FactSet Research Systems Inc. (FDS) Earnings Call Transcript & Summary

Good morning, and welcome to today's webinar, Making The Most of Alternative Data, which is being conducted by AsianInvestor in partnership with FactSet. I am Richard Morrow, the editor of AsianInvestor, and I'll be acting as the moderator for what we hope will be a lively and informative discussion. Today's ultra low rate environment, combined with the uncertainties that are being generated by the ongoing COVID-19 pandemic is leaving many investors in an uncertain place. It's possible that next year, it will offer strong or consistent investment opportunities as the world returns to normal. Alternatively, markets may well prove subdued or volatile as vaccines prove difficult to distribute, and the economic impact of the pandemic continues to stifle economic growth. Now this lack of clarity surrounding the future only underlines how much investors need to turn to cutting edge data collection and analysis, in order to identify and take advantage of areas of potential outperformance. There are many types of data management professionals, quants, research teams and data scientists, all of whom are looking for new sources of alpha to help differentiate their investment offering. They need to spot quantitative patterns and unusual forms of information if that should maximize investor returns. And that, of course, is easier said than done. There is an enormous amount of potential data that investors can turn to from geotagging to sentiment analysis, to natural language processing. But even the best broad data cannot be useful in isolation and requires processing and context. In addition, most asset owners, in particular, lack the internal capabilities, expertise or budgets to build such software themselves. To do so they need to partner with outside fintech experts and companies that can meet these data needs. So how can asset owners best ascertain what they need? And what types of nontraditional data can best complement their long-term investment objectives? And once they arrive at an idea, how can they identify the best partners? We hope today's webinar will offer a little bit of guidance about some of these questions and at least give you all an idea of some of the right questions to ask about alternative data, to get the answer. To discuss this topic, we're joined by 3 experts on the effective use of data to help invest. First of all, I'd like to introduce Puneet Singh. He's the Head of Asia Pacific Quant at Societe Generale, Corporate and Investment Banking. Puneet, could you please give a quick introduction of yourself?

Thanks, Richard. As you already said, I work with SocGen. I run their Asia quant team. My team basically looks at equity and cross-asset country search. I have been in the market for about 20-odd years, doing this pretty much way before the financial crisis around the team developed.

That's great. Thank you. We also have Marko Milek. He's the Managing Director and Head of State Street Global Exchange for Asia Pacific at State Street.

Well, thank you, Richard and good morning, everyone. I am responsible for our data and analytics business within State Street in Asia Pacific. And this really is about client-facing capabilities. It's not internal IT. I come from an academic and technology background and having spent most of my career, prior to State Street, at IBM. So looking forward to the discussion.

Thank you. And last but not least, we're joined by Michael Rhodes. He's the Vice President and Regional Sales Director of Content and Technology Solutions at FactSet, Michael, please introduce yourself.

Yes. Good morning, everyone. And thanks for joining us. So as Richard mentioned, I lead the FactSet sales team for our content and technology solutions. So that's all of our off-platform offering, including real-time data feeds, company analytics, a host of data management solutions; as well as, what we'll discuss today, of course, alternative data. I've been with FactSet for just over 8 years now, both in our London and Hong Kong teams.

Thanks very much, Michael. This is going to be a broadly free flowing webinar around 5 main talking points. And I would welcome all of the audience members to make use of our Q&A function to send in questions to our panelists and speakers. During this event, I will pick the best of these and ask them to the speakers. In addition, we are -- we have 3 poll questions that we're going to ask you, all of you; during the course of this discussion. I would urge you to participate in it to give us immediately helpful data on your opinions on some relevant areas to discuss. And indeed, we will begin with the first of those poll questions right now. And if we could bring that up, the first poll question. That poll question is: What is your level of engagement with alternative sources of data, when it comes to investment decision making? There are 3 answers we've offered: Not used regularly. Referenced in some cases. And frequently relied upon. Please, everyone who's listening to this, please do vote. And please do participate, it would be helpful for us as we go on and discuss. Perhaps I could begin with Marko, do you have a perspective on what might be the answer on this?

I believe that most people will be somewhere within the middle category. I mean, obviously, there is a lot of interest, most of the clients and peers that we talk about are seriously looking into nontraditional data sources, which we will define. But again, I would say, other than really specialized shops, probably something that Puneet would represent. I think most of the industry is still heavily relying on, let's call it, traditional rows and columns of mostly financial data to make their investment decisions. Happy to be proven wrong though. I think it would be a good thing.

