17 | Mio Kato, Don’t Dread Data

My guest today is Mio Kato. Mio is the founder of LightStream Research, and was previously the head of research at Uzabase. LightStream Research melds traditional research with alternative data and AI to provide deeper insights into companies, industries and economic conditions. LightStream’s Asian focus is centred on Japan and India with a particular emphasis on the automotive, machinery and materials sectors and the technological developments impacting them.

In this conversation, we dive deep into the world of data and investing, Japanese equities, and Japanese companies expansion strategies.

I hope you enjoy my conversation with Mio Kato.

Show Notes:

[00:00:40] – [First question] – Mio’s background and history
[00:17:06] – Why are forecasts traditionally so bad?
[00:24:54] – Common pitfalls hedge funds make in regards to data?
[00:38:50] – Why do managers only use data for short term decisions currently?
[00:46:46] – Why have Japanese equities turned a corner?
[01:02:04] – Why have Japanese companies traditionally struggled overseas?
[01:05:39] – Common misconception around Japanese equitites
[01:15:51] – Undervalued skill or life experience?

Connect with Mio:

Listen to this episode on Apple PodcastsSpotifyStitcherCastboxGoogle Podcasts, or on your favourite podcast platform.


Kalani Scarrott (00:00:40): Alrighty. How are we doing? My guest today is Mio Kato. Mio is the founder of LightStream Research and was previously the head of research at Userbase. LightStream Research melds traditional research with alternative data and AI to provide deeper insights into companies, industries, and economic indicators.

LightStream’s Asian focus is centered on Japan and India with a particular emphasis on the automotive machinery and material sectors, and the technological developments impacting them. In today’s conversation, we dive deep into the world of data and investing Japanese equities as well as Japanese companies expansion strategies.

I had a freaking blast with today’s conversation. So please, enjoy my conversation with Mio Kato. So, Mio, thank you so much for being here today. But maybe for listeners who are new to you and may not know, you could you just give me a quick rundown of maybe you and your background?

Mio Kato (00:01:23): Sure. Yeah. It’s a pleasure. And thanks for the invite. So my background, I mean, essentially I run independent equity research company called LightStream Research which mainly focuses on equity research on Asian stocks, but particularly on Japan. So I’m actually half Japanese, half Sri Lankan. And most of my life, actually, grew up in Sri Lanka and actually Singapore. But for the last 10, 11 years, I’ve actually been living in Japan.

I got my break in the industry actually through one of the outsourcing companies in Sri Lanka. This was sort of 2004 when they were actually just getting started. And they were basically four guys from Deutsche, Goldman, JP Morgan who basically had some research in Singapore. Got together and started up this company called Amber Research which was one of the first equity research outsourcing companies. And they came to Sri Lanka which was a bit of surprise given that India was all the rage then.

But I had just gotten back from university in London doing mathematics and economics which is kind of where I get my bent for equity research, I think, since they tie in quite well. And at that time, Amber was just starting up. So I joined them. They put me through like this big training course of how to look at developed market equities and kind of teaching us the ropes which is a really great experience because we got hands-on training from some very senior people because I was probably something like the 16th employee or something like that.

That seems to be where I always end up. Whenever I actually work for another company is I’m always employee 50 or something like that. I’ve generally always worked in relatively small and growing companies in a relatively start-up environment. But, yeah. So they put us through a training course. And then, the whole idea was to basically sell our time as analysts to either hedge funds or investment banks in Europe, in the US, Hong Kong, Singapore.

And because of my background, there was actually a Japanese hedge fund looking for an analyst to support them. So I was the most natural fit. Obviously, not too many Sri Lankans speak Japanese. So, yeah. I got my start as an outsourced analyst helping out this Japanese hedge fund which is actually at the time one of the biggest hedge funds operating in Japan.

And I used to do a lot of fundamental work researching companies. And they’d sell send us basically overflow, as in work that their analyst team couldn’t get around to. So we’d go away. We’d crunch all the numbers. We do a lot of desk research, and we’d come back to them with a bespoke research report. And I was doing that for about a year and a half. And the PM actually liked my work. It was this really fiery Italian job.

So very interesting to work for and a very smart guy. So he used to like my work because he felt that I actually put thought into the work which, I guess, is a contrast to a lot of sell-side research which I’m not the huge fan of. But, yeah. He asked me to join the fund in Tokyo. So my option was either stay in Sri Lanka running Sri Lankan salaries or go work for a hedge fund in Tokyo. And that wasn’t the toughest decision I ever had to make.

So I came here. And it was a real experience because it felt like plugging into the matrix. I mean I’d been working for a year and a half. And before that, all my university learning and everything like that. But probably in about two, three months actually being on the ground and learning, I’d picked up more, I think, than probably four or five years of university and working in Sri Lanka. So that was an amazing experience.

And it certainly helped me a lot in terms of developing as an analyst being able to speak to a lot of companies. And I think that’s one of the best sort of perks of being an investment analyst, is actually being able to speak to companies whether it’s IR or management. In Japan, in particular, you get a lot of sort of semi-engineering types actually doing IR as well. So you can ask them relatively technical questions. And you basically get a lot of very high quality answers. And you can get information that if you try to find it online, you probably have to spend three or four days. And you just ask some guy. Five minutes later, you kind of know everything, right?

So, yeah. I mean I think that really appealed to me in terms of my personality. I think analysis is something that I really enjoy doing. I mean you know how all these professional sports stars say that they never work a day in their life, right? It’s the same thing, just a lot less boring… Sorry, a lot more boring. But, yeah.

