CFA L1 | Alternatives | Free Study Tools | Zorroh

Core Idea

The portfolio approach evaluates each investment by how it affects the risk and return of the entire portfolio, not in isolation.

Key Principles

• Securities should not be analyzed independently
• Focus on interaction between assets
• Emphasizes diversification
• Combine assets that are not perfectly correlated

Analogy:
Building a portfolio is like forming a sports team — the best individual players don’t always make the best team together.

• Portfolio risk depends on:
  • Individual asset risk
  • Correlation between assets
• Lower correlation ⇒ greater risk reduction
• You don’t need “safe” assets — you need different assets

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Diversification Ratio

$$\text{Diversification Ratio}=\frac{\text{Risk of equally weighted portfolio of }n\text{ securities}}{\text{Risk of a single random security}}$$

• Lower ratio = better diversification

CFA Insight:
A ratio close to 1 means little diversification benefit.

Step 1: Planning

• Understand client needs
• Prepare Investment Policy Statement (IPS)
• Identify:
  • Objectives (return)
  • Risk tolerance
  • Constraints (liquidity, taxes, time horizon, legal)

Analogy:
Planning is the blueprint — mistakes here affect everything downstream.

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Step 2: Execution

Asset Allocation

• Decide weights of asset classes
• Most important driver of portfolio returns

Security Selection

• Choose specific securities within each asset class

Portfolio Construction

• Combine assets into a cohesive portfolio

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Step 3: Feedback

• Monitor portfolio performance
• Rebalance when needed
• Measure results and report to client
• Ensure objectives are being met

CFA Exam Tip:
Feedback is ongoing — not a one-time step.

Individual Investors

• Highly variable:
  • Time horizons
  • Risk tolerance
  • Liquidity needs
• Needs differ significantly across individuals

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Pension Plans

Defined Benefit (DB) Plans

• Employer promises specific retirement payments
• Employer bears investment risk
• Less popular with companies

Analogy:
Employer guarantees a pension paycheck regardless of market performance.

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Defined Contribution (DC) Plans

• Employer contributes a fixed amount
• Employee bears investment risk
• Employee controls asset allocation

Analogy:
Employer fills the bucket — employee decides how to invest it.

Industry Overview

• Extremely competitive
• Includes all firms providing asset management services

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Sell-Side Firms

• Provide:
  • Trading services
  • Research
  • Recommendations
• Examples:
  • Brokers
  • Dealers
  • Investment banks

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Buy-Side Firms

• Manage money on behalf of clients
• Examples:
  • Mutual funds
  • Hedge funds
  • Private equity firms

Buy-side uses sell-side research — but makes independent decisions.

Active Strategies

• Goal: Outperform a benchmark
• Require:
  • Research
  • Skill
  • Higher fees

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Passive Strategies

• Goal: Replicate benchmark returns
• Lower fees
• Minimal trading

Analogy:
Active = trying to beat the race
Passive = matching the race pace

Traditional Investments

• Long-only:
  • Equity
  • Fixed income

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Alternative Investments

• Broad set of instruments
• Includes:
  • Hedge funds
  • Private equity
  • Real assets

1. Rise in Passive Investing

• Driven by:
  • Fee sensitivity
  • Difficulty of consistent outperformance

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2. Growth of Big Data

• New data sources:
  • Structured (financial statements)
  • Unstructured (social media, satellite data)
• Improved technology enables analysis

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3. Robo-Advisors

• Algorithm-based investment advice
• Rule-driven and constraint-based
• Lower fees
• Popular with younger investors and smaller portfolios

Mutual Funds

• Investors pool money
• Own shares proportional to contribution
• Value increases via income and capital gains

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Net Asset Value (NAV)

$$\text{NAV per Share}=\frac{\text{Assets}-\text{Liabilities}}{\text{Number of Shares}}$$

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Open-End Funds

• Accept new investors
• Shares issued and redeemed at NAV

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Closed-End Funds

• Fixed number of shares
• Trade in the market
• Price may differ from NAV

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Load vs No-Load Funds

• Load funds: charge purchase or redemption fees
• No-load funds: no transaction fees

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Types of Mutual Funds

Type	Invests In
Money Market	Short-term securities
Bond Funds	Fixed income
Stock Funds	Equities
Index Funds	Track an index

Exchange-Traded Funds (ETFs)

• Passive
• Track an index
• Tax-efficient (low capital gains distributions)
• Trade intraday like stocks
• Can be:
  • Short sold
  • Bought on margin

Hybrid Nature:

• Like open-end funds (NAV-based)
• Like closed-end funds (exchange trading)

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Separately Managed Accounts (SMAs)

• Portfolio owned by a single investor
• Customized management
• Direct tax consequences on trades

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Hedge Funds

• Long/short positions
• Use leverage and derivatives
• High minimum investments
• Charge:
  • Management fee
  • Performance fee

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Private Equity & Venture Capital

• Invest in early-stage or private companies
• Limited Partners (LPs) provide capital
• General Partners (GPs) manage investments

Portfolio Management Process

Step	Purpose
Planning	Define objectives & constraints
Execution	Build the portfolio
Feedback	Monitor & rebalance

Buy-Side vs Sell-Side

Buy-Side	Sell-Side
Manages money	Provides research & trading
Mutual funds	Investment banks
Hedge funds	Brokers

Pooled Investment Comparison

Vehicle	Liquidity	Customization
Mutual Funds	High	Low
ETFs	High	Low
SMAs	Medium	High
Hedge Funds	Low	Medium

• Portfolio risk depends on correlation, not just individual risk
• Diversification reduces risk without sacrificing expected return
• Asset allocation is the primary driver of portfolio performance
• IPS is the foundation of portfolio management
• DB plans shift risk to employers; DC plans shift risk to employees
• Buy-side manages money; sell-side facilitates markets
• Passive investing continues to grow due to fee pressure
• ETFs combine features of mutual funds and stocks
• SMAs offer customization but create tax implications
• Hedge funds and PE involve higher risk, fees, and complexity

Holding Period Return (HPR)

$$\text{HPR}=\frac{P_1}{P_0}-1$$

• Measures return over one holding period
• Ignores compounding beyond that period

Analogy:
Buying a stock at $100 and selling at $110 → HPR = 10%

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Arithmetic Mean Return

$${\bar{R}}_{\text{arith}}=\frac{R_1+R_2+\cdots +R_n}{n}$$

• Average of periodic returns
• Best estimate of expected return for a single future period
• Overstates long-term growth

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Geometric Mean Return

$${\bar{R}}_{\text{geo}}={[(1+R_1)(1+R_2)\dots (1+R_n)]}^{1\mathrm{/}n}-1$$

• Measures compound annual growth rate (CAGR)
• Always ≤ arithmetic mean

Analogy:
Geometric mean shows how fast your money actually grew over time.

