4. ASSET ALLOCATION STRATS Flashcards
AA performance explanation
Explains 40% of variation of returns among funds
90% of variability of a funds returns over time
- but AA is also decided by client risk tolerance, time horizon, indiv wealth objectives
Objectives of LTAA
- customer profile reqs (financial circ, needs, priorities, capacity tto take risk)
- formulation of strat to achieve income/capital needs
(liabilities, ethical considerations, remaining within risk tolerance, liquidity and accessibility)
widely used approach - LT SAA with TAA overlay
Qual vs Quant approach to AA
Quali = required PM to use skill/judgement/knowledge of markets and econs to establish which asset classes to invest in
Quant - associated with computer algos that analyse large amounts of data to produce optimal short and long term AA decisions
- include buy and sell price targets
- macro trend signals
SAA
Widely used approach is SAA with TAA overlay
SAA is
- relatively static
- usually set using proportions of externally recognised global BM
- peer group/fund compositions
- extensive risk/return numerical data
infrequently changed and reflecting a ‘neutral’ LT position
Usually managers given permitted range to operate within
diversified within and across asset classes
be suitable for client risk profile/needs/circs/time horizon/liquidity needs/liabilities/ESG preferences
TAA
Often used as an overlay to SAA - TAA considered relative to the long term strategic benchmark
chasing alpha by ow/uw asset classes from their LT SAA weight
taking advantage of short term market movements, development
more frequently adjusted - monthly/quarterly
May use derivs to provide tactical overlays given the shorter time period - can deploy rapidly and cost effectively (cheaper and quicker)
Asset liability management (ALM)
= practice of managing financial risks that arise due to mismatches between the assets and liabilities as part of an investment strategy
ALM = common with pension funds where this is a future liabilty to be met - penalty for not funding liabilities can be severe with legal and reg consequences
MVO formulas
MVO - Mean Variance Optimization
in 2 asset portfolio - MVO will give you the optimal proportions of each asset to hold
M = optimal portfolio = highest excess return per unit of risk on curve (tangent on curve from RFR = capital markets line)
MVP = minimum variance portfolio = left most point on curve and least risky portfolio
Efficient frontier in bold = optimal portfolio for any given level of risk
Involving 3rd asset = rfr -can borrow @ risk free rate and invest in M OR invest in M and save at RFR
curve is a plot of all all the permutations of Wa/Wb
Issues with MVO
Historical data used to estimate future returns/risk which may not be predictive
MVO models v sensitive to changes in estimates in expected returns and can generate unreasonable solutions
How to cal weightings for MVP
Weighting in other asset = 1-answer
CovAB = CorrAB x SDA x SDB
Constrained vs unconstrained MVO
Standard MVO
- No negative weights
-Weights must add to 100
Black theorem allows you have to have negative weightings and gearing (>100%) = unconstrained MVO
Shifts efficient frontier up and left = higher return with decreased risk through gearing and shorting (HF love this topic)
Obvs only works like this when longs/shorts work the right way or efficient frontier can move down and right
Black Theorem and Unconstrained MVO
THEORY IS with the two corner portfolios you can recreate the entire efficient frontier
MVP = corner 1
100% in most risky asset = corner 2
Theory also says - there is a portfolio with zero correlation to M. Rz tangent to M - may give better combo of returns than investing in the rfr
Risk budgeting
Involves setting LT plan of risks to take on portfolio - rive tolerance of investor is critical
- set tolerable risk target for portfolio over specified time horizon
- construct portfolio that matches target risk profile
- implement policy and manage so risk remains close to target
e.g. equal distribution of risk with risk parity management
Risk premia/return driver approach
Approach diversifies portfolio @ level of risk and return streams
Control £ value of risk buckets instead of £ value of asset classes
HF stats aim to systematically isolate and harvest excess returns from exposure to specific risk factors e.g. rates, inflation, credit, growth, value etc
Useful given traditional asset classes have varying level of correlation - e.g. HY bonds may be correlated to equity, as with infra trusts - may be running more equity risk than intended
New approach - may need to reskill IMs
Additional complexity - does this raise costs to client?
Market timing
Assumes markets are inefficient at least ST - rejecting strong form EMH - markets must over/underreact to new info and mispricing can therefore be exploited