Standards of professional conduct:

1.      Professionalism

A.     Knowledge of the law

B.     Independence & objectivity

C.     Misrepresentation

D.     Misconduct

2.      Integrity of capital markets

A.     Material nonpublic information

B.     Market manipulation

3.      Duties to clients

A.     Loyalty, prudence & care

B.     Fair dealing

C.     Suitability: client-investment mandated objectives/constraints

1.      Advisory role

2.      Managing role

D.     Performance presentation

E.      Preservation of confidentiality: unless

1.      Illegal activities info

2.      Disclosure required by law

3.      Client permits disclosure

4.      Duties to employers

A.     Loyalty

B.     Additional compensation arrangements

C.     Responsibilities of supervisors

5.      Investment analysis, recommendations & actions

A.     Diligence & reasonable basis: (1) & (2)

B.     Communication with (prospective) clients:

1.      Disclose basic format, general principles clients use

2.      Disclose significant limitations/risks

3.      Use reasonable judgment in identifying important factors

4.      Distinguish between fact & opinion

C.     Record retention

6.      Conflicts of interest

A.     Disclosure of conflicts

B.     Priority of transactions

C.     Referral fees

7.      Responsibilities as a CFA Institute member/candidate

A.     Conduct as participants in CFA Institute programs

B.     Reference to CFA Institute, designation, program

1.      Strategic asset allocation (SAA): based on long-run capital market expectations & IPS; long term policy target portfolio

2.      Tactical asset allocation (TAA): deviation from SAA or benchmark to take advantage of perceived short-term (mean reverting returns to long term level) opportunities (mispricing) in the market; increment to SAA not replacement (small increment in return); undertaken infrequently or regularly by monitoring the market and reacting accordingly; active management (if value added > costs, internally or by outside firm)

1.      Loss aversion: take greater risk to recover from a loss

2.      Mental accounting: identifying & immunizing individual goals; more important less risk; pyramid

3.      Regret: bad feeling/disappointment/shame from admitting bad decision; avoided if not recognized (loss)

4.      Fear of regret: avoid taking actions that could lead to regret

Asset/liability management (ALM) approach: SAA based on liability modeling (not asset-only approach)

1.      Static asset allocation: no link for asset allocation between different time periods

2.      Dynamic asset allocation: multi-period view of investment horizon; Monte Carlo simulation; unanticipated changes; difficult & costly; ALM

Utility risk-adjusted return: return, risk aversion score, variance

also 0.5 for decimal %

Roy’s safety-first measure (shortfall/downside risk):

return, min acceptable return, σ

Asset classes:

1.      Contain similar assets from a descriptive & statistical perspective

2.      Are not highly correlated to provide diversification

3.      Should be mutually exclusive (each asset only in one class)

4.      Cover majority of investable assets (exhaustive)

5.      Have sufficient liquidity for rebalancing

Adding a class: theoretical effects (risk, return, correlation), practical effects (liquidity, legal, tax, political, currency); a class could be added just for TAA; distinct classes: TIPS (inflation), international investments (return) & alternative assets (diversification); decision to add class/security:

if   Sharpe ratios, correlation

Asset allocation process:

1.      Determine IPS & formulate capital market expectations

2.      Determine asset allocation (appropriate mix) & construct portfolio; TAA also considered

3.      Monitor & revise allocation & portfolio accordingly

CAL/CML allocation = risk free asset + tangency portfolio (highest Sharpe ratio corner portfolio = market); risk free lending (Rp < Rm) or borrowing if possible (Rm < Rp)

International assets:

1.      Currency risk: local market return (LCM) + local currency return (LCR), if correlation +/- high/low volatility; small impact over currencies & time; σ bonds < FX < stocks

2.      Political risk: monetary & fiscal policy, legal & regulatory rules, FDI, capital flows

3.      Home country bias: overweight domestic investments; lack of familiarity; match domestic assets/liabilities

Costs: high transaction costs (market impact), withholding taxes & DTT issues, free-float issues, inefficient market infrastructure;  currency devaluation; crisis contagion (high correlation, instead use conditional return correlation depending on volatility level)