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Gross vs Net Return

• Gross Return:
  • Return before fees & taxes
  • Commissions already included
• Net Return:$$\text{Net Return}=\text{Gross Return}-\text{Fees}-\text{Taxes}$$
• Adjusting for inflation → Real Return

Money-Weighted Return (MWR)

• Equivalent to Internal Rate of Return (IRR)
• Accounts for:
  • Timing of cash flows
  • Size of cash flows
• Affected by investor behavior

Use When:
Investor controls cash flow timing

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Time-Weighted Return (TWR)

• Measures compounded growth of $1 invested
• Eliminates impact of external cash flows

Calculation Steps

1. Divide horizon into sub-periods
2. Compute HPR for each sub-period
3. Compound sub-period returns

$$(1+HP{R}_1)(1+HP{R}_2)\cdots -1$$

• For periods > 1 year → use geometric mean

Use When:
Evaluating investment managers

Analogy:
TWR measures manager skill; MWR measures investor experience.

Risk-Return Trade-Off

• Higher expected return ⇒ higher risk
• Investors demand compensation for bearing risk

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Key Assumptions

• Returns are normally distributed
• Markets are operationally efficient

When assumptions fail:

• Skewed returns
• Illiquidity
• Fat tails

Variance & Standard Deviation

• Measure dispersion of returns
• Standard deviation = square root of variance
• Most common risk metric used by portfolio managers

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Covariance

• Measures how two assets move together
• Ranges from −∞ to +∞
• Hard to interpret alone

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Correlation

$${\rho }_{ij}=\frac{{\text{Cov}}_{ij}}{{\sigma }_i{\sigma }_j}$$

• Scaled version of covariance
• Ranges from −1 to +1
• Easier to interpret

Key Insight:
Lower correlation → better diversification

Variance & Standard Deviation

• Measure dispersion of returns
• Standard deviation = square root of variance
• Most common risk metric used by portfolio managers

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Covariance

• Measures how two assets move together
• Ranges from −∞ to +∞
• Hard to interpret alone

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Correlation

$${\rho }_{ij}=\frac{{\text{Cov}}_{ij}}{{\sigma }_i{\sigma }_j}$$

• Scaled version of covariance
• Ranges from −1 to +1
• Easier to interpret

Key Insight:
Lower correlation → better diversification

Investment Opportunity Set

• All possible portfolios formed from risky assets
• Each portfolio has:
  • Expected return
  • Standard deviation

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Minimum Variance Frontier (MVF)

• Set of portfolios with lowest risk for each level of return

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Global Minimum Variance Portfolio (GMVP)

• Portfolio with the lowest possible standard deviation
• One specific point on the MVF

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Efficient Frontier (EF)

• Upper portion of MVF
• Portfolios that:
  • Offer highest return for given risk
• Portfolios below EF → inefficient
• Portfolios above EF → unattainable

• Straight line connecting:
  • Risk-free asset
  • Optimal risky portfolio

$$E(R_p)=w_{rf}{R}_f+w_{rp}E(R_{rp})$$$${\sigma }_p=w_{rp}{\sigma }_{rp}$$

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Optimal Risky Portfolio

• Tangency point between:
  • CAL
  • Efficient Frontier

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Leverage

• Investors can:
  • Borrow at $R_f$Rf​
  • Invest more than 100% in risky assets
• Results in points beyond risky portfolio on CAL

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Investor Portfolio Choice

• Depends on:
  • Risk tolerance
  • Shape of indifference curves
• Optimal choice = highest indifference curve tangent to CAL

Analogy:
CAL shows all possible mixes; indifference curves show preferences.

Return Measures

Measure	Best Use
Arithmetic Mean	Single-period expected return
Geometric Mean	Long-term growth
MWR (IRR)	Investor cash flows
TWR	Manager performance

Risk Measures

Measure	Purpose
Variance	Total risk
Std Dev	Volatility
Covariance	Joint movement
Correlation	Standardized relationship

Frontiers & Lines

Concept	Meaning
MVF	Lowest risk per return
GMVP	Minimum risk portfolio
Efficient Frontier	Superior portfolios
CAL	Risk-free + risky combo

• Arithmetic mean overstates long-term growth
• Geometric mean reflects true compounding
• MWR is affected by cash flow timing
• TWR isolates manager skill
• Diversification reduces risk via correlation
• Portfolio risk ≠ average of individual risks
• Efficient frontier defines optimal risky portfolios
• CAL expands choices using risk-free asset
• Optimal portfolio depends on investor preferences

Investor Expectations & Efficient Frontiers

• Efficient frontiers depend on:
  • Expected returns
  • Risk (volatility)
  • Correlations
  • Market conditions

👉 If investors have different expectations, they will have:

• Different efficient frontiers
• Different CALs
• Different optimal risky portfolios

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Key Assumption

All investors have identical expectations

Under this assumption:

• All investors share:
  • Same efficient frontier
  • Same Capital Allocation Line
  • Same optimal risky portfolio

This shared optimal risky portfolio is called the:

Market Portfolio

Analogy:
If everyone agrees on weather, ingredients, and recipe, everyone bakes the same “best cake.”