Puneet, would you agree with that?

I think so. Even in my conversation with a whole host of clients, ranging from retail investors to asset owners, i.e., pensions and sovereigns, the utility or the usage of alternative data sets has been fairly limited with only the very, very, I would say, advanced clients. So I would agree completely with Marko, that I would say most people would be at the, "Referenced in some cases" level.

Okay. I'm looking to see if we've got any -- if we got some results coming through yet. If we have, can we please push them out. It would be helpful to see if -- particularly if the audience are engaging with us. Okay, that's good. So not being used regularly, well, that gives us meat for -- meat to discuss. So that's probably a good thing. And then just over 1/3 "Referenced in some cases," with a small percent of the audience, "Frequently relying upon it". Okay. That's good. Let's mid leap take that as we move into our first actual discussion area. And this is going to be looking into how to deploy and execute an effective overall data management strategy. Now I think, just to put things in context, why don't we get all from you, what you consider alternative data to mean; just so, we can really identify that for the audience. Michael, why don't I turn to you for what your perspective on that is?

Yes, sure. I mean, there's lots of different ways in which people can view alternative data. I think the way that we look at it in-house is data that comes from nontraditional means. So if you exclude data that comes from regulatory bodies or company reports or government agencies, would probably fit everything that falls outside of that into this alternative [indiscernible].

Okay. Marko, would you agree with that?

I would absolutely agree to that. I would add a little bit, though, I think more than the source, I would look at how frequently has a particular type of data been used in the investment work [ was ] previously. And I think it has to do with the structure of the data set, like if it's something more complex, whether it's text or images or you mentioned geotagging, weather, things that traditionally were difficult to analyze. I think there's a lot of opportunity there. Volume, so Internet scale data sets definitely haven't been fully explored. And then timing, particular data sets, where the signal simply expires, we need to be able to address that timelines between signal and reaction to get an outside [ round ].

Okay. And Puneet, would you -- from your perspective, is there anything you would add to that? Or do you want to -- we can move on to the actual talking point itself?

I would just say that, as both Michael and Marko have said, completely agree with that. I would just add that, in my role, we are also seeing what we call as alternative data, where we're using different processing techniques on stuff which potentially was traditional, for example, analyst calls, but using machine learning or using AI-driven techniques, give you different insights. So I would just add that to the list.

That's great. Thank you. So Marko, perhaps I can address the main point of view about how you deploy and execute an effective overall data management strategy. That's a big topic. But how do you think, if you're an investor and you're looking into and exploring particularly alternative data areas. Where do you start? What's the guidance you would offer?

I was thinking of a cute way to segue into the answer and couldn't come up with anything better than, how long is a piece of string. I mean, as you said, it's a really, really broad, really complex area, but I'll try and talk about a couple of elements that we see pretty consistently. I think the trifecta of problems that need to be addressed with spend, understanding, really, what's the business process? What's the operations? What's the outcome you're looking to achieve? Secondly, it's -- what's the scope and the underlying data sets? Are you thinking about enterprise data management? Or are you thinking about data management for, let's call it, a very particular business vertical? You would approach it differently. And then the last would be technology and infrastructure. And there's a plethora of solutions, anything between do-it-yourself 100%, to outsource virtually anything, get all the help you can get, talk to your peers, get vendors to come in, get consultants to help you and try to really understand, what is the purpose of your particular company? Why are you around? What is the value that you bring to the market? Is it massaging data day in, day out? Or is it generating alpha for your clients? And then also, what are your capabilities? And I think that should be -- that should guide you towards what to do yourself and where to get help.

That's great. Michael, this is obviously something you're doing, I mean, from a daily basis into clients getting their perspectives on how cohesive their thoughts are about this. So again, is there a huge variety of understanding of this? And how much guidance do you offer? And where do you start?

