This repository stores the Case Studies I carried out as a part of the Finance & Econometrics Course in the curriculum of B.Tech. Data Science & Engineering at Manipal Institute of Technology.
A brief description of the Case Studies:
The object of this Case Study was to analyze the relationship between two numeric variables. This was achieved through the calculation of the Correlation Matrix and visualization through Scatter Plots.
This Case Study was carried out to predict the values of one dependent numeric variable based on multiple independent numeric variables. This was achieved by building a model using the Principle of Least Squares and analyzing the Coefficient of Determination (R2) and the Statistical Significance of the independent variables using the p-values.
In this Case Study, we identify and confirm the presence of Heteroscedasticity (inconsistent variance of errors) by conducting the White Test and testing our hypothesis.
The objective of this Case Study was to identify and confirm the presence of Multicollinearity (high intercorrelations among two or more independent variables). This was achieved by building a Multiple Regression model and taking inference from the Coefficient of Determination (R2), Correlation Matrix, Scatter Plots, and Statistical Significance of independent variables.
This Case Study was carried out to identify and confirm the presence of Autocorrelation (degree of correlation between the same variables across different observations in the data). This was achieved by conducting the Durbin-Watson test.
In this Case Study, we deal with Dummy Variables, which allows for sophisticated modeling of data by incorporating qualitative information into regression analysis.
The objective of this Case Study was to identify and analyze the effect of a Structural Break in the dataset indicating a period of significant change using the Chow Test.
This Case Study was carried out to analyze and identify the best regression model built on Simultaneous Equations containing Exogenous and Endogenous variables by lagging them and using Two Side Least Squares.
In this Case Study, we study and analyze the results of the Fixed Effect and Random Effect models and choose the better of the two by comparing them using the Hausman Test.
Based on Return and Risk, we analyze the pros and cons of investing in two stocks and choose the better of the two in this Case Study. We compare the two stocks using the following parameters: Average Daily Returns, Annualized Returns, Standard Deviation on Returns, Annualized Standard Deviation on Returns, Variance on Returns, Annualized Variance on Returns, and Coefficient of Variation (CV).
I would like to thank my Professors: Dr. Suman Chakraborty and Dr. Venkatamuni R Reddy from the Department of Commerce, Manipal Academy of Higher Education, for guiding me throughout.
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