Opportunities (also emerging markets): long-run diversification (decline over time, integration); foreign undervaluation; economic growth; bonds less correlated than stocks

Segmented to integrated market: share price rise, expected return increase & decrease, stand-alone risk decrease (variance down), efficiency increase, diversification benefits decline (covariance up)

Corner portfolios: define segment adjacently with same assets (shift when weights go +/0 or 0/+) & constant rate of change of weights; include global minimum variance portfolio (GMVP); efficient portfolios are linear combinations (interpolations) of adjacent corner portfolios; 2-porfolio/(corner portfolio) theorem in unconstrained/constrained (0 or positive weight) optimization

Asset allocation approaches

Mean variance optimization (MVO)

Resampled efficient frontier (REF)

Black-Litterman model (BL)

Monte Carlo simulation (MCS)

Surplus asset liability management (ALM)

Experience based techniques (EBTs)

Exchange rate = (price currency)/(base currency); buy or sell base currency in units of price currency; T+2; quotes: bid/ask price, (receive less, buy)/(deliver more, sell) of price currency

Mark to market = PV of gain/loss

FX swap: roll over forward with spot; call option to buy one currency = put option to sell other currency (out of/at/in the money)

5.      Index: represents the performance of a specified group of securities

6.      Benchmark: reference point for evaluating portfolio performance (indexes are often used as benchmarks)

Market index uses:

1.      Asset allocation proxies (risk/return of classes)

2.      Investment management mandates (specifying style ex-ante)

3.      Performance benchmark & portfolio analysis/attribution (evaluating results ex-post, GIPS, legal compliance)

4.      Gauging market sentiments

5.      Design of index funds, ETFs, derivatives (passive investment)

1.      Asset-based benchmarks: focus on assets/returns & the ability of managers to meet/exceed them

2.      Liability-based benchmarks: focus on liabilities/payments & the ability to meet/fund them (duration matching, relatively low risk)

Index construction tradeoffs:

1.      Completeness vs investability (liquidity & ownership constraints)

2.      Reconstitution (addition/removal of securities) & rebalancing (adjustment of weights) frequency to align characteristics vs low turnover (transaction costs)

3.      Objective & transparent rules vs subjective judgement

Equity (US 50%, EU 25% allocation):

1.      Passive management: indexing (minor)

2.      Semiactive/risk-controlled active management:

enhanced indexing; highest IR

3.      Active management: highest active return & risk

Information ratio (IR) = active return / tracking risk TE(V)

good inflation hedge (low taxes, low competition)

Indexes:

1.      Price-weighted index: arithmetic average; adjustments for stock dividends, stock (reverse) splits & addition/removal of stocks; biased toward higher priced stocks

2.      Market capitalization value-weighted index: adjustments for stock issues/buybacks & addition/removal of stocks; biased toward large cap, mature, overvalued companies; (less diversified)

3.      Free float-adjusted market capitalization value-weighted index:

float-weighted, publicly available shares; biased like market cap

4.      Equal-weighted index:

$ per stock; periodic rebalancing; biased toward small-cap

Investment costs (high > low):

1.      Index mutual funds: admin fees, liquidation impacts

2.      ETFs: more tax efficient, higher license fees

3.      Separated & pooled accounts: institutional, securities lending

4.      Equity futures: finite live (roll over), basket shorting/uptick rule

5.      Equity total return swaps: cheap, tax advantages, TAA

Indexed portfolios (combinations possible):

1.      Full replication: < 1000, liquid stocks; self-rebalancing (except for dividends & adjustments); low TE(V); index return - admin fees, cash drag (for redemptions) & transaction costs (dividends reinvestment, reconstitution & rebalancing)

2.      Stratified random sampling/representative sampling: index subset; multidimensional market cap cell structure/matrix with representative stocks (random sample) segmented by uncorrelated characteristics (industry, size, P/E); TE(V) decrease with more cells (finer divisions); can mimic concentrated positions

