ATTENTION: NEW FORMAT SINCE FEBRUARY 2023
Previously, the first session included only essay-type questions associated with a case (item set) for which a written answer was expected and the second session only multiple choice questions also associated with a case.
Each session (morning and afternoon) will now include 6 sets of multiple choice questions and 5 sets of essay questions.
The CFA Level III exam will still consist of 11 essay and 11 item set cases with multiple choice questions.
All topics may be taken in the first or second session, or both sessions.
The number of points attributed to each case will be mentioned.
The CFA Level III exam is also 4 hours and 24 minutes long, divided into two equal sessions of 2 hours and 12 minutes, one in the morning and the second in the afternoon with an optional break in between.
For more information:
|ETHICAL AND PROFESSIONAL STANDARDS||10-15%|
|PORTFOLIO MANAGEMENT AND WEALTH PLANNING||35-40%|
In the initial study session on portfolio management, behavioral finance is introduced to acknowledge that behavioral biases can affect all market participants, regardless of their level of expertise. Behavioral finance helps to understand how emotional biases and cognitive errors can influence individuals' perceptions and investment choices.
Recognizing these biases can be beneficial in comprehending client objectives, constructing investment portfolios, and identifying inconsistencies in decision-making. Furthermore, behavioral finance provides insights into market anomalies. The readings suggest that integrating behavioral and traditional finance can lead to superior outcomes compared to relying exclusively on either approach.
Behavioral biases have the potential to influence the behaviors and decisions of participants in financial markets.
A key step towards improving economic outcomes is recognizing and comprehending these biases.
They can be categorized as either cognitive errors or emotional biases, and addressing each type requires a different approach.
Cognitive errors result from statistical, information-processing, or memory inaccuracies, which can be rectified more easily due to their reliance on faulty reasoning.
On the other hand, emotional biases stem from impulses and feelings, making them more resistant to change. Adapting to biases involves acknowledging and making adjustments, while moderating biases entails recognizing and reducing their impact.
Cognitive errors encompass biases such as belief perseverance biases and information-processing biases, while emotional biases include loss aversion, overconfidence, self-control, status quo, endowment, and regret aversion.
Detecting and understanding these biases is crucial for mitigating their influence on financial decisions, ultimately allowing market participants to enhance their economic outcomes.
The reading delves into the incorporation of behavioral considerations in financial decision-making. It explores the impact of including behavioral factors in adviser-client relationships and portfolio construction. Additionally, it examines how behavioral factors influence analyst forecasts, committee decision-making, and market behavior.
The reading emphasizes that behavioral biases can manifest in all market participants, regardless of their expertise. It suggests caution when classifying investors into different types based on their characteristics, although such classifications can offer valuable insights.
Considering behavioral factors can enhance the effectiveness and satisfaction of adviser-client relationships and financial decisions. By integrating behavioral biases into portfolio construction, portfolios can better align with the efficient portfolio of traditional finance while remaining accessible and appropriate for clients.
The reading also highlights the susceptibility of analysts and committees to behavioral biases, including overconfidence and availability bias. Recognizing and addressing these biases can lead to improved decision-making processes.
Finally, behavioral finance provides explanations for market anomalies and deviations from market efficiency.
In the investment management process, formulating capital market expectations is a crucial task. These expectations, which involve predicting the risk and return of different asset classes, serve as the foundation for constructing portfolios that aim to maximize expected return while considering the desired level of risk.
This study session explores the process of establishing capital market expectations and explores key tools of economic analysis. Both readings emphasize the importance of a disciplined approach when setting expectations, highlighting that it can yield favorable outcomes.
The first module outlines a framework for developing expectations and addresses common challenges, with a particular focus on using macroeconomic analysis. Building upon this framework, the second reading delves into setting expectations for specific asset classes, including fixed income, equities, real estate, and currencies.
