In terms of staffing or managing people, my approach has always been that you have to empathize with what the other person is, with what he's doing, what is going on in his background whether it's family issues that come to work or what not, right? You help him compartmentalize and you try to help him perform. EQ definitely plays a part as much as the IQ side. So I mean it's a blend of both. And it's really a case by case basis since different people may have different issues. By and large, the team at RV (RealVantage) is a small one where everybody is performing well. So it's performance-driven, a lot of accountability, processes that govern our work, so it's quite clear. - Mao Ching Foo
Mao Ching Foo is the Co-Founder at RealVantage, South East Asia’s leading real estate co-investment platform which provides access to vetted private equity institutional grade properties, small investment sizes to enable greater risk diversification and better returns for an investor’s capital in order to help build a high-performing global real estate portfolio.
Previously Mao was the CTO & Chief Data Scientist of Funding Societies | Modalku, leading and growing the technology and data teams while working closely with the business teams to scale the platform for users. Prior to Funding Societies, Mao founded QVantage, a startup empowering equity traders to make smarter decisions using professional quantitative insights. He has also served as Chief Data Scientist of Paktor, a technology startup with presence in 8 countries in the region, and over 5 million users. In this role, he set up the full data science infrastructure and pipelines, built up a team of data analysts, scientists and conducted various studies / models to advance Product & Marketing metrics lift.
Before the world of startups, Mao was a quantitative equities trader / portfolio manager in Barclays Global Investors in San Francisco, and subsequently at Ronin Capital, a proprietary trading firm in Chicago. In these roles, he managed all aspects of a market neutral quantitative equities long-short global portfolio. This included researching alpha signals, constructing country specific Barra-type daily risk models, automatic sub-hourly portfolio optimizations, automatic trade execution algos & T-Cost analysis. Trade execution was through a Java OMS on FIX connectivity to DMA destinations. Market exposure was 100 x 100 mio long short with consistent after cost IR performance of 2+ on the overall global portfolio.
Mao graduated from Stanford with a MSc. and a MComp, BComp Hons in Computer Science from National University of Singapore. He has publications in top ranked journals, refereed conferences and book chapters. He is a strong believer of building technology startup ecosystems and is an angel investor and mentor to several startups in the region.
This episode is produced by Kyle Ong.
Jeremy (00:00):
Hey, Mao! Good to have you on the show.
Mao Ching Foo (00:02):Thanks, Jeremy. Excited to be here!
Jeremy (00:04):
Yeah well, really excited because it's a small world in Singapore. I've heard great things about you through Funding Societies. But also happens that you know my sister works with you, and so it's nice that you know practically family I guess at this rate!
Mao Ching Foo (00:21):Wonderful, yeah. That's great. Happy to share.
Jeremy (00:26):For those that don't know you yet, why are you awesome and what have you done?
Mao Ching Foo (00:32):
So a little bit of background on myself. Well, when I was younger, I was in the quantitative finance space. On Wall Street, they would call us "Quants."I was running through a long-short market neutral fund. We were running a $100 million long by $100 million short portfolio. It was a lean team. I was in Barclays, and then subsequently a couple of us moved on to a proprietary trading firm in Chicago. We were very hands-on, and it runs end to end. This spans from research to implementation to coding up the order management systems, FIX messages, and operational stuff. It was very quantitative. It was a geek's heaven if you will. From hardcore research in market inefficiencies to really developing algos and the strategies that would trade the markets. And you get a lot of satisfaction from that. So that's when I was a Quant in the States, I spent about 10 years there and then subsequently I came back to Singapore with my family.
As a Quant trader in the States, bonuses were good and I invested personally in various properties as well as startups. When I came back to Singapore I joined Paktor to run the data team. At that point, it was up and coming like Tinder.
A year later, I started QVantage, and along the way was also consulting with several other startups, some VC firms as well, and I was helping this startup called "Funding Societies" where we spoke for 2-3 months. Subsequently I decided to join them full time. It was a very promising startup. It was like “Lending club” “Prosper.com” in the States. I spent maybe 2 1/2 years with Funding Societies. When I joined them, the founders were still in school. It was a small team. The technology was from a contractor. So I joined them and I took over the technology side. At that point, many crowdfunding events that we did in Funding Societies were unstable. There were a lot of crashes and stuff. The volume was huge; there was a lot of demand. Online investing was the next wave and when we put up a deal on the platform at that point in time in 2016, things was just a mess.