So I’ve been doing that basically ever since I’ve been working at hedge funds. There’s a brief period where kind of every hedge fund in Japan basically moved out. And I think most of them moved to Hong Kong especially or Singapore. And I spent a year in Hong Kong. But I didn’t particularly like living there. I mean I’m kind of a homebody. And I like a quiet life. And Hong Kong is all about excitement which is actually nice for a break. But it’s not somewhere where I want to live for an extended period of time. So I came back to Japan.

And it was difficult finding jobs in hedge funds. But I managed to join another startup. And they were again sort of probably 50, 60 people doing basically a financial information database. So I helped them build out research offices in Sri Lanka, in Shanghai, and Singapore as well as looking after some of their Tokyo research helping out with some of their data acquisition and even some of the kind of customer support which was quite high touch consulting type work. And, yeah.

So there was startup. They listed on the Tokyo Stock Exchange in 2016, I believe. So I was there until then. And I left shortly afterwards. What had actually happened is while I was there, the research they do is mainly industry focused which is it’s fine, but it’s a little bit basic compared to really deep private equity research. And I think around that time again, there’s this company called Smartkarma which is an investment research portal based in Singapore.

And they had reached out to me. And I was actually allowed to sort of write research for Smartkarma as under the name of the company I was working for which is called Userbase. They run the [inaudible 00:09:05] platform. So I was writing on Smartkarma. And then, I kind of really got the edge again.

So I actually tied up with another data company to launch LightStream Research. And a lot of it was just simply that I enjoyed doing equity research. And I think there’s a lot of interesting things going on in Japan. And I think Japan has kind of finally turned the corner and potentially could do some interesting things over the next five, 10 years.

But at the same time, I also was interested in building out the team in Sri Lanka because part of it is kind of to give back or give people in Sri Lanka at least similar opportunities to what I had. And part of it is just that it also works, I think, relatively well from a business standpoint simply because there’s a huge wage differential. But I think Sri Lanka is actually a great place to outsource if you’re looking for accounting skills because a lot of people there don’t follow university necessarily because the system there is extremely clunky.

I mean every few weeks, there’s a strike. So you complete a three-year university course in eight years. That might be a slight exaggeration. But it’s not the ideal setup for someone who’s young and talented. So a lot of them actually do the British CIMA course which gives them a strong grounding in management accounting. So if you want people with financial analysis and accounting knowledge, actually Sri Lanka has a very rich hunting ground.

So it actually works very well for the kind of work that I wanted to do. And in addition to that, simply because I was one of the first employees of Amber Research doing equity research outsourcing. A lot of the people who are now running the place were basically my peers or even trainees. So I had a good way to tap into the talent pool there and get first-hand accounts of whether somebody was actually good or not rather than having to do the traditional hiring process which I think is a lot more hit and miss especially if you’re trying to find people who can do analysis at a very high level.

So, yeah. I mean I think everything kind of came together very naturally. And at the same time, the current age is all about data and data analytics. And so, since I have a background in mathematics, I think that actually jives very well with the direction that the industry is heading in.

And we’ve done a lot of work with alternative data and data analysis. We’ve seen how various funds try to use it as well. I mean I think it’s all very new. And I’m not convinced that the funds really know what they’re doing just yet. A lot of these guys, they’re very well-funded, and there’s a lot of talent in them. So they are making some use of it. But I think that it’s still a very new area. And the application of data analytics, I think, is still quite nascent and, in a lot of cases, quite weak.

One reason is simply because I think that they’re still going through the kind of research and testing process. The advantage we had is that we actually were much more hands-on with the data since we actually were working with the data provider whereas in a lot of cases with funds, they have to first make the decision of whether to actually buy the data. It tends to be extremely expensive. But there’s a lot of crap as well. There’s some data which is good. But a lot of data sets are just completely useless.

So it takes a long time to actually test it out. And data analysts, data scientists are not cheap. So that entire process is extremely expensive. And therefore, I think in a lot of cases, the research process has taken quite long. And they probably haven’t always gotten immediate access to all the data that they need taking it in sort of bytes to ensure that there’s some actual value there.

So in that sense, I think we got quite a head start in terms of figuring out what can actually be done with data and what actually it is useful for, what its limitations are because we spent a lot more time playing around with it. Plus, because I’ve actually always been a traditional fundamental analyst, I could combine the data analysis side with more traditional research to try and figure out how they fit together and also kind of test them against each other which I think is pretty lacking even in a lot of the big quant funds.

Certainly, it was a few years ago. I don’t know if they progress from that. But like with all things, if you have data scientists and programmers on one side and red-blooded intuition-based PMs on the other side that there is definitely the potential for things to be lost in translation, and maybe for people not to completely cooperate. So, yeah.

I think in a lot of cases, funds eventually are probably going to need people who combine both skill sets. But that’s actually relatively rare at present. So I think that, yeah, I just lucked out in a lot of ways landing in the right place at the right time. And so, yeah, that’s an area where I think that we’ve found a lot of incremental agile advantage that people still haven’t even started to take advantage of. And just being exposed to that and looking at a lot of alternative data actually made me go back and look at a lot of traditional financial data in a very different way.

And so, when I started as an analyst, I just kind of did the same thing that everybody else does and accepted it as kind of best practice. But when I actually went back and dug into it, I started applying a lot more things from very basic mathematical and statistical first principles. And when I started doing that, and then I compared the results I was getting with a lot of the estimates that the sell side makes, so that even I would make using a traditional approach, I realized that, “Hang on.” The traditional way sucks. So, yeah.

I think that we are currently doing a lot of exploring in terms of how to apply very basic mathematical techniques to really deepen the quality of forecasting. And we’ve been doing it for five years. Every six to 12 months, we have some sort of breakthrough. And I have this real eureka moment. And I think, “That’s it. I’ll never get a forecasting wrong again.”

And then, the next set of quarterly results come out. And, sometimes, it’s good. But there’s always the occasion of disaster as well. And then, it’s back to the drawing board to incrementally improve again. But since we followed that process for about five years now. I feel that we’re really starting to make significant progress.

