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Lec1PortfolioManagement optimizing ‘utility’. The M PT notion that all investors will hold the ‘tangent portfolio’ with maximum Sharpe ratio estate, global small cap Alpha or beta?a) Enhanced index – Both. Mainly beta, b) ‘Equity long-short’ hedge fund – PM process①Plan(Governance,Objectives&constraints, Asset allocation)②Implement(Asset class strategy & portfolio assume the existence of a risk-free asset and the ability to borrow (i.e. leverage the portfolio). This assumption is Should be pure alpha, providing market exposure neutralized by longs and shorts..c) ‘Macro’ hedge fund – Seeks alpha structure, Manager or asset selection, Execution)③Review(Performance measurement &evaluation, Monitor& adjust) tenuous in practice.⑧Need to settle on a specific investment horizon. As an essentially single-period model, a choice of by timing beta. d) “Opportunistic” direct property fund – Combination of both alpha and beta. There is also an element Agency problemP1(Key)fund management companies& fund managers:↑fees and FUM (profits and bonus motives); specific investment horizon needs to be made to do the analysis, e.g. quarterly, yearly, multiple years, etc. This choice is of returns attributable to the management process, although they do not fully adhere to the traditional view of alpha as investors: max portfolio return①Managers:keephigh fees, but this ↓ investorreturns.②If FUM increase beyond not straightforward, and can often be arbitrary. Further, the choice matters to the results if returns are not skill-based. Some returns also arise from adding economic value to the underlying assets thems elves. reasonable capacity for strategy, returns are eroded③Result: the value-add from investment s kill may be secured by iid⑨Assumption of normal returns may not be accurate (although it may act as a working approximation - hence Lec6Equity:Role①Widely available – the ‘key’ growth asset, easy access,dorminant portfolio,②Dominant source of manager themselves, rather than accruing to investors.P2Managing to benchmarks①Benchmark need not align with analysis may still be valid to an extent).⑩Implementation matters to outcomes, but is not reflected in the analys is, e.g. both risk and return in most portfolios, high weight in equity; higher risk, higher return; equity 可解释 90%portfolio variation ③ Liquid(mostly) ④ Relatively efficient markets: excess returns hard to come by:Equity market highly investor objectives②Mangers may game benchmark by seeking risk premiums or off-benchmark exposures③Varying accessing and managing the assets; impact of intra-period cash inflows and outflows. risk to manage relative performance (i.e. hug benchmark if ahead, go for broke if behind). Investor gets not enough risk, Lec4Asset Assumption Data-based vs model-based:①Non-parametric/data-based: analysis performed by drawing competitive, it depend on sub-class, small cap/EM less efficient than US ⑤Focus for attention and resources, huge or too much.P3Peer ranking considerations①Reluctance to try something new, even if in investor’s best interest, as directly on the actual data series:Simulation by randomly drawing from the data(Bootstrap)/Use historical data series to amount $ invested in equity market.Drivers:fluctuation reason:Fundamental view: Change in expected cash flows raises risk of underperforming peers②Manager “career risk” (i. e. risk of losing one’s job due if one of the worst analyze a ‘hypothetical’②Parametric/model-based:imposes s ome structure, a model to repres enting the (profitbility)/in discount rate. Macro: Economy positive corr. Stock;lag stock market; Profit-share: wage rise then performers) leads to benchmark hugging and other forms of herding.P4Ongoing evaluation of manager performance return-generating process(a)Simple M V(b)Factor-based models(e.g.CPAM,FF,Carhart,industryfactors)(c)More complex profit&share drop; Inflation: inflation rise, dis count rate drop; Resource utilization: low capacity correspond recession ; (‘short-termism’,i.e.managers trying to max short-term returns not longer-term performance.)P5Incentives to increase distributions,e.g.copulas,mixed–Factor-based models(d)Stochastic asset models.Data choose:①Measurement interval Monetary environment (equity booming because monetary policy is easy; low interest rate cause easy borrow& increase risk when ‘optionality’ exists for manager①Options are more valuable under high volatility=>incentive to take –Align with investment horizon if possible (Comment-holding or investment period versus review period) –Influence of liquidity,cause bullish market ); Buying and s elling power (funds flows: a lot s elling cause equity market drop, portfolio risk②Investor subjected to more ris k than appreciated③More of an issue in hedge funds, private equity)P6Brokerage serial correlation/appraisals/thin trading fades as measurement interval is lengthened–Longer intervals = less data positions) Risk aversion& risk premiums: high risk aversion cause drop in equity value; Valuation (relative, absolute) and other costs paid by investor, but controlled