Yes. We're having a lot of conversations with clients, particularly as it relates to data management strategies. I think one thing that plays well into the topic area of alternative data is just the pure scale of it, and how firms set themselves up, and not just for what they need access to today, but how that might change over the next 3 to 5 years. So even if you think about traditional data that's housing relational databases, that presents a lot of challenges to our clients around the ETL process, around the existence of data silos, there exists a lot of duplication of resources as well, when you think about data architecture work and IT work that goes into integrating all of that content. And at the end of the day, and to Marko's point, none of that necessarily adds value to the firm's bottom line. So where you can look for ways in which you can streamline that process, look to vendors, look to consulting partnerships, but also look to alternative ways of storing the data. So a lot of the discussion we've had has been around cloud-based solutions. And that's certainly something that's accelerated as a result of the pandemic. The firms that we've seen been able to withstand a lot of the challenges associated with work from home have been the ones that have adopted cloud-based approaches, it allows them to provide remote access to their end users at speed, but it also provides that level of flexibility. So a lot of our clients, where they might have local or bespoke data management setup, don't necessarily have the capacity to scale or it might be very expensive to do so. When you think about the likes of Google or Amazon; even Alibaba, to some extent; they offer far more elasticity, when you think about the demands that are being placed on them, but also the advances in technology that the industry can take advantage of when working with those kind of partners. So I think one anecdotal piece of evidence we've seen around this is when it comes to testing data, the cost of data itself only forms about 1/7 of the cost to the firm. Once you factor in all the storage and IT resources that go with that. So it's on vendors like FactSet to find ways in which we can work more upstream and provide testing capabilities that don't result in firms experiencing those costs throughout the testing and onboarding process.

How do you convince firms that it's worth doing? How do you convince them that collecting this alternative data has material outcomes, be it alpha, be it performing in line with market. What's the rationale you use?

When it comes to the alternative data itself, we work with a lot of third-party partners to provide a host of different use cases. They might be anecdotal, they might be related to current market volatility. We work with a lot of academics in that regard as well. I think going back to the original point around data management, though, we really got to practice what we preach. So FactSet started life as a data integrator, 40 years ago. So we went through a lot of the challenges that our clients are going through now. And we're very much embracing that same technology by putting our ticker plant up on AWS. So the clients don't have to hit our servers in the U.S., they can access that data anywhere in the world and do so at speed. We're partnering with cloud-based data platforms like Snowflake and AWS to ensure we are able to deliver our data in a seamless way as well. So it's really a case of, we need to be able to practice what we preach in that regard and ensure that our clients have the confidence that they're working with a partner that's adopting the same kind of strategies that we're pitching.

Okay thanks. And Puneet, what's your perspective on this? And is that -- is there advice you would offer from your own experiences on how best to identify, I guess, the goals and then executing a data management strategy to meet them.

I mean, what both Michael and Marko have said kind of, completely makes sense, in that you need to invest for something which is scalable and you need to invest for the future rather than just looking at what you have now. In terms of what Michael referred to as silos, as an example, where I work; we, of course, have tons and tons of internally generated data from the trading desk. The simplest of this is tick data from all the price feeds get. Now if we, on the research side or the development side, have to go out and access tick data, it's not necessarily always straightforward in being able to access the same source of tick data. So those silos need to be broken down because the data might exist, and it needs to be shared. One other point, which I have noticed, when trying to examine some of these alternative data sets for investable or actionable points, is the legality. There is a lot of controversy nowadays and has been for a while around personally identifiable information, PII. And we always need to be cognizant that whatever data sets we are onboarding, whether they are web scraped or whether they are using any sort of additional techniques to parse what we have are all anonymized, are all cleared. And that should, in my opinion, be a part of the data management strategy as well because you don't want to get on the wrong side of an insider trading lawsuit.

Wise advice. Let's -- with the idea of sort of going into the types of data that we're looking at in the -- all types of alternative data we're seeing. Let's move on to the second panel question. And this is about the analysis of key trends in data usage, and alternative data demands this year. I'm very curious to know what's been -- what have -- we've seen, obviously, the pandemic has been a extremely unusual circumstances. I'm curious to know whether that has emphasized the need and desire for alternative data or deemphasized it and what trends, indeed, we have started seeing. Marko, perhaps I can turn to you, what do you think we've been seeing this year in terms of data usage and particularly on alternative data.