• Special case of CAL
• Risky portfolio = market portfolio
• Shows best possible risk–return combinations

📌 S&P 500 is commonly used as a proxy for the market portfolio.

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Interpretation of the CML

• Points on the CML represent:
  • Different weights between:
    • Risk-free asset
    • Market portfolio

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Lending vs Borrowing Portfolios

Type	Description
Lending Portfolio	Invest partly in risk-free asset
Borrowing Portfolio	Uses leverage (borrows at $R_f$Rf​)

• Points below CML → inefficient
• Points above CML → unattainable

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CML Equations

Expected return:$$E(R_p)=w_{rf}{R}_f+w_{m}E(R_m)$$

Risk (standard deviation):$${\sigma }_p=w_m{\sigma }_m$$

Total Risk

$$\text{Total Risk}=\text{Systematic Risk}+\text{Non-Systematic Risk}$$

• Measured by standard deviation

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Non-Systematic Risk (Unsystematic)

• Company-specific risk
• Examples:
  • Management decisions
  • Product failures
• Eliminated through diversification

Analogy:
One company slipping doesn’t matter if you own 100.

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Systematic Risk

• Market-wide risk
• Examples:
  • Recessions
  • Interest rate changes
• Cannot be diversified away
• Investors are compensated only for systematic risk

 Key Principle:
Diversification removes non-systematic risk, not systematic risk.

Used to estimate expected returns based on factor sensitivities.

Types of Factors

• Macroeconomic (GDP, inflation)
• Fundamental (earnings, leverage)
• Statistical (factor analysis)

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Market Model

$$R_i={\alpha }_i+{\beta }_i{R}_m+{\epsilon }_i$$

Where:

• $\alpha$ = abnormal return (intercept)
• $\beta$ = sensitivity to market (slope)
• $\epsilon$ = unexplained return (residual)

Interpretation:
This is a regression of a stock’s returns on market returns.

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Beta (β)

$${\beta }_i=\frac{\text{Cov}(R_i,R_m)}{\text{Var}(R_m)}$$

• Measures systematic risk
• Indicates responsiveness to market movements

β Value	Meaning
β = 1	Moves with market
β > 1	More volatile than market
β < 1	Less volatile than market

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CAPM Equation (Must Memorize)

$$E(R_i)=R_f+{\beta }_i[E(R_m)-R_f]$$

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Key Implications

• Expected return depends only on beta
• Two securities differ in expected return only if beta differs
• Non-systematic risk is irrelevant

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CAPM Assumptions (High Exam Value)

• Investors are risk-averse
• Markets are frictionless
• Same holding period & expectations
• Investments infinitely divisible
• Competitive markets → price takers

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Security Market Line (SML)

• Graphical form of CAPM

Axis	Meaning
X-axis	Beta (systematic risk)
Y-axis	Expected return

• Y-intercept = $R_f$
• Slope = market risk premium

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CML vs SML (Classic Exam Question)

Feature	CML	SML
Risk Measure	Total risk (σ)	Systematic risk (β)
Applies To	Efficient portfolios only	All assets
Slope	Sharpe ratio	Market risk premium

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SML Applications

• If a security plots:
  • Above SML → underpriced
  • Below SML → overpriced

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CAPM Applications

• Required return in Dividend Discount Models
• Cost of equity in WACC
• Benchmark for performance evaluation

Sharpe Ratio

$$\text{Sharpe}=\frac{R_p-R_f}{{\sigma }_p}$$

• Uses total risk
• Best for undiversified portfolios

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Treynor Ratio

$$\text{Treynor}=\frac{R_p-R_f}{{\beta }_p}$$

• Uses systematic risk
• Best for well-diversified portfolios

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Jensen’s Alpha

$$\alpha =R_p-[R_f+{\beta }_p(R_m-R_f)]$$

• Measures return above CAPM expectation
• Positive alpha = superior performance

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M² (Modigliani–Modigliani)

• Converts Sharpe ratio into percentage return
• Easier to interpret
• Higher M² = better performance

Risk Types

Risk Type	Diversifiable	Compensation
Systematic	❌	✅
Non-systematic	✅	❌

Performance Measurements

Measure	Risk Used	Best Use
Sharpe	Total	Undiversified
Treynor	Systematic	Diversified
Jensen’s α	Systematic	Skill vs CAPM
M²	Total	Intuitive comparison

• Identical expectations → market portfolio
• CML applies only to efficient portfolios
• Only systematic risk is priced
• Beta measures market sensitivity
• CAPM links expected return directly to beta
• SML applies to all securities
• Above SML = underpriced; below = overpriced
• Sharpe for total risk, Treynor for systematic risk
• Jensen’s alpha measures true outperformance

Definition

The IPS is a written roadmap that summarizes the client’s:

• Risk objectives
• Return objectives
• Constraints
• Investment guidelines

Insight:
The IPS is the foundation of the portfolio management process — all decisions must align with it.

Analogy:
The IPS is like a GPS route — once set, you don’t keep changing the destination every time markets move.

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Components of the IPS

1. Introduction

• Summarizes:
  • Client circumstances
  • Expectations
  • Background information

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2. Statement of Purpose

• Explains why the IPS exists
• Defines scope of the portfolio manager’s mandate

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3. Duties & Responsibilities

• Responsibilities of:
  • Client
  • Portfolio manager
• Includes communication and decision-making roles

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4. Investment Objectives, Constraints & Guidelines

• Risk & return objectives
• Asset classes allowed
• Restrictions on leverage or investments

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5. Performance Evaluation

• Benchmarks used
• Frequency of evaluation
• Reporting standards

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6. Appendices

• Supporting documentation
• Legal or special provisions

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Risk and Return Objectives

Portfolio managers must clearly understand both.

