurate predictions, analyse trends and also make future predictions based on inferences drawn from analysed datasets. To take advantage of this econometric and financial tool, business managers must be up to date on the latest data analysis tools and methods. Where they are lacking in knowledge and in the use of tools such as Regression analysis, business managers can hire freelance Data and financial analysts from Fintalent, the hiring and collaboration platform for tier-1 Strategy and M&A professionals to meet all their data analysis requirements.