We started hiring back-end, front-end engineers, we also developed mobile apps, products, infrastructure and so on and so forth. We put in technology sprint structures, processes, and migrated from the monolithic architecture we had to a microservices architecture. We were licensed in three countries and we went through the MAS requirements, the TRF frameworks, the tech audits. And in Indonesia, we were also certified ISO 27001, certified for the OJK license. We set up data analytics, engineering, data science teams as well and then just before I left we added on a cybersecurity team as well. We saw a lot of security attempts, none of them succeeded but we also saw the need to be more beefed-up on the cybersecurity front.
It was a great company. When I joined them, it was after the seed round, pre-Series A, and then when I left they had closed the B round, moving on to raising the C round. When I was there, we were also driving technology products on the micro-lending side because after all the technology components were sorted out, the people were in place, the processes were laid out, then we could start to work on other problems. There was a technology product that we were pushing on, looking to drive more revenue and then I started to take on other problem areas in the Firm that I could solve as well. Because it's not just the technology function that makes a company, there are many other parts to it, so you look to value-add in other ways since there were many gaps. So that's the journey at Funding Societies.
I then formed RealVantage with my co-founder Keith. Keith is from the property investment side. He was doing real estate investments his whole career, 20+ years of experience. Deploying capital across the various countries into large office buildings, into large retail malls, and residential properties . So a very experienced property investment specialist. After a lot of chats, drinks, research and thinking, we decided to take the plunge and build RealVantage. It's the next wave forward from what we see. Online investing is something that folks are getting comfortable with and real estate is an asset class that Southeast Asians are all excited about. We put together RealVantage and hope to serve the population in this space.
Jeremy (05:55):Wow, what an amazing journey! And I have so many questions, right?
Mao Ching Foo (06:01): Sure.
Jeremy (06:01):
You know, I mean this is an interesting life that you have had. Obviously the first one is obviously at least in business school, in the MBA, etc. people use the word "Quants" and everyone is like "Wow! These samurai who are roaming the financial data internet and making trades. All these normal value investors, these hold- and long-term value. No, these Quants are the guys who are making the real bucks, right?" Well so that was the story and some of us were like, "Why are we even in this room learning fundamental financial strategy when the Quants seem to be you know doing stuff, making collaterals, making securities, making plays?" It felt like you know playing defense while the Quants are like the wonderful rock-star strikers out there. So what was it like? I mean, I'm sure that was the outside-in. But what was it like being a Quant in the States?
Mao Ching Foo (07:04):
I guess the grass is always greener on the other side. I think there's always room for multiple players. I do believe in value investing as well. From the Quant’s perspective, it's largely statistical. So you look at research or rather, you conduct your own research into the market data and then you find different inefficiencies which you can potentially look to make the markets more efficient with.
And of course there are challenges in a quantitative space. Research (for new strategies), takes quite some time. After everything is done up, you find a strategy that looks like it's going to be working. So you do your back-test, it looks great, you perform simulations from live data feeds, wow it's awesome. And then you turn it on to production with a small stake. And then whereas in simulation, it keeps going up; in production, it keeps going down profit-and-loss wise. So then you scratch your head and you are puzzled and you go back to your research. What's wrong? Compare and contrast? And you find - oh, you are slower than the market, everybody else has moved (their bids) but you haven't moved yet (for that precious milliseconds). So you need to be co-located, your servers need to be closer (to the exchange) and then there's the additional costs involved, you have to bake it into the models, compute again see if it would work, and then try it again.
So it's a lot of trial and error, it's a lot of experimentation. It's fun but there's some frustration. It’s also stressful because when, let's say your models in all your different tests, you know that it's going to work over the (longer term) time horizon, - this is your information ratio, this is your sharpe ratio, it's going to be let's say 4.0 and when you actually run it, the (short term) day-to-day (PnL) movements actually may make you nervous.