3.      Optimization approach: index subset; use of multifactor risk model to match index factor exposures

accounts for factor dependence; use of objective function to minimize TE(V) (lower than sampling); data overfitting; historical bias; needs frequent exposure updates & rebalancing; many solutions

Investing style:

1.      Value investing: low P/E, P/B; focus on numerator; depressed earnings will rise as reverting to the mean; low turnover; substyles: high dividend yield, low price multiples & contrarian

2.      Growth investing: high P/E, P/B; focus on denominator; earnings growth; do better during economic contraction; high turnover; substyles: consistent earnings growth & momentum (RSI)

3.      Market-oriented/core/blend investing: must outperform broad market indexing strategies; substyles: value tilt, growth tilt, growth at reasonable price (GARP), style rotation

Market capitalization-based investing: micro-cap, small-cap, mid-cap, large-cap

Style indices: value, growth, blend/neutral; holding-based construction by assigning securities by category (no overlap) or by quantity (market cap category split, overlap); buffering rules to reduce reconstitution

Style identification:

1.      Returns-based style analysis (RBSA): weights bi > 0, Sum(bi) = 1, (i > 0)

regression against security indexes (well specified, mutually exclusive & exhaustive); style fit = coefficient of determination R2 (unexplained variation = 1 - R2, error, selection return); construction of custom/normal benchmark with bi weights (regression coefficients)

2.      Holdings/composition-based style analysis (HBSA): determination by averaging/comparing security selected characteristics (dimensions);

2.1.  For value: high earnings volatility (cyclical firms), finance, utility, basic & energy

2.2.  For growth: technology & health care

RBSA

HBSA

1.      Low cost & data, quick execution

2.      Could be misleading, many solutions

3.      Focus on full portfolio

1.      Subjective, data intensive

2.      Detect style drift quickly

3.      Focus on securities

Style box: (market caps) x (styles) matrix; sub-detailed HBSA

Style drift: leads to outside exposure/expertise problems

Socially responsible investing (SRI): ethical, social, religious; positive/negative screens (look for/avoid); tilt toward small-cap growth stocks

1.      Long strategies: buy undervalued stock, avoid overvalued; asymmetric active weight distribution (- bi limits)

2.      Long-short strategies: buy undervalued stock, sell overvalued stock (2 alphas, long-short spread); symmetric active weight distribution; can use market neutral strategy, pairs trade (arbitrage) with beta = 0 & leverage

3.      Equitizing a market neutral portfolio (100/0 + 20/20):

cash + long/short  + (long equity futures or ETFs)/cash; portable alpha (transportable); seen as alternative investment

4.      Short extension/partial long-short strategy (120/20):

typically beta = 1; seen as long-only substitute

Enhanced indexing strategies: stock-based/derivative-based (yield & duration play, lower IB than stocks)

Short side price inefficiencies (overvaluation):

1.      Barriers: find security lender

2.      Accounting manipulation to look better (performance)

3.      More buy than sell recommendations by sell-side analysts:

buyers > sellers; not anger large stockholders; firm management pressure (cut off communications &  investment banking/corporate finance deals, lawsuits)

Sell disciplines (after tax): opportunity cost (substitution), deteriorating fundamentals, valuation level, down-from-cost, up-from-cost, target price

Fundamental law of active management:

Information coefficient (IC): knowledge depth, forecast/actual return correlation

Investor breadth (IB): number of independent investment decisions

Information ratio:

Optimal allocation:

active return, risk aversion, active risk (variance)

Higher aversion to active than total risk:

1.      Pick possible active manager to produce possible active return

2.      Take away positions from judged passive index

3.      High active return needs focus on highest active manager (less diversification)

1.      Core-satellite approach: active risk mitigated by core (passive/enhanced investor benchmark) & active return added by the satellites (manager portfolios); uncorrelated manager active returns

2.      Completeness fund: combined with manager active portfolios to make risk exposures similar to the investor benchmark; neutralizes misfit risk; passive/semiactive indexing of fund & regular rebalancing