The first reading on establishing capital market expectations aims to provide investment professionals with a comprehensive framework. It begins by outlining the process of developing capital market expectations and addressing the potential challenges and pitfalls in the forecasting process. Moreover, the reading emphasizes the application of macroeconomic analysis in setting expectations. Here are the key points covered:
Firstly, capital market expectations are crucial for both strategic and tactical asset allocation. Additionally, emphasis should be placed on internal consistency across asset classes and time horizons rather than the accuracy of individual asset class projections. In terms of the process of setting capital market expectations, several steps are involved, such as specifying expectations, researching historical data, selecting appropriate methods and models, gathering relevant information, interpreting the current investment environment, and documenting the conclusions.
However, various challenges are encountered when setting expectations. These include limitations of economic data, measurement errors and biases, limitations of historical estimates, biased risk measures, method biases, failure to consider conditioning information, misinterpretation of correlations, psychological biases, and model uncertainty. Therefore, it is crucial to maintain a clear understanding of the relationship between investment outcomes and the overall economy when setting expectations.
Shocks from factors such as policy changes, new products and technologies, geopolitics, natural disasters, resources, and financial crises can have significant impacts on growth trends. Consequently, the trend growth rate of an economy serves as an important benchmark for estimating bond returns and long-term equity appreciation.
When it comes to economic forecasting, there are three primary approaches: econometric models, indicators, and checklists. Each approach has its own advantages and limitations. Furthermore, the business cycle consists of distinct phases, namely initial recovery, early expansion, late expansion, slowdown, and contraction. Translating business cycle information into capital market expectations and investment decisions is complicated due to variations in cycle phases, challenges in distinguishing between cyclical and secular forces, and uncertainty in market responses.
It's worth noting that business cycle information is most reliable and valuable for setting expectations within the range of likely expansion and contraction phases. Monetary and fiscal policies play significant roles in the business cycle, and their impacts on interest rates, inflation, and yield curve slope should be considered.
Moreover, macroeconomic linkages between countries are expressed through their current and capital accounts, with interest rates and exchange rates being interconnected. Expectations of currency movements and exchange rates have implications for investment decisions, along with nominal returns and changes in exchange rates. Furthermore, real interest rates tend to move together globally due to the requirement of global savings equating to global investment.
In conclusion, the reading offers guidance on approaching capital market expectations and underscores the complexities involved in forecasting and making informed investment decisions.
The reading covers a range of important topics. It discusses the selection of forecasting techniques based on the information used and its incorporation into forecasts. Common methods for forecasting capital market returns include statistical approaches, discounted cash flow models, and risk premium models. Shrinkage estimation is a technique that combines multiple estimates to yield more precise results.
Discounted cash flow models are employed to estimate the expected return implied by an asset's current price, while risk premium models express expected return as a sum of risk-free rate and one or more risk premiums. There are three main methods for modeling risk premiums: equilibrium models (e.g., CAPM), factor models, and building blocks.
When it comes to forecasting fixed-income asset class returns, various methods are utilized, such as discounted cash flow, building blocks, and inclusion in an equilibrium model. The yield to maturity (YTM) metric can be enhanced by considering the impact of yield changes on reinvestment and valuation at the investment horizon.
Building blocks for fixed-income expected returns consist of the short-term default-free rate, term premium, credit premium, and liquidity premium. Credit spreads reflect both the credit premium and the expected losses due to default. The liquidity premium can be estimated by comparing the yield spread between high-quality and next highest-quality issuers.
Investors interested in emerging markets must take into account factors such as corporate governance, accounting standards, property rights laws, and government actions. Real estate returns are subject to smoothing effects, requiring careful handling of the data for meaningful analysis. Real estate experiences boom-bust cycles closely tied to the business cycle.
About real estate returns' forecasting, the cap rate, which measures net operating income relative to property value, is the standard valuation metric for commercial real estate.
A model similar to the Grinold-Kroner model can be utilized to estimate expected returns on real estate. These returns include term premium, credit premium, equity premium, and liquidity premium.
Forecasting exchange rates poses challenges and necessitates evaluating various influencing factors. While purchasing power parity (PPP) is not a reliable short-term predictor, it becomes more relevant over longer time horizons. The impact of the current account balance on exchange rates depends on its persistence and sustainability. In the long run, exchange rates are influenced by expected returns on domestic and foreign assets, although short-term overshoots can occur.
As for forecasting currency exchange rates, this is worth pointing out that carry trades can be profitable despite violating uncovered interest rate parity.
