Lessons
- CFA Level 2: Portfolio Management – Introduction
- Mean-Variance Analysis Assumptions
- Expected Return and Variance for a Two Asset Portfolio
- The Minimum Variance Frontier & Efficient Frontier
- Diversification Benefits
- The Capital Allocation Line – Introducing the Risk-free Asset
- The Capital Market Line
- CAPM & the SML
- Adding an Asset to a Portfolio – Improving the Minimum Variance Frontier
- The Market Model for a Security’s Returns
- Adjusted and Unadjusted Beta
- Multifactor Models
- Arbitrage Portfolio Theory (APT) – A Multifactor Macroeconomic Model
- Risk Factors and Tracking Portfolios
- Markowitz, MPT, and Market Efficiency
- International Capital Market Integration
- Domestic CAPM and Extended CAPM
- Changes in Real Exchange Rates
- International CAPM (ICAPM) - Beyond Extended CAPM
- Measuring Currency Exposure
- Company Stock Value Responses to Changes in Real Exchange Rates
- ICAPM vs. Domestic CAPM
- The J-Curve – Impact of Exchange Rate Changes on National Economies
- Moving Exchange Rates and Equity Markets
- Impacts of Market Segmentation on ICAPM
- Justifying Active Portfolio Management
- The Treynor-Black Model
- Portfolio Management Process
- The Investor Policy Statement
Impacts of Market Segmentation on ICAPM
- The ICAPM is used by some investors to value any and all risky assets on a global basis.
- The ICAPM is only valid if international capital markets are integrated.
- If international capital markets are segmented, then assets with the same risk characteristics will be priced differently in different national markets.
- When barriers to capital market integration cause investors to avoid the balanced world market portfolio, higher allocations will be made to specific countries and specific securities, causing inefficient asset pricing.
- This inefficiency makes an argument for active portfolio management.
Data Science in Finance: 9-Book Bundle
Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.
What's Included:
- Getting Started with R
- R Programming for Data Science
- Data Visualization with R
- Financial Time Series Analysis with R
- Quantitative Trading Strategies with R
- Derivatives with R
- Credit Risk Modelling With R
- Python for Data Science
- Machine Learning in Finance using Python
Each book includes PDFs, explanations, instructions, data files, and R code for all examples.
Get the Bundle for $29 (Regular $57)JOIN 30,000 DATA PROFESSIONALS
Free Guides - Getting Started with R and Python
Enter your name and email address below and we will email you the guides for R programming and Python.