Asset Allocation

Framework for capital markets expectations

1. Specify the set of expectations needed including the time horizon to which they apply

2. Research the historical record

3. Specify the methods and/or models that will be used and their information requirements

4. Determine the best sources of information

5. Interpretation of current investment environment

6. Provide the set of expectations set out in Step 1

7. Monitor actual outcomes and compare with expectations

Challenges in developing capital market forecasts

1. Limitations of economic data

2. Data measurement errors and biases

3. The limitations of historical estimates

4. Ex-post risk can be a biased measure of ex-ante risk

5. Biases in analyst’s methods

6. The failure to account for conditioning information

7. Misinterpretation of correlation

8. Psychological Traps

9. Model Uncertainty

4) Ex-post risk can be a biased measure of ex-ante risk

may reflect a fear or concern that never materialized

returns would show the effect, but economic data would not

6) The failure to account for conditioning information

are there relevant new facts in the present when forecasting the future

Anchoring trap

initial impressions, data, or estimates - discuss anchor subsequent judgments

Status-quo trap

perpetuate recent observations

predict no change from the recent post

commit an error of omission rather than an error of commission

Overconfidence trap

overestimate the accuracy of forecasts

tending to believe that most people share our views

predicting to narrow a range of possibilities

Prudence trap

tendency to temper forecasts so that they do not appear extreme

Availability bias

tendency of forecasts to be - discuss overly influenced by events that have left a strong impression

a personal/vivid experience comes easily to mind, so you give it too much weight when assessing what could happen

Exogenous shocks

events from outside the economic system that affect its course

can be positive or negative for growth

typically not built into prices → unanticipated or only partially anticipated

policy changes, new tech, geo-politics, natural disasters, financial crisis etc

Approaches to economic forecasting

1. Econometric modelling

2. Economic indicators

3. Checklist approach

Checklist approach to economic forecasting

forecaster asks a series of questions about the economy (substantial amount of subjective judgment)

Business Cycle Phases

Initial recovery

early upswing

late upswing (contraction)

slowdown

recession

Initial Recovery (Business Cycle)

economy picks up from the slowdown/recession

business confidence rises, consumer confidence low

fiscal/monetary stimulus still present

typically matched by an inventory upswing CME effects/

gov't. yields start bottoming

Stocks rally sharply

cyclical assets & riskier assets outperform

Early upswing

confidence grows, economy gains momentum without inflation, unemployment starts to fall, business investment picks up

CME effects: short rates begin to move up, long rates stable

stocks → rising

Late upswing (contraction)

output gap closed, inflation looming, confidence high, unemployment low, wages grow

CME effects

• interest rates rising

• stocks rising but volatility increases

• monetary policy may shift from neutral to hawkish

• short-term interest rates, bond yield do too but less

• yield curve steepens

Slowdown

economy slowing, business confidence dropping

inflation strong, downswing in inventory cycle

CME effects

• short rates are topping out

• bonds begin to rally → long bonds by more

• yield curve often inverts

• stock soft = falling

Recession

large inventory pullback, large drop in business investment, durable goods sales drop

consumer and business confidence decline

CME effects

• short rates drop as do all bond yields

• stocks drop & bottom before end of recession

Econometric modelling

quantitative modelling grounded in economic theory using only economic variables

(e.g. structural models reduced-form models)

one, or a system, of equations, estimated with OLS, used to forecast the future value of economic variables - exogenous variables supplied by forecaster

Economic indicators

economic statistics provided by government and private organizations that contain information on economy’s past (lagging), current (coincident) and future (leading) performance

YTM

YTM → PV(CF) = Po

It is the rate that would make all the present value of future cashflow equal to the current price

MacDur

MacDur = ModDur(1+YTM)

the weighted average time (in years) required for an investor to recover the price of a bond through its cash flows

Building Block Approach

one period default- free rate is

• one period = forecast horizon (complicates term premium)

• or/ (more commonly) return generated by rolling over the 30-day T-Bill over the forecast horizon (keeps the term premium clean)

4 main drivers of term premium (maturity premium)

1. level-dependent inflation uncertainty - higher level of π (inflation rate), higher θ (inflation uncertainty

2. ability to hedge recession risk

   • good hedge against recession = low term premium

3. supply/demand of bonds at various maturities

4. cyclical effects - shape of the curve changes over the course of the business cycle .