The module concludes with the necessity to forecast volatility of différents asset classes, sample variance-covariance matrices provide unbiased estimates but face limitations when dealing with large asset classes. Linear factor models handle such cases but can be biased unless the assumed structure is accurate. Shrinkage estimation combines sample matrices with a target matrix to account for prior knowledge.
Lastly, adjusting for smoothing effects in observed return data for real estate and private assets is crucial to avoid distorted portfolio analysis. Financial asset returns exhibit volatility clustering, which can be addressed using ARCH models.
This reading provides an overview of asset allocation and covers various important aspects. Effective investment governance plays a vital role and involves decision-making by competent individuals or groups, allocation of decision rights and responsibilities, formulation of investment policies and strategic asset allocation, establishment of a reporting framework, and periodic governance audits. Asset allocation decisions are influenced by the economic balance sheet, which includes both financial and non-financial assets and liabilities. Different approaches to asset allocation focus on asset-only objectives, liability-relative orientation, or achieving specific financial goals. The concept of risk varies depending on whether it pertains to individual assets, risks in relation to meeting liabilities, or the probabilities of not attaining financial goals.
Asset classes serve as fundamental units of analysis in asset allocation and represent systematic risks with varying degrees of overlap. When considering asset classes, it is important to assess their homogeneity, exclusivity, diversification potential, and ability to accommodate a significant portion of a portfolio. Risk factors associated with systematic risk and expected return premiums also come into play during asset allocation, and in some cases, complex spread positions may be necessary to identify and isolate specific risk factors. The global market portfolio is a diversified asset allocation benchmark that can serve as a reference point.
Passive/active choices within asset allocation involve decisions about managing the strategic asset allocation itself and implementing strategies for specific asset classes. Risk budgeting plays a crucial role in determining the types and levels of risks to be taken, with active risk budgeting considering risks relative to benchmark performance. Rebalancing is an essential practice that involves adjusting portfolio weights to align with the strategic asset allocation. This can be done through calendar-based or range-based approaches, with the latter allowing for tighter control of the asset mix compared to fixed calendar intervals. Strategic considerations in rebalancing encompass factors such as transaction costs, risk aversion, correlations among asset classes, volatility, momentum beliefs, taxation implications, and the liquidity of different asset classes.
This reading provides an overview of determining appropriate asset allocations to meet the needs of different investors. It highlights several key points:
The objective of asset-only mean-variance optimization (MVO) is to maximize expected return while considering risk aversion and expected variance of the asset mix. However, MVO has been criticized for its sensitivity to input changes, concentration in certain asset classes, neglecting characteristics of asset class returns, lack of diversified risk sources, and limited connection to liability or consumption streams.
Reverse optimization and the Black-Litterman model can address the limitations of MVO by deriving expected returns or incorporating investor views on asset returns.
Placing constraints on asset class weights and resampling inputs are additional approaches to address concerns about portfolio concentration and lack of diversification.
Including illiquid asset classes in optimization may be problematic when satisfactory proxies are unavailable.
Risk budgeting optimizes the use of risk in pursuit of return by maintaining a consistent ratio of excess return to marginal contribution to total risk across all assets in the portfolio.
Liability-relative asset allocation considers characteristics of liabilities such as fixed vs. contingent cash flows, legal vs. quasi-liabilities, duration and convexity of cash flows, value of liabilities compared to the organization's size, factors driving future cash flows, timing considerations, and regulatory impacts.
Approaches to liability-relative asset allocation include surplus optimization, a hedging/return-seeking portfolios approach, and an integrated asset-liability approach.
A goals-based asset allocation process combines sub-portfolios designed for individual goals with their respective time horizons and required probability of success.
Disciplined rebalancing reduces risk and potentially increases returns. Optimal corridor width for rebalancing depends on factors such as transaction costs, risk tolerance, correlation with the rest of the portfolio, and volatility of the rest of the portfolio.
Other asset allocation approaches mentioned include age-based rules, stock/bond ratios, the endowment model, risk parity, and the 1/N rule.
