Dr Tatjana Puhan, deputy chief investment officer at ‘anti-benchmark’ manager Tobam, argues that criticisms of quantitative investing are misplaced.
Recent months have seen some sharp criticisms of quantitative investment approaches. While it is true that poor performance has been recorded in some areas, the recent narrative around ‘quants’ within the media – which, to varying degrees, has been pushed by certain parties to support their own commercial interests – neglects a number of important points which should be part of industry discussions.
This, it has to be said, is not unusual. Generalised critiques of quants often occur when one particular type of strategy has experienced difficulties. Indeed, there is a tendency to adopt a ‘one size fits all’ perception when it comes to assessing quants, with little consideration given as to the nuances between different types of strategy. Regardless of how different approaches might be, systematic strategies tend to be treated the same and therefore all subject to the same stereotypes and criticisms – data-mining, excessive risk-taking, ‘black box’ approaches – that have historically been directed toward this type of investing.
Such perceptions are, however, flawed. Quant strategies are not a single homogenous group and it would be a mistake to treat them as such. Yet, periods of poor performance within elements of the quant space seem to lead to assumptions that this is reflective of the whole sector, in turn generating far more interest and scrutiny of this area of asset management than of the flaws or pitfalls of fundamental investing by comparison.
Why, then, do quants attract so much attention? One of the main reasons for quant investing to be eyed more suspiciously is that, by their very nature, human beings do not like to be left out of events. There is a general perception that at least some element of human control is needed, even if only to act as a final check. If humans are not involved, the inherent presumption is that something will go wrong; when this does happen, it is taken as confirmation of the faults and limitations of quant investing.
Interestingly, this type of confirmation bias, as behavioural finance would term it, is applied much less to fundamental managers where human involvement is emphasised above all.
This is not to say, of course, that systematic strategies are without flaws – there are many things that can go wrong with this type of investing. However, there is a multitude of objective evidence to show that fundamental strategies are subject to at least as many potential pitfalls – ironically, the reason why investors first began to develop systematic strategies was to overcome human biases! To group all quant strategies together and assess them as a single style, while paradoxically recognising the nuances of different fundamental types of investing, is therefore a misnomer.
Recent ire towards quants has largely arisen due to the performance of certain strategies – specifically those focusing on certain factors, particularly value. Much of this attention can be attributed to the popularity of ‘smart beta’, an area of investing that has become hugely in vogue over the last 10-15 years.
Originally based on the idea of delivering a better or ‘smarter’ premium than market cap-weighted indices (which are very biased dynamic risk allocators and so not representative of true market diversity), smart beta has increasingly become associated with using certain proxies that should represent certain risk factors to make systematic bets on future stock returns.
There is a lot of merit to the theory underpinning factor models and the ideas that for a homogenous set of assets, differences in returns only arise because of different exposures to common components that make the returns of assets take different paths over time. The problem with this theory is exactly that: it is theory and gives little guidance as to how these principles can practically be implemented within a real portfolio.
Endless academic papers have been written looking about how factors can be used, with almost as many factors being proposed to clients by banks and asset managers over the last 20 years. The dispersion of outcomes of these factors is huge, even if some share the same names.
More recently, multi-factor portfolios have appeared, promising investors diversified exposure to the most relevant risk factors, to the extent that investors should not seemingly need to worry about how to construct their portfolio anymore. Yet, these approaches are still ultimately built with the same underlying factors, therefore suffering from the same problems and, as recent history has shown, subject to underperformance.
Within the multi-faceted world of investing, it is important to make sure things do not become overly simplified. Words and definitions matter: it is important not to reduce ‘quants’ and risk-based strategies simply to ‘factor investing’.
Unfortunately, this is exactly what has happened and the difficulties these factors strategies have faced recently have been presented as reflecting the quant industry generally. Yet, in reality, factor investing is only a tiny proportion of the quant world.
Inevitably, quant investing will continue to be attacked in the future. Much of this will likely be a result of comparisons to fundamental strategies due to the inherent preference of humans to exercise control themselves, rather than rely on machines, even if this is logically the least rational thing to do. However, a more objective discussion is required moving forwards, based on a clear understanding of the different types of quantitative strategies. Only by doing so can investors truly understand the merits, pitfalls and flaws of different strategies, be these quantitative or fundamental, and take informed decisions accordingly.
Reducing huge depths of research, expertise and strategies to only one small corner of the market is folly, and ultimately not in investors’ best interests.
Dr Tatjana Puhan, is deputy chief investment officer at quantitative asset manager Tobam.
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