Q&A with Raul Leote de Carvalho
Does the quant investment industry need to consider reforms? Raul Leote de Carvalho, board member of Inquire Europe, provides his perspective in a new, extensive Q&A.
How would you describe the changes that have transpired since 23 March, specifically from the perspective of a quantitative investment professional?
For me, the changes started on 17 March when the French government imposed general confinement rules to halt the spread of the pandemic in France and in the European Union.
On the positive side, it was great to see how those highly restrictive measures had little impact on my professional activity and all of us at BNP Paribas Asset Management successfully continued to work from home. We were able to do this because we have technology to communicate efficiently with each other, to access the data we need remotely, to run our models remotely, and to stay in contact with colleagues and clients while away from the office. I can also say that I am very happy that the social distancing effort seems to have paid off when I see how low the number of new coronavirus cases is now in France and in most of the European Union.
On the downside, I miss the human contact, the one-to-one discussions, the networking, the face-to-face client meetings, some of the travelling to meet clients and people in different places, and my daily commute by bicycle from home to the office in the centre of Paris. I was very much looking forward to the Inquire 2020 Spring Seminar in Riga in March - we had a great line-up of speakers and presentations. I was sad to have to cancel some of the presentations I was supposed to make at some other conferences. While technology allowed me to do my work, I do not think it can completely replace human interaction.
Some contend the quant industry needs a wholesale revamp after a decade of bull-market underperformance. Was the quant investment industry experiencing a ‘quant winter’? If so, has it now ended?
This question really concerns value investing in US equities. Fortunately, the world of quant investing is much more than that. Take, for example, low volatility equity investing. I just contributed to a paper “The low volatility anomaly in equity sectors: 10 years later!” which shows that the low volatility factor behaved much as we projected it to 10 years ago when the researched commenced. It was extremely reassuring to confirm the importance of sector neutrality.
Concerning multi-factor equity investing, it pays off to put significant focus on diversification and on neutralising unwanted risks in the factors. If you did so, then it is likely that your quality and low-risk factors have been doing well, as expected. Similarly, for Europe, diversified and properly neutralised value and momentum factors have also been doing well. For the US, paying enough attention to unwanted risk exposures in the value factor should at least have limited the adverse impact on performance.
Much as is the case with equities, placing significant focus on removing unwanted risk exposures has proven important for the performance of multi-factor investing in corporate bonds. Such strategies have been performing well and are in line with expectations at least for those who pay enough attention to this, and even throughout the recent crisis resulting from the coronavirus pandemic.
Another important axis of quant research and development is portfolio construction; I believe that using robust optimisation to construct portfolios can be advantageous. More quants, and even fundamental managers, should be using robust portfolio optimization algorithms to better construct their portfolios.
Finally, with any strategy, quantitative or not, it is important to consider risk when assessing returns. For example, when it comes to equities, it is important to adjust returns when taking the beta of the strategy into account. If the beta is not 1, then the absolute returns are not directly comparable with those of the corresponding market indices. Not adjusting returns for beta often leads to great confusion.
For example, a long-short US equity market neutral strategy with beta close to zero, or even a long only US low volatility equity strategy with beta of 0.6, simply cannot be expected to outperform the S&P 500 index in a strong bull market. It does not make sense. Similarly, irrespective of their long-term performance, when markets severely crash, out-performance of low volatility equity strategies tends to be a function of their level of beta and those with the lowest beta on average are more likely to out-perform.
While quant reformists shift long-standing allocation practices, many quant purists are in fighting spirits. Which camp are you in?
Let’s be careful not to throw the baby out with the bathwater at the first sign of difficulty. If the old guard is good then keep it, but if it is not, then improve or replace it. That is how science works and that is how quant investing should work.
I find that it is important is to remain critical and to solve all potential problems before releasing investment solutions that rely on quantitative techniques. For example, there are factors that are useful to predict the cross-section of stock returns, which were proposed many years ago by academics; these continue to be extremely useful and I plan to continue using them.
Nevertheless, it is true that more often than not, I find that the results from academia tend to be over-simplistic for practical use. In that sense, I would not classify myself as being in the purist camp because I do not engage in quant investing in the most traditional ways. I work with my colleagues to make improvements or adjustments based on the information we derive from academia.
I do not see myself as a purist nor a reformist. One just needs to do what one needs to do in order to deliver the best performance and risk management.
If you had the opportunity to start a new research project today, what would the primary research question be?
I am looking at new data sources, particularly relating to unstructured data. I believe that an important area of research is natural language processing, which is used to prepare text and model input for investment signals. These techniques will be used increasingly by quants, not only for systematic strategies but also for fundamental managers.
But in general, even for fundamental active managers, it is important to investigate increasing overall productivity with quant methods, to explore how to handle more data, models and portfolio construction. I believe reliance on quants methods is only likely to increase in the asset management industry.