Rebecca Hotsko (04:18):

Can you talk a bit about who’s mainly using this and then why you’re using it?

Mike Chen (04:24):

To use alternative data, in a sense I describe, I think mainly started with quantitative investors. These are relatively large investment houses, as such as ourselves, such as BlackRock, and lot of quantitative hedge funds, you think millennial, Two Sigma, these investment houses historically has been using them. And because people have found success with using alternative data in investing and the fact that they can get information that’s really not readily available, using traditional data sources. Even fundamental investor, people that basically invest according to their own analysis to how they feel about a certain company. Even fundamental investors are now actually in hunt for Alpha via alternative data. So really anybody nowadays, on the institutional side, are actually using these data sources.

Rebecca Hotsko (05:17):

Then you named a couple sources of alternative data. I’m curious to know where are the providers of that? And is that available to everyone? Or are you purchasing it and then it’s solely yours? How does that work?

Mike Chen (05:30):

There is actually quite a diverse alternative data ecosystem in investing. Some of these data are actually for free, anybody can use them. For example, if you want to look at an employee sentiment, how does a company’s employees feel about the company, the management, the outlook, the benefits, et cetera. A lot of information is actually available for free. A lot of NGOs, they find malfeasance with certain company’s operation, with their conducts, they are public listed. A lot of government agencies when they find a given company, that information is public listed. Some of these data do have to be purchased.

Mike Chen (06:05):

For example, I mentioned a shipping manifest. This is what’s called as exhaust data. These are data that’s generated through a company’s normal operation, freight company here shipping these cargos from A to B, you actually know what you’re shipping, you have to know. Because historically what happens that these companies just set this data, they say, “Oh well, this is something that we use just to keep records and maybe we can file tax.” And regulator comes and say, “Hey, what’s it? That’s great. We know.” But in recent year, they say, “Wow, hey people are actually looking for this data so maybe I can sell it.” Some of these data you do have to buy, some are available for free.

Rebecca Hotsko (06:40):

Are you also creating in-house data through different machine learning techniques or anything like that?

Mike Chen (06:48):

We don’t create data per se. There are techniques in machine learning where if the data is very imbalanced … I’ll define what that means later. We can use … There’re techniques you can use to make the data more balanced so that machine learning algorithms perform better. What I mean by imbalanced data is … For example, say medical research, you want to study rare disease. You look for key metrics or KPIs that would indicate a presence of these disease. Now these disease, because they’re rare by definition only happens a very small sample set. When you study them, a lot of people do not have them, so then what happens is that this creates a problem for machine learning because there’s not a lot of samples for it to learn, so then the machine learning can create artificial samples to make it larger.

Mike Chen (07:34):

We do use these techniques to create artificial samples to make the data set more balanced. But in general we don’t create data sets because we want data to be from real life, real events, real people, real society, et cetera. So that we can invest accordingly. But what we do actually is in addition to data that’s curated, that’s put out there for sale, or just for public consumption from NGOs, et cetera, we do collect our own data sometimes. And for example, there may be a data set that we find, “Hey. This is interesting and useful but it’s not really available in general.” What we do is sometimes we go out and collect the data ourselves. For example through web scrapping, we web scrap the data. Once we have that data, it’s something that we have to access to. But these are all public data.

Rebecca Hotsko (08:20):

A couple of the alternative data sets that really stuck out to me were credit card transactions and then the digital or satellite, because it just seems like there could be so many applications for those having that data. I’m wondering if there are any others that would be maybe surprising that retail investors would be interested to learn about.

Mike Chen (08:41):

Definitely. For example your credit card transaction, what do you buy and what you sell. Not just credit card, debit card and satellite. I think these stuff directly impact the retail investor because they become the data. Others of that nature would be, for example, their [inaudible 00:08:58] traffic. Where do they go actually? Because you have a cell phone, you have a smartphone with you and every few seconds it’s actually sending out a location information to the base station. Technically, that tracking information is there. Your internet search event, or your purchase information, that’s actually also available.