And, yeah. I mean it’s encouraging. But at the same time, sometimes, I look at these consensus forecasts. And it just frustrates me because some of them are so bad. I don’t know. Maybe, it’s unfair. But at the same time, I find myself thinking like, “You guys have been doing this for 10 or 20 years. Figure it out already.”

Kalani Scarrott (00:17:06): Why do you think forecasts are so bad?

Mio Kato (00:17:08): Well, I think there are a number of reasons. One is I don’t think it’s true that analysts are 100% doing their very best to adopt the most accurate forecasting technique. I think if you’re on the sell side, priority number one is just don’t lose your job. And that means basically hurting.

One of the advantages of actually working for an outsourcing shop is that a lot of my friends used to work for sell-side analysts and support them. And, they get friendly with them. So they also know what they do and what they’re up to, and maybe the stuff that they don’t want the buy side or their bosses to know in terms of how they focused.

So they tell me all the stories of like, “My analyst looked at my model and said, ‘Oh, we’re way too far from consensus and we need to adjust this assumption and that assumption.'” So you get to see how the sausage gets made. And so, I think there’s a lot of that in that you’re not always getting their best effort and their best guess. You’re getting like safe guess.

And, at the same time, the typical way that forecasts are made in a lot of cases, people forecast segmental revenue on something like a year-on-year basis. And then, they guess what the margin is going to be. And that’s just there’s a lot of volatility in that. And it introduces a very large margin of error. And a lot of it is based on actually them going to the company and talking to them and asking them what they think.

But the company is not always completely honest either. Sometimes, they are a little optimistic. Sometimes, they’re low balling. So you always have to read what they are trying to do as well. So you need to have a certain amount of knowledge of behavioral psychology and what the company’s tendencies are. Maybe, the individual person you’re speaking to as well as the CEO or the management committee.

So there’s a lot involved in that. And I find that sell-side analysts, a lot of the time, just swallow whatever they’re told and kind of regurgitate it with a little bit of spin. If they have a buy, you spin upwards. And if you have a sell, you spin downwards. And that’s how things end up. But the way we do it is, as much as possible, we try to forecast margins using a marginal profitability and correlation analysis.

So when we do that, there’s a couple of things we can learn. One is exactly how stable the profit structure is. In some cases, it’s basically a straight line. And if you get a $20 increase in revenue, that means you’re getting $10 extra in profit. And the operating margin might only be 10%. But you know that the larger the company grows, it’s going to head towards 50%. And then, you have really attractive operating leverage.

And sometimes, it’s pretty much metronomic. Other times, basically, it grows 50%. But in any given quarter, it can be off by 50% because there’s a lot of noise. So at least, we understand then exactly how bad our estimates might be if we follow that process. And so, we then can go back through history and try to identify the different errors and try to see what drove the largest errors. So we at least know what to try and check when we speak to the company.

And rather than just asking them like, “Hey, what’s your margin going to be,” it’s more like, “Okay. Is it commodity prices or what’s going to be the timing of the commodity price change impact? Is it going to come to this quarter and next quarter?” And so, we can make adjustments. And they’re still not perfect in terms of accuracy. But at least, there’s an actual proper framework and structure to actually try and understand these things.

And on the revenue side, because of that data background, we troll through masses of public data as well as, in some cases, proprietary data. And so, we use that to try and forecast what revenue is likely to be. And in addition to that, we actually apply a lot of mathematical models as well. So for example, one thing which I’m constantly screaming about is I look at a lot of cyclical companies. And if you look at cyclical company estimates, they’re always going up.

For the sell-side, cyclical companies are misnamed because they always grow which is extremely frustrating. And I think anyone on the buy side would just constantly be bitching about that. But what we do is you can actually just apply a very simple sine wave over the revenue trend. And that gives you an inherent cyclicality. And so, then, you can actually adjust that to try and see what the periodicity is. For example, does the cycle typically last, say, seven quarters, 15 quarters?

And that’s nowhere near perfect. But at least, you’re starting to give yourself… or you’re getting yourself closer to the actual underlying structure of the revenue-generating process for the company. So you’re getting close to kind of the proper way of looking at it. And so, you might still be off. And you have an error term. But at least, you’re measuring the error term against the proper benchmark then. And so, you’re more informed about what’s actually going on.

And so, when we see that something typically has a seven-quarter cycle and we’re coming up to the seventh quarter of growth, something like that, then, we know we need to be looking for what are the signs that the downturn is coming. And then, we can break down data and look at it on country by country basis. So we’re just looking at the news or macro or things like that.

But we know what to look for whereas with the sell side, it’s like do you expect revenue to be up next quarter? You do? Great. Just forecast it up forever. And then, at some quarter, there’s a massive downward revision, and everybody’s like, “What? What happened?”

So yeah. I mean I think a lot of the improvements we’ve made are kind of obvious. And I think a lot of buy side analysts follow them to some extent or the other. But what we do, I guess, is we can spend a little bit more time on each company because a buy-side analyst might look at, say, 50, 60 companies whereas we generally look at maybe 30 for an honest which is still quite a lot.

But as I mentioned, since we do have Sri Lanka, we can leverage some of our juniors to help us build models and things like that. So I think the process is relatively efficient. And we can spend a reasonable amount of time actually looking at these companies and trying to understand them and be much more comprehensive about how we actually drill down into what’s actually going on with the company. So, yeah.

And I think that helps a lot in terms of trying to extricate a lot of the unforced errors that crop up because of the sell-side process which I think is quite weak, in a lot of ways, maybe intentionally so.

