Evaluating investment managers is a difficult undertaking. Why else would asset owners spend a lot time and resources, often with the assistance of consultants, conducting manager searches? Properly choosing and evaluating managers requires thorough due diligence, but a comparatively easy filter can function a helpful initial number of potential investment managers.
There are three basic questions asset owners should ask any quantitative manager before initiating their due diligence process with that manager. If a manager doesn’t provide appropriate answers, they might not be price further consideration. Although our focus is on quantitative managers, the identical questions apply to fundamental managers, particularly with regard to the quantitative filters or signals they use of their investment processes.
1. What are the drivers of your investment process?
Investment managers should have the option to clarify which aspects they imagine are most significant to their investment decision and supply conceptual justification for these aspects. For example, their equity aspects needs to be economically intuitive and comprehensible, moderately than opaque or synthetic. As an example, consider the definition of the worth factor. A single, comprehensible metric just like the price-to-book ratio has benefits over hybrids like a “value” factor that consists of a mix of price-to-book and price-to-earnings ratios.
Why avoid such hybrid approaches? First, the empirical evidence that the price-to-earnings ratio is a rewarded risk factor is far weaker than the price-to-book ratio. Second, even when we were to make use of each metrics, a hybrid that mixes the 2 individual metrics in a roundabout way, resembling 50% price-to-book and 50% price-to-earnings, doesn’t make economic sense. That is, what’s the return current of the hybrid “factor” a return current from? Third, combining different metrics can lead us to take risks we don’t desire. Even if we mix the aspects as above, we eventually must apply some form of weighting scheme, be it static or dynamic. But then we still have to supply a justification for our weighting scheme. If our only justification is that it worked well in a backtest, then we’re succumbing to probably the most fundamental mistake in each investing and statistics: basing a supposedly generalizable investment strategy on an overfitted metric.
Therefore, using a transparent set of things that make business sense and might be conceptually defended is critical to assessing whether a manager has a sound and well-constructed investment process or is making investment decisions based on less sound considerations.
An vital additional component of equity factor strategies is controlling the possible negative interaction effect between the assorted equity aspects. For example, the stocks in a worth strategy are no less than partially depending on momentum and size, amongst other aspects. If exposure is large and negative, the strategy could wash away the premiums earned from value exposure. Therefore, managers should have a procedure that enables for factor biases but controls for these negative interaction effects. If not, a specific strategy deviates from its stated mission. Managers should have the option to clarify how their process ensures the intended exposure within the presence of interaction effects.
Finally, a crucial aspect when assessing a manager’s answers to our first query is their consistency. What if different members of an investment team, resembling the pinnacle of research and senior portfolio managers, have different views on what an important aspects are of their investment process? Then your strategy might not be fully developed yet. This “inconsistency risk” can burden each quantitative and fundamental managers, but could also be more common amongst fundamental managers, who often have less disciplined investment processes in comparison with their quantitative peers.
2. What evidence is there that your investment process shall be effective?
A well-constructed investment process needs to be validated by ample empirical evidence and a comprehensive set of statistical tests. For example, a quantitative process needs to be supported by very large data sets, tests with different subsamples, and several types of simulations. All of those validation methods needs to be documented, ideally in peer-reviewed journals. For example, Scientific Beta’s investment team has collectively published dozens of articles through the years expressing their views and supporting their approach to equity factor investing with facts.
Why does publishing articles in journals make sense? Because it gives the broader investment community the chance to judge an investment team’s ideas. And since the reviewers don’t share any business interests with the authors, their assessments are more objective. Publishing research helps establish the legitimacy of quantitative investment processes. Not only does it provide insight right into a manager’s investment methodology, however it also aligns a manager’s research efforts with real scientific practice.
In science, answers to questions are derived from consensus. That is, different research teams working independently of one another come to similar conclusions. As a result, their results reinforce one another. If a manager cannot explain or support why their process works, empirically or otherwise, asset owners should consider this a red flag.
Of course, some investment firms don’t publish their research because they are saying they wish to protect the proprietary elements of their investment process, their “secret sauce.” But that is not convincing. After all, other firms publish their research results without fear of misappropriation. In any case, an organization’s methods needs to be supported by each in-house manager research and research external to the corporate.
3. What risk controls are a part of your investment process?
Ensuring that a method delivers what it is meant to and that it doesn’t expose itself to unwanted risks is a vital a part of effective investment processes. For example, the goal of an equity factor strategy is commonly to supply targeted exposure to at least one or more aspects. Therefore, the return of a worth strategy needs to be determined primarily by exposure to the worth factor. When an element strategy’s return depends upon other aspects or the idiosyncratic risk of individual stocks, unwanted risk exposures creep in. Lack of risk control can subsequently result in unintended consequences.
Misspecification of the model represents a possible risk for any investment strategy. Quantitative strategies specifically often determine the asset weights of their portfolio using optimization. Although any optimization could also be limited, it could still unduly expose a portfolio to concentration risk in certain securities, regions or sectors and other kinds of risk. After all, no model is ideal and each model processes inputs in a different way. Therefore, managers should have controls in place to forestall a specific model from biasing the portfolio toward undesirable or overly concentrated exposures. One solution to achieve that is to make use of multiple model to find out asset weights.
When applying a model, selecting which inputs to make use of is a crucial consideration. Does a process rely totally on more stable metrics resembling volatility or on more unpredictable variables resembling expected returns? Managers must provide this information to reassure asset owners that their models are robust and stable.
Final thoughts
Of course, these three questions are just the start of the due diligence process. However, as a primary filter, they’re a very good start line for evaluating a manager. If the answers to any of those questions are unsatisfactory, the manager’s process can have fundamental flaws and the manager could also be unsuitable for further review.
If you enjoyed this post, do not forget to subscribe.
Photo credit: ©Getty Images / Alex Liew