Mike Chen (09:19):

Now I have to say that obviously all of this is anonymized so your credit card purchases, it doesn’t identify you personally, individually. I say, “Okay, this person … For example, myself, Mike, went and bought a new iPad this weekend in downtown Boston. That information level is actually taken up by … These data are what we call anonymized in the business, but they do look at a group of people or a geographical area so that can tell what’s happening say in this specific region for this specific demography, what’s happening, what they purchasing, are they cutting back of their purchase, where they spending more, this area, et cetera.

Rebecca Hotsko (09:56):

I’m wondering if you can talk a bit about which sectors stand to benefit the most from the use of alternative data.

Mike Chen (10:04):

I think actually all sectors can benefit because … For example, for the energy sector, people have been using satellites to see whether the oil storage facilities are full or are they empty, to track the shipment of oil tankers. Industrial sector, they actually look at smoke stack, whether the factories running or not, using infrared signatures. For transportation, they count how many trucks are in the various transportation centers, is it empty or is it full. But I think two sectors above and beyond all these other sectors really benefit are the consumer facing sectors, consumer discretionary and consumer staples. Mainly really because there’s just more data out there for consumers.

Mike Chen (10:46):

I just mentioned that people … There’s credit card transactions being tracked. Where they go, are they going to certain stores. You can look at parking lots to see if the parking lots are full for certain retailers. You can see what people search for in Google, using Google trends, which is publicly available to see how are they searching for … We have an Apple event coming up, are they searching for iPhone 14 Pro as an indication of interest? Consumer facing, I think, above and beyond all the other sectors that typically … Probably benefit more from the rise of alternative data simply because there’s more data available.

Rebecca Hotsko (11:22):

When I was looking into this I found that there were three main use cases for alternative data to inform investment decisions. One would be to find new investment ideas. You mentioned this, using a satellite to examine the number of cars in a parking lot of a retail store to gauge consumer spending. Two would be to validate an existing investment idea. Use satellite images to track the number of vehicles in a car dealership, for example. And this could be used as information that the dealership is doing well. And then three, monitor investments. A quant or hedge fund might use social media data to track consumer sentiment about a particular company and then if the sentiment is negative, the hedge fund may decide to sell it shares in the company. I’m wondering if those are all accurate? And then if there’s any more that are used that I didn’t name there?

Mike Chen (12:15):

That is definitely very accurate. You basically named the entire investment cycle, from idea generations to validation to exit decision. The three examples you give actually cover the whole spectrum. I think those are very good examples. You can also use it many different ways. For example, you can use NGO data to see if a company is being fined by committing violation that are serious or not. There’s data you can use to see how many lawsuits are against a given company. Even more than just which investment you need to get into you, it could actually analyze do these companies in general have good characteristics, strong management, so on, so forth. There’s definitely a lot of ways to apply alternative data. And I think alternative data, although very interesting, it is just data at the end of the day. It doesn’t make it any better or stronger than the footed traditional or fundamental data that people have been using.

Mike Chen (13:08):

I think what’s really interesting is actually the creativity, how you want use this data. And I think more important data itself … Really it goes down to the individual investor. Whether you’re quant or fundamental, it’s actually the same. What is your investment hypothesis and your thesis that you want to apply? Then you go out and hunt for the data. But I think the benefit of alternative data is that it gives you so much more possibility to answer questions that you’ll always have but perhaps we’re not able to answer. And I’ll give you an example. Corporate culture is very important. If you worked in the industry for a while, you know that if you work in the company where morale is very high, people tend to be more productive people, there’s tends to be less workplace accidents, people tend to … Their likely to be more innovative, because they actually want the company to succeed.

