This course covers all readings of Quantitative Methods (QM) of Level 2 of the CFA program (CFA Institute, USA). The course includes video lectures for readings on Correlation & Regression, Multiple Regression, Time Series Analysis and Probabilistic Approaches (Simulations, Decision Trees and Scenario Analysis). The course also includes solved questions of the QM module in the form of instructor-led video lectures.
"Learn the concepts of Quantitative Methods in this comprehensive course"
"Be prepared for about 10% of the Level 2 exam in under 6 hours!"
This course is ideal for you if you are enrolled in Level 2 of the CFA program. However, it can also be taken up by those doing majors in Finance or working in the investment management industry.
This video gives an introduction to Correlation and Regression analysis and discusses the concept of covariance.
Calculating and interpreting the correlation coefficient, r.
Scatter plots and correlation coefficient
Testing the significance of correlation coefficient
This video discusses the regression model and the concept of sum of squared errors.
Subtopics covered are- Assumptions of linear regression, correlation coefficients, R-square and Standard of Error Estimate
Testing the significance of the independent variable in explaining the variation in the dependent variable
Calculating the value and confidence interval of the dependent variable
Introduction to ANOVA and the components of total variation
This lecture discusses ANOVA table with an example.
In this lecture, we'll learn about F-statistic and the limitations of regression analysis.
This lecture given an introduction to Multiple regression and discusses the interpretation of the result of multiple regression analysis.
This lecture shows 2 ways of testing for the statistical significance of the independent variable in explaining the variation in dependent variable- confidence interval and t-test.
In this video, we look at some different cases for conducting the t-test.
Here, we'll learn about p-value and about predicting the value of the dependent variable.
In this video, we will discuss ANOVA (Analysis of Variance), R-square and adjusted R-square.
This video discusses F-statistic used to test the utility of the model as a whole.
Here, we look at a comprehensive example of Multiple Regression.
In this part, we'll learn how to do regression analysis with dummy variables.
This video starts the discussion on issues in regression analysis, and explains the concept of heteroskedasticity.
In this video, we continue with the discussion on heteroskedasticity.
This video discusses autocorrelation, also known as serial correlation.
This video discusses the concept of multicollinearity.
This video given an introduction to time series and discusses a linear trend model and a log-linear trend model.
Linear trend model versus Log-linear trend model
Here, we discuss an autoregressive model.
Here, we discuss about the mean-reverting level of a time series and the in-sample & out-of-sample forecasts.
This video discusses the concept of a covariance stationary time series. We look into the three conditions of covariance stationarity and Dicky Fuller test used to detect if a time series is covariance stationary or not.
Here, we see how we can correct a time series if it is not covariance stationary, which is by first differencing.
This video discusses seasonality in an autoregressive model.
This video discusses Autoregressive Conditional Heteroskedasticity (ARCH) models.
In this last part of Time series, we discuss the concept of cointegration.
We look at simulation as a risk assessment tool.
We continue with the discussion on simulation analysis.
I have done MBA in Finance, post which I worked for two years as an equity research analyst for a multinational finance company. I have cleared all 3 levels of CFA and have also taught a course on Business Analysis and Valuation at a Business school. Last year, I started Infinity Institute of Professional Studies to pursue my passion for teaching and sharing my knowledge with millions across the globe.