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Risk–Return Tradeoff

• Higher expected return ⇒ higher risk
• Clients must understand this tradeoff to avoid:
  • Excessive risk-taking
  • Emotional reactions during downturns

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Risk Measurement

Absolute Risk

• Defined without reference to a benchmark

Examples:

• “Portfolio should not decline by more than 5%”
• “Probability of losing more than $15,000 < 15%”

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Relative Risk

• Defined relative to a benchmark

Examples:

• “Return should be within ±2% of the S&P 500”

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Return Measurement

Can also be:

• Absolute (e.g., 6% annual return)
• Relative (e.g., outperform benchmark by 1%)

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Risk Tolerance

Risk tolerance has two distinct components (very testable).

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Ability to Take Risk

Depends on objective factors:

• Time horizon
• Wealth level
• Income stability

📌 Long time horizon + stable income ⇒ higher ability

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Willingness to Take Risk

Depends on subjective factors:

• Personality
• Expectations
• Psychological comfort with losses

Insight:
If ability and willingness conflict, ability dominates.

Analogy:
Someone may want to skydive (willingness), but poor health limits their ability.

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Investment Constraints (L-T-T-U)

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1. Liquidity Requirements

• How frequently funds must be withdrawn
• Higher liquidity needs ⇒ lower risk tolerance

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2. Time Horizon

• Length of time until funds are needed

Horizon	Impact
Long	Higher risk tolerance, lower liquidity
Short	Lower risk tolerance, higher liquidity

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3. Tax Concerns

• Different tax rates across clients
• Influences:
  • Asset location
  • Turnover
  • Security selection

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4. Legal & Regulatory Requirements

• Limits on:
  • Asset classes
  • Leverage
  • Concentration

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5. Unique Circumstances

• Client-specific preferences
• Examples:
  • ESG mandates
  • Concentrated stock positions

Strategic Asset Allocation (SAA)

Definition

Long-term allocation of capital across asset classes.

• Specifies:
  • Asset classes
  • Target weights
• Primary driver of portfolio returns

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Systematic Risk Exposure

• SAA exposes portfolio to systematic risk
• Systematic risk:
  • Cannot be diversified away
  • Explains most portfolio value changes

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Common Asset Classes

Equities

• Large-cap
• Small-cap
• Emerging markets

Fixed Income

• Government bonds
• Corporate bonds

Alternative Investments

• Private equity
• Hedge funds
• Commodities

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Correlation Principles

• Within asset classes:
  • High correlation
  • Similar characteristics
• Across asset classes:
  • Low correlation
  • Diversification benefits

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Determining Ideal SAA

Combines:

• Client objectives & constraints
• Long-term capital market expectations

Tools used:

• Optimization
• Simulations

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Tactical Asset Allocation (TAA)

Definition

Short-term deviations from SAA weights to exploit market opportunities.

• Temporary
• Opportunistic
• Increases active risk

Analogy:
SAA is the cruise route; TAA is making short detours for better weather.

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Security Selection

• Selecting individual securities within asset classes
• Goal: outperform asset class benchmark
• Overweight or underweight securities

Insight:
When deviations from benchmarks occur → strategy becomes active, not passive.

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Risk Budgeting

Definition

• Total acceptable portfolio risk
• Allocation of risk across:
  • Asset allocation
  • Security selection
  • Active strategies

Analogy:
Risk budgeting is like allocating calories across meals — overspend in one place, and you must cut elsewhere.

Environmental Factors

• Climate change & carbon emissions
• Air & water pollution
• Biodiversity & deforestation
• Energy efficiency
• Waste management

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Social Factors

• Customer satisfaction
• Product responsibility
• Data security & privacy
• Diversity & inclusion
• Worker treatment
• Health & safety
• Human rights

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Governance Factors

• Board structure
• Audit committees
• Executive compensation
• Bribery & corruption
• Shareholder rights
• Lobbying & political contributions

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ESG Investment Approaches

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Negative Screening

• Excludes companies/sectors that violate ESG values

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Positive Screening

• Selects companies with strong ESG practices

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ESG Integration

• Incorporates ESG factors into traditional financial analysis

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Thematic Investing

• Focus on a specific theme:
  • Renewable energy
  • Water
  • Human rights

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Active Ownership

• Shareholders influence company behavior
• Voting & engagement used to achieve ESG goals

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Impact Investing

• Explicit intention to:
  • Generate financial returns
  • Create measurable social & environmental impact

Risk Tolerance Components

Component	Determined By
Ability	Time horizon, wealth, income
Willingness	Psychology, preferences

Asset Allocation Types

Type	Horizon	Purpose
Strategic	Long-term	Core portfolio
Tactical	Short-term	Opportunistic

ESG Approaches

Approach	Description
Negative Screening	Exclude harmful firms
Positive Screening	Select ESG leaders
Integration	ESG + financial analysis
Thematic	Focus on a single theme
Active Ownership	Influence via engagement
Impact Investing	Financial + social goals

• IPS governs all portfolio decisions
• Risk and return objectives must be realistic and aligned
• Risk tolerance = ability + willingness (ability dominates)
• Constraints shape feasible portfolios
• Strategic asset allocation drives most portfolio returns
• Tactical asset allocation is short-term and active
• Security selection introduces active risk
• Risk budgeting allocates risk, not just capital
• ESG investing can be implemented in multiple ways
• Impact investing targets measurable real-world outcomes

Behavioral biases cause investors to deviate from rational decision-making, leading to:

• Suboptimal portfolios
• Excessive trading
• Poor diversification

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Two Major Categories

1️⃣ Cognitive Errors

• Errors in:
  • Memory
  • Reasoning
  • Statistical processing
• Often caused by mental shortcuts (heuristics)
• Usually mitigated through education

Mnemonic: RICCH + FAMA

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2️⃣ Emotional Biases

• Driven by:
  • Feelings
  • Impulses
  • Intuition
• Harder to correct
• Often require behavioral constraints

Mnemonic: LOSERS

Definition

The tendency to cling to prior beliefs even when new information contradicts them.

• Caused by cognitive dissonance
• Investors ignore or reinterpret opposing evidence

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RICCH Biases

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1. Representativeness Bias

Classifying new information based on past experiences or stereotypes rather than proper analysis.

