This course provides a practical and analytical foundation in probability trees and conditional expectations, with a focus on their applications in investment analysis and financial decision-making. Participants will learn to calculate key statistical measures (expected values, variances, and standard deviations), structure investment problems using probability trees, and update probabilities using Bayes’ formula. Through case-based examples, exercises, and visual tools, learners will gain the quantitative reasoning skills required for dynamic investment environments.
Course Objectives:
By the end of this course, learners will be able to:
- Understand and compute expected values, variances, and standard deviations in probabilistic scenarios.
- Represent multi-stage investment problems as probability trees.
- Apply conditional expectations to make data-informed investment decisions.
- Use Bayes’ formula to update probabilities and improve forecasting accuracy.
- Interpret the results in a financial context to evaluate investment risks and returns.
Course Features
- Lectures 19
- Quiz 0
- Duration 6 weeks
- Skill level Beginner
- Language English
- Students 26
- Certificate No
- Assessments Yes
- 6 Sections
- 19 Lessons
- 6 Weeks
Expand all sectionsCollapse all sections
- Module 1: Introduction to Probability in Finance3
- Module 2: Expected Value, Variance, and Standard Deviation4
- Module 3: Introduction to Probability Trees3
- Module 4: Conditional Expectations in Investment Decisions3
- Module 5: Bayes’ Formula and Posterior Probabilities3
- Module 6: Application and Integration3
Target audiences
- CFA candidates and finance students
- Investment professionals
- Analysts and risk managers
- Anyone seeking to enhance their quantitative skills in finance