If I were to pick a category, it wouldn't really relate to COVID. That's probably a question of a time line more than anything else. I think ESG would be one theme, I would single out that's really picking up very clearly Australia, Japan, the Nordics. And I think the interesting aspect here is, there is both the investment use case, sort of looking for alpha, especially long-term performance based on whether you believe in the governance element more than the others. You know, that's debatable. But as a data set, it definitely has some predictive value. And there is also the number of, let's call it, regulatory use cases that are developing around reporting, monitoring and so on. Virtually impossible to have a conversation with asset owners, particularly around anything data related that doesn't touch on ESG. And again, an interesting element from the alternative or nontraditional data set is there are 2 -- at least 2 very different paradigms about how ESG data is collected, right? There will be data providers or generators that really depend on human research or augmented human research, but basically going through pages and pages of documents to construct an ESG score. The alternative way of looking at it is some vendors have adopted through machine learning on large-scale data sets, including sentiment. So we have these 2 very different data sets that can give us very timely information with momentum, but then also the more, let's call it, traditional solid type of curated very, very cleanly managed data. That would be my boat.

Could you explain to me, from your experience, what's been the motivation behind the interest in ESG, for example? I'm well aware that Europe is the leading region for ESG, but it has been growing in Asia. So what -- has it been a box ticking exercise? Has this been a genuine desire that they see this potential material benefits to be had from a more alternative data collection in ESG? Or what? Just could you clarify that one?

I think there is -- I think there's genuine interest. It's not just, tick the box. I think -- and we've seen, relatively recently in Australia, especially with the younger generation, there is an expectation of that transparency and understanding and really a group of retail investors, which I believe is developing, who will want to invest in things that are aligned with their principles; saving the planet could very well be one of them. And I hope that's not too controversial. So I think it's genuine. I think on the government side of things, we've probably seen the biggest historical, let's call it, impact of ESG elements to performance.

That's great. Thank you. Michael, I mentioned FactSet has been engaged with this with clients all over the world. Have you seen that these sort of trends, the data usage, alternative data interest in this region varies much from Europe or the States? And again, would you point to sort of sentiment and ESG as being areas of principle [indiscernible] data interest.

This one is on me. The phrase of 2020. I would say that Asia, I think -- well, I've been out here 5 years now. And I think the general trend is that Asia is generally a couple of years behind what we see in other regions, whether that's a shift from active to passive or the adoption of ESG. But I'll follow-up from what Marko was saying about ESG because I think it's -- you don't escape the news one day without a mention of ESG. And I think what's been most noticeable in Asia, over the last year or so, is that it's been heavily institutionally investor led. So whether that comes from sovereign wealth funds or 80% of pension funds now saying that they want to adopt some sort of sustainable strategy associated with their investment. I don't think this is a trend that's by any means going to go away. But also there's research that clearly demonstrates that firms that score highly from an ESG perspective and deliver long-term value for investors, commitments to sustainability, be it social cohesion or just general strong governance. Yes, we are looking out for the future generations, but there's also returns to be had there. So I don't think it should necessarily be looked at as a tick-box exercise, but how the positive implications of us looking out for future generations can also generate returns for investors.

And is that across all asset classes, I mean, I always think of ESG, particularly in Asia, as being mainly a listed equities led game. To some degree, we're seeing bonds. I think alternative asset class is far less. Are you seeing there being ubiquitous interest across the portfolio? Or is it quite focused?

We're seeing interest across the board, primarily a lot of the work we do focuses on the equity and fixed income space, and there's certainly been a lot of interest there. Outside of that, yes, we do see interest. But I think the data is probably a little bit behind what the market makes available for the equity and fixed income space.

That's great. Is there -- I think Puneet's camera may have temporarily frozen. Hopefully, you can still hear us. But while we just don't see it, but that's a technical glitch. Let me go back to Marko, maybe what's your take on other types of alternative data, or maybe rising interest in alternative data from where we are today? Do you see there being areas that maybe are taking off in America or Europe that are less evolved here in Asia that maybe we can look forward to in 2021.

We have definitely seen interest and made some investments in behavioral data, whether it's sort of investment behavior, we certainly see interest in people's understanding of flows. I think there is a lot of interest in sort of economic attributes that could be calculated from nontraditional sources that can impact how foreign exchange moves. Again, having worked predominantly with large institutional investors, I don't have direct experience with people who really are at the forefront of investing in weird signals that no one else is using. I would say that in the industry, there is some dose of skepticism because I've certainly heard the question to a data vendor. And I don't mean to single anyone out. But if this is such a strong signal of alpha, why don't you build a fund out of it, why you're trying to sell me the data set. So I think time will tell what bio patterns are out there. And I would say, most importantly, if anyone has found something and is utilizing it, it's heavily proprietary, and we wouldn't really be talking about it.