Mike Chen (13:57):

Versus the company where everybody’s apathetic, they couldn’t wait to for the bell to ring so they could go home. They just tried to do the minimum to get by, what’s called quiet quitting. Culture and sentiment is something that’s very important and it’s not really particularly related to any investment hypothesis. But just generally, are these, in general, good companies to invest it? Or are they … Apart from the catalyst, right? “Oh well, it’s good company to invest in because they have a new product coming up.” But just general quality of a given company. And I think this is where alternative data can also be used. You have this question that you always wanted to answer, it’s certainly very important and when you’re investing in a company, but you never really had the opportunity to. But now without alternative data, analyzing what people say, how they behave, do they complain a lot on the internet, or they don’t complain a lot, you get a sense of what’s going on inside a company.

Rebecca Hotsko (14:50):

That was really interesting, that piece you said about how it’s not necessarily better than the traditional data, it can be applied differently. I’m wondering, it does seem like these large institutions have gained an advantage over retail investors with this data. We can access it or use it maybe to the same extent. But I’m wondering is there any of these data sources you think retail investors should be paying attention to and maybe use in addition to their fundamental analysis?

Mike Chen (15:21):

Institutional investors have always had advantage compared to retail investors, simply because this is what they do, it’s their job. My job is to look at this 10, 12 hours a day and trying to create value for our investors, whether they be institutional investors or retail investors. But Robeco, as an institutional investment firm, this is our job. And we do this all day long and there’s a lot of people looking … Doing this. And we all discuss. And this is what we do. Just the amount of effort and the time and the people spent, I think it’s difficult for our retail investors to match. A lot of data sources I mentioned here, such as social media, what NGOs put out, for example, bulletin boards and chat groups, et cetera. These are all available to retail investors. I think if retail investor want, they can access them and I think you should actually monitor, when you’re an investor, whether you’re institutional or retail. Fundamental analysis is obviously important but you also need to monitor for the sentiment, et cetera.

Mike Chen (16:27):

You can have the best fundamentals in the world, if the sentiment isn’t great, it still not go to perform. As a retail investor, I think you should monitor the [inaudible 00:16:35]. The data access isn’t necessarily the problem but what you with do it. There is a lot of data out there, you can look at all kinds of different sources. And even more financial data is very famously noisy. What we do … Obviously, I’m a quant.

Mike Chen (16:51):

What we do is we take all this data and then we apply these statistical techniques such as natural language processing, such as machine learning, trying to analyze and gain insight into these data. And then once we gain insights, we don’t just say okay, “Well, this is right.” We actually want to test it to see if it holds water historically. See if we could use the … What’s called a scientific process to explain whether ideas are right or not. I think if you’re a retail investor that might be a very hard threshold to cross, to write up all these machine learning algorithms to analyze the data you get and then to put everything together, synthesize it, into a portfolio that makes sense.

Mike Chen (17:31):

And also, by the way, you typically, as a fund investor, typically hold reasonable number of stocks. Anywhere between 100 stocks or more. As a retail investor you probably don’t hold that number of stocks. What might happen is that you could a few stocks, they could work out really well, you make a lot of money, which is great, or they actually could not work well. Because there are a lot of idiosyncratic risks that affects how a given security performs, more than just the fundamental, more than just the sentiment. For example, we all know what’s going on right now that FED is hiking rates so a lot of these previously high flyers are not doing well. But going back two years, all of a sudden world breaks down in the pandemic. What traditionally has been a very stable company, say a retailer, that all of a sudden nobody’s going to retailer anymore. These are just things you cannot predict.

Mike Chen (18:19):

I think what the advantage of institutional investor, at least the quant institutional investor over a retail investor, we not only are able to process these huge amounts of data using vested Cisco techniques and put them together, we also hold a lot of these names so that we hedge out as much as we can the idiosyncratic risks. And we basically hedge our bets. We have an idea, we think this can work, but we’re not sure if any given stock that exhibit these traits can work well, so we buy a lot of them so that overall we believe the idea that we have can work. I think that’s the challenge of the retail investor.

