Financial Analysis. Lesson 26. Advanced Portfolio Theory and Optimization
Financial Analysis. Lesson 26. Advanced Portfolio Theory and Optimization
Portfolio optimization aims to maximize returns for a given risk level.
Mean-variance optimization balances risk and return using asset variance and covariance.
Efficient frontier represents optimal portfolios offering highest return for risk.
Capital Market Line (CML) shows risk-return combinations in a perfectly efficient market.
Security Market Line (SML) illustrates expected returns for assets at various risk levels.
Sharpe ratio measures excess return per unit of portfolio risk.
Risk-adjusted return considers both returns and risks to assess performance.
Alpha generation represents earning returns above a portfolio’s benchmark.
Modern portfolio theory (MPT) emphasizes diversification to reduce unsystematic risk.
Systematic risk is unavoidable market-wide risk affecting all asset classes.
Unsystematic risk is asset-specific risk reduced through diversification.
Beta quantifies an asset’s volatility relative to market risk levels.
Tactical asset allocation involves making short-term shifts in portfolio allocations.
Strategic asset allocation sets long-term portfolio targets and periodic rebalancing.
Factor investing targets specific attributes like value, growth, or size.
Momentum investing capitalizes on ongoing trends in asset price movements.
Volatility drag reduces portfolio returns due to fluctuating asset prices.
Monte Carlo simulation generates possible outcomes based on probabilistic models.
Tracking error measures deviation of portfolio performance from benchmark.
Active management seeks to outperform benchmarks through tactical investment decisions.
Passive management aims to replicate benchmark returns with minimal trading.
Core-satellite strategy combines passive core investments with actively managed satellite holdings.
Smart beta adjusts portfolio weights based on alternative factors, not market cap.
Risk parity allocates capital so each asset class contributes equally to risk.
Currency hedging reduces the impact of foreign exchange risk on portfolios.
Black-Litterman model integrates market views to improve asset allocation decisions.
Optimized asset allocation seeks efficient distribution based on return expectations.
Drawdown is the decline from peak to lowest portfolio value.
Maximum drawdown measures worst peak-to-trough loss over a period.
Value at risk (VaR) estimates maximum loss within a given confidence level.
Technical Examples:
Mean-variance optimization helps investors balance risk and return in portfolios.
Tracking error is essential for assessing portfolio deviation from benchmarks.
Core-satellite strategy offers diversification by mixing passive and active investments.