Credit premium

Credit premium = expected loss + risk of default or credit migration

Conclusion: credit spreads driven primarily by credit premium + financial market conditions, but only secondarily by expected level of default

Liquidity premium

Liquidity for bonds are better when:

• priced near par

• relatively new

• from a relatively large issuer

• from a well known issuer

• standard/simple structure

• high quality

Forecasting equity returns

1. historical statistics approach

2. Discounted cash flow approach

3. risk premium approach

Grinold-Kroner Model

Gordon Growth Model (GGM)

Two risk premium approaches

ERP

equilibrium approach - (Singer & Terhaar)

Equity Risk Premium (ERP)

historical equity premiums are subject to substantial estimation error

ERP = historical equity returns - historical risk-free returns

Equilibrium approach (singer & Terhaar)

Real estate risk premium

Term premium + credit premium + equity risk premium + liquidity premium

Risk premium should be between corporate bonds and equity

Term premium: long-lived asset, very sensitive to level of long-term rates, therefore high term premium

Credit premium: exposed to the credit risk of tenants, lease → bond-like cash flows, therefore credit premium commensurate with the average tenant credit risk

Equity risk premium: relative to corporate debt, owner bears full risk of fluctuations in property value, (+ uncertainty in rent growth, occupancy etc)

Real estate in an equilibrium framework

1. unsmoothed data series

2. equilibrium models assume fully liquid assets, therefore add a liquidity premium

3. far less integrated (.50-.75), more closely related to local economic factors

Exchange rate influences

1. trade flows

2. purchasing power parity

3. competitiveness and sustainability of the current account

Expected percentage change in the exchange rate

Forecasting volatility methods

1. Sample statistics

2. VCV-matrix from multi-factor models

3. Shrinkage estimation of VCV-matrices

4. Estimating volatility from smoothed returns

5. Time-varying volatility: ARCH models

VCV = Variance-Covariance Matrix

Sample statistics

estimating a constant VCV-matrix

computed from historical return data

with small sample periods, cannot be used to estimate VCV-matrix for large number of assets

subject to substantial sampling error given typical small sample sizes

VCV-matrix from multi-factor models

Number of assets can be very large when exposures are to a small number of common factors

will contain less estimation error than using small historical samples

will also improve cross-sectional consistency

will handle a large number of assets

BUT- factor models are rarely correctly specified, therefore VCV-matrix will be biased and inconsistent

Shrinkage estimation of VCV-matrices

Estimating volatility from smoothed returns

Ex: real estate, private equity, hedge funds

smoothed returns dampen volatility, distort correlation\

Traded REITs correlation with equity (short vs long-run)

Highly correlated in the short-run less correlated in the long-run

Real estate expected return (long-run)

Real estate expected return (short-run)

g = growth in NOI, NOI/p = cap rate

Metrics for investing in emerging market debt

• Fiscal deficit/GDP: < 4%

• Debt/GDP: < 70%

• Current account deficit/GDP: < 4%

• FX reserves / short-term debt: > 100%

3 levels of governance structure

1. governing investment committee

2. investment staff

3. third-party resources

6 common elements of effective governance

1. articulate the long and short-term objectives of the investment program

2. Allocate decision rights and responsibilities among the functional units in the governance hierarchy effectively

3. Specify the process for developing and approving the investment policy statement

4. Specify processes for developing and approving the strategic asset allocation

5. Establish a reporting framework to monitor the programs progress towards the agreed don goals and objectives

6. periodically undertake a governance audit

3. Specify the process for developing and approving the investment policy statement features

Statement of investment objectives

Investment constraints

Duties and responsibilities

• investment committee

• Investment staff

• 3rd party providers

Investment guidelines

Reporting

Extended portfolio assets and liabilities

human capital

PV of pension income

PV of expected inheritance

PV of future consumption

3 broad approaches to asset allocation

Asset-only

• focus solely on the asset side of the investor’s balance sheet

Liability-relative

• choose an asset allocation in relation to the objective of funding liabilities

Goals-based

• used primarily for individuals & families

• Specify asset allocations for sub-portfolios, each of which is aligned to specified goals

