TOPIC 5 : Strategic Asset Allocation Flashcards
(26 cards)
What is Strategic Asset Allocation (SAA)?
SAA is a long-term asset allocation that focuses on choosing proportions of asset classes in a portfolio. It’s the main driver of portfolio returns — more than selection or market timing.
Why do we use indexes to test SAA?
Because we want pure, representative returns for each asset class — not influenced by fund manager skills — to base allocation decisions on.
Why is historical information useful?
It highlights past behavior — returns, variance, and covariances — which can be indicators of future volatility and correlations.
Why might we control software with constraints?
To avoid portfolios with concentrated holdings — pure optimization might produce portfolios with very large weights in a few assets — adding minimum and maximum bounds prevents this.
Why is the main MPT (Modern Portfolio Theory) assumption of market efficiency questionable?
Because markets aren’t fully efficient, future returns may differ from historical patterns — MPT may produce portfolios that are undiversified or unreliable.
Why do we use historical covariances?
To approximate future relationships between asset classes — covariance drives portfolio risk.
Why were equities and small-caps more volatile?
Because small-caps and growth stocks typically experience greater fluctuations — reflecting higher risk and uncertainty — than large-caps or value stocks.
Why were international stocks weak performers in the long term?
This reflects a combination of factors — different economic conditions, currency effects, and lower growth — yielding weak annualized return (about 3.5% over 20 years).
Why were equities more prone to drawdowns and crashes?
Because stocks are riskier and more susceptible to market shocks — financial crises, wars, policy upheavals — which can produce large downturns.
Why do we need to control for constraints when using historical data to form portfolios?
To avoid portfolios that are overly concentrated and unrealistic. Constraints help produce portfolios that reflect reasonable diversification.
Why is volatility a key consideration in portfolio choices?
Because it directly impacts portfolio risk — large fluctuations can undermine financial goals — and it’s a useful indicator of future risk.
Why do we sometimes combine historical data with forward-looking judgments?
Because future conditions may differ from the past — pure historical data may be a poor guide — adding judgment lets us account for future risks and opportunities.
Why might we ignore pure MPT optimizations?
Because ignoring forward view and ignoring constraints can produce portfolios that are undiversified or overly reliant on a few assets.
Why do we need to consider constraints and bounds on weights?
To control for concentration risk, liquidity, and policy limits — yielding portfolios that are more realistic and implementable.
Why are bonds often less volatile than equities?
Because bond prices reflect fixed income streams and maturity payback, making their returns more stable and less prone to dramatic fluctuations.
Why do we say diagonal elements of covariance reflect variance?
Because the diagonal elements (cov(x, x)) represent an asset’s variance — or its squared volatility.
Why do we consider off-diagonal elements of covariance?
Because these show how two different assets move in relation to each other — their co-movements — which is key for diversification.
Why were small-caps and growth stocks more weakly correlated with large-caps and value stocks?
Because small-caps and growth companies often respond to different market conditions, adding diversification benefits.
Why were international stocks weak performers during 2000–2008?
Because many non-US markets were impacted by weak growth, financial crises, and unfavorable exchange rate movements
Why might we constrain portfolio weights to minimum and maximum bounds?
To avoid portfolios that are overconcentrated in a few assets, adding stability and diversification.
Why do many portfolios underperform pure MPT portfolios?
Because pure MPT portfolios ignore real-world constraints, forward view, and judgment — yielding portfolios that may be theoretically “optimized” but practically unsuitable.
Why do we consider volatility to be a forward indicator of risk?
Because historical volatility is often a reasonable approximation for future risk — although it’s not perfect.
Why is return-covariance a key input to portfolio optimization?
Because it shows not just how much each asset varies, but how they move together — affecting total portfolio risk.
Why might we adjust historical data?
To reflect forward-looking conditions — for example, ignoring periods we think are non-recurrent or adding judgment about future policy or economic regimes.