Kalani Scarrott (00:24:54): Man, I don’t even know where to start. But to touch on, maybe, you mentioned how sort of managers get data on. Let’s just say for example, I parachute you into a hedge fund manager role, what’s maybe the first thing you do in regards to data wise or what are some common pitfalls hedge fund managers make in regards to data?

Mio Kato (00:25:11): I think it depends a lot on the type of fund. As I mentioned, I think in terms of where I see the industry eventually ending up, I think you need to have people trained deeply in both the fundamental analysis side as well as in data analysis.

And for that, I mean more sort of statistics and math rather than necessarily data science which is basically a lot of basic programming, plus a little bit of statistics. So the application isn’t always that simple. But it sounds a lot more glamorous than it really is, I think.

So if you look at a lot of confronts or fronts which consume a lot of alternative data or big data, they spend a lot of resources either getting data cleaned or cleaning it themselves and actually sort of converting these massive data files into something usable. And I actually compare it a lot to all mining which, I guess, [inaudible 00:26:19] you should be relatively familiar with. But, yeah.

2% of what you have in there is actually usable. And you don’t know which 2% immediately. So the problem I see with the way a lot of funds are using this is that they’re getting people to analyze that data and figure out what’s useful who don’t actually have the fundamental skills to comment it from a different angle and sort of double check that. It’s actually logically useful rather than just having a high correlation which could be spurious.

So I think that needs to be strengthened. And I think they probably do realize that. And I think they have been moving in that direction. But it’s kind of that interconnect between the sort of programming and statistical skills and the fundamental analysis skills which I think is the big bottleneck right now because you either need people who have both skill sets themselves.

I mean they’re not ridiculously rare. But they’re not that common either. And especially if you want people who are good at both, then, it starts to get tougher. But you either do that or you have to have your data scientists speaking to your fund managers and analysts. And then, you start running into problems like they might feel threatened or that they’ll be replaced or this isn’t my comfort zone, and I’ve always done it this way. Why do I need to change.

And you have all the traditional problems that come up whenever you’re kind of transitioning like a technology or a method of doing things. So that’s not entirely easy either. And I think the personality types for a lot of analysts and PMs also kind of clash with the personality types of a lot of statisticians and data scientists or programmers.

So that’s an additional kind of wrinkle. And I don’t think it’s very easy to sort that out. So I think actually doing the work maybe wouldn’t take all that long. But I think building the structure that can actually do it well, that I don’t think would be an easy process at all. And for a lot of these funds, they want to do it at a relatively large scale. So if you want to build a team of three or four people, I think you can do it. If you want to build a team of 30 or 40 people, that is a pretty big challenge in my view. So, yeah.

And I mean everybody’s trying to do it at the same time. So if you have somebody who’s good, every billion-dollar fund is going to be making them an offer. So how long do you keep them, six months, 12 months?

And then, if you lose them, you have to rebuild your process all over again. So I think these are some of the issues which will delay how quickly it matures. And I think the other issue is, of course, that in terms of decision makers, what I feel has been going on is initially everybody just invested in this area because, firstly, it was hot. And if you told your investors, “Hey, we use alternative data and we use it to generate so much alpha,” they’re not going to know.

So it makes them that much more likely to invest with you. But I think the reality has been that whatever alpha generation there was has been kind of mediocre and, in a lot of cases, in relatively small cap stocks or from relatively obvious ways to use it like I think a lot of companies were just using it to forecast revenue which I mean fair enough. It’s a worthwhile thing to do. But it’s not exactly the most sophisticated thing in the world.

So you’re not going to get a massive sustainable edge from that. If it’s possible to do, every fund is going to be doing it sooner or later. And in terms of data, I think a lot of funds were quite careful about trying to make sure that data providers didn’t spread the data too far and wide. But at the same time, some data providers I think were trying to be exclusive or to try and kind of monopolize their data sets.

But I’ve always felt that’s kind of silly because there’s too much of it out there. And a lot of people think that you need massive amounts of data or massive amount of coverage to actually understand what’s going on. But the problem is there’s a lot of noise in it as well. So if you have, say, 10% of the sales of a company covered, you might be able to get a very good estimate. But from what I’ve seen, if you can get the right one or 2%, you can actually sometimes do a better job.

And if you only need one or 2% coverage, you do find that somewhere. I mean somebody will get it. So as a data provider, just trying to be the only unique data provider for a particular set of data series covering some sort of metric, I think, it’s a losing game. And it’ll become increasingly difficult as time goes by because everybody is now kind of trying to unearth the data that they’ve had and actually recording it because some of the discussions I had with people were like about, “Oh, we have all this data. And, oh, how much history do you have?” They’re like, “One year or something like that.” You’ve been doing this for the last 20 years. They’re like, “Oh, we threw it away.”

But now, I think everybody realizes. So everybody’s storing their data and trying to keep it clean whereas before, I mean it’s not just that they were throwing it away. In a lot of cases, they’re quite careless and very small differences. Even whether you capitalize something or not can mean that if you do a search, you don’t pick it up or if you sort it gets lost or just small areas like decimal places, formatting. There’s all sorts of things which can kind of mess up the data set.

So initially, you need a lot of data scientists to correct all those errors. But as companies start to understand exactly what the value of data is and how you actually need to process it and store it so that it’s actually usable without a massive amount of processing. Then, I think you’re just going to have data coming out of everywhere. And so, the value of that is probably going to diminish very quickly. Well, maybe saying very quickly is wrong. I mean I think it will take time to really get going.

But once it gets going, I think you’re going to be able to get data from pretty much anywhere unless it’s really proprietary in a closed system. So that sort of method of gaining an advantage by having unique access to data, I think, it won’t go away completely. But I think it’ll be difficult to maintain that especially for very large liquid stocks and trying to trade this stuff with very illiquid stocks. I mean it’s worthwhile doing to a certain extent. But it also carries its own risks.