For example, let's say after three days, it goes down for three days, okay sure that's expected, it's part of the model predictions. It goes on for five days, it goes on for seven days, hmm is there something wrong with the model? Let me check. After it goes down for 10 days and 15 days, okay no something's off. Let me check it out more. There's a lot of stress involved. But more often than not, the models are performing properly. It's the human emotions that get into the way, the fear, then you start to scale down a bit, maybe I should do these other backtests. Yeah so there's good sides and bad sides. I just shed more color so that you get some appreciation on the Quants side of things I guess.
Jeremy (09:36):There's always a light in the normal day-to-day of a Quant. Is it like you wake up in the morning, you read research papers, you do a bunch of meetings, what's it like actually on a day-to-day basis?
Mao Ching Foo (09:47):
Okay. We don't really go for a lot of meetings to be honest. There's a lot of research, a lot of data cleaning, testing, some discussions, and then a lot more on the ... the life revolves around markets, models, research performance. You’ve got to make sure all your algorithms are performing out there that your (trading) strategies are performing out there. And you keep abreast of the markets and you get a lot more insights into the data by doing more research, not by doing more meetings. So it's pretty much a very geeky environment I would say.
Jeremy (10:23):So it wasn't too different from the DSO time? Your time as a Masters in Computing? Did it feel very similar like that academic research dynamic and it's just adding on the real-life component of it?
Mao Ching Foo (10:37):
Adding on the real-life component, adding on the stress component, adding on the component that there is a $100 million long-short portfolio at stake. So I mean, $100 million by $100 million is small compared to the regular value funds where they manage tens of billions or hundreds of billions, right? Capacity is smaller in a quantitative firm. But to answer your question, yes it's ... in some aspects, it's similar to the research days. But there's a lot more urgency, there's a lot more performance required as a Quant.
Jeremy (11:10):So there you are eight years in the States. So from Singapore to eight years in the States, being that Ronin Capital samurai Quant there.
Mao Ching Foo (11:21): Okay!
Jeremy (11:21):And then you add on another two more years in the S&P area and so why is it that you decided first to move back to Singapore?
Mao Ching Foo (11:31):
So it was family. I had three children when I was there from the first to the last. Just after my third child came out, we decided to come back to Singapore. The last child was quite premature at birth so we had to move back to Singapore for extended family help.
Jeremy (11:54):Mm-hmm (affirmative) yeah that makes a lot of sense. I mean that's where family is really powerful in helping raise a family, right?
Mao Ching Foo (11:54): That's right.
Jeremy (12:00):
So there you are back in Singapore and you built 10 years of deep experience as a Quant, a financial Quant. And then like you said, you somehow make a jump to startups and not any other startups, not a fintech startup. You joined Paktor, right? Which is the dating app, right? So I got to get this story, right? So you're doing financial quant algorithms, and now you're doing human dating algorithms, right? So how does that happen yeah?
Mao Ching Foo (12:28):
It was actually quite a big leap and I was thinking quite a lot before I made the switch. And it required a lot of thought before I made the move. Actually before anything major that I do, I do think quite a bit before I proceed. So when I joined Paktor, it was as a role on the data side where you run the data team. And as a Quant, I think at that point in time, this term called Data Science became fashionable. Previously it was essentially statistics with computer science or statistics with programming. But at that point in time, data science was something that was fashionable.
I joined them largely for a few reasons. You know when you ... in the States, you see a lot of different startups that grew exponentially. And as a Quant professional, you also keep abreast of the different investments that's going on. Like I mentioned earlier, I invested in startups and when I made the switch, it looked like Paktor was also going the way of Tinder. They served the market need and there was this role where I could add a lot of value to the Firm. There was a lot of data that was being collected and you could make a lot of sense from it. And so I joined them.
Essentially there were a lot of things that we built during the time I was there. From analytics, the dashboards, you look at the Product side (of things) and you see how data can help. You look at the Marketing side and you see how data can help. On the product side, I think one very interesting finding was that there is an auto-correlation to swipe activity, the conditional probability of the next swipe being the same as before gets higher as you swipe. So when it's the left, then the probability gets higher. Or you go to the right, and the probability that the next swipe is a right gets higher than before. So you can make use of that information and try to tune the product to improve it. I'll leave it to your imagination how you can do it. I won't share more here.