The main factors that constrain asset allocation decisions are the size of the assets, liquidity, time horizon, and external considerations like taxes and regulations. The size of a portfolio can limit access to certain asset classes or strategies, while complex assets require proper governance capacity. Large-scale asset owners may face challenges in effectively deploying capital due to liquidity conditions and trading costs. Conversely, smaller portfolios may struggle with diversification and monitoring complex investments due to staffing constraints. Access to certain asset classes, such as private equity or hedge funds, may be restricted for smaller portfolios, but pooling assets can help meet minimum investment requirements. The available investment opportunities are influenced by both the liquidity needs of the asset owner and the characteristics of different asset classes.
The time horizon of the investor is also a crucial consideration in asset allocation. External factors like regulations, tax rules, and funding needs further impact the asset allocation decision. Taxes have an effect on return distributions and should be taken into account when determining asset values and risk assumptions. Periodic portfolio rebalancing is important to maintain the target asset allocation, and tax implications need to be considered for taxable portfolios.
Strategic asset location involves placing assets with different tax efficiencies in accounts with favorable tax treatment. Regular review of the asset allocation policy is recommended, even in the absence of changes in the investor's circumstances. Changes to the asset allocation strategy may be implemented without a formal study in response to anticipated milestones, known as a "glide path." Tactical asset allocation allows for short-term deviations from the strategic asset allocation targets and aims to increase risk-adjusted returns. However, trading and tax costs, as well as concentration of risk, should be considered when implementing tactical trades. Behavioral biases, such as loss aversion and recency bias, can impact asset allocation decisions.
Mitigating these biases requires a formal asset allocation process with objective frameworks, governance, and controls. Goals-based investing and framing strategies can help address cognitive biases. A strong governance framework, combined with well-documented investment beliefs, enhances the likelihood of making objective asset allocation decisions and mitigating the impact of behavioral biases on long-term investment success.
This module examines the concept of structured fixed-income investing and the strategies used to immunize portfolios.
Assets and liabilities are classified based on the certainty of cash flows, with Type I having known amounts and payment dates, and Type II, III, and IV having uncertain amounts and timing. Various duration statistics are employed depending on the type of asset or liability.
Immunization aims to minimize the rate of return variance in a fixed-income portfolio over a specified investment horizon. It involves matching the portfolio's duration with the horizon date and periodically rebalancing as bond yields change. The objective is to secure the cash flow yield of the portfolio rather than the weighted average yield to maturity.
Immunization risks stem from non-parallel shifts and twists in the yield curve. Structural risk can be mitigated by reducing cash flow dispersion and concentrating them around the horizon date. Cash flow matching is a technique employed for immunizing multiple liabilities.
Laddered portfolios offer diversification across the yield curve and increased convexity. They provide liquidity through maturing short-term bonds that can be used as collateral. Immunizing multiple liabilities requires aligning money durations and ensuring that the market value of assets exceeds or equals liabilities.
Derivatives like interest rate futures contracts and swaps can be utilized in immunization strategies. The number of futures contracts required depends on the difference in basis point value (BPV) between assets and liabilities. Contingent immunization involves actively managing the surplus to reduce the cost of fulfilling liabilities.
Liability-driven investing (LDI) is commonly employed for rate-sensitive liabilities such as defined benefit pension plans. Effective duration and BPV are used to assess the interest rate risk of liabilities. Interest rate swap overlays can be employed to minimize the duration gap between assets and liabilities.
LDI strategies face model risks and spread risks due to assumptions and variations in spreads. Investing in bond market index funds provides diversification and cost-effectiveness. Matrix pricing is used to estimate the fair value of illiquid bonds. Index replication and enhancement strategies are implemented to accurately track bond indexes.
Total return swaps (TRS) enable investors to transform assets or liabilities between categories. They offer advantages such as lower initial cash requirements but involve counterparty credit risk. The choice of a bond index depends on investment goals and factors like changing durations and index composition over time.
The par yield curve represents yields-to-maturity at different maturities and is derived from bond yields using a model.
Active management of fixed-income portfolios focused on spread-based strategies involves taking positions in credit and other risk factors to achieve higher returns compared to an index. The key points discussed in the reading are as follows:
The effective management of equity portfolios is essential for achieving investment success, given the substantial representation of equity securities in numerous portfolios.