Rebecca Hotsko (18:56):

I guess I’m just wondering how you think that impacts retail investor’s ability to earn Alpha, especially on the investments that large firms are using alternative data techniques to analyze and make buy and sell decisions on? Or is it not an issue because, as retail investors, we aren’t subject to the same constraints as larger firms and don’t need to go to the same lengths to generate Alpha on our investments. What are your thoughts on that?

Mike Chen (19:23):

Definitely. I think … You are right, retail investors are not subject to … First of all, you can buy a small company, without really moving the market, which for some of the larger institutional investors, such as ourselves, that’s very difficult, where we have what’s called a capacity. If we invest in these smaller companies, we can only buy a certain amount. It depends on how much money we’re trying to put to work, that might or might not move the dial. So I think retail investors do have a certain advantage. There’s also … If you have smaller positions you can be more nimble, only not moving the market, but you can also trade in and out of certain names much quicker. I think retail investors can try to understand quantitative investing a bit more. And I think in particular too for millennial investors, I think this is actually something that could be very interesting because I think millennial investors can probably investigation, do some education on how do quant funds really work, how do they add value.

Rebecca Hotsko (20:17):

I really wanted to talk with you about this today because I think it’s important for retail investors to know what they’re up against in the other side of the trade. And I like that you mentioned it’s not necessarily a bad thing, we have our edge with smaller accounts. But I guess I am still wondering that piece of this evolving quant space, are there any implications that we should be aware of?

Mike Chen (20:40):

I think we’re actually … Retail investors probably have enough edge over institutional investors, are these micro stocks. Very small stocks that are less liquid. These are things where retail investors probably have an advantage over institutional investor because no institutional investor … First of all, we typically invest in larger stocks just because of the size of the asset we’re trying to employ. It’s really difficult to put a lot of money to work for smaller stocks. And a lot of these smaller stocks, when you’re a larger investor, you probably would not invest in them. But I think perhaps some of these smaller names, less traffic in what’s considered lower quality stocks, retail investors do have an advantage. Having said that, I do want put in the caution that some of these stocks such as game stock, during the [inaudible 00:21:29] stock [inaudible 00:21:30], their price and their fundamentals are completely out of whack. Buyer beware. I would just say that.

Rebecca Hotsko (21:37):

I want to kind of jump back to the machine learning piece because I think that’s really interesting. And how that technique and technology is used. Can you talk about what machine learning is? And then how it’s used to make sense of alternative data?

Mike Chen (21:52):

What is machine learning? Machine learning … There’s a lot of talk about machine learning, there’s a lot of hype about machine learning. I think machine learning is just … If you want to think about it, it’s just a continuous evolution. At least in a financial sense, it’s just a continuous evolution of what quant investor have been using historically. Historically, quantitative investor have a certain set of tools that we use, such as regression, et cetera. Basically we look at the relationship between input data and output data, subject to our investment hypothesis of course. Machine learning is really powerful in a sense that machine learning allow us not just to examine linear relationships. That means that if an input goes higher, then output goes higher, if it’s positively related, otherwise it’s goes lower if it’s negatively related. But it also allow us to examine non-linear relationship. What happens to a given security price in the presence of two indicators, for example, simultaneously? Or if an indicator goes really high, maybe the price will go really high.

Mike Chen (22:52):

I’ll give you an example. Typically, higher the earning, the better the stock price. That’s very basic. But could also be that a company has really high earning. But hey, guess what? There’s some strong indication that this company could be cooking its book. In the prices of these two indicators happening is that typically the stock price might not go higher because we don’t even know if their earnings can be trusted, this is what’s called interaction effect. Machine learning can detect all these different combinations, more than just linear effect [inaudible 00:23:24], and also non-linearity. That’s what makes machine learning so powerful.