Active return:

Active risk (uncorrelated returns):

Total active return = true active return (A) + misfit active return (S)

Manager true active return = total return (P) - normal/neutral portfolio (B)

Manager misfit active return = normal portfolio (B) - investor benchmark (M)

Total active risk =

True information ratio (IR) = (true active) / (true active risk)

Optimization objective: maximize total active return with given level of total active risk with optimal amount of misfit risk (not 0)

Alpha & beta separation approach: systematic risk exposure (beta) through low-cost index fund/ETF + portable alpha through long/short strategy (more expensive); use of derivatives if restrictions; clear separation of risks

1.      Buy-side (private) vs sell-side (public) research

2.      Top-down approach: macroeconomic

3.      Bottom-up approach: stock & industry level

Manager selection: quantitative (style, valuation) & qualitative considerations

1.      Past performance (quant): evidence of superior results (not proof)

2.      Manager questionnaires: manager staff/organization; investment philosophy/process; resources (research & trading); performance; fees

3.      Fee schedules (quant): sliding ad valorem (AUM); performance-based (more complicated, volatile, align interests if symmetric), fee caps, high water mark provisions (cover negative return first)

Take necessary risks (information/advantage to generate return), reduce/eliminate (hedge), avoid unnecessary risks

Risk management process: continuous analysis & data update

1.      Identify & measure specific risk exposures

2.      Define specific tolerances (will/able to bear) & adjust risk levels (upward/downward execution)

3.      Report risk exposures to stakeholders (capital/risk budgeting)

4.      Monitor (limits) & take corrective actions (long-run issues)

Risk governance (corporate governance): overall process to develop & put a risk management system (infrastructure) into use

1.      Transparent, clear accountability, cost efficient, outcome effective

2.      Decentralized (business units); centralized (top-down, aggregate diversified firm-wide risk, overall VaR, economies of scale, ERM)

3.      Front office (trading & sales); back office (administrative, transaction processing, record keeping, regulatory compliance, custodian relations, STP)

Financial risks (high > low):

1.      Market risk: interest rates, exchange rates, equity prices, commodity prices

2.      Credit risk: debtor default on promised payment

3.      Liquidity risk: bid-ask spread, trading volume (often not in valuation)

Non-financial risks:

1.      Operational risk: loss due to failure in company processes or from external events

2.      Model risk: bad inputs & assumptions

3.      Settlement (Herstatt) risk: counterparty failure to deliver its obligation (asset/payment) in a transaction

4.      Legal/contract risk: legal contract enforcement, litigation

5.      Regulatory risk: regulation

6.      Tax/accounting risk: laws/rules, taxes, reporting (IASB)

7.      Sovereign/political risk: government willingness/ability to pay (financial), political conditions

Other risks: ESG risk, performance netting risk (multimanager asymmetric fee), settlement netting risk (challenged netting in liquidation)

Value at Risk (Var): minimum expected $/% value loss (to be exceeded) for a given probability (confidence level) over a specified time period; downside left tail; industry standard & part regulatory

·        |loss| >= |VaR|

·        z (score) = (x - μ)/σ

single tail: 5% is 1.65 σ, 2.5% is 1.96 σ,  1% is 2.33 σ

·        n (periods): R/n & σ/√n; 250 days, 52 weeks, 12 months

1.      Analytical VaR method: parametric variance-covariance, normal distribution; easy to compute/understand, mostly used

(positive z for 1 - Prob)

many covariances, issues in leptokurtosis & skewness (delta-normal method adjustment)

2.      Historical VaR method: nonparametric, rank/quantile past data returns (sample); easy to compute/understand; data intensive, past not future

3.      Monte Carlo VaR method: parametric computer simulation, rank/quantile generated data returns; input assumptions (many solutions, GIGO, overconfidence), difficult, computation intensive & expensive