by the manager:Open for abuse by manager–brokers can offer benefits to points②Timeperiod:Longer= more opportunity to see the entire distribution/Longer=more exposure to any structure Risk & return within equity portfolios: Tracking error:1In equity, typically measured using benchmark equity manager (e.g. entertainment) to get them to trade. Eg: ‘soft dollar’ transactions (these involve brokers paying for certain change.Lack of reliable data history:Problem:new asset(no retuen history)/illiquidity (appriaisal bas ed)/structural index.2Historical / realized vs Predicted (factor model). Beta(eg. single factor CAPM, multi-factor models)Common 1 ‘style’:How a services in return for a promise to pay brokerage - mostly frowned upon)P7Commissions on product sales(Investor change(decades old data still relevant today?)/latent(unobserved)risk:(a)liquidity back holes:flood of s eller&no buyers factors: 1.Carhart 4-factor model (Fama-French 3-factor + price momentum) 2.Investment style:○ 2 Value investors find undervalued stock, hope they turn directed towards managers paying highest front-end loads and trailers, not necessarily best managers for investor. ) causes liquidity dries up like credit mkt in GFC(b)peso (Taleb)problem:return series that can appear deceptively low-risk manager approaches investments the way manager select. ○ with steady returns but it experiences periodically catastrophic drawdowns like dodgy gov,naturalization¤cy around. Growth investor will go for stocks they expect to have earing growth rate beating the broad market TYPES OF INVESTORS 3 .Identified by: a Return correlations: same style managers tend to be highly correlated. b.Portfolio characteristics: collapse in South Americanbond mkt in 70s-80s)/large outliers in the data) Response (Interpolate from like as sets given ○ Private DC DB Endowments Sovereign Insurance Fund Investors, Pension Pension & Wealth fundamental nature:pubic equity exposure is consideriably low/ Draw on other representative data/ Adjust the available whether stocks are value or grow in nature. c.Factor exposures:Factor model or regression. d. Philosophy and process. Companies Foundations Managers including Funds Funds Funds SOME OBJECTI VES: 5 US: common to market (and benchmark) fund by SMSFs 4. Style divers ification important in multi-manager investing. ○ data seriesi:if structure changes,shorting the period or remove the outlier before changes happen/ Ad-hoc ○ Maximize E[U(W)] or E[U(C)] ? ?  6 AU: Style is far less distinct 3.Industry:fund is highly adjustments:made or happening only for a purpose or need,not planned before it happen) Methods to calibrate styleValue /growth, Size (large, mid, small), ‘Market-oriented. ○ Meeting Liabilit ies   ? ? expected return:1.Sample mean:assumes the future will look like the average of the past&issue for asset classes with exposed to specific industry 4.Factor models, eg. BARRA,Northfield, proprietaryStock-specific risk (should be largely Minimiz e Contributions  clear time series trend; 2.Implied views-- expected returns for a particular portfolio of assets and covariance matrix(a)let diversified away).Sub-clas ① Domestic equities(Familiarity, easy access; a limited universe) ② Overseas equities Impact on Profits    the model determine expected returns conditional on the variance-covariance metrics so that a user-specified portfolio (Diversification potential;currency, access can be harder)③Large/mid/small cap(Trade-off between liquidity/capacity & Consumption in Retirement (the benchmark) is on the efficient frontier(b)Benchmark should be a reasonable portfolio that reflects available assets market inefficiency) ④ Emerging markets- Frontier markets(High return; high risk (beta), liquidity/capacity/access   class(e.g.peer group risk)(c)mitigates the hypersensitivity problem of M-V model3.Impose expected returns.a) Asset issues- Even further out along the spectrum)⑤Specialist funds-sector, country, regional (Targeted exposure, specialist Support Spending Programs  ? ? pricing model, e.g. cross-sectional risk (i.e. factor) model. b) Linking to economy. (St=GDPt*Kt*Mt)Decompose stock management; constrained in breadth of potential bets)⑥Global TAA(Market timing at sector/country/ currency level) Absolute targets e.g. wealth accumulation or   ? ?  market level (S) into product of GDP, share of earnings in GDP (K), and PE multiple (M).Price Return ≅ Nominal GDP Accessing:Ways of investing: Direct/Investment managers (closed-end or open-end) Fund-of-funds (popular in PE HF, protection, returns (CPI-plu Relative targets Growth Rate)c)Investor’s forecasts.‘equilibrium’ 4. Bayesian techniques, e.g. James Stein- combine prior information diversification benefit multi-manager)Passive: Index funds/Exchange-traded funds (ETFs)/Futures Active: Traditional  ?  ? e.g. return vs benchmark, with new information to generate a more refined estimate-“shrinking” the individual sample means toward a common long-only/Active extension(can s hort),eg.130/30/Long-short hedge funds.Other (inc ‘Smart Betas’): Enhanced index peers Other: ? ? ? ? ? ? ? value referred to as the grand mean5.Mixed estimation, eg. Black-Litterman-combine implied views with specific funds (passive+small active position, keep low tracking error)/Factor mimicking(mimicking size value of index) - Sustainability, e.g. ESG, SRI - Public Policy Impacts ?  