Mike Chen (23:29):

And I think another thing that makes machine learning so powerful is that machine learning is really trained to sift through a lot of information. And so when you have a lot of big data inputs, machine learning is trained to sift through all of that to see if you can detect any relationship between the input and output. Having said that, that’s also where the danger is, because financial investing is famously noisy. It’s famously high dimensional. If you think about it, pretty much anything in the world can really affect a stock’s price, fundamental to the sentiment, what the FED is doing, maybe a CEO tweeted something. There’s just all sorts of information that can affect the price, so it is really very high dimensional.

Mike Chen (24:11):

Now we don’t have nearly as much data and history to train a machine learning algorithm as compared to the dimensions that can affect the price. So what it means is that there is a … The degree of freedom is very high, to use a very technical term. In this situation, what you find is that if you just say, “I just want to throw this data into this machine learning algorithm, I have to see what it finds.” It could find something very ridiculous. I’ll give you an example, it could actually … For example, if you take the letters in a stock’s ticker, like JOOG for Alphabet or Meta for Meta. What you might find is that actually the third letter in the ticker begins S, alphabet S. In short all the stocks with third letter with alphabet O. You would actually have a phenomenal return over the last 60 years. That actually is an example that somebody has worked up. Obviously to a human, say, “This is total nonsense, this a total happened by chance.”

Mike Chen (25:11):

But this is what I mean by very high dimensionality. But if a machine learning algorithm … When you do that, it actually might not know, “Oh this is nonsensical.” What we need then is actually … Even though machine learning is very powerful, it can detect a lot of information, you still need human oversight. Humans still need to impose some structure on it, precisely because the dimension is so high. Machine learning will do what it does, but it cannot interpret if it makes sense or not. It’s a powerful tool that’s [inaudible 00:25:38] a good and bad thing. And I think, even for a quant, you do need to have human in there. The quants need to design the experiment where the machine learning is set up so that the stuff that you learn is actually sensible. Rather than say, “Oh I’m going to buy every stock that begins with A, and [inaudible 00:25:55] every stock that ends with C.” This is an example what I mean by high order, high degree of freedom.

Rebecca Hotsko (26:00):

Would it be fair to say it’s still in the early stages of any firm’s investment process and informing decisions because it’s still being worked out, like you said?

Mike Chen (26:11):

Machine learning is definitely in use. It’s definitely being applied by large institutional investors such as ourselves. We definitely … We are applying it for our investment process to the benefit of our investors. But I think the general public, and even maybe perhaps is even the [inaudible 00:26:28] of some of investment professionals, there tends to be a hype around machine learning. Now you think machine learning is this wonder drug that can cure all ills. It really isn’t the case. Machine learning is another tool in the toolbox. It is a powerful tool. It can do stuff that previous tools cannot do. For example, to detect non-linearity, but you have to know how to use it.

Mike Chen (26:52):

If you use it in an informed manner, it can actually lead to a lot of problems for you. And the worst thing is that you actually might not not even know what the problem are. I think machine learning is being applied. I personally believe we’re still very early in seeing the power of machine learning as an application of financial investment, but its being applied. But at the same time you need to be very careful with. It’s like that saying, “With great power comes great responsibility.” Because machine learning can do a lot of really unintuitive stuff, if you don’t put structure or constraints on it, you do need to be very careful. You need to know what you’re doing.

Rebecca Hotsko (27:28):

I want to switch gears a little bit because as we know, AI, machine learning, all these technologies are really shaping the way that investment and quant firms can include them in their investment process. They’re also just shaping our future in the companies and our world in different applications, in healthcare vehicles. I’m wondering how do you think investors can participate in these trends? What is the best way that we can also, I guess, reap the benefits of some of these? And should we be looking at investing in specific companies that are exposed to these trends? Or you more in favor of just us buying maybe a basket of these companies in a thematic ETF? What are your thoughts on that?