VaR used in/with: capital/risk budgeting (allocation to business units), projections backtesting, before/after effect incremental VaR (IVaR), cash flow at risk (CFaR), earnings at risk (EaR), tail VaR (TVaR) = average of losses beyond VaR, credit VaR (CVaR), stress testing

Stress testing: focus on extreme adverse outcomes

1.      Scenario analysis: different states, model variables/(risk) factors; use of actual extreme & hypothetical events; potential weaknesses/inability to accurately measure simultaneous shifts, causal relations & correlation change effects on factors

1.1.  Stylized scenarios (type involving at least 1 change in):

·        Interest rates: yield curve levels (parallel shifts & twists, swap spreads), volatilities

·        Exchange rates: key FX levels, volatilities

·        Stock prices: equity index levels, volatilities

·        Commodity prices

2.      Stressing models (scenario analysis extension): range of states

2.1.  Factor push: simple combination of (simultaneous) most disadvantageous shifts in factors (independent)

2.2.  Maximum loss optimization: math & computer modelling,  sophisticated worst combination of factors (dependent)

2.3.  Worst-case scenario analysis: likely to occur

Credit risk (default probability, expected loss): current (jump-to-default) vs potential; cross-default-provisions (default on all & other creditors); linked to market risk

Market order: immediate; execution certainty, price uncertainty

Limit order: execution at the limit price or better that expire; (partial/full) execution uncertainty, (price certainty)

Inside/market spread = quoted ask - quoted bid

Midquote = (quoted ask + quoted bid) / 2

Effective spread = |execution average price - midquote| * 2, leads to price improvement or price/market impact; simple/volume-weighted average

1.      Brokered markets: search by agent (in illiquid markets); block trading (upstairs market, listed on OTC 3rd market)

2.      Quote-driven/dealer markets: (closed-limit order book) over the counter (OTC) market; bridge liquidity by sell-side traders/market makers

3.      Order-driven markets:

3.1.  Auction markets: natural liquidity by buy-side traders/investors; competitive price discovery, periodic/batch vs continuous market

3.2.  Automated auctions: same but computerized, Electronic Communication Networks (ECNs)

3.3.  Electronic crossing networks: batch cross matching at midquote at fixed points in time; no price discovery, low cost, anonymous, institutional (4th market)

4.      Hybrid markets: ex NYSE - specialists, call/batch at open & close

Dealer: sell-side principal, inventory, liquidity, bid-ask spread, adverse selection risk

Broker: sell-side agent of buy-side, commission

1.      Represent the order (principal-agent relationship)

2.      Find/be counterparty (principal trade)

3.      Supply market information

4.      Provide secrecy & discretion

5.      Provide other supporting services (prime brokerage)

6.      Support the market process

Market quality (quantitative, qualitative):

1.      Liquidity: low costs - small bid-ask spreads, market depth, resilience; factors - many buyers & sellers, diversity of opinions, convenience, integrity

2.      Transparency: pre-trade (quotes, spread) & post-trade (completed transactions) information availability

3.      Assurity of completion: clearing & settlement guaranties

Explicit costs: commissions, taxes, stamp duties & fees

Implicit costs: bid-ask spread, price/market impact cost, missed trade opportunity cost, delay (slippage) cost; require a benchmark

VWAP: share volume weighted average of execution prices for a trading period (benchmark); better than midquote, open & close prices

Implementation shortfall (IS) costs = (OC + DC + MC) + EC

IS for a buy [+] = (paper portfolio - gross actual portfolio) + EC

= paper portfolio - net actual portfolio

IS for a sell [-] = (gross actual portfolio - paper portfolio) + EC

1.      OC: missed trade opportunity cost (unrealized profit/loss)

2.      DC: delay cost (slippage cost)

3.      MC: realized profit/loss (market impact cost)

4.      EC: explicit costs (commissions …)

Decision Price, Arrival Price, Execution Price, Cancellation Price

Costs: total or per share $, %, bp

Market adjusted IS = IS - beta(Rp) * Rm

VWAP

IS

Advantages

1.      Easy to understand

2.      Easy & quick to compute

3.      Good for comparing smaller trades in nontrending markets

1.      Links trading to portfolio management (cost/value)

2.      Useful in portfolio optimization (performance)

3.      Recognizes time/price tradeoff

4.      Allows cost attribution/decomposition

5.      No gaming

Disadvantages

1.      No account for delay or opportunity costs

2.      Can be gamed

3.      Not good for substantial trades in trending markets

1.      Requires extensive data & analysis

2.      Unfamiliar to traders

Econometric models estimate trading costs (regression) nonlinearly related to:

1.      Liquidity: trading volume & frequency, market cap, index membership, spread, price level

2.      Trade size relative to available liquidity

3.      Momentum (up-down market)

4.      Risk (stock volatility)

5.      Trading style (aggressive, passive)

Usefulness in:

1.      Trading effectiveness: comparing actual (ex-post) costs to forecasted (ex-ante) costs to assess execution quality

2.      Portfolio management: trade size/position determination (cost/value)

Traders

Motivation

Preference

Order Type

Information-motivated

Time sensitive information

Time

Market

Value-motivated

Security misvaluation

Price

Limit

Liquidity-motivated

Liquidity & reallocation

Time

Market

Passive

Liquidity & rebalancing

Price

Limit

Others: dealers, day traders

Trading focus/tactic

Strengths

Weaknesses

Motivation

Liquidity-at-any-cost

Quick, certain execution

High costs, info leakage

Information & liquidity

Costs-are-not-important

Quick certain execution at fair market price

Loss of costs control

Various

Need-trustworthy-agent

Broker skill, time to obtain lower price

High commissions, info leakage

Not information

Advertise-to-draw-liquidity

Price, sunshine trades

High admin costs, front running

Not information

Low-cost-whatever-the-liquidity

Price

Uncertain timing, bad news

Value & passive

Algorithmic trading: automated quantitative systems using rules, benchmarks & constraints to exploit market patterns (price/volume) and minimize trading costs & risks

1.      Logical participation strategies:

1.1.  Simple logical participation strategies: VWAP, TWAP, percentage of volume (POV); small break up through time, minimize market impact costs by matching or improving upon the benchmark

1.2.  Implementation shortfall strategies: quick & early (front-loaded), high market impact costs vs high volatility of delay & opportunity costs tradeoff considering risk aversion; objective function to minimize total cost & its variance; full portfolio

2.      Opportunistic (participation) strategies: passive with opportunistic liquidity seizing

3.      Specialized strategies: passive, hunter, market on close price target, smart routing & others

Strategy choice driven by: relative(%) order size, bid-ask spread, urgency of the trade

Best execution (prudence): use of best means to trade securities

1.      Linked to investment decision (cost/value)

2.      Not known with certainty ex-ante, depends on circumstances & parties

3.      Assessed ex-post over time

4.      Ongoing & integrated with relationships & practices

Trade management guidelines:

1.      Processes: have formal policies & procedures that assist in best execution

2.      Disclosures to (potential) clients periodically:

2.1.  Information on trading techniques, venues, agents

2.2.  Conflicts of interest related to trading

3.      Record keeping:

3.1.  Compliance with policies & procedures

3.2.  Disclosures to clients

Buy-side traders:

1.      Should always act in the best interests of clients (first)

2.      Have a fiduciary duty to maximize portfolio value

Trust is more important now: lower commissions, agency/explicit to adversarial/implicit cost shift; new tech venue complexity & anonymity

Performance evaluation: fund sponsor/investment manager perspective; feedback & control mechanism linked to IPS, effectiveness check

1.      Performance measurement (quantification of return, what)

2.      Performance attribution (sources of return, how)

3.      Performance appraisal (raisons for return, why)

External CF: [+] contribution, [-] withdrawal

At end, MV1 after CF

At start, MV0 before CF

MWR (IRR): cash/return dependent, return on average investment

TWR (GM): return per unit of money, data intensive & expensive

LIRR: chain-linked MWR, approximate TWR for CF < 10% of value & low volatility

Portfolio = Market + Style + Active, A = (P - B), S = (B - M)