On the marketing side, you look at data from digital marketing and you can see how to make sense of it and essentially how to drive ROI for marketing in a better way. You measure your CACs, you measure your LTVs, you measure your funnel conversions across the whole marketing funnel, across your different campaigns. You test across your different creatives or copywriting and see which one is really resonating and then you move from there.
If there is a spike in your user activity, whether it is your DAU or MAU, or your user views or your engagements, you can correlate that with if it's something that's done. Based on a product launch, is it a feature launch of the product, or whether it, or is it an email blast that goes out or is it a PR that was sent out, or is it another campaign that was just pushed out? So you can correlate these different metrics with what you actually push out on the marketing site. And you can see which one is really performing. Yeah so in short, you can put a lot of Quant or quantitative-ness or study of analytics into a regular startup that has good volume. So I mean essentially that's where I came from.
Jeremy (15:38):
Wow, that's an amazing story! And it's not a common one, right? I mean, OKCupid was one of the first few people to really put together that data science dynamic to human dating which ... obviously I think before that had all been like grandmother stories or bro-y / comrade stories about what works, what doesn't work. And it's interesting that he did that. I remember his quantitative eye with their blog right on data science. And actually for us you know, Peng the founder of Monk's Hill .
Mao Ching Foo (16:11): Ah yes.
Jeremy (16:12):Used to be one of the leaders and co-founders of Match.com and also takes the same quantitative point of view on the human dating side of it. So very interesting times for everybody.
Mao Ching Foo (16:25): Mm-hmm (affirmative) it's true.
Jeremy (16:26):
So there you are. You're a Quant and you've been ironing out the inefficiencies of the financial markets, right? And then now you're working with people who are dating, right? So you're looking at the efficiencies and the inefficiencies of the market. What parallels did you see between both markets from your perspective?
Mao Ching Foo (16:44):
I think as a Quant versus as a data professional running the team, for me I found that I got to interact a lot more with people across multiple teams. So because as a Quant you don’t need to worry about the sales, you don't need to worry about let's say business development whereas you focus on performance models, research performance markets. Whereas as a Senior Exec running the data side, you look at many other things. You get a lot of interaction with the marketing team, you get a lot of interaction with the product team, and then you have to manage the data part of things. From the infrastructure all the way down to the dashboards to all the minute details as well. So that's where I would say the biggest difference would lie. As a Quant versus as a startup Exec looking at the data side
Jeremy (17:36):
Yeah. And there you are, Chief Data Scientist right? Then you decided to make a move from there as well. This time you eventually transitioned back to something that was much more finance-oriented in some ways, right? So kind of like both a combination of both sides which is the human psychology dating side but as a startup operator versus the fintech and the Quant and you became eventually like the CTO for Funding Societies. So how did you get from point A to point B here?
Mao Ching Foo (18:10):
So like I mentioned earlier, I was speaking to the founders, more over WhatsApp, for quite some time. I think two to three months before I actually made the move. After Paktor, I was looking to start my own startup. Combining the mobile experience from Paktor's side and the digital marketing experience and skill, with the trading models, trading strategies or algos that we had as Quants, and put it into the hands of all traders and level the playing field. That was the idea for QVantage which I wanted to build.
Along the way I was consulting with other startups. Funding Societies was one of them and after two months of discussions with the founders, I decided that this looks like a startup that does have the legs to run and needs somebody to look into this whole technology side on their end. And what I was looking to do on QVantage - to put trading models out for everyone could still be done later down the road. But for P2P lending, it has to be now or never, right? So that's why I took the plunge.
Jeremy (19:29):Yeah amazing and there you are working with Kelvin and Reynold who are both Harvard MBA classmates of mine.
Mao Ching Foo (19:34): Oh? Nice.
Jeremy (19:35):
I remember chatting with them at HBS about their return back to Southeast Asia and their plot to build out Funding Societies. So there you are as a CTO. So there you are, you've been a Quant, then you've become Chief Data Scientist and now you're becoming the CTO, right? So what was that professional journey like? Because you see that very intentional building from my side like an eye of building skillset on skillset, right? But what was it like transitioning to become a CTO?