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Base-Rate Neglect

• Ignoring historical probabilities
• Overweighting recent information

Sample-Size Neglect

• Assuming small samples represent the population

Consequences

• Decisions based on limited data
• Poor forecasting

Guidance

• Consider long-term base rates
• Increase sample size

Analogy:
Judging a whole movie from one scene.

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2. Illusion of Control Bias

Belief that one can control or influence random outcomes.

Consequences

• Excessive trading
• Under-diversified portfolios

Guidance

• Recognize investing is probabilistic
• Seek contrary opinions
• Focus on downside risk

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3. Conservatism Bias

Failure to properly update beliefs when new information arrives.

Consequences

• Holding outdated views
• Delayed reactions
• Holding securities too long

Guidance

• Actively incorporate new data
• Acknowledge bias
• Seek expert input

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4. Confirmation Bias

Focusing only on information that supports existing beliefs.

Consequences

• Ignoring negative signals
• Biased screening
• Poor diversification

Guidance

• Seek contradictory evidence
• Use multiple research sources

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5. Hindsight Bias

Belief that past events were predictable all along.

Consequences

• Overestimating forecasting skill
• Poor manager evaluation

Guidance

• Record investment rationale before decisions

Analogy:
“Of course that stock crashed — it was obvious!” (only after the fact)

1. Framing Bias

Decisions change depending on how information is presented.

Consequences

• Sub-optimal decisions
• Short-term focus

Guidance

• Evaluate outcomes neutrally
• Focus on net gains/losses

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2. Anchoring & Adjustment Bias

Fixating on an initial value and insufficiently adjusting.

Consequences

• Ignoring new information

Guidance

• Re-evaluate assumptions
• Identify anchor points

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3. Mental Accounting Bias

Separating money into mental “buckets” rather than evaluating the total portfolio.

Consequences

• Ignoring correlations
• Poor portfolio-level risk management

Guidance

• Analyze portfolio holistically

Analogy:
Treating each pocket of money separately instead of your full wallet.

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4. Availability Bias

Decisions based on easily recalled or familiar information.

Includes:

• Retrievability
• Categorization
• Narrow range of experience
• Resonance

Consequences

• Limited opportunity set
• Under-diversification

Guidance

• Use IPS
• Rely on long-term data
• Conduct thorough research

1. Loss Aversion

Losses feel more painful than gains feel pleasurable.

Consequences

• Holding losers too long
• Selling winners too early
• Disposition effect

Guidance

• Use disciplined investment rules

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2. Overconfidence Bias

Overestimating knowledge and ability.

Types:

• Prediction overconfidence
• Certainty overconfidence

Consequences

• Underestimating risk
• Excessive trading
• Poor diversification

Guidance

• Review historical performance
• Track decisions and outcomes

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3. Self-Control Bias

Failure to prioritize long-term goals.

Consequences

• Inadequate saving
• Excessive borrowing

Guidance

• Written investment plan
• Structured asset allocation

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4. Endowment Bias

Overvaluing assets simply because you own them.

Violates: Law of one price

Consequences

• Failure to sell or rebalance

Guidance

• Objective valuation

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5. Regret Aversion

Avoiding decisions due to fear of making mistakes.

Consequences

• Overly conservative behavior
• Herding

Guidance

• Quantify risk and return
• Accept losses are normal

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6. Status Quo Bias

Preference for doing nothing.

Consequences

• Inappropriate risk levels
• Missed opportunities

Guidance

• Regular portfolio reviews
• Education

Momentum Anomaly

• Securities that performed well recently continue to perform well short-term
• Typically lasts up to 2 years
• Eventually reverts to mean

Caused by

• Availability bias
• Recency effect
• Hindsight bias
• FOMO

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Market Bubbles & Crashes

Driven by:

• Overconfidence
• Confirmation bias
• Self-attribution bias
• Herding
• Cognitive dissonance during downturns

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Value vs Growth Anomaly

• Value stocks:
  • Low P/E
  • Low P/B
  • Low P/Dividend
• Tend to outperform growth stocks

Possible explanation:

• Halo effect
• Underestimating risk in growth firms

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Home Bias

Preference for domestic assets.

Consequences

• Poor global diversification
• Inflated domestic asset prices

Behavioural Bias Categories

Category	Bias Type	Mitigation
Cognitive	RICCH, FAMA	Education
Emotional	LOSERS	Constraints

Mnemonic Summary

Mnemonic	Meaning
RICCH	Representativeness, Illusion of Control, Conservatism, Confirmation, Hindsight
FAMA	Framing, Anchoring, Mental Accounting, Availability
LOSERS	Loss aversion, Overconfidence, Self-control, Endowment, Regret, Status quo

• Behavioral biases lead to predictable errors
• Cognitive biases can often be reduced with education
• Emotional biases are harder to mitigate
• Loss aversion explains the disposition effect
• Overconfidence leads to excessive trading
• Diversification helps combat several biases
• Market anomalies can arise from widespread biases
• IPS helps constrain irrational behavior
• Awareness is the first step toward mitigation

Risk

Exposure to uncertainty

Risk exists whenever outcomes are uncertain and can deviate from expectations.

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Risk Exposure

• Degree to which an entity is sensitive to a specific risk
• Higher exposure ⇒ greater impact from adverse events

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Risk Management (RM)

The process used to determine risk tolerance, measure risk taken, and adjust exposures.

Insight:
The goal of RM is NOT to eliminate or minimize risk.

True Goal:
✔️ Understand risk taken
✔️ Allocate risk efficiently
✔️ Maximize firm value

Analogy:
Risk management is not about avoiding storms — it’s about sailing with the right equipment.