Mike Chen (28:10):

I definitely think machine learning is an important trend. Just like computing is an important trend. Computing changed the world. Cellular phone, mobile communication changed the world., internet changed the world. These are all major trends. I think machine learning is a major trend. I think it will impact the world. To see which one is going to be a winner is very difficult. If you think about it, back in the early internet revolutions, you have all these dot-coms, how many of them turned out to be winner? Probably not as many as the names that were listed, if you can recall that far. But it is a very real trend. But I think investing in any trend …

Mike Chen (28:51):

You mentioned thematic, I think that really is the way to do it. You want to identify a theme, you want to spread your bets to identify companies that will ultimately benefit from this theme and emerge the other side. As with any investing, you have the beginning stage where ideas being created, business model are being explored, we don’t really know who’s winning towards the hype stage, to the growth stage where you … Some are practical, tangible results are seeing, you see some benefits and then you have a lot of people jumping in. And then you get to the over hype stage where valuation becomes very stretch, becomes very dangerous. Then you have the reckoning, a bunch of companies implode and a lot of consolidation. And then it goes into more steady state development. We’ve seen that with the internet, we’ve seen that with computing, I’m certain we’re going to see it with machine learning.

Mike Chen (29:40):

It is very difficult to tell who’s going to win, mainly because this thing is still being developed, it’s still being explored. People are still trying to apply in a way that makes sense. And there’s still a lot of unseen factors that could cost … Even very big companies just happen not to make it because external environmental impacts that may … Perhaps funding rates has gone up. It’s very expensive to borrow money, just don’t have runway to grow your business. I think if you want to invest in a theme, and I definitely think machine learning is a theme that’s worth investing in. I think the best way to do it probably is to buy a basket of related companies, and hopefully you have some companies that will survive, that do well. There are many thematic funds you’ve been investing in that can help you do this as well. I think that’s the best way to do it.

Rebecca Hotsko (30:28):

You mentioned that once these trends become popular, sometimes they can become over hyped and they run the risk of overconfidence and then overpriced. Do you think we’re there yet with some of these themes? Or not yet?

Mike Chen (30:44):

We’ve seen it before. Some of the themes I personally do think are probably a bit more over hyped. I think, for example, machine learning is very powerful, and it’s very powerful in finance, it allow us to do a lot of things that it was not able to be done before. But if you think machine learning is something that can help you invest basically perfectly to generate basically a smooth monotonic increasing line, with no draw downs, no volatility, I think you’d be either misinformed or the person that telling you this is not being completely transparent. I do think that for some of these newer technologies, perhaps the promise or the hype is still more than where the technology is at the moment.

Rebecca Hotsko (31:27):

I’m wondering if you can talk about any other themes that are maybe important and should be on investor’s radars that we haven’t talked about yet.

Mike Chen (31:35):

I think, first of all, sustainability is a huge thing. Another thing is, I think, the application of machine learning and biology. Protein folding is one of the toughest problem that exists in [inaudible 00:31:48], because it’s so complicated. But it actually was solved recently, by Google’s [inaudible 00:31:53] so now we can begin to understand how protein folding happens and think about the implication. Another example is gene sequencing, just generally biotech, I think, there’s a huge promise. Our lives are back to normal because of breakthrough in biotech. This is a tremendous technology. And I just think that we’re barely scratching the surface of the biotech revolution. We’re thinking about actually possible cures for Alzheimer’s and all these other disease. Think about how much that’s going to change the world really. I think sustainability and biotech are two very broad trends that’s definitely worth watching. There are obviously sub themes within it, but these two trends are massive and I think it’s going to really affect us from years to come.

Rebecca Hotsko (32:36):

I want to talk a bit more with you about sustainability because you have a background in sustainable investing, you have lots of papers written on this. I’m wondering if you can talk a bit about why investors should care about ESG and sustainability. What would be the benefits of including these considerations in their investment process?