So, yeah. I think what really needs to happen is you need to develop that understanding of taking data that’s maybe not of the highest quality, but being able to process it well enough and intelligently enough that you can actually take sort of subpar data and turn it into actually usable signal in the same way that I guess Australian walls tend to be extremely high quality. But you can still use lower quality walls and get similar product as long as your processing technology is very accurate.

And the thing with data is obviously it’s not finite in the same way that physical resources are. So as long as your process is extremely good, you should be able to apply it extremely broadly. And you don’t have to tailor it quite as much. So that, I think, is really the key in terms of being able to really generate alpha and generate a sustainable advantage.

But I think that getting to that point will require combining skills in a way that hasn’t been done until now. And I’m not convinced that the guys who are extremely well funded are necessarily going to do this simply because, in a lot of cases, they can just throw money at things, and it solves the problem. But I don’t think that’s really the case here simply because it’s too new.

Yeah, I think whoever gets the culture right and can actually get their fund managers properly speaking and communicating to your data geeks, they, I think, would have a significant advantage and potentially be the funds which actually we’re not. There’s actually one other major point that I think is worth noting, is that a lot of funds actually use this data for relatively short-term trading.

So like I mentioned, you know that revenue is going to beat or something like that. And you try to play earnings which is quite volatile. But if you can spread it across enough names and you have a 55% hit rate, then, that’s good enough.

But I think a lot of more traditional fund managers like long-onlys are kind of missing a trick and not appreciating enough the value that they can get out of data because you can really drill down into what companies are doing. You can get from credit card data, in a lot of cases, you can actually get demographics. So if a company is trying to expand from, say, the 30s demographic and they’ve said, “We’re going to create products for 40-year-olds,” you can actually go in and check if they’re actually successful with that.

You can go in and check what’s the churn rate for particular demographics. Maybe, a company has an equal number of people in their 30s and 40s. But if those 30-year-olds are the same 30-year-olds every month and the 40-year-olds are being churned consistently, then, you start to understand qualitative aspects of the company. And that’s important, I think, for a fundamental analyst because you can get a significant edge in terms of understanding exactly what’s happening within the company.

I’d argue that, in a lot of cases, if you really know how to look, you can probably understand the companies better than management because they’re probably not looking at the stuff yet either. So I don’t think that’s being done. And I don’t think that, in a lot of cases, long-onlys even aware of exactly what can be done because a lot of the decision makers probably won’t be going into this stuff and actually getting hands-on with it.

And then, if you’ve been in a long-only and everybody you know and your entire team is basically fundamental-research oriented and some guys come in who are data scientists and they start telling you need to do this and that, I mean they might be perfectly right. But that doesn’t necessarily mean you’re going to listen to them. And it certainly doesn’t mean you’re going to listen to them right away.

So these things can potentially take time. And I think even the work we’ve done, I see a lot of possibilities. But there’s still more work to be done. On every end of the supply chain in terms of data collection, data cleansing, data processing, and then the final analytics and actually applying it into the portfolio management process, there’s a lot of advancements that need to happen to really utilize this fully.

But I think that the scope and potential is really masked. What we’re trying to do is just use at least a lot of publicly available data and try to really train our processing and analytical skills and interpretation skills so that we can really be kind of data native or data literate in a way that a lot of analysts aren’t which is partly for survival. But I think it will also give us a significant edge as people start incorporating this more and more to that process.

So although we’re a very small company, I mean the whole idea is to try and develop a skill set that’s relatively unique and also try to get ahead of the curve mainly because I think that my background and the sort of circumstances, I found myself in, were just dumb luck in a lot of ways. But I think they worked out in a way that is ideal to kind of get ahead of what could be a very big trend in future.

Kalani Scarrott (00:38:50): You mentioned about how sort of managers are happy to take advantage of data for short-term training. But why do you think they’re more reluctant to use that data for long term long-only positions? Is that just because they might be working themselves out of a job or do you reckon?

Mio Kato (00:39:03): I think it’s probably a number of things. One is probably familiarity. I think a lot of fund managers have a process, and they generally don’t want to deviate it because they’ve usually put a lot of years into it. And there’s usually a lot of trial and error as well. And it takes a long time before you really figure out exactly how to think and trade in a way that allows you to actually generate any sort of sustainable advantage.

And markets are always really tricky. So when you mess with that process, you don’t learn whether your adjustment is actually good or not for quite a long time. So there’s a huge opportunity cost involved in terms of actually abandoning your process because you might, I don’t know, make an extra 50 basis points or something like that in a given year by adopting this. But then, if you lose, say, 50 basis points compared to what you would have done otherwise the next year, I mean do you stick with the process? Do you go back to your process? Do you try something entirely different?

That discovery process, I think, is quite expensive. And depending on where you are in your career, it might not be worth doing. And then, on top of that, like you mentioned, they might be worried that, “Oh, if I do this, then, everything can be automated. And then, I’ll be on a job.” I’m 99% sure there’s no way that happens. From what I’ve seen all the data, that can’t be done as far as I’m concerned. It’s just it’s too complex.

You can’t just like apply an AI and suddenly get everything you need to know about companies partly because the data is too noisy. And there’s a lot of adjustments companies make. They change their segments. They reclassify things. They have unusual items. Sometimes, they book unusual items as operating profit. Sometimes, they don’t. So there’s a lot of adjustments you need to go back and make as an analyst or a data analyst to try and actually understand what each metric is in reality versus how it’s presented in the financial statements. And I don’t think we’re anywhere close to where AI could do that.

In a lot of cases, companies don’t even tell you until you actually go and ask them. So that sort of fear, I think, is completely misplaced. But it’s natural that without really understanding this that people will be kind of hesitant. And then, there’s simply the fact that if you’re an analyst with 10 or 20 years of experience and you’re extremely respected and people know that you know what you’re doing, if you suddenly go into this data field, it’s sort of like learning a new foreign language.