Mao Ching Foo (20:04):
First and foremost, when I first joined there were a lot of crashes, a lot of downtime on the site. So you look to putting a stop to that, which we quickly did. And then subsequently I started to bring on other members of the team, so team leads for back-end for front-end, for mobile as well as for product. And to get the platform or to get the technology stack into a safer place into a place where it's more stable, more reliable. So that was the first part. And then subsequently we started adding in the data analytics and data engineering, data science teams to make sense of the data. And following that it was adding on QA and cyber security teams.
So that's what we did on the technology side, in terms of staffing up. On the process side, we had the engineering team moved to using sprints - two weekly sprints; Then we had more processes installed. As mentioned earlier, we were also going through the MAS technology risk framework, the technology audits, the ISO certifications, and so on and so forth. And then driving technology products subsequently. It was a fine journey I would say.
Jeremy (21:22):
Did you feel it was tough transitioning into bigger roles because each stage you know you're taking on more and more leadership roles, managing more people, you're also taking on different things because as a Quant, it's not the same as a data scientist and then it's not the same as being CTO. So how did you go about learning the skills? Did you ask your friends or did you read a lot of books? I'm just kind of curious there.
Mao Ching Foo (21:46):
I think humans are very adaptable. You learn as you go along. The learning curve has always been I would say moderate in my opinion. You build on what you have before. You put a lot of hard thought and thinking into it and then you try to anticipate different problems that could come up. You anticipate what the Firm or what different processes that you would need and the staffing requirements that you have. And you work with the budget that you have. And then you devise a plan and you go from there; so from strategy to execution. So I wouldn't say it was ... I mean, I'm like that - as a geek you think a lot! So that's how I've been approaching things. Of course I read as well, a lot of reading material that one goes through. Paul Graham’s notes have always been very helpful. Even today in my own startup, I read that as well.
Jeremy (22:46): Yeah.
Mao Ching Foo (22:47):As well as different books like Phil Knight’s Shoe Dog and stuff like that. I hope that answers the question.
Jeremy (22:53):
Yeah it does, I mean it's good, right? Because I think a lot of people go through that role expansion and building up and they're often asking themselves: what do they need to read or learn to get there. Because you know not everybody gets to progress from the data side to the CTO side both in terms of vertical in some ways but also in terms of role progression.
Mao Ching Foo (23:16):
So just to add a little bit more. My training and my background was in CS / software engineering as an undergrad. That's where I pick up everything in the computer science side of things from databases to operating systems to networks and so on and so forth. So that basically has always been my first strength to rely on, so you can engage engineers easily because you are speaking the same language as well. There are other parts of the role that you pick up on the fly. So it all falls into place as well.
Jeremy (23:50):
All right that makes a lot of sense. And so there you are at the end. This time around you're like, "Okay. I tried to found a company once and I decided now I want to go and become CTO of Funding Societies. But now this time I'm going to found for real a second time around with RealVantage." So what was so appealing about the problem that said I must work with this co-founder and I want to tackle this problem?
Mao Ching Foo (24:18):
So RealVantage is a co-investment platform for real estate. And essentially what RealVantage does is that it offers real estate investors a place to get access to quality, institutional grade deals. So investors get to choose the deals that they are interested in, they get diversification of their real estate portfolio, and it's all available in smaller investment quantums. Essentially when investors or real estate investors, let's say yourself - you're going to invest in real estate, typically the general population would choose an apartment unit in Singapore and then put in close to a $1 million with some leverage and then you try to make your returns. The yields in Singapore are very low, sometimes you may not even have enough to cover the interest payments to the banks. So that's the access that currently folks would get.
There are many ways to invest in real estate. There are professionals that invest on behalf of institutions into real estate, investing for a living. What RealVantage seeks to do is to bring these expertise into the hands of the public. So you get, as an investor into RealVantage, you get access to very good deals. All of them are tightly screened institutional quality deals. Users get access to good deals, get to diversify into different real estate strategies, across real estate sectors, whether it is retail, industrial, commercial, and you get to invest in different sectors, different countries, different sub-markets as well.