A comprehensive, firm-wide system consisting of six components:

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1️⃣ Risk Governance

• Sets overall risk levels for the entire firm
• Top-down process
• Typically set by the Board of Directors
• Uses an Enterprise Risk Management (ERM) perspective
• Focus: maximize value of the entire organization, not individual units

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2️⃣ Risk Identification & Measurement

• Identifies sources of risk
• Uses:
  • Quantitative analysis (metrics, models)
  • Qualitative analysis (judgment, experience)

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3️⃣ Risk Infrastructure

• People, systems, and processes
• Tracks firm-wide risk profile

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4️⃣ Risk Policies

• Day-to-day operational rules
• Translate governance into action

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5️⃣ Risk Monitoring

• Ongoing:
  • Measurement
  • Mitigation
  • Control

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6️⃣ Communication

• Risk reporting
• Feedback loops
• Supports decision-making at all levels

Focuses on:

• Identifying value-adding risks
• Eliminating or reducing non-value-adding risks

Board Responsibilities

• Define:
  • Firm goals
  • Risk levels
  • Risk constraints
• Ensure consistency with long-term strategy
• Apply an ERM perspective

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Risk Tolerance

• Amount of risk the firm is willing to accept
• Determines risk appetite

Includes:

• Acceptable losses
• Maximum downside exposure

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Inside View vs Outside View

View	Focus
Inside View	Internal shortfalls that could cause failure
Outside View	External forces & risk drivers

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Better Risk Preparation Depends On

• Firm expertise
• Ability to respond to adverse events
• Amount of losses the firm can sustain
• Competitive environment
• Regulatory environment

This analysis must be done before a crisis, not during one.

Board Responsibilities

• Define:
  • Firm goals
  • Risk levels
  • Risk constraints
• Ensure consistency with long-term strategy
• Apply an ERM perspective

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Risk Tolerance

• Amount of risk the firm is willing to accept
• Determines risk appetite

Includes:

• Acceptable losses
• Maximum downside exposure

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Inside View vs Outside View

View	Focus
Inside View	Internal shortfalls that could cause failure
Outside View	External forces & risk drivers

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Better Risk Preparation Depends On

• Firm expertise
• Ability to respond to adverse events
• Amount of losses the firm can sustain
• Competitive environment
• Regulatory environment

This analysis must be done before a crisis, not during one.

Financial Risks

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Market Risk

Uncertainty from movements in:

• Interest rates
• Stock prices
• Exchange rates
• Commodity prices

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Credit Risk

Risk of loss if a counterparty defaults.

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Liquidity Risk

Risk of loss due to:

• Inability to sell quickly
• Selling at a depressed price

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Non-Financial Risks

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Operational Risk

Losses due to:

• Human error
• System failures
• Process breakdowns

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Solvency Risk

Risk that the firm:

• Cannot meet short-term cash obligations

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Legal Risk

Risk of:

• Lawsuits
• Contracts not being enforced

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Compliance Risk

Uncertainty from:

• Regulations
• Accounting rules
• Tax laws

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Model Risk

Risk that:

• Valuation or risk models are incorrect
• Assumptions are flawed

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Tail Risk

Risk of extreme, low-probability events occurring.

Tail risks are often underestimated because they lie in the “fat tails” of distributions.

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Risk Correlations

• Risks are often interconnected
• Example chain:
  • Market risk ↑
  • Credit risk ↑ (defaults)
  • Legal risk ↑ (lawsuits)

Key Point:
Risks rarely occur in isolation.

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Individual Risks

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Health Risk

Risk of illness

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Mortality Risk

Risk of premature death

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Longevity Risk

Risk of outliving assets

Mitigation Tools

• Health insurance
• Life insurance
• Lifetime annuities

Standard Deviation

• Measures volatility
• Captures total risk

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Beta

• Measures sensitivity to market movements
• Captures systematic risk

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Duration

• Measures sensitivity of fixed-income prices to interest rate changes

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Greeks (Derivatives Risk)

Greek	Measures
Delta	Price sensitivity
Gamma	Change in delta
Theta	Time decay
Vega	Volatility sensitivity
Rho	Interest rate sensitivity

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Value at Risk (VaR)

Minimum loss over a given time horizon at a given probability

Example:
1-month 2% VaR of $1M
→ 2% probability portfolio loses at least $1M in one month

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Conditional VaR (CVaR)

• Expected loss given that VaR is exceeded
• Focuses on tail losses

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Stress Testing

• Evaluates portfolio under extreme events
• “What if markets crash 30%?”

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Scenario Analysis

• Similar to stress testing
• Uses multiple input assumptions
• More flexible and descriptive

Risk can be:

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Avoided

• Do not invest in risky assets

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Accepted but Mitigated Internally

• Diversification
• Self-insurance

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Transferred

• Insurance
• Surety bonds

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Shifted

• Derivatives (hedging)

Always perform a cost-benefit analysis before choosing a risk management method.

Risk Management Framework

Component	Purpose
Governance	Set firm-wide risk
Identification	Identify & measure risks
Infrastructure	Track risk
Policies	Operational rules
Monitoring	Control & mitigate
Communication	Feedback & reporting

Sources of Risk

Category	Examples
Financial	Market, credit, liquidity
Non-Financial	Operational, legal, model
Individual	Health, mortality, longevity

Risk Modification Models

Method	Example
Avoid	Exit risky assets
Mitigate	Diversification
Transfer	Insurance
Shift	Derivatives

• Risk management aims to maximize value, not eliminate risk
• Board sets firm-wide risk tolerance
• Risk governance is top-down and enterprise-wide
• Risk budgeting allocates risk, not just capital
• Risks are often correlated
• Tail risk involves extreme, low-probability events
• VaR measures minimum loss at a confidence level
• CVaR focuses on losses beyond VaR
• Stress testing examines extreme outcomes
• Risk modification requires cost-benefit analysis

What Is Technical Analysis?

Technical Analysis uses price and volume data to estimate future price movements.

• Prices change due to supply and demand
• Does not require detailed knowledge of the underlying asset
• Especially useful for assets without predictable cash flows (e.g., commodities, crypto)

Analogy:
Technical analysis is like reading crowd behavior at a concert—watch how people move rather than asking why they came.

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Three Core Principles

1. Market Discounts Everything

• All information (economic, fundamental, psychological) is already reflected in price
• No need to analyze earnings, GDP, etc.

