Evaluating investment managers is a difficult task. Why else would an asset owner spend so much time and resources, often with the help of consultants, researching a manager? Choosing and evaluating the right manager requires thorough due diligence. However, a relatively simple filter can serve as an initial screen for potential investment managers.
There are three basic questions that asset owners should ask their quantitative managers before they initiate the due diligence process. If the manager does not provide a suitable answer, it may not merit further consideration. Although our focus is on quant managers, the same questions apply to fundamental managers, especially regarding the quant screens and signals they use in their investment process.
1. What drives the investment process?
Investment managers should be able to explain and provide a conceptual rationale for which factors they believe are most important for investment decisions. For example, capital adequacy factors should be economically intuitive and understandable, rather than opaque or synthetic. For a good example, consider the definition of a value factor. A single understandable indicator like price-to-book has advantages over a hybrid indicator such as a “value” component that consists of a combination of price-to-book and price-to-earnings.
Why avoid such a hybrid approach? First, the evidence that price-to-earnings ratio is a risk factor is much weaker empirically than price-to-book ratio. Second, even if both metrics are used, hybrids that somehow combine two separate metrics (e.g. 50% P/E and 50% P/E) are economically meaningless. In other words, what are the return streams for hybrid “elements”? Third, combining different metrics can expose you to undesirable risks. Finally, even if the coefficients are combined as above, some form of weighting scheme, static or dynamic, must be applied. But then you have to provide a justification for the weighting scheme. If backtesting success is the only justification, then we are committing one of the most fundamental mistakes in both investing and statistics. It’s based on overfitting metrics in what should be a generalizable investment strategy.
Therefore, using a distinct set of factors that are economically rational and can be defended on conceptual grounds is a question of whether a manager has a solid and well-structured investment process, or a more flimsy one. important for assessing whether investment decisions are based on a broad set of considerations.
An important additional element of equity strategy is controlling potential negative interaction effects between various equity capitals. Value stocks, for example, have at least some exposure to momentum and size, among other factors. If the exposure is significantly negative, this strategy could wash out the premium you get from the value exposure. Therefore, managers should introduce procedures to control for these negative interaction effects while taking factor tilt into account. Otherwise, a particular strategy will deviate from its stated mission. Management should be able to explain how their processes ensure intended exposures when interaction effects are present.
Finally, an important aspect when evaluating managers’ responses to the first question is their consistency. What if different members of the investment team, say the head of research and the senior portfolio manager, have different views on what the most important factors in the investment process are? Maybe not. This “mismatch risk” can plague both quantitative and fundamental managers, but is perhaps more common among fundamental managers, whose investment processes are often less disciplined than quantitative managers. Common.
2. What is the evidence that your investment process is effective?
A well-structured investment process must be validated through a large amount of empirical evidence and comprehensive statistical testing. For example, quantitative processes must be supported by very large datasets, tests using different sub-samples, and different types of simulations. All these validation methods should be documented, ideally in a peer-reviewed journal. For example, Scientific Beta’s investment team has compiled and published dozens of papers over the years that articulate its views and provide evidence to support its approach to equity factor investing.
Why is publication in a journal useful? It gives the wider investment community an opportunity to evaluate the investment team’s ideas. Also, the evaluators do not share a business interest with the authors, which makes the evaluation more objective. Publishing research results helps establish the legitimacy of the quantitative investment process. This not only provides a perspective on the manager’s investment methodology, but also aligns the manager’s research efforts with true scientific practice.
In science, answers to questions are consensual. This means that different research teams working independently have come to similar conclusions. Because of this, their results are mutually reinforcing. When managers, empirical or not, cannot explain or support why their processes work, asset owners should take that as a red flag.
Of course, some investment firms do not publish their findings, arguing that they want to protect a unique element of the investment process – the “secret sauce.” But it’s not convincing.After all other companies do Publish research results without fear of misuse. In any case, a firm’s methodology should be supported by both management’s own research and research outside the firm.
3. What risk management is included in the investment process?
Ensuring that a strategy performs as expected and is not exposed to unwanted risks is essential to an effective investment process. For example, equity factor strategies often aim to provide concentrated exposure to one or more factors. Therefore, a value strategy’s returns should be primarily driven by its exposure to value factors. Undesirable risk exposure creeps in when a factor strategy’s revenue streams come from other factors or the idiosyncratic risks of individual stocks. Lack of risk management can therefore have unintended consequences.
Model specification errors are a potential risk in any investment strategy. Quantitative strategies in particular often use some kind of optimization to determine the weights of assets in a portfolio. Although optimization may be subject to limitations, the portfolio may still be overly exposed to concentration risk in particular securities, geographies, sectors and other types of risk. After all, no model is perfect, and different models handle inputs differently. Managers should therefore put in place controls to prevent certain models that tip the portfolio towards undesirable or overly concentrated exposures. Using multiple models to determine asset weights is one way he does this.
The choice of inputs to use is an important consideration when applying the model. Will the process primarily rely on more stable indicators such as volatility, or more volatile variables such as expected return? You must provide this information to ensure that you are
thoughts in conclusion
Admittedly, these three questions are just the beginning of the due diligence process. However, as a first filter, it is a good starting point for evaluating managers. If the answers to any of these questions are unsatisfactory, there may be a fundamental flaw in the manager’s processes and the manager may not be suitable for further scrutiny.
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All posts are the opinion of the author. As such, they should not be construed as investment advice, and the opinions expressed do not necessarily reflect those of the CFA Institute or the author’s employer.
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