So we have invested in Australia, we have also invested in the States, we are running our first deal right now in UK as well. In Singapore, we recently invested into a defensive suburban mall in Bukit Batok. We have family offices that have invested with us, and we currently provide this access to accredited investors in Singapore. Where else would you be able to be a co-owner of a shopping mall with a button click? That's what I see as very compelling. It's a product that I love. Personally I invest in every deal. Co-founders co-invest into every deal that comes along on the platform to be aligned with investors into the deal - we believe in each of the different deals that come along on RealVantage.
Jeremy (26:59):
So what's so broken about the current approach that retail investors look at property? Can't they just buy a house? Can't they just go get a REIT and get exposure that way? So what's going wrong that we need RealVantage to come in?
Mao Ching Foo (27:20):
Retail investors definitely could purchase apartment units or buy a house or something. But there's a lot of concentration risk, you are putting a lot of capital into a single asset, single location. Capital appreciation, or depreciation, it could go either way. And you are very much exposed to an unpredictable market. Diversification is very important.
Further, you can actually make your money work harder as well if you get access to some very good deals. These could be deals of the development nature, where the IRR could go north of 20%. There are also deals where it's a lot more lower risk where you get into an asset that is stable and well tenanted, generally after leverage, net of fees, net of tax you are getting circa 7-8+ percent year on year in terms of cash-on-cash return.
So you get access to these different deals which typically a retail investor will never get access to. Or, if you somehow could get access, typically you have to put in a huge amount to invest into these deals. So that's the value add that RealVantage would bring to the users. Access to good deals at lower investment quantums.
Mao Ching Foo (28:47):
You mentioned something about REITs, right? So what's wrong with REITs?
There's nothing wrong with REITS and it's great. REITs play a part in an investor's portfolio as well. But for REITs, it is typically investing in a pool of assets that's already purchased and you get the stable DPUs. The Distribution Per Units. Depending on the REITs that you get into, yield range is from 3% to 7% or 8% for the more risky ones.
For REITs, firstly, you don't get to pick and choose your own asset. You don't get to choose, strategy-wise. Let's say I'm a higher risk appetite person where I want to invest more into development deals to get a lot higher IRRs. As an individual investment to REITs, I wouldn't get that exposure. Secondly, leverage ratios are also different. REITs are regulated and there is a cap to the leverage at 55%. For individual deals that you get into through RealVantage, the leverage could go to 60, 65, sometimes 70%. Thirdly, REITs move with the equity markets. Yeah, so there are several differences between REITs and co-investment. We do have an article in the blog that addresses these differences in the platform www.realvantage.co. I hope that answers the question.
Jeremy (30:15):
Yeah. Okay so what I'm hearing is you're basically giving more investors the strategies that would only be available to very large institutions and so you are effectively giving them better return for an equivalent amount of risk.
Mao Ching Foo (30:32): Correct.
Jeremy (30:32):
So it's really about that. Being slightly better on a curve. Is that a fair statement?
Mao Ching Foo (30:38):Yes. Providing better access and getting better risk-adjusted returns.
Jeremy (30:42):And how does your time being a Quant and your prior startup experiences feed into the way that you're building the company today?
Mao Ching Foo (30:51):
As a founder, you get involved in many parts of the Firm. You anticipate and try to see different bottlenecks that would come up in the horizon, and then you try to address them before it appears. So the experiences, I think from day one from the software engineering training back in school all the way until the most recent efforts, all these parts would play a role in shaping one’s thoughts, one’s attitude, one’s beliefs.
By and large I think a few key points that always hold true. The first is to be humble and to learn from the different experiences. You don't know everything; there's a lot of things that a person doesn't know so there's a lot of learning to be had. So that's one. And then the second part is really to test. You have different assumptions in your head and you want to test to make sure that the assumptions and your hypothesis is true before you push further. Whether it's a marketing test, whether it's a product test or a feature test or whatnot. These are two points which have been helpful in the different parts of my career, even as a co-founder.
Jeremy (32:02):
Is co-founder life what you thought it was going to be? Because before that, you had been a Chief Data Scientist watching the founder, you'd been a CTO watching the founder. Is founder life the way that you thought or expected it to be? Or how is it different from what you initially thought it was going to be like?