investor forecasts.Tu4Considerations in selecting measurement interval trade-off including the following three /Fundamental indexation /Minimum variance(choose index, then minimum variance in portfolio) - Regret ? ? ? ? ? ? ? considerations:1.Ideally align measurement interval with investment time horizon (or ‘review’ period) if practical.2. Lec7Fixed Income:Role①Volatility reduction - ‘ballast’ for the portfolio (lower risk asset class provides stability, the - Career of manager ? ? ? ?   Finer data means more data points. This is useful if there is a requirement to res trict the time period from which the data avergae SD of FI is way lower than that of equity)②Diversifier of risks: Economy: to an extent (every new asset you - Enjoyment ? ? ? ? ?   Lec2Objectives&Dimensions of risk Risk measures①MaximizeE[U(W)] Ass et/portfolio volatility (drops) ②Meeting is drawn in order to generate ‘fresh’ estimates of risk measures s uch as standard deviation and covariance. An example bring in that is not perfectly positively correlated with your portfolio will have diversification properties); Deflation: liabilities Surplus volatility/shortfall③Minimize contributions Shortfall④Profit impact Profit volatility would be if there has been a structural change so that older data becomes less relevant. Estimates can be drawn from the increasing purchasing power of nominal coupons; Equities: low correlation with FI(on average, the corr. between equity ⑤⑥⑦Consumption in retirement/ Support spending programs/Absolute targets: Shortfall⑧Relative: benchmark/peers recent part of the available history by cutting the data into finer intervals (e.g. monthly instead of annually).3.Prevalence and FI over the past hundreds of years has essentially to be zero, very slightly positive)③Liability matching (LDI) (FI Tracking error⑨Regret: Probability of wrong choice Issues with quantitative risk measures(mean, variance): of serial correlation and/or measurement lags in the data should be considered, e.g. thin-trading/appraisals.Lengthening has a higher corr. with the liabilities in your fund compare to equity(surplus risk))④Source of ‘alpha’ generation? Mixed ①Characterizing the distribution-Parametric (model-based) vs non-parametric(data-based):Instability in return the measurement interval will dilute the impact. The relevance of this issue can relate to the type of assets beingevidence that FI managers outperform; Often you end up with market bets like interest rate direction (duration) and generating process(volatility varies)/Investment horizon(relative risk between asset class vary with different investment analyzed(eg.liquidity, valuation method).Considerations in selecting measurement period1.Availability of data credit exposure; problems with FI benchmarking and performance evaluation.(for FI, only a narrow indice as the horizon)/Data problems:limited history, thin trading/appraisals②True nature of risk can be hidden (GFC:low i.r, easy 2.Which period is most representative looking forward3.Whether any instability can be successfully modeled Benefit benchmark and it is not easy to replicate) Sub-class: Sovereign debt/Corporate credit(Investment grade; High yield)/ credit,highed leveraged property mkt, regulationgap,issues with rating agencies, mismanagement of risk)③Much matters from adding new assets(Potential diversification benefits): Adding new assets offers the potential for a more efficient Structured s ecurities(‘alphabet soup’)–ABS,MBS,CMBS,CMO,CLO,CDO,CDO2,/Emerging market debt/Bank loans/ that cannot be easily incorporated –Possibilities in extreme markets (‘black swans’:the Russian gov.’s default for portfolio, with a higher Sharpe ratio. This can work because the benchmark portfolio does not hold all available assets, Inflation-linked(or index-linked, indexed,inflation-protected)/ Derivatives(futures,options ,interest rate swaps,CDS) Risk: LTCM)/Business ris k, personnel risk, counterparty risk, governance-related failures, changes in public policy/Funding or hence may be sub-optimal;adding new assets may generate a higher efficient frontier. Parametric vs non-parametric ①Interest rates: Price fluctuations (when interest rates↑, bond prices↓)/Reinvestment rates (coupon-paying: reinvest cash flow uncertainty, and illiquidity/Personal assets, eg. human capital,family home)Investment horizon effects −Non-parametric methods two advantages:1. Can be more effective where return distributions are complex(data do not coupon; zero coupon: multiple period investors reinvest)②Credit (e.g. default risk)③Liquidity (many debt markets less ①Changes in investment opportunity set②Risk of capital loss vs reinvestment risk③Serial corr.