Performance Measurement and Attribution
- DST Anova
- SS&C Sylvan
- Eagle Performance
- StatPro Revolution
- CIBC Mellon Performance Measurement
- Market Street Advisors Archer
- Wilshire Analytics
A recurrent theme here is that it's all about the data - the computer science basic rule of "garbage in equals garbage out" applies here to a great extent. Let's work through a simple example.
At time t a portfolio manager decides to sell stock x at a limit price of $5.08. At t stock x the last traded price for x was $5.10 and the market bid/ask is $5.09/$5.11.
For other exchange traded instruments such as options and futures
Similar to equities in the sense that there is an exchange and therefore a stream of pricing information.
For foreign exchange and fixed income
The problem here is simple - how do you compare an executed price level to a "market" when the market is decentralised and trades over-the-counter. There are a number of ways that firms perform this function but there is a greater degree of uncertainty in the significance of the results.
And for Performance Measurement and Attribution?
Another simple example. An equity investment portfolio of size z has a mandate to invest in the FTSE 100 stocks and to maintain a cash balance of as near to zero as possible. As such, a decent benchmark may be 99% of the performance of the FTSE-100 plus 1% of the performance of cash.
- Asset Allocation. The manager has invested 100% in equities and 0% in cash, whereas the benchmark is 99% equities and 1% cash.
- Stock Selection. The manager has invested all of the portion invested in equities in one stock rather than spread between the stocks in the FTSE 100 index.
- Stock selection effect
- Asset allocation effect
- (Sometimes) Interaction effect
- Stock and cash positions at start of period (period being defined by the degree of granularity required - nowadays this may be at the level of intraday, historically often on a month-by-month basis).
- Stock and cash positions at end of period
- Cash inflows during the period
- Cash outflows during the period
- Stock prices at start of period
- Stock prices at end of period
- Cash interest rate over the period
- Index/indices weights at start of period
- Index/indices weights at end of period
- Index/indices changes during period
- Index/indices level at start of period
- Index/indices level at end of period
- Composite index weights at start of period
- Composite index weights at end of period
- Composite index changes during period
For a fixed income portfolio
There are a few challenges here:
- Bond valuation. For an equity a simple measure is used - the mid-price on a particular date. That's often flawed since if the holding of the equity is large relative to the available market liquidity then there would be market impact if a portfolio manager actually tried to trade at that level. However, this is an accepted limitation. The challenge for fixed income is that there is not really a worthwhile mid price available for the majority of bonds in size. As such this is a matter for debate and in some cases this is a modelling exercise rather than a true valuation exercise.
- Universe size. For an equity portfolio the universe is generally clear and quite small. For fixed income portfolios the universe can be huge see for example here.
- The nature of fixed income returns is different. An equity portfolio has stock selection and asset allocation effects. A fixed income portfolio has returns from changes in spreads, changes in the slope of the yield curve and overall yield curve level.
- In some cases, rather than benchmark to a constituent level index a Fixed Income portfolio may look to other measures such as yield or duration.
And for Behavioural Analytics?
- Price of W at start of year = W0
- Price of W at six months into the year = W6
- Price of W at end of the year = W12
- Price of X at start of year = X0
- Price of X at six months into the year = X6
- Price of X at end of the year = X12
There are a range of further analytics possible here - one could be to judge performance of a holding versus a ex-ante price target. In some cases, portfolio managers sell when a target is hit, sometime they will hold anticipating further gains. Can an analysis of sales made and not made when targets are hit reveal a behavioural bias? That could be holding a position for too long or selling too quickly. If the analysis shows that the positions are sold too soon, then perhaps the evidence points to research and analytics providing target prices that are not punchy enough.
Another way to view this is that the "anti-monde" construct (the counterfactual case - what would have happened instead of what did happen) for this analysis is based predominantly upon the behaviour of the portfolio manager rather than an index or composite.
So how does this work? The role of the data scientist is key to the delivery of meaningful behavioural analytics but this requires all of the groundwork to be completed - the data requirements for a sensible anti-monde can be onerous for an absolute return portfolio with no index weight style benchmark. Consider the case where the price of X halves due to a two for one stock split - unless the data is adjusted to compensate this will lead to incorrect conclusions. Hence the GIGO problem.
One can almost argue that TCA and PMA are rather like crosswords (finite solution set) whereas behavioural analytics can take the form of a murder mystery - tracing the decision making process through the firm.