Mao Ching Foo (32:18):
It's very much the same if you ask me. So I wouldn't say "watching the founder" in the previous roles. So let's say in Funding Societies, right? You look to grow the company. There are bottlenecks on the technology side when I first came in and then I resolved those. It's very much problem-solving along the way. You see different problems, you address it. And then you think ahead and see what other problems will come up and then you solve those as well. And, this problem solving mentality does not just stop at the technology front, I apply it to solve issues in many other parts of the firm from the marketing side to ops processes to sales to so on and so forth that you can, as a Senior Exec, you have to solve.
And likewise as a co-founder in a firm, you work on different problems that come along your way. At RealVantage, technology was quite quickly sorted out in a sense because I had some folks who could assist on the technology side. So once the sprints, the processes, the specifications were sorted out, and once the initial platform was built and the features were able to be rolled out in a very consistent manner, then as a co-founder you start to look at other growth bottlenecks / problems to solve, right? For instance staffing up other departments. Or let's say on the user’s side, there's a bottleneck. Okay let's quickly solve the problem. Or let's say on different parts of the marketing funnel, you see that you can improve this conversion or you can install these processes. There are many demands as a co-founder and you do your best to solve the different growth bottlenecks along the way.
Likewise, as a senior management exec in the other firms as well, it's the same thing. You just have to give your best in any role that you are in and then you problem solve.
Jeremy (34:06):
So your span is larger as a result now, right? Because now you're not just doing the technical leadership. You're also doing the staffing, the investor relations. How do you feel about all those new responsibilities? Do you feel like ... I mean I guess I'm kind of curious how do you approach it? Do you use your IQ to tackle EQ problems, like staffing and all that stuff? Or how do you think about your leadership style now that you're kind of managing the whole company from the business functions to the internal to the investor relation functions as well as the technical leadership that you've already mastered over the years?
Mao Ching Foo (34:41):
There's operation and marketing work that I have oversight on. In terms of staffing or managing people, my approach has always been that you have to empathize with what the other person is, with what he's doing, what is going on in his background whether it's family issues that come to work or what not, right? You help him compartmentalize and you try to help him perform.
EQ definitely plays a part as much as the IQ side. So I mean it's a blend of both. And it's really a case by case basis since different people may have different issues. By and large, the team at RV (RealVantage) is a small one where everybody is performing well. So it's performance-driven, a lot of accountability, processes that govern our work, so it's quite clear.
Jeremy (35:53):
Yeah and when you think about all that you've done so far, you've kind of had a really established career from Singapore and the DSO all the way to Stanford and as a Quant. And then becoming a Chief Data Scientist and you know CTO at two name brand Singapore startups and now being a founder of your own, could you share with us a time when you've been brave?
Mao Ching Foo (36:19):
I think every time is a brave time in a sense. When I moved from let's say the quant space into Paktor, that's a brave move. When you move from Funding Societies into a new venture, that's a brave move. When you move from Singapore to overseas to do your studies, I mean every move in some sense has that bravery component to it. Yeah so it's there all the time I would say.
Jeremy (36:52):So out of all those things when you stack rank them, which one do you think was your bravest move?
Mao Ching Foo (36:58):
The bravest I think is trying to create a firm from nothing, right? Trying to solve a pain point for users with technology and skill. That you are solving this for a large number of users and in taking the plunge with my co-founder, I think so far that has been the bravest to date I would say. But of course there’s a recency memory effect so you forget things that's further on behind that as well. So I would say just because of recency I would say that's the bravest move so far.
Jeremy (37:32):I like how you just straight away just pulled out a cognitive bias card of yourself!
Mao Ching Foo (37:38): I do that quite a bit.
Jeremy (37:43):
That's good. And you know I think what advice would you give to people? You know, technical leaders, data scientists who are thinking about setting up their own startup and founding? What advice would you give them to think about or to have some self-awareness about as they make the decision or have made the decision and are going to transition to that founder role?