At first, you seem like a complete idiot. On a personal level, that might be kind of uncomfortable especially if the guy teaching is some 22-year-old who just came up with a data science degree from university and this 45, 50-year-old fund manager who’s been there and seen it all. Yeah.

So I think there are a lot of these sort of frictional issues, but yeah. I mean I think over time, it’s probably going to percolate. Right now, I think they probably don’t necessarily feel that they need it. But I think there’s a lot of pressure from things like passive investing. And so, as an active investor, I think you need to be honing your skillset and trying to kind of develop your edge. And I think this is one way that you can do it.

And especially if you’re very good at deep fundamental analysis, this actually massively broadens the scope of what you can do because your access to information increases significantly. So, you might understand the company and know what you need to know. But that information, in a lot of cases with traditional information, might not be available.

But if you can turn to alternative data or big data, you might start to be able to get a lot of this stuff. And then, what you can do improves significantly. And I think, like with a lot of things, if you’re already a good analyst, you can increase the gap between you and the next guy even more by developing this skillset even if they do the same thing because you can utilize the advantage you already have in terms of fundamental knowledge. And you should, hopefully, be better at the data analytics eventually as well. And then, when you combine the two, it starts to become impossible for everybody else to catch up. So, yeah.

I mean I think that, over time especially the better analysts are going to be incorporating this more and more, and you can start with very simple things. I mean Google Trends if you use that, a lot of time, you can make better revenue estimates than a lot of sell side ones, honestly, especially for easy and things like that.

You do need to kind of put some time in to figure out exactly how to do it because it’s not completely obvious initially. But there’s relatively good correlations especially when companies are growing at the initial stage between sort of the amount of discovery that’s happening at the amount of transactions that are occurring. So you can get actually very good correlations.

And especially if a company is new, as a sell-side analyst, you don’t know how to forecast it. So the accuracy tends to be extremely low. So as a buy-side, honest, if you start using that and put some time into it, you can actually get pretty good results. And it’s completely free, very easy to do. I mean Google’s very intuitive and the data comes out in a very clean format. So, yeah. You can start very easily.

I think if you try to be really fancy and incorporate massive amounts of AI and racks and racks or servers of data, you’re probably going to spend an enormous amount of money, not get much valuable out of it. And then, everybody’s going to give up. But if you keep it simple at the start and just try to incrementally improve on your existing process a little bit, once you kind of have a few positive experiences and results to show, then, you can start really thinking about how to expand on that process and incorporate more complex data sets.

And I think that’s probably the right way to go. But I don’t know. As a big fan, I think the temptation might be there of these guys are spending $10 million a year. We need to keep up. And so, we need to get the same data sets and get the same expensive data scientists. And can we afford that? Do we want to spend on that?

And so, I think a lot of these things are kind of causing delays. But I think it’s only a matter of time. In a lot of ways, it’s so simple that it’s ridiculous that people aren’t using it more. And I think some people make very good use of it. Eventually, it’ll start happening. And I think once it starts happening, I think the people who use it effectively will have a significant edge. So then, everybody else will have to kind of incorporate it into their process as well. That I think is what’s holding back the industry for now. But I don’t think it’ll last that long.

Kalani Scarrott (00:46:21): So it’s probably fair to say that data is basically leverage for traditional financial analysis then?

Mio Kato (00:46:25): Yeah. I think that’s a great way of putting it, I think, yeah, probably one of the problems is that a lot of people don’t seem to look at it exactly like that and kind of look at it as something kind of different with different teams and different skillsets. I think it has to be integrated and embedded into the current process rather than being a different process.

Kalani Scarrott (00:46:46): Maybe, to move to Japanese equities, at the start of the conversation, you mentioned how you think Japanese equities have maybe turned the corner. Why do you think that is?

Mio Kato (00:46:55): I think it’s very simple stuff. Like I mentioned, I look at cyclicals a lot. And I think the micro-cycle is also there. And I think it’s just a natural thing that they would. People talk about the declining population and sort of Galapagos syndrome and a lot of kind of lack of flexibility especially with the workforce and things like that. Yeah. All these things exist. There were problems in the ’80s when Japan was booming.

It’s just that when things are going well, people point to the things that are good and attach that value to them. And when things are going bad, people look at the inherent problems and kind of just say that because those won’t change like, “Things will always be bad.” And it’s silly. If you actually track Japan, what you’d see is that since the ’90s, they’ve been two lost decades basically.

But from about 2014, ’15, you’d see that pretty much all of the large corporate balance sheets were cleaned up. And I think over the last five years, you’ve even seen that in a lot of cases with the SME sector as well. They’re not over leveraged. They’re not just trying to get rid of debt and kind of get their balance sheets healthy again.

Even some of the weaker companies, their balance sheets are actually pretty good now. Like in the auto sector, Mazda was traveling for a long time now. Their net cash just slightly net debt. So, yeah, the balance sheets have strengthened a lot. You also have another factor which is that, yes, the workforce is declining. But you also had this particular dynamic in Japan which is that nobody gets fired.

So if you were sort of in your 40s when the bubble burst, you were probably stuck at an inflated salary level for the last 30 years. Now, you’re coming up on 70, you’re going to retire. That’s a lot of overpaid people who are coming out of the books of a lot of companies. For example, it’s actually looking at NTT about 20 years ago… not 20 years, 10 years ago.

And it looked very boring. But they had one chart of their workforce by demographic. And I looked at that. And I was like, “Oh, free money,” because it’s just a lot of people in their late 50s and 60s very high salary. They’d be coming off the books replaced with people in their 20s who generally have much lower salaries.

