CFA Level 2 - Portfolio Management

Active management seeks to outperform a benchmark to add value. Choose a benchmark that fits the investment thesis, represents assets, and offers low-cost, verifiable weights.

Decompose value added into active asset allocation and securities selection by expressing portfolio and benchmark returns as weighted sums, then quantify each contribution.

The information ratio measures active return over active risk, showing how well a fund outperforms its benchmark; positive ratios signal outperformance, while negative ratios indicate underperformance.

Explore the correlation triangle, linking forecast, realized active return, and active weights, and assess information coefficient, transfer coefficient, and value added for portfolio implementation.

Explore the fundamental law of active management by applying mean-variance optimization to compute active weights, using active returns, active risk, variance, breadth and information coefficient to maximize portfolio outperformance.

Apply the fundamental law of active management, relating expected active return to information coefficient and the square root of breadth, with transfer coefficient one, shown via a four-security benchmark.

Apply the full fundamental law of active management to relate expected active return to the transfer coefficient, information coefficient, breadth, active risk, and correlation between optimal and actual active weights.

Explore ex-post performance measurement by linking actual active return to the conditional expected active return, and decompose variance into realized information coefficient and constraint-induced noise.

Compare active management strategies by stock selection and sector selection, showing how breadth, information coefficients, and constraints affect expected active returns and risk-adjusted performance.

Learn how exchange traded funds grew due to lower costs, transparency, and index-based investing, and how to assess ETF trading costs, creation, tracking error, and choice.

Explain how ETFs differ from mutual funds through the creation and redemption mechanisms and intraday trading, enabling lower costs and tax efficiency.

Explore how authorized participants create and redeem ETF shares by exchanging baskets of securities with the issuer in the primary market, while arbitrage keeps prices near fair value.

Arbitrage opportunities arise when the ETF trades at a discount to its underlying basket, as AP market makers buy the ETF and redeem it for basket securities with the issuer.

Explore how the ETF creation and redemption process shifts trading costs to the AP, with issuer fees and processing costs, while shielding non-transacting investors and enabling tax-efficient cost-basis management.

Explain how fixed income ETFs use cash creation and redemption to handle illiquid bonds, balancing liquidity, costs, and tax, and why leverage and commodity ETFs prefer cash creation for swaps.

Understand how etfs deliver on promises through low expense ratios, close index tracking, and low tax exposure, while offering prospectuses and marketing materials with clear structure, performance, and risk information.

Explore how ETF expense ratios compare to mutual funds, driven by index-based management, portfolio complexity, and scale, with typical fees well below traditional funds.

Examine ETF tracking error by comparing ETF and index returns, considering expense ratio, fees, sampling, and valuation differences, using rolling 12-month tracking differences.

Compare ETF and mutual fund costs, noting lower management fees for many ETFs but higher trading costs from spreads and premiums or discounts; liquidity and holding period matter.

Use etfs to improve portfolio efficiency, liquidity management, and rebalancing across stocks, bonds, and commodities. They enable strategic, tactical exposure management and combine top-down and bottom-up approaches.

Explore how fixed income etfs provide efficient, liquid exposure to bond markets for institutional investors and an adviser, enabling strategic and tactical asset class exposure across equities, bonds, and commodities.

Learn how active and multifactor ETF strategies use value, quality, momentum, and other factors; design weighting schemes and risk management to target factor returns and manage portfolio risk.

Explore multifactor models that increase explanatory power and flexibility for portfolio construction, risk management, and analytics, contrasting them with CAPM's single-factor approach.

Arbitrage pricing theory models expected returns as a linear function of factor risk, using diversified portfolios to remove idiosyncratic risk and reveal no-arbitrage opportunities via factor sensitivities and long/short strategies.

Explore the Carhart four-factor model, extending the Fama and French framework with momentum, and analyze market, size, value, and momentum factors to explain expected portfolio returns.

three multifactor model types by factor: macroeconomic, fundamental, and statistical; macro uses surprises in rates and inflation, fundamental uses stock attributes, statistical derives factors from historical returns.

Explain macroeconomic factor models where asset returns respond to surprises in inflation and GDP growth, with betas as sensitivities and an intercept for expected returns in portfolio and alpha calculations.

Fundamental factor models express factors as returns, use beta to compare sensitivities, and apply regression to estimate return effects for performance attribution and risk analysis, with a dividend yield example.

Describe how to decompose a portfolio's active return into the manager's factor tilts and security selection using a multifactor model, then assess mandate alignment and sources of return.

Compute active return and active risk, then decompose into factor and specific risk. Use the information ratio and tracking error to assess benchmark-relative performance amid multifactor and security selection risk.

Multifactor models help investors identify risks they can bear and those they should avoid, guiding asset allocation and diversified portfolios with risk premiums beyond the market factor.

Understand market risk from price movements in stocks, rates, currencies, and commodities, and learn how models help measure, manage, and test portfolio exposure.

Understand the parametric VaR method and other VaR approaches by modeling portfolio exposures to stock and yield-curve factors, assuming normal distributions and z-score scaling for daily or annual VaR.

Understand historical VaR through the historical simulation method, using a look back period to model portfolio losses from actual historical market moves, accommodating options and non-normal distributions.

Simulate portfolio risk using Monte Carlo VaR by drawing random returns from asset distributions such as stocks, bonds, and real estate, with specified mean returns, standard deviations, and skewness.

Explore the advantages of value at risk as a simple, communicable measure for risk budgeting and performance evaluation, and examine its limitations in subjectivity, liquidity, and tail risk.

Examine extensions of value at risk, including conditional VaR (expected shortfall), incremental VaR, and marginal VaR, along with extended tracking error and relative benchmark deviation.

Explore sensitivity risk measures across asset classes, from equity beta and CAPM expectations to fixed-income duration and convexity, and option Greeks (delta, gamma, vega) with pricing implications.

Learn how scenario risk measures model portfolio returns under multi-factor moves, compare to sensitivity measures, and use historical and hypothetical scenarios plus reverse stress testing to identify exposures.

Explore sensitivity and scenario risk measures as alternatives to historical models, using hypothetical scenarios to address non-normal distributions, liquidity, concentration, and correlation limits in stress testing.

Explore how banks and asset managers measure liquidity gaps, asset-liability mismatches, and risk using VaR, sensitivities, and scenario analysis, plus glide-path strategies for pension funds and liability hedging for insurers.

Explore how firms set risk appetite and allocate capital across market, credit, and operational risk, using limits, stop-loss rules, and scenario analysis to manage tail risk.

Backtesting evaluates risk-return trade-offs by using historical data to simulate strategy performance, guiding acceptance decisions and revealing how criteria may contribute to excess returns across varied environments.

Define the investment hypothesis and goals for active strategies, translate them into rules and parameters, then backtest on historical data and assess performance with Sharpe, Sortino, and drawdown.

Perform historical scenario analysis to test portfolio performance across regime shifts, including expansions, recessions, and volatility changes, using metrics like Sharpe ratio, density plots, skewness, and kurtosis.

Historical simulation selects past period returns via bootstrapping to form a non-deterministic rolling backtest. Monte Carlo simulation assigns distributions to key variables and generates random outcomes to model non-normality.

Explore inputs and decisions in portfolio simulation, computing portfolio return from asset weights and returns, and analyze results with Monte Carlo and historical methods, including Sharpe ratio and drawdown metrics.

Sensitivity analysis tests how input changes affect portfolio returns using Monte Carlo simulations, comparing baseline normal with skewed t to assess robustness, Sharpe ratio, and conditional VAR.