Risk management plays a central role in our Core Quant strategies. Key element is that we distinguish between risks we take to enable long-term risk-adjusted outperformance on the one hand, and unrewarded risks that we avoid on the other.
Risk is a multifaceted concept. We distinguish three main financial risks: market, counterparty and liquidity risk. In this article we focus on market risk. We describe the risk management approach in our Core Quant strategies.
The Core Quant strategies use an active share framework to manage the risk objective of the portfolio. Active share is a measure of ‘activeness’ of the portfolio, and is defined as the fraction of the portfolio that is different from the benchmark.
The outcome of our stock selection model is combined with a proprietary portfolio construction algorithm and a unique set of risk controls. The portfolio construction algorithm calculates the most optimal transactions that have to match a pre-set active share range. In this way, investment decisions and risk management are fully integrated.
Restricting the active share alone is not sufficient to define the risk budget. The active share measure is indifferent to ‘many small’ versus ‘few big’ active bets. Portfolios with the same active share can therefore have widely varying tracking error levels: from low (diversified stock pickers) to high (concentrated stock picks). Our goal is to achieve controlled factor exposure by diversifying over a large number of stocks and thereby reducing stock specific risk.
As a result of our factor-based stock selection approach and diversified portfolios, performance contributions of individual stock holdings are usually small and total contribution is driven by a large number of stocks. The aim of our process is to take tilts to investment factors, not concentrated exposures to individual stocks. This approach allows us to reduce active risk, without giving up active return.
The active share ranges we apply in our strategies are selected to ensure that the portfolio does not exceed a set ex-ante tracking error limit. For the enhanced indexing developed markets and emerging markets strategies this allows us to consistently achieve a low realized tracking error.
The aim of the stock selection model is to create a portfolio with holdings that, taken collectively, have the ultimate stock profile: attractive valuation, high quality, higher than average analyst revisions and positive share price momentum. Some stocks within the universe may score better on – for example - valuation than our portfolio, but no single stock scores better than our portfolio on all factors. The strategies have been developed to maximize exposure to high ranked stocks (by means of overweights) and low ranked stocks (underweights) while maintaining cash, country, sector, size and beta neutrality.
We aim to maximize the active risk contribution stemming from tilts to our identified factors, and reduce exposure to non-rewarded risks.
We strive for an optimal balance between value & quality on the one hand and price momentum & analyst revisions factors on the other. A diversified, balanced model will outperform single-factor models over time because value and momentum will each experience periods of weak performance. By combining factors, we can realize maximum diversification benefits. It is critical to manage downside risk.
Risks that are not adequately rewarded should be avoided. This has implications for both the definition of our factors and our portfolio construction process.
The idea behind our integrated risk management approach is that if factors have certain undesirable properties, it is best to address them immediately in the definition of the factors. For each alpha factor we first identify its exposures to other factors, and then eliminate undesirable exposures.
An example is the way in which we enhance a generic momentum strategy. Generic momentum strategies exhibit large dynamic exposures to various risk factors. For instance, during bull markets a generic momentum strategy is typically biased towards high-beta stocks because these stocks tend to have the highest return when markets go up. The dynamic beta exposures of a generic momentum strategy contribute a lot to its risk but hardly anything to its return. This inspired us to develop a ‘residual momentum’ technique, which isolates the momentum that is really stock-specific by eliminating the part of the momentum that is simply due to the beta characteristics of a stock..
One should reduce - or where possible even eliminate – the allocation of risk to low-breadth decisions. For us that implies that we aim to be neutral towards sectors, industry groups, regions, countries and beta. By avoiding exposures to these unrewarded systematic risks, we are able to maximize the available risk budget in areas where we do add value.
The portfolio managers exercise a human overview to ensure that there are no additional sources of risk that have not been captured through the rules-based investment process. This human overview is exercised for the purpose of risk reduction only.
Examples of situations in which we may consider intervening are specific issues that drive stock prices like takeovers, disasters or political risks. A merger activity may increase a stock’s momentum and as a result also lead to a more favorable stock ranking. However, the stock price may no longer be driven by underlying model factors.
Taking risk is a necessary condition to outperform, but by no means a sufficient condition. The key takeaway is that risk management has important lessons and implications for any investment strategy. We take these lessons to heart in developing and managing our Core Quant strategies.
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