And you could even argue that they may not be the most dynamic people in the world especially in Japan in terms of IT skills and things like that. And Japan has amazing engineers who are sort of like, I don’t know, 80 and still going in a lot of cases. But I think that there’s been kind of this weird dynamic where I think if you enter the workforce after the bubble burst, you just didn’t have that many opportunities.

If you’re the best of the best, of course, you are still kind of fine even though Japan is more seniority based. But if you are kind of the ordinary guy, the ability to move up and kind of aspire to higher wages and things like that just wasn’t there. So with that starting to change, you can’t get increasing consumer spending I think even without a real increase in the population simply from a lot of these kind of legacy costs coming off the books.

And, perhaps, the younger demographic just getting a little bit of air to breed. And at the same time, that’s happening in certain traditional sectors. What you’re seeing is the startup ecosystem in Japan is actually not too bad. I mean it’s not glamorous and sort of rolling in money the way Silicon Valley is. But you are getting some reasonable innovation.

And even in terms of mentality, I think, yeah, there is a little bit more dynamism coming in. So because I worked in startups and I’ve been around startups, I’ve kind of seen the change. But you’re not everybody wants to go and work for like Mitsubishi or Mitsui or these kind of crusty companies which have a very particular culture.

And a lot of them actually choose to go work for startups not because they get paid more or anything like that, but because they’ll actually get the opportunity to do things and actually make decisions and try to kind of have some sort of impact. And it’s not necessarily the majority of people. But the proportion is increasing. And you’re starting to see an encouragement of that mentality even in the universities.

The University of Tokyo I think started up a fund to invest in internal startups. So that mentality is changing. And I think Japan is becoming more dynamic. And some of the work-life balance stuff is also improving in terms of just not getting people to work ridiculous over time hours and kind of put in sort of FaceTime which tends to happen a lot.

But I think they’re starting to address that. To a certain extent, even a lot of more traditional companies are trying to be a little bit more dynamic now. I speak to some of them, and they have no idea how to change. But they want to which is at least the first step. So I think that’s also another positive.

And in addition to that, I think if you’ve been tracking a lot of companies, you’re starting to see some, I don’t know if visionary is the right world. It’s probably a bit of an exaggeration. But you’re starting to see some leaders who you could imagine like books being written about them in the future. Kazuo Hirai who turned around Sony, I think, did a remarkable job. He didn’t stay there that long. But I think what he did with the company has been pretty fantastic,

And, Sony, I think is just in an extremely good position now. But for most of the 2000s, it was kind of a basket case, always had massive potential. But nobody just executed. And it was dealing with a lot of problems.

You look at Toyota. I think Akio Toyoda is one of the best CEOS in the world. There are a few others who potentially might be better. But I don’t think he’s generally talked about in that regard. But if you really look at what he’s done, I mean Toyota obviously is a fantastic company and always has been. But what it wasn’t in the past was agile or particularly dynamic or it was boring.

But I could throw it up basically like he was race driver, rally driver. And I think he really came in and shook things up a little bit and also made Toyota more international. I mean one of the things he said is basically he doesn’t want people saying the Toyota cars are boring anymore.

And if you look at the design, I think they’ve become a lot more attractive over the last five, 10 years. I mean before, it was just like what your dad or mom would drive to the shops. But now, they actually look like sauna, pretty nice. And I think they’ve turned around Lexus to a significant extent as well.

I think they took a little bit more time with that. But you’re not starting to see a lot of new models coming out for Lexus. And at the same time, Toyota traditionally would have tried to do everything in-house and especially in Japan. But for example, they came out with a new pickup in the US, the Tundra. And the infotainment system for that was designed entirely in the US.

I mean pretty much the entire truck was designed in the US. But I love Japan. But if you’re talking to infotainment like US stuff, please, software. But at least they’ve recognized that and kind of incorporated the strengths of a lot of their Toyota personnel in the US. And it’s not that they didn’t do that at all before.

But I think he’s really accelerated that and kind of put the right people in the right place. And so, while Toyota is still very much Japanese, it has a very international Japanese flavor now. So, yeah, you have some of these leaders who have actually addressed some of the core problems of these companies and kind of set them on the right path.

And I see little bits of that everywhere. In the startup space, we were very positive on this company called Mercari which they’re basically an online free market. So you can sell your old stuff. But it’s amazing how convenient it is.

I mean you just take a picture of your stuff. In some cases, it uses AI to actually detect it and write down all its features and sort of what it is basically. And they have parcels, and you can easily put it in a post box and send it, and you get your money. They have a digital payment app. But everything’s really integrated into the convenience store system here.

And it’s just super convenient which is why they’ve been completely dominant in Japan. But what I liked about it is they announced that they were going to enter the US. And I first looked at it. And I thought, “Okay. That’s going to be a disaster because usually it is.” I mean if anyone ever analyzes Japanese companies, I think if they’re not manufacturers, if they ever invest overseas as a rule of thumb, you can like roll a six-sided dice. And in that many years in the future, you just write off the entire amount plus 20%.

And I mean I think that’s probably a better way of forecasting than whatever the company gives you. Now, that just couldn’t be 100% accurate. But that’s typically the case for Japanese companies. And so, I kind of assumed that would be the same for Mercari. But I spoke to them. And then, they’d actually tried the Japanese style of doing things at the US. It, as expected, didn’t work.

And I guess they were surprised. But to that credit, they actually was self-aware enough to understand that it wouldn’t work in future either. I think they fired a guy whose ex-Facebook, I think, this guy called John Lagerling. And he came in. And he seems to have really turned things around. I mean it’s not completely just run by the US. I mean there’s a lot of cooperation.