This study session concentrates on elucidating the role of equity investments while taking into account costs and the responsibilities of shareholders. It delves into two distinct approaches to equity portfolio management: passive/index-based investing and active equity strategies.
The section dedicated to passive equity investing explores important subjects such as various techniques for replicating indexes and employing factor-based passive strategies. Furthermore, it examines considerations related to tracking error, risk, and returns from an indexing standpoint.
This reading provides an overview of how equity investments can contribute to a client's portfolio. It explains how asset owners and investment managers categorize the equity market to establish investment mandates and discusses the costs and responsibilities associated with owning equities, including engaging with shareholders.
The reading also examines the decision-making process involved in choosing between active and passive management of an equity portfolio. Key topics covered include the advantages of including equities in a portfolio, the segmentation of the equity market, sources of income and expenses in equity portfolios, shareholder engagement, drawbacks of engagement, and the spectrum between active and passive management.
The selection between active and passive management is viewed as a continuum rather than a binary choice, influenced by factors such as performance expectations, client preferences, appropriate benchmarks, specific mandates, costs, and tax considerations.
This reading explores the reasons behind passive investing and provides an overview of equity market index construction and tracking methods.
It covers important topics such as the growing popularity of passive investing due to the underperformance of active equity managers, the goal of passive investors to replicate benchmark index returns, the process of selecting appropriate benchmarks based on investor objectives and constraints, different weighting methods used for benchmark indexes, rebalancing and reconstitution policies, the utilization of index-based strategies to target specific risk factors, the measurement of portfolio tracking error for passive investors, various approaches to pursuing passive investing including mutual funds, ETFs, and derivatives, the factors contributing to passive portfolio tracking error, and the role of investor activism in passive equity investment.
To achieve successful passive equity investment, it is essential to understand investor requirements, index construction principles, and available tracking methods.
In this study session, active equity portfolio management is thoroughly explored, encompassing a range of strategies and concepts. The session commences by delving into quantitative and fundamental equity strategies, providing insights into their underlying reasoning and the approaches used to develop them, whether from a top-down or bottom-up perspective. Factor-based investing and specialized equity strategies, including activist investing and statistical arbitrage, are also examined. Lastly, the study session concludes by addressing significant factors in active equity portfolio construction, such as active share, active risk, risk budgeting, and portfolio construction constraints.
This reading provides an overview of active equity management approaches and discusses the creation of various strategies. It highlights the distinction between fundamental and quantitative approaches, emphasizing their differences in decision-making processes, forecast focus, information used, analysis depth, data orientation, and portfolio risk approaches.
The main types of active management strategies, including bottom-up, top-down, factor-based, and activist approaches, are described. The reading also covers quantitative equity investment strategies, such as factor-based models, as well as specialized strategies like activist investing, statistical arbitrage, and event-driven strategies. It outlines the steps involved in the fundamental and quantitative active investment processes, along with potential pitfalls in each approach.
Additionally, the reading mentions investment styles and the use of style analysis, including returns-based and holdings-based approaches, to classify and analyze portfolios.
Active equity portfolio construction aims to effectively translate forecasted returns into actual portfolio performance by considering return objectives, acceptable risk levels, and potential obstacles. This applies to various strategies such as long-only, long/short, long-extension, and market-neutral approaches. The reading emphasizes four key elements of portfolio construction: adjusting the weights of rewarded factors, leveraging alpha skills for timing factors, securities, and markets, sizing positions based on risk and active weights, and incorporating independent decisions to broaden expertise. Managers have the flexibility to combine systematic and discretionary methods, employ bottom-up and top-down evaluations, and adopt benchmark-aware or benchmark-agnostic strategies based on their core beliefs. The reading also delves into risk budgeting, measuring active risk through tracking error, assessing absolute risk, evaluating active share, implementing risk management processes, and adhering to different risk constraints.
Moreover, it addresses portfolio management costs, including explicit and implicit expenses, and underscores the significance of well-constructed portfolios with a clear investment philosophy, consistent processes, suitable risk characteristics, and efficient operational costs. The compromises and considerations associated with long/short investing are also explored.