3 super classes of assets

1. capital assets - an ongoing source of something of value

2. consumable/transformable assets - commodities

3. Store of value assets - currencies, gold, art

Criteria for specifying asset classes

1. assets within a class should be relatively homogenous

2. asset classes should be mutually exclusive

3. asset classes should be diversifying

4. asset class as a group should make up a preponderance of world investable wealth (large enough to be widely investable)

5. asset classes should have the capacity to absorb a meaningful proportion of an investor’s portfolio (liquidity and transaction costs)

8 steps for asset allocation selection

Mean-variance optimization formula

Human capital (equity vs bond split)

Most human capital is 70% bond like and 30% capital like

Absolute contribution to risk (ACTR)

MCTR - marginal contribution to total risk

Liability relative asset-allocation approaches

1. Surplus optimization

2. Hedging/return seeking portfolio

3. Integrated asset-liability approach

Surplus optimization

an extension of mean variance optimization (MVO)

optimize over all assets and liabilities

institution is constrained to hold this ‘negative asset’

outcome optimizes over asset surplus

Hedging/return seeking portfolio

two-portfolio approach most appropriate for conservative investors or when the size of the PVL is large in relation to the size of the firm

Basic - surplus available

1. hedging portfolio

2. return seeking portfolio

Variants - deficit

1. partial hedged portfolio

2. return seeking portfolio

Integrated asset-liability approach

when decisions regarding the composition of liabilities must be made in conjunction with the asset allocation decision

• e.g./capital-adequacy ratios

• often implemented in the context of multi-period models

decisions about asset allocation will affect the amount of business

• loans a bank can make, policies an insurance company can underwrite

Pension funding ratio

After-tax return formula (interest and equity)

After-tax standard dev

Taxes hurt gains and help losses therefore reduce the standard dev on expected return

Tactical Asset Allocation (TAA)

• business or monetary cycle opportunities

• temporary price dislocations

• objective → increase risk-adjusted return by taking advantage of short-term market conditions

• predicated on the belief that some short-term returns are predictable

Discretionary TAA

based on manager skill in predicting & timing short-term market moves

Systematic TAA

based on technical analysis/market signals

look for asset class level return anomalies

• (value, momentum, valuation, trend-following)

Loss Aversion - Behavioural Bias

may interfere with an investor’s LOS e ability to maintain the SAA through - identify periods of negative returns

People feel the pain of a loss more strongly than the pleasure from an equivalent gain, This can cause investors to hold losing investments too long because they don't want to realize the loss, while potentially selling winners too early.

Illusion of Control

believing you have more influence over outcomes than you actually do

may result in:

• extreme TAA (all in/all out market calls)

• belief in superior resources (institutional investors)

• excessive trading, use of leverage

• eliminating/shorting asset classes

Mental accounting

treating money differently depending on the mental “bucket” you put it in, even though money is fungible.

goals-based investing incorporates this bias into the process

Representative Bias

Seeing a pattern/recent trend and assume it is representative of what will happen going forward.

• ex: this company looks like the next Apple, so it will probably be hugely successful

• Equities have recently done well so they will continue to

TAA particularly susceptible to this bias (e.g. return chasing)

Framing Bias

choice of asset allocation may be influenced merely by the manner in which the risk-to-return trade-off is presented

Availability Bias

giving more weight to information that is familiar or easily recalled

Ex home bias, investing more in your own countries economy

Grinold-Kroner model (long-term)

Taylor Rule

Nominal GDP growth

Nominal GDP growth = Forecasted increase in labor input + Forecasted increase in labor productivity + Forecasted inflation

Monte Carlo Simulation

• potential future outcomes

• likelihood of meeting various goals

• distribution of the portfolio’s expected value through time

• potential maximum drawdowns

• the effects of trading/rebalancing costs & taxes

Criticisms:

• outputs highly sensitive to small changes in the inputs

• the asset allocations tend to be highly concentrated in a subset of the available asset