–Positive (several occurexactly in the same sequence it occur, u can mix it up&Statistically, generate far more database to analyze)e.g. liquid than equity and can dry up in times of crisis, eg GFC)(U.S. on the run bonds are one of the most liquid market. days)=> ‘momentum’–Negative(long time) Investor directed towards managers paying highest front-end loads and non-normal.2.Readily supports simulations (bootstrap) without having to impose a particular joint distribution But, even the U.S. treasuries market, if it goes off the market, liquidity will start to dry out.) ④Optionality (hence trailers, not necessarily best managers for investor.=> ‘mean reversion’–Discussion: short vs long term④Influence of -providing the data is independent and has no significant time dynamics, e.g. serial correlation. Parametric(hypothetical) volatility), e.g. corporate bonds with puts or calls; redemption option in MBS (Mortgage Backed Securities)(when the measurerment period under thin trading or appraisal valuations(can result ‘smoothed’ return=>understate risk). methods-two advantages:1. Easier to generate assumptions for assets where historical data is either non-existent or int rate drop, borrower could redempt, but the lender los e)⑤Currency (typically hedged; but some managers take R2.Liability-driven Investment Management①definition:LDI stragety s eek to manage the gap between the ass ets unreliable. Eg.under mean-variance analysis an asset can be incorporated or revised inputs be invoked by specifying bets.won’t look at unhedged FI) Credit as equity beta exposure: poor economy => lower profits + more bankruptcies and liabilities.②Immediate Risks-Interest Rate risk;Market risk;Assumption risk③Emerging Risks -Longevity E[R], standard deviation and correlations.2. Time dynamics and conditioning can be handled within parametric models, /higher beta, more vulnerable to economic downturns; The “structural” view (Black & Scholes, Merton) Debt holders risk;Inflation risk;Operational risk;Legislative risk;Business (peer group) risk④LDI Strategy: Strategic Asset e.g. VAR (vector auto-regression) models. Dealing with new assets with no history1. Extrapolate from like assets, e. g. have written a ‘put’ to equity holders; Value of put dominates as asset value nears ‘exercise price’; Underpins credit Allocation involves setting the strategic allocation of the assets backing the benefit liability in a way that reduces the treat asset as part equity, part FI, plus idiosyncratic term2. Use a factor model, specifying factor exposures and hence models s uch as Moody’s KMV; Credit return spreads are correlated with equity returns; equity betas rise as credit rating mismatch risks between the assets&liabilitiesso reduces the impact of market and interest rate risk.Derivatives Strategies E[R] given nature of asset, e.g. nominate a plausible beta if operating within the CAPM3. Make ad hoc assumptions (last deteriorates. Key Driver: interest rates(for FI port, ↑duration(a proxy: with greater duration,you see greater price involves entering into certain derivative contracts or purchasing particular instruments that might hedge part of the resort when modeling)4. Employ some judgment, e.g. the ‘stage 1 / stage 2’ approach discussed by Ansley (2004); changes to a 1% change in i.r.) would ↑volatility; for the multi-asset class port, low corr between equity and int.rate risk would diversify the rsk) credit (for FI pf, high yield ↑ from 0% to ~30%, FI volatility ↓ (diversification due to risk.Dynamic Asset Allocation a range of strategies stretching from mechanistic application of portfolio-insurance style fundamental risk approach as per Warren (2008) strategies through to tactical strategies that seek to exploit views on interest rates, equity premia etc.Special Purpose Lec5Alpha&BetaLDI Liabilitycan be viewed as a negative asset Difference: it is not usually a choice variable Trade-off:equity like nature of Junk bond, BUT for whole pfs, diversification benefit disappears (as already have equity) reason Vehicles includes special purpose vehicles offered by investment banks that offer an agreed stream of cashflows.Annuity Surplus risk vs.expected return (or cost) •Measure: 1）Surplus (Deficit) = Assets – Liabilities 2）Funding Ratio = Assets why duraiton need not↑total port risk: depend on corr of equities vs bond(average close 0, slightly +) ; when eco↓, FI Contracts It may be poss ible to purchase annuity contracts from major onshore, or potentially offs hore, insurers. / Liability One approach: Identify minimum risk portfolio, i.e. bes t liability hedge b) Find preferred position, i.e. hedge equity at their best, help hedge equity mkt risk; LDI(the best match for long duration liabilities is long duration Reinsurance:a form of insurance contract that provides cover under s pecified (usually extreme circumstances). consider taking on risk to increase return (or reduce cost) Another approachbased around cash flow matching. assets, long duration FI)) Accessing①Traditionally an institutional mkt ②Portfolio management comes in various Managing Liabilities Proponents of LDI strategies focus primarily on solutions that affect the disposition of fund Implementing LDI 1.Identify the measure of liability value (and hence surplus)–For a DB fund, this migh...