2. Prices Move in Trends

• Prices move in trends and countertrends
• Once a trend starts, it tends to persist

3. History Repeats Itself

• Price patterns recur because human behavior is repetitive
• Fear and greed show up repeatedly in charts

Behavioral Finance	Technical Analysis
Explains why investors behave irrationally	Measures and visualizes that behavior
Focus on biases	Focus on price & volume patterns

Key Insight:

Technical analysis is the measurement tool; behavioral finance is the explanation.

Technical	Fundamental
Price & volume-based	Intrinsic value-based
Practical & visual	Theoretical
Best in liquid markets	Long-term valuation
Needs trends to work	Prices can deviate for long periods

Line Charts

• One data point per period (usually closing price)
• Best for identifying trends

Scaling Rule:

• Narrow price range → Linear scale
• Large price range → Logarithmic scale

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Bar Charts

Show four prices per period:

• Open
• High
• Low
• Close

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Candlestick Charts

Also show four prices, but visually clearer:

• White candle → Close > Open (bullish)
• Black candle → Close < Open (bearish)

Analogy:
Candlesticks tell a story of the battle between buyers and sellers in each period.

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Trading Volume Charts

• Typically below price charts
• Confirms price movements

Key Insight:
📈 Rising prices + falling volume = weakening trend

$$\text{Relative Strength}=\frac{\text{Asset Price}}{\text{Benchmark Price}}$$

• Rising line → Asset outperforming benchmark
• Falling line → Underperforming

Relative strength ≠ RSI (Relative Strength Index)

Trends

Uptrend

• Higher highs & higher lows
• Demand > Supply
• Trendline drawn across lows

Downtrend

• Lower highs & lower lows
• Supply > Demand
• Trendline drawn across highs

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Support & Resistance

Concept	Meaning	Analogy
Resistance	Price ceiling	Roof
Support	Price floor	Floor

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Change in Polarity

• Resistance broken → becomes support
• Support broken → becomes resistance

Reversal Patterns (Trend Changes)

Head and Shoulders

• Left shoulder, head, right shoulder
• Neckline connects lows

$$\text{Price Target}=\text{Neckline}-(\text{Head High}-\text{Neckline})$$Price Target=Neckline−(Head High−Neckline)

• Volume confirmation is critical

Inverse Head and Shoulders

• Bullish reversal

Double / Triple Tops

• Bearish reversal

Double / Triple Bottoms

• Bullish reversal

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Continuation Patterns (Trend Continues)

Triangles

• Formed by converging trendlines

Type	Interpretation
Ascending	Bullish
Descending	Bearish
Symmetrical	Neutral

Price Target:
Distance between trendlines at the start of the pattern

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Rectangles

• Price bounces between parallel support & resistance
• Continuation pattern

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Flags & Pennants

• Short-term (≈ one week)
• Occur after sharp price moves

Pattern	Description
Flag	Slopes opposite prior trend
Pennant	Small symmetrical triangle

Price-Based Indicators

Moving Averages

• Average closing price over time

Signal	Meaning
Golden Cross	Bullish
Death Cross	Bearish

(Usually 50-day & 200-day)

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Bollinger Bands

• SMA ± k standard deviations
• Wider bands = higher volatility
• Prices usually remain within bands

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Momentum Oscillators

Used to measure trend strength and reversal signals

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1. Rate of Change (ROC)

$$ROC=\frac{P_t-P_{t-n}}{P_{t-n}}\times 100$$

• Oscillates around zero
• Positive → bullish
• Negative → bearish

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2. Relative Strength Index (RSI)

$$RSI=100-\frac{100}{RS}$$

$$RS=\frac{\text{Sum of up moves}}{\text{Sum of down moves}}$$

RSI Level	Interpretation
< 30	Oversold (buy)
> 70–90	Overbought (sell)

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3. Stochastic Oscillator

• Compares closing price to recent range
• Ranges from 0–100

Signal	Meaning
K crosses D from below	Bullish
K crosses D from above	Bearish
> 80	Overbought
< 20	Oversold

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4. MACD

• Difference between 12-day & 26-day EMAs
• Signal line = smoothed MACD

Signal	Interpretation
MACD > Signal	Bullish
MACD < Signal	Bearish

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Convergence & Divergence

Type	Meaning
Convergence	Trend strengthening
Divergence	Trend weakening

Indicator	Interpretation
Opinion Polls	Crowd psychology
Put/Call Ratio ↑	Bearish
VIX ↑	Fear ↑
Margin Debt ↑	Bullish

• Examines relationships across asset classes
• Uses relative strength analysis
• Identifies leading vs lagging markets

• Identify reversals & continuations
• Assist portfolio managers
• Improve timing of trades
• Must justify recommendations logically
• Avoid non-technical bias

Major Chart Patterns

Pattern Type	Signal
Head & Shoulders	Reversal
Triangles	Continuation
Flags & Pennants	Continuation

Key Indicators & Uses

Indicator	Purpose
MA	Trend
RSI	Overbought/Oversold
MACD	Momentum
Bollinger	Volatility

• Technical analysis focuses on price & volume
• Markets reflect all known information
• Patterns exist due to repeating human behavior
• Indicators confirm, not predict
• Best used for timing, not valuation

Fintech

Fintech = Finance + Technology

Refers to technological innovation in the design and delivery of:

• Financial services
• Financial products
• Investment management processes

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Key Applications of Fintech

• Analysis of large data sets
• Automated trading
• Automated investment advice
• Record keeping and settlement

Analogy:
Fintech is the engine upgrade for finance — the destination (investment goals) stays the same, but speed, efficiency, and scale improve dramatically.

Big Data

Refers to the vast and continuously growing amount of data generated by:

• Industry
• Governments
• Individuals
• Electronic devices

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Traditional Data Sources

Source	Examples
Financial Markets	Prices, volumes
Companies	Financial statements, payroll
Government	GDP, inflation

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Alternative Data Sources

Source	Examples
Individuals	Social media, clicks
Sensors	Satellites, mobile phones
Business Processes	Credit cards, receipts

Alternative data is often unstructured and requires advanced tools to analyze.

