In 29 years as a “proud quant” Arup Datta has experienced and learned from three major macro events – the dotcom bubble, the global financial crisis, and the Covid-19 pandemic. Along the way his experience has led him to create more well-rounded quantitative strategies that are designed to better handle stretches of rough road. It was by increasing the number of growth signals in his quant strategies that he created the resiliency that saw them through the most challenging parts of 2019 and 2020 – a time of mighty struggle for many quant shops. Datta has been Senior Vice President, Head of Global Quantitative Equity Team, at Mackenzie Investments, since 2017. He recently spoke to II about his investment philosophy, which he says has stayed the same his entire career “but the underlying details keep morphing.”
When what you refer to as big macro events occur what happens to quant strategies?
Datta: Valuation gets hammered. Cheap gets cheaper and expensive names become more expensive. If you have some value factors in your process – and I would argue all quants do, including me – that part gets decimated because you’re betting on the cheap and expensive names moving toward each other in valuation, not going apart. When value disparities correct – which I believe began to happen when the first Covid vaccine results were announced – quants have a tailwind. In addition, most active managers – including all quants – have a cap tilt against the benchmark. And that’s a double whammy. But we are feeling the tailwinds now. We’ve had one of the best starts of any year in my 29 years in the business.
Perhaps because of your experience you were able to safely navigate this most recent big macro event while it doomed some quant shops. What’s the takeaway from the rough ride quants experienced?
Datta: Quants need to have a good arsenal of growth signals in their process to carry the day when their value signal is out of favor. They can’t be so dependent on value for alpha – they have to be more all-weather. I’m a core manager, and that means I use value, growth, and quality in my process. We were able to come through 2019 and 2020 because our growth signals did their job. My view is that most quants fail to get it right – they should have focused more on robust growth signals, so that even in growth environments they don’t always lag the benchmark. No one predicted the pandemic – we had growth in our process because of what I learned from 1998 and ’99 [the dotcom bubble] and 2007 and ’08 [global financial crisis].
Is the light at the end of the value tunnel visible globally?
Datta: It seems a bit brighter in the U.S. than internationally and in emerging markets. The U.S. might get out of the Covid era ahead of others due to vaccines, and for that we can thank the amazing health care system and pharmaceutical companies we have. If you compare the U.S. and international perspectives, you’ll see the valuation disparity rolling over in the U.S. in November 2020. Internationally, it happened a couple of months later, hampered a bit by the political show over vaccines in Europe.
What I’m a bit puzzled by is why it has been slower in emerging markets [EMs]. Value in EMs should have started working ahead of the U.S. and international because the three largest countries market cap-wise – China, South Korea, and Taiwan – did a much better job of dealing with Covid than the U.S. and the effects didn’t linger. It has really only been since February that emerging markets arrived at the value party, and I don’t have a rationale for why that is the case. However, being unable to identify the cause for the lag in EMs underscores my point that quants need to be better prepared for the unexpected.
Clearly you believe quants on the whole should be more well-rounded. Do you feel the same way about your own strategy?
Datta: All good quant teams focus on new research and try stay ahead of the competition with new ideas within value, growth, and quality. For me, the overall philosophy has remained the same my entire carrier, but the underlying details keep morphing. As I mentioned, we have focused a lot of effort on the growth side. We look at analyst revisions – are analysts raising the revenue forecast for the company, for example, or the earnings forecast – and that provides a shorter-term signal. More recently we’ve added some longer-term growth ideas, such as how a company looks compared to its peers both looking forward and looking backward. For example, has the company had better growth than its peers? We’re striking more of a balance between short- and long-term growth. Instead of just looking at individual stocks’ earning or revenue increases, as quants have historically done, we are looking at peers and you can define peers in many ways. We could be looking at the fact that your customer companies are doing well, for example, and that it’s only a matter of time before those numbers come through to your earnings. That’s part of making all of our signals more robust, and it turned out to be a timely improvement when the pandemic struck.
Are there other game changers taking place in the quant world?
Datta: Part of the investment process for a good fundamental equity analyst is to meet with a company’s management and do a deep dive into the financials. Not being able to do that has historically been a weakness for quants. Today, with natural language processing [NLP], we can take any bunch of texts – word usage – and use computer models to figure things out. For example, are companies becoming more positive in their word usage in financial statements or earning transcripts? You can look at pattern data. Natural language processing has opened up a new world of insights and a whole new area of research. I cannot get too specific, but we have NLP-based alpha factors in our process. Our entire team of nine people all write code proficiently, and once you open up English language text and – hopefully, in the future, foreign language texts – there are a lot of ideas that will come to good quant teams. The quality of code writing will be the difference maker among quant managers.
You’re a big believer in capacity limits for your strategies. Why is that?
Datta: We run our models twice a day and we rebalance our portfolios on a daily basis. We look at all of our trades from when we execute them until the close on that day, and then look at the next five-day performance. Our typical trade makes us 12 to 14 basis points of alpha in the first five days. We couldn’t rebalance every day without capacity limits. There’s a certain size at which you can no longer be nimble enough to take advantage of opportunities that arise on a daily basis – you can be too big. Every strategy we manage has a set capacity at launch. Once we reach the capacity number the strategy is closed to new capital coming in.
For example, we’re aiming to build a $600 million book in emerging markets small caps. We are currently at about 25% of that capacity. By staying small and rebalancing daily we can get into a name before larger players do. As soon as the name becomes highly ranked, we run the model twice a day. As soon as the model becomes highly ranked it tells us to buy the name. At that point there is human vetting, too – something most quants don’t do. We have a human overlay on any name we are buying to make sure the data is clean and that we’re not missing anything. We are not trying to be a $30 billion emerging markets manager. We’re trying to be a $5 billion EM manager, and we’re at about $1 billion right now across a suite of EM strategies.
You joined Mackenzie Investments in 2017. Before you made the move you presumably needed buy-in from the firm’s leaders regarding your approach to capacity.
Datta: I explained my approach early on with the leaders at Mackenzie and they never pushed back. The nimbleness of a capacity constraint strategy is especially important in less efficient markets like EMs or small caps because stocks tend to have more dramatic shifts in performance. If a stock is suddenly ranked highly and you’re rebalancing every day, you can get into that name the next day. If you’re rebalancing once a week, you miss several days of alpha and if you rebalance once a month you’re missing out on significantly more alpha. I look at capacity holistically, too. If we launch a China-only strategy, for example, it will come out of that $5 billion EM pool. We won’t increase capacity to expand the number of strategies. Managing $5 billion compared to $30 billion is akin to managing a speed boat versus a large ship.
During a rough time for quants, we grew our team when others were retrenching. There was no way to know what would happen in 2019 and 2020, but the firm has been supportive of me throughout. We have sown a lot of seeds and now they’re all beginning to grow. All my team’s strategies have performed well, which most quants cannot say in a tough environment. So, hopefully, I’m returning the favor to Mackenzie in that regard.
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