Mike Chen (32:56):

There are two reasons why you should care about it. I think one reason is personal, it’s your personal preference, something I don’t want to go into because we all have different beliefs. I think that’s actually something that you to be very clear about when we talk about sustainability. Sustainability is really an individual preference. Sustainability means a lot of that, and it means different things to different people. You could care about climate, you can care about societal issues, you can care about whether the company is paying his worker living wage and provide reasonable benefit. These are all under definition of sustainability. I’m not saying whether one is more worthy than the other. It’s actually all important depending on your definition. And I think it’s an individual preference. You should care sustainability mainly if you’ve wanted your money to express your personal value and preference. That’s one thing. And I don’t want to talk about that because everybody’s different. But you should also care about sustainability if you actually want better returns. That’s a pretty bold statement.

Mike Chen (33:55):

But it’s not every single case of sustainable investment will generate higher returns. That’s something that people need to be very clear about. Sustainability can only lead to higher return under the condition what’s called financial materiality. And what do I mean by that? A sustainable issue is material to a company, whether a company does well or not well on this sustainable issue. Sustainability issue has a dramatic impact on its operation. I’ll give you an example, Robeco, we’re actually one of the world leaders in sustainability. Robeco’s very serious about sustainability. Robeco definitely very careful of our carbon footprint, our actions impact the environment and the community we operate in. So Robeco should score very high, depending how you evaluate, but should score very high on E. And yet E is not a financially material sustainability topic for Robeco, because we’re an investment company. We don’t have the carbon footprint of power generator, of a steel manufacturer, of a heavy industry company. We do well on E and yet whether we do well or maybe perhaps not so well has very little impact on Robeco’s performance as a company.

Mike Chen (35:12):

E is not financially material topic for Robeco. Another topic that [inaudible 00:35:17] sustainability is employee’s talent, motivation, happy employees and how satisfy employee are, how well employees are taken care of by the company when the needs arise. Robeco also does very well on this issue, could be called the S, the social aspect, or the employee wellness aspect of sustainability. Robeco does very well on this topic as well. And this is actually a very important financially material metric for Robeco because we can deliver value to our clients through the action and ingenuity and motivation of our employees. Whether our employees are very happy … I really identify with this company. I want to do … I want this company to do well, I want to deliver value to our investors so that we can thereby make Robeco as well. This is actually a topic that’s financially material to Robeco. The S aspect of sustainability is material for Robeco. E which we also care very much about is not as material. On the other hand, if you are a …

Mike Chen (36:23):

For lack of better word, a cog in the machine. Whether any individual person has very high sentiment or very happy with the way he or she’s being treated by the company. While it’s very important, just from a humanist perspective, it’s important to make sure that your employees are satisfied and happy, it might not impact the financial performance of your company that much. Where the environmental aspect, if a company’s very efficient in their operation, thereby they have lower environmental footprint compared to the competitor, that can actually impact the financial bottom line of a given company. Sustainability … What I’m trying to say is that actually has a link to financial performance, but only through this channel called materiality. And I think this is something that I hope the listener can understand. Not all sustainability, higher sustainability rated company will lead to better stock, better price return, but only those issues material to how a company operates. And there’s actually emerging literature for academia now that’s actually discuss this topic. We’re now finding more and more evidence of the linkage between sustainability and financial performance.

Rebecca Hotsko (37:35):

Just to tie this all together. For investors looking to incorporate this into their investment process, should we be looking at … You mentioned … Look at … Think about what’s material to that company. For an energy company, we know it’s largely environmental concerns and then maybe the other two are a little less important, but they’re still important, figuring out that material aspect. And then I guess can you just walk us through maybe what else are we looking for? What type of information on the company’s financial statements, and that type?

Mike Chen (38:08):

I think sustainability is an aspect of investing. And in fact, all of the information you can derive from alternative data, apart from the more traditional factors that my colleague, Pim, has discussed with you, are important. But the traditional factors, the financial statement data, they also matter. When you’re investing, it is a high dimensional problem you’re solving and every single piece of information is just part of the puzzle. Maybe traditional factor investing with traditional data gives you a pretty good holistic … Pretty good view of say the front facade of a building, if you think about investing as buying a house. Gives you a good facade of what looks like from outside, perhaps on the inside.