But basically, they have the confidence and the wisdom to actually hand things over to a local. Like I mentioned, I did work for a Japanese startup. And I mean they tried to do the same thing. But they went and found a local who thinks like a Japanese. And I mean, yeah, I was telling them like, “No. You’re missing the point.” That’s not that [inaudible 00:57:56]. But that’s not what Mercari did. So I speak with them. And I kind of realized, “Okay. I think these guys actually get it.”

And so, we were initially very bearish on them when they listed. And it actually went down a lot. I think it was worse than halved. But after speaking to them and understanding this, these guys might actually get things done. And so, we turned bullish on them. And they actually came through. And they were helped a lot by the pandemic. But they had something like $100 dollars a month GMV target for the US which at one point there was something like 30 million or something like that with maybe about 12 or 18 months to go.

So everybody had given up. Even I’d given up. But they actually managed to hit it which was just amazing. So that company, I think, has all the hallmarks of a software company which I think could actually be globally competitive and potentially even globally dominant. It’s the first Japanese software company that I think that can be said of. I mean [inaudible 00:59:07] kind of makes an effort. But they’re basically 90% Japanese.

These signs I see that Japan’s kind of figured out a lot of its problems and weaknesses at the same time that they now have stronger balance sheets to actually go out and start getting more aggressive. So, yeah. And I think last time, you had this sort of wave of Japanese manufacturers going overseas and dominating a lot of aspects.

And I think those companies are still competitive. But I think potentially you could see another wave of more service-oriented companies from Japan, companies like Mercari. There’s another interesting company we just listed called Safie which makes surveillance cameras. But they’re ex-Sony guys. But there’s surveillance cameras which are kind of 200 bucks and 10 bucks a month. But you just plug and play them. You just need a power socket and an internet connection. And you can control them through your smartphone or from the web.

And they’ve been growing something 100% every year. But that sort of thing right now, they’re domestic. They do have plans to expand overseas. And it’s not a guarantee that they’ll succeed. But it’s just such a simple product. And it’s so easy to understand because you can basically place it wherever you want like a webcam. And you can monitor your store or monitor your employees and see are they following your best practices.

You can use it as a normal security camera. It’s just so simple and so cheap that sort of thing can really do extremely well. And I mean it’s not something that’s particularly Japanese. You can apply that concept anywhere. So just starting to see a few of these companies come up tells me that a lot of Japan’s latent potential is actually starting to be realized.

And when you start to see a lot of different companies kind of executing this at the same time, what that tells me is there’s probably been enough soul-searching done over the last 20 years that they’ve kind of figured out where the deficiencies have been. And the only thing is as long as they try to maintain the traditional strengths and try to address the uncompetitive areas versus a lot of the global industries, I think you can really see a resurgence in Japanese competitiveness.

And the main thing is that I don’t think that’s in any way a consensus view. I don’t think too many people who don’t look at Japan closely would actually not be surprised by that view. So as far as investing goes, you’re going to get pretty good odds if you want to bet on that case because not too many people are going to believe that that’s going to happen. So, yeah. That’s what I kind of see with Japan hopefully having a second win now.

Kalani Scarrott (01:02:04): Why have other Japanese companies struggled overseas? Is this just that cultural aspect, do you think? And does it go both ways, do you reckon?

Mio Kato (01:02:07): Yeah. I think it does go both ways. I think the cultural bridge between Japan and other countries is just wider than between any two other countries. I guess it’s probably the widest. I mean I’m not aware of any others. I mean I think in any case, expanding overseas is difficult in any case. Maybe, if it’s from the US to the UK, or Australia or something like that where the language is the same, the culture is actually quite similar, it might be easier.

But it’s still not like trivially easy. But especially a country with a different language and also where Japanese culture, I think, is quite particular. And there’s a lot of non-explicit expectations. And so, that I think makes it very difficult to integrate especially quickly. And things like the sales process and what customers expect and what their standards are and things like that, I think like the expectations are almost the opposite.

In a lot of cases in Japan, when you’re buying something or the customer, you kind of expect everything to kind of work properly and as expected and kind of do what it says. But you might be willing to wait for it whereas overseas, in our case, if you have a need, you don’t necessarily need the product or service to be perfect. You just needed to take care of that thing as quickly as possible and for whoever you’re dealing with to try and address whatever other issues crop up.

And I mean this is a bit of a stereotype. But I think broadly that is the difference. And I think that as much as people say that they’re aware of it, when they actually try to actually bridge that gap, it’s much more difficult than you’d think. In particular in terms of comfort level, you might theoretically believe that, “Oh, it’s fine. I’ll just change the way I do things.”

And when things are going well, that might be fine. But if you’re not selling and you’re piling up losses and stuff like that, you still have the conviction to do things the way that are sort of supposed to be done in that country versus what you’ve been doing for the last 10 years. That’s a lot more difficult. So I think, yeah, that’s probably the main issue.

But I think what really compounds it is I think, in a lot of cases, there’s a lack of self-awareness. So like I mentioned, with Mercari, the thing is the reason I turned positive on them is that I felt they were self-aware of their problems whereas, in a lot of cases, I talk to people. And I’d say, “Look, this is what you’re doing wrong overseas.” And they go like, “Well, yeah, maybe.”

But you could see that they really didn’t want to accept it. And I think emotionally, it’s difficult. But if you don’t, you’re just not going to succeed. And I think that’s kind of the hurdle that a lot of Japanese companies don’t get over.

But that kind of works for a lot of companies coming to Japan as well because, in a lot of cases, they just come here, and they try to apply the same strategy that they do overseas. And it fails. Then, they’re shocked. I just sit here and just like, “What do you expect?”

But I don’t know. That just happens. Despite the fact that a lot of the people doing these things are very accomplished and intelligent people, yeah, I can’t explain it.

Kalani Scarrott (01:05:39): Are there any maybe opinions that really annoy you when outsiders express their opinