Matt Armstrong, Analyst, Investment Strategy
The Financial Services Industry is notorious for creating obscure jargon to describe straightforward concepts. Alpha is one of the main offenders within investment language. It is a term typically applied in the context of how much (alpha) a specific investment strategy or manager has generated. It’s considered both scarce and valuable, with successful exponents rewarded handsomely. The presence of financial incentives also offers powerful motivation for the broader industry to devote significant resources each year towards discovering new sources of alpha. So what exactly is it?
At the most basic level, alpha simply refers to the difference between an investment outcome and its associated benchmark (e.g. ASX200, S&P500). Instances of performance above expectations are characterised as good while underperformance is considered bad. The size of the difference is used as a measure of success or failure.
One of QSuper’s Investment Principles specifically focuses on alpha. This principle acknowledges that it can exist in any asset class and outlines a process for inclusion within one or more of our investment portfolios1. Central to this is a notion of reliability. After all, there’s no sense appointing a manager on the basis of their ability to beat a benchmark (and compensating them accordingly), if their ultimate results are not likely to differ significantly from those that could be obtained through random chance. This doesn’t imply managers can never make a mistake or suffer periods of underperformance, merely that there needs to be a sound basis on which to expect long term outperformance. It turns out that separating luck from skill is a much more challenging task than you might initially think.
Rigorous analysis of a manager’s underlying investment decision making process would be an obvious starting point. Unfortunately managers are far more comfortable sharing the outcomes of their process than details of the process itself. This is not surprising given that the costs of (even inadvertent) intellectual property leakage are significant. Next cab off the rank is statistical analysis which consists of mathematical techniques specifically designed to distinguish signals from noise. Used correctly, they are excellent at uncovering relationships and quantifying future expectations. Their major weakness is the (relatively) large amount of data necessary to support definitive conclusions. Most managers don’t have multi-decade track records, and by the time sufficient statistical evidence exists to confirm whether or not skill truly was present, the original principals may have retired.
This leaves us with a market efficiency assessment. It’s based on the fundamental assumption that less efficient markets (e.g. infrastructure, real estate) contain more alpha opportunities and vice-versa. By restricting exploration to asset classes exhibiting more favourable market characteristics, you automatically improve the chances of identifying a suitable candidate.
QSuper utilises not one, but all of these approaches when searching for an investment manager, so you can be confident in the quality and calibre of all appointments.
1. The term ‘portfolios’ is used to refer collectively to the underlying portfolios of assets which in combination make up the individual asset allocations of QSuper Lifetime and the Balanced, Moderate and Aggressive investment options.
The views of the author and those who provide the responses to the comments posted on this blog are not necessarily the views of the QSuper Board. We’ve put this information together as general information only and as such it doesn’t take into account your personal financial objectives, situation or needs. You should get professional advice before relying on this information.
Past performance is not a reliable indicator of future performance. Each of our investment options has a different objective, risk profile, and asset allocation.
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Analyst, Investment Strategy
Matt is responsible for researching alternative investment strategies, implementing and maintaining asset class forecasting models, and providing support to the portfolio managers.