Introduction
Portfolio Risk Optimization through OLS Regression, is a short-duration course that benefits the finance students, financial researchers, financial analysts, brokers, and investors to understand how the impact of regression techniques can benefit them in understanding the statistical long -term relationships between the accounting information and the stock prices. The use of OLS regression tool is common in setting almost all financial relationships with market-driven information like stock prices. A small exercise is also planned for the viewers to practice and learn the aspects of the captioned topic faster.
Objective
This programme will :
- Introduce the learners to how the aggregate financials provide efficient information for the stock price prediction by the use of the regression tool
- Enable the participants to know how to determine whether the underline variable is truly defining the stock price movements
- Help the students to learn about the process by which OLS (ordinary least square) regression help in establishing the relationships between the stock prices and the internal aggregate accounting variables
- Facilitate the participants to learn How the risk of “regression residuals” can be used for portfolio risk optimization purposes
Benefits
The scope of this program is immense since, through audio-lectures and slides, the viewers can learn to use the companies's historical financial statements in managing their long-term portfolio risks. The method will use the optimization tools which are available in Spreadsheets now-a -days.
Topics Covered
- Portfolio risk optimization
- OLS regression
- Regression coefficients
- Regression error/residual risk
- Test of Normality, Heteroskedasticity, Autocorrelation, and Multicollinearity
- Portfolio optimization (2 Asset case)
Intended Participants
- Economics Honours Students
- Statistics Honours Students
- Other Quantitative Discipline Students
- Anyone having a basic knowledge of statistics and wanting to know how to optimize their portfolio risk
Summary
- 3 Videos
- 25 mins of Content(approx)
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