Mike Chen (38:55):

But with sustainability consideration, while alternative data, you can get a broader understanding. You can maybe look at the foundation, whether the foundation is good, maybe the house wiring is good. You still cannot understand everything, I guess, because in order to [inaudible 00:39:10] you have to be an insider and, I must say, disclose that having inside information is illegal. But you can try to get more information than what you can do with traditional approaches, traditional data. Having said that, traditional data and traditional approaches are all important. And I think alternative data through sustainability data, sustainability consideration, can compliment the information where you would traditionally get and give you the more holistic view of the investment puzzle.

Rebecca Hotsko (39:38):

The last piece I want to talk to you about, because we just learned from Pim all about factor investing. I’m wondering, is sustainability almost becoming a factor in of itself? And what is the link between sustainability and factors? Do some factors score higher on sustainability that we should know about?

Mike Chen (39:57):

In general, traditionally, I think there are a lot of definitions of sustainability. And traditionally though, higher quality companies typically have higher sustainability score, because of perhaps they have better governance, perhaps because higher quality they would consider more issues that could affect how their company’s reputation, for example, their indigenous group, how maybe their operation will impact indigenous groups or the community they operate in. I would say that … I guess the more common factors that Pim discussed probably has more to do with sustainability, but we believe … At least I believe sustainability is a totally different new factors than the traditional factors. There’s positive correlation to quality, but I think there’re other dimensions of consideration that is not captured by traditional factor investing.

Mike Chen (40:51):

For example, resource efficiency, we talked about. Having a [inaudible 00:40:56] environmental footprint. Some of the higher quality companies are heavy industry companies that they … Although very stable cashflow, they’re probably not the most environmentally friendly companies around the world. These are additional dimensions that sustainability can capture, that traditional cannot capture. Long story is short, I think, sustainability is emerging as another definition, another factor within the world of factor investing. And in fact, this is what we have done at Robeco, we have … Using the sustainability to rise sensibility, create lot more new factors into our portfolio. And the benefit of this is that actually what we end up with is that … Under the condition of materiality, we end up with [inaudible 00:41:40] of that. We expect [inaudible 00:41:40] with a higher Alpha. We’ll also have better sustainability characteristics, such as more satisfying employees, happier employees, lower carbon footprints, et cetera.

Rebecca Hotsko (41:50):

That was great. Thank you so much for joining me today, Mike. Before we close out the episode, where can the audience go to learn more about you, your work, and everything you do?

Mike Chen (42:00):

Thanks for that question. I’m a member of the Robeco Quantitative Investment Team, it’s obviously not just me. We have a very large and talented group of people that is working very hard and we like to write about our findings and talk about our findings. So anything you want to learn, you can find us at www.robeco.com. You could also connect with all of us, Pim, myself and others, on LinkedIn. We like to post our latest research, our latest findings or just general thought of the day. The social media channels. Please reach out and we’d love to discuss these interesting findings with you.

Rebecca Hotsko (42:37):

Thank you so much. All right. I hope you enjoyed today’s episode. Make sure to subscribe to the show on your favorite podcast app so that you never miss a new episode. And if you’ve been enjoying the podcast, I’d really appreciate it if you left us a rating or review. This really helps support us and is the best way to help new people discover the show. And if you haven’t already, be sure to check out our website, theinvestorspodcast.com. There’s a ton of useful educational resources on there as well as our TIP finance tool, which is a great tool to help you manage your own stock portfolio. And with that, I will see you again next time.

Outro (43:17):

Thank you for listening to TIP. Make sure to subscribe to We Study Billionaires by The Investor’s Podcast Network. Every Wednesday we teach you about Bitcoin. And every Saturday, We Study Billionaires and the financial markets. To access our show notes, transcripts, or courses, go to theinvestorspodcast.com. This show is for entertainment purposes only. Before making any decision, consult a professional. This show is copyrighted by The Investor’s Podcast Network. Written permission must be granted before syndication or rebroadcasting.