Okay. I think Puneet is back with us. So that's great. Is there -- would you sort of -- would you underline the points we've already heard? Would you say there are other areas of alternative data that you think are either on the cusp of what will continue to gain interest among the sort of fund managers and institutional investors in the Asia region going forward.

So just going back for a second to ESG data and what Marko mentioned, there are clearly 2 types of data set, like he said, I would classify them slightly separately in that the third [indiscernible] data set, which is used by most [indiscernible] pour over the [ internal ] and then create the ESG rankings. It's most what is supported by the companies themselves. Now the reliability, of course, we -- these are audited statements and all, but the reliability is always a question mark or believability maybe is a question mark. To augment this has seen tons of data set coming up, especially used for analysis, which looks at or using natural language processing around news flow, which dig into ESG and provide a big indicator [ end ] to the ESG sub complexes. So you could have a mantle issues gain but [indiscernible] specifically climate change effort. In terms of data sets, which I have seen in Asia, I think a lot of interest is centered on sentiment [indiscernible] signals and processing of data around that. About [indiscernible] company data and using sort of alternative techniques to analyze that, perhaps using company reporting, company filings and using machine learning or NLP techniques. It's been challenging because English phraseology and the way that you read English reports, of course, pretty different from a lot of the Asian languages, which are more, I would say, potentially graphic based or a script which is radically different. The phonetic construction is different. So it's a challenge, which is being addressed by some vendors, but that is one of the most -- one of the key areas of interest that I have seen here.

Okay. Thanks Puneet. That's really good. [indiscernible] we don't have an unlimited time to the third panel question, which we're going to be discussing. And that is: How to get the most out of alternative data and data science programs. Michael, perhaps I could start with you. Do you have a take on what sort of advice you give when you're speaking with potential clients? So what, I guess, the goals should be and how they -- what's the best way for them to get the most out of executing those goals.

Yes, absolutely. I mean, I think the first thing is around sourcing. There's over 2,000 alternative data providers out there at the moment. So JPMorgan's Big Data report [indiscernible]. And I think the biggest challenge we see our clients facing is, where do you start. Even when you take those 2,000 and you drill it down to a theme, let's say, the ones we've talked about around sentiment or ESG, there's still 200 ESG providers out there. So where do you start? And I think that's probably why we've seen the emergence, ourselves included, in production of data marketplaces. A central location that allows our clients to browse through and compare and contrast based on coverage, based on scope, and based on use cases. So I think what we're trying to do is provide almost a consulting service in that regard to help clients narrow down the focus to a predefined list of providers that adhere to all kinds of compliance purposes, as Puneet mentioned, but then also that cater to the various markets that our clients are interested in. I think the other thing is around the fact that alternative data shouldn't necessarily be seen as something that will replace traditional data, but how alternative data can augment the current research and research and investment process. So we've seen some really good examples, where clients have integrated alternative data alongside their own proprietary content or more traditional data to deliver insight. And they can be quite vanilla approaches, where it's around looking at job postings through comparing web scrape data providers; whether it's around using sentiment for fundamental analysts, when it comes around to earnings season to be able to not just look at one earnings report in isolation, but compare the sentiment on that earnings report to, say, the last 9 to 12 quarters. So I think establishing a good framework for not just the search process, but how it's going to augment the current research and investment process will probably be the 2 factors of highlight in terms of the advice we give to clients.

So it sounds like it's -- a very much a healthy way of approaching it is, it's a complementary way of traditional analysis and data finding as opposed to replacing. And I guess, is that a means of people, asset owners or for managers basically just adding to the data and that will be their ability to analyze and ultimately pick a stock pick or instrument.

I think the way I've described it recently is if you think about the way in which Facebook, Twitter and social media companies use our data, if they just had our age our location, they couldn't really do much with that. Their power comes from the ability to gather more and more information from multiple sources to deliver predictive analytics to an individual, be it around advertising or the content you want to see. So I think when it comes to our clients' use of data as a means to analyze risk and investment opportunities, the more diverse data they bring on board, the more they can augment their traditional process to deliver more timely insights.

Okay. Marko, to sort of follow-on from that; what's the sort of advice, when you're speaking with your clients on the -- what do you tell them about how they should approach this? Or do you -- is there particular areas, where you sort of finesse their ideas and try to get them, so that they'll get a result that is satisfactory as possible?