Mao Ching Foo (38:09):
I would say: do think things through. Building a company is not easy. It's not just the particular quantitative skill that you have nor is it just the technology skill. Because as a technologist, there's always a tendency to build platforms, to build stuff, right? Fundamentally, you need to solve a pain point for the users. And once you're clear that you have a pain point that you are solving, then you think about what components do you need to make this work? And then you embark on that journey. That's what I would advise fellow co-founders to do.
Jeremy (38:56):Yeah that's a classic problem for everybody, right? Not just technologists. We're just overbuilding a solution and over-fitting you know ...
Mao Ching Foo (39:04): Yep.
Jeremy (39:05):
... the solution to what we think is the problem and kind of being surprised it doesn't work. I'm just kind of curious, we're almost out of time. What advice do you have around how do you know if you're actually solving the problem, right? Because everybody has that problem. I hang out with founder friends they're always like, "Oh does this actually solve the problem or not? What is the problem?" So how do you ignore I guess the biases where founders are like, "Oh my solution is definitely going to solve this problem!" The optimism, the over-fit. How do you solve for that dynamic?
Mao Ching Foo (39:41):
I think you need to listen to your customers. You need to listen to the target audience that you are solving the problem for. So take for instance this particular feature (at RealVantage) that we launched very recently. It came from a problem that our users face. They love investing in foreign properties but there is this part about foreign currency conversion - that the banks are awfully expensive.
So we took note of that and after digging deeper, we looked to solve the problem through an integration with Wise. After the feature was deployed, the users started using it on their own volition, and they received very good exchange rates from that integration. I think circa 30 bips from the spot, that's including the fees itself. So it's very, very efficient from the transaction perspective and users loved it. And you know that.. Okay… you have finally solved this pain point for the users. And you look at which other pain points there are to try to make the product even better for the users. So that's what we do. I hope that answers your question.
Jeremy (41:04):
I feel like all the non-technical people go back disappointed because they're hoping for a very technical answer. You know of a model, of a quant, this is like, "Listen to your customers and just solve what they are screaming at you to solve." Right? But it's awesome. I think it's great. I mean that's the correct answer I think. I mean not the correct answer but I think that's the real answer, right?
Mao Ching Foo (41:26): Mm-hmm (affirmative).
Jeremy (41:27):
So wrapping things up here, if you could travel back 10 years in time, 10 years ago you're still back in the States, still hadn't made a lot of your transitions into the technology world yet either. What advice would you have given to yourself back then if you had a time machine?
Mao Ching Foo (41:49):
10 years ago? I haven't really thought about that. There are many things that I know today that 10 years ago I didn't know. So it's a learning process you know, you just have to go through it and you learn those parts. And in fact even if I advise myself, I still may want to test it out to make sure that it's legit before I execute on that. If the advice makes sense of course, sure let's do it.
So for example, one thing that I learnt over time is that people’s core values may change along the way. Surprising. But that's what I learnt. Secondly, integrity matters a lot. So you work with people with high integrity and you try to solve a bigger problem together. That's the advice I would give myself.
Jeremy (42:58):
Awesome. Well I love it. I love the fact that you gave yourself both real advice and you're so self-aware that you probably have trust but verified yourself. You see a time machine coming that's like, "Okay thanks for the advice but let me just verify this and test it out."
Mao Ching Foo (43:18):Are you sure? Are you sure you tested it?
Jeremy (43:19):
Yeah, are you sure you're Mao? Are you really me 10 years in the future? Is this advice legit? Let me back-test this, right? So obviously thank you so much, Mao, for coming on the show. I really appreciated you really sharing three major parts of it. I think the first part of course was your transition into the quant life and what it actually meant because it's such a black box for so many people in the world today, right? You only see them either in whispers or in the movies, you know? And then the second is thank you so much for also sharing your transition from the U.S. back to Singapore but also in parallel from finance to the dating market and the P2P funding market.
Last thing I think I really appreciated also was your role transformation from a Quant to a Chief Data Scientist to CTO and now a founder. And I really appreciate your honesty not just in the advice that you gave about what people should be aware of but also I think the self-awareness of how you would have responded to that same advice 10 years ago which I think is hilarious. But you know, it's true right?
Mao Ching Foo (44:36):
Okay yeah. I mean it was interesting, definitely entertaining talking to you. Thanks for having me on the podcast, Jeremy.