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The 3 Vs of Big Data (Highly Testable)

Characteristic	Meaning
Volume	Massive amounts of data
Velocity	Speed of data generation (real-time)
Variety	Many formats and sources

Analogy:
Big data is like drinking from a firehose — the challenge isn’t access, it’s processing.

Artificial Intelligence (AI)

Definition

AI refers to advanced computer systems that:

• Simulate human intelligence
• Perform tasks such as pattern recognition and prediction

AI is commonly used to organize and analyze big data.

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Machine Learning (ML)

What Is Machine Learning?

Algorithms that:

• Learn from data
• Generate predictions
• Identify patterns without explicit programming

Algorithms are trained on large datasets until they can perform tasks independently.

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Types of Machine Learning

1️⃣ Supervised Learning

• Inputs and outputs are labeled
• Algorithm learns patterns from historical examples

Example:
Training a model to predict stock returns using past returns and known outcomes.

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2️⃣ Unsupervised Learning

• Inputs are not labeled
• Algorithm identifies patterns on its own

Example:
Clustering stocks based on similar return behavior.

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Model Limitations (Exam Favorite)

• Models are not perfect
• Risks include:
  • Overfitting (too complex, fits noise)
  • Underfitting (too simple, misses structure)

Analogy:
Overfitting is memorizing practice questions instead of learning the concepts.

Practical Applications of Fintech

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Text Analytics

• Analyzes unstructured text or voice data
• Common use:
  • Social media sentiment
  • News analysis

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Natural Language Interpretation (NLP)

• Interprets human language
• Extracts meaning from:
  • Analyst reports
  • Earnings calls

Used to identify market sentiment based on wording and tone.

Robo-Advisors

What Are Robo-Advisors?

Online platforms that provide automated investment advice using:

• Questionnaires
• Algorithms
• Predefined rules

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Information Collected

• Financial situation
• Ability to take risk
• Willingness to take risk

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Typical Services

Service	Description
Asset Allocation	Portfolio construction
Rebalancing	Maintain target weights
Tax Management	Tax-loss harvesting
Trade Execution	Automated trading

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Characteristics

• Usually offer passive investments
• Low cost
• Low account minimums
• Best suited for simple client needs

Analogy:
Robo-advisors are like cruise control — great for steady conditions, not complex terrain.

Distributed Ledger Technology (DLT)

Definition

A database where:

• Transactions are recorded
• Copies are distributed to all network participants
• Each participant holds a matching ledger

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Consensus Mechanism

• Validates transactions
• Ensures all ledgers are updated consistently

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Blockchain

A form of DLT where:

• Transactions are recorded chronologically
• Data is stored in blocks
• Blocks are linked using cryptography
• Creates a permanent, tamper-resistant record

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Permission Models

Network Type	Access
Permissionless	All participants see all transactions
Permissioned	Different authority levels

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Benefits of DLT

Benefit	Explanation
Security	Cryptographic validation
Transparency	Shared ledger
Speed	Faster settlement
Peer-to-Peer	No intermediary
Decentralization	No central authority

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Applications of DLT

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Cryptocurrency

• Peer-to-peer exchange of value
• Transactions recorded on a distributed ledger
• No financial intermediary required

---

Automatic Clearing & Settlement

• Trades verified immediately
• Eliminates third-party clearinghouses

---

Smart Contracts

• Self-executing contracts
• Triggered when predefined conditions are met

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Tokenization

Other physical assets

Digital representation of ownership

Used for:

Real estate

Art

Fintech Applications in Investment Management

Area	Use Case	CFA Focus
Big Data	Process massive datasets	Data-driven decision making
AI / ML	Pattern recognition & prediction	Model risk
Robo-Advisors	Automated portfolio management	Suitability & cost
Algorithmic Trading	Automated execution	Speed & efficiency
DLT / Blockchain	Record keeping & settlement	Transparency & security

Traditional vs Alternative Data

Feature	Traditional Data	Alternative Data
Source	Markets, companies, governments	Individuals, sensors, processes
Structure	Structured	Often unstructured
Volume	Limited	Massive
Processing	Conventional tools	AI / ML required

The 3 Vs of Big Data

V	Meaning	Example
Volume	Quantity of data	Social media posts
Velocity	Speed of generation	Real-time prices
Variety	Different formats	Text, images, clicks

Machine Learning Types

Type	Inputs	Purpose	Example
Supervised	Labeled	Prediction	Return forecasting
Unsupervised	Unlabeled	Pattern discovery	Stock clustering

Robo-Advisors Overview

Feature	Description
Advice Type	Rule-based
Investments	Mostly passive
Costs	Low
Best For	Simple portfolios

Algorithmic Trading vs High-Frequency Trading

Feature	Algorithmic Trading	HFT
Speed	Fast	Extremely fast
Holding Period	Short	Very short
Objective	Efficient execution	Exploit mispricing
Technology	Advanced	Cutting-edge

Distributed Ledger Networks

Feature	Permissionless	Permissioned
Visibility	All transactions visible	Restricted
Authority	Equal participants	Tiered access
Use Case	Public blockchains	Financial institutions

DLT Applications

Application	Description
Cryptocurrency	Peer-to-peer value transfer
Clearing & Settlement	Instant verification
Smart Contracts	Self-executing agreements
Tokenization	Digital ownership tracking

• Fintech enhances efficiency, scale, and speed in investment management
• Big data is defined by volume, velocity, and variety
• Alternative data is often unstructured and requires AI to analyze
• AI and ML enable pattern recognition but introduce model risk
• Overfitting and underfitting reduce model usefulness
• Robo-advisors provide low-cost, rule-based advice
• Algorithmic trading emphasizes speed and execution efficiency
• HFT relies on ultra-fast execution to exploit mispricing
• DLT provides secure, transparent, decentralized record keeping
• Blockchain is a cryptographically linked, permanent ledger
• Smart contracts self-execute without intermediaries
• Tokenization enables digital ownership of real assets