Benchmarks (asset-based):

1.      Absolute (return objective, MAR, not investable)

2.      Manager universe (only measurable, ambiguous, not specified/appropriate/investable, survivor bias)

3.      Broad market index (specified, measurable, unambiguous, investable)

4.      Style index (maybe ambiguous, maybe not appropriate, maybe not accountable)

5.      Factor model-based (ambiguous, maybe not investable, expensive)

6.      Returns-based (maybe not appropriate, maybe not accountable)

7.      Custom security-based (maybe ambiguous)

Valid properties (SAMURAI):

1.      Specified in advance

2.      Appropriate (style/expertise)

3.      Measurable

4.      Unambiguous (IDs/weights)

5.      Reflective of current investment opinions (knowledge)

6.      Accountable (owned, accepted)

7.      Investable (replication)

Custom security-based (strategy) benchmark: expensive to construct/maintain

1.      Identify manager’s investment process/style

2.      Select securities (including cash)

3.      Weight securities

4.      Review & adjust to process as needed

5.      Rebalance benchmark portfolio on schedule

Benchmark quality tests:

1.      Systematic bias: P = beta(1) * B;  Corr(A, S) = 0;  Corr(P - M, S) = 1

2.      TE(V): low σ(A) < σ (P - M)

3.      Risk characteristics: same exposure to systematic sources of risk

4.      Coverage: high % ratio = (P intersect B) / P

5.      Turnover: low % ratio = (buy-sell of B) / B

6.      Active positions: small (number of) negative active weights

Hedge funds:

MV0 = 0

1.      Value added Rv = Rp - Rb with separate/sum individual long/short positions

2.      Combine returns-based/holdings-based long & short benchmarks

3.      Use of absolute return, style/manager universe benchmarks & Sharpe ratio (but skewness)

Macro performance attribution ($, % incremental):

1.      Net contributions

2.      Risk free asset

3.      Asset categories (SAA)

4.      Benchmarks (TAA, style bias, misfit return)

5.      Investment managers (active return)

6.      Allocation effects (plug)

Inputs: policy allocations; benchmark returns; fund returns, valuations, external CFs

Micro performance attribution (%):

1.      Pure sector allocation:

weighting, [+] * [-] = [-]

2.      Allocation/selection interaction

3.      Within-sector selection: stock picking

Value added return: Rp - Rb

Fundamental factor models in micro attribution (many solutions):

(x 2)

1.      Identify fundamental/economic factors generating systematic returns (market timing, allocation/selection, sector rotation, company size, growth, leverage, financial strength)

2.      Determine active exposures = actual - normal from portfolio & benchmark (normal portfolio) exposures for each factor at start

3.      Determine the active impact: added return due to active exposures of the portfolio (not unexplained return)

Fixed-income attribution (external + management):

1.      External interest rate effect: expected + unexpected return on a passive default free bond benchmark

2.      Interest rate management effect: duration, convexity, yield curve shape change

3.      Sector/quality effect (allocation/weighting)

4.      Security selection effect (picking)

5.      Trading activity effect (plug)

Risk adjusted performance appraisal measures (ex-post):

1.      Jensen’s alpha (SML):

2.      Treynor ratio (SML):

3.      Sharpe ratio (CAL):

4.      M2 measure (CAL):

5.      Information ratio (IR):

active return / active risk TE(V)

Manager Continuation Policy (MPC): manager continual monitoring & periodic review

Value added return = portfolio - benchmark (independent, normally distributed, consistent)

1.      H0: manager adds no value, Rv = 0 (null hypothesis)

2.      HA: manager adds value (alternative hypothesis)

1.      Type I error: rejecting the null when true, keeping bad manager; P(T1) > P(T2)

2.      Type II error: accepting (failing to reject) the null when false (& HA true), firing (not keeping) good manager; P(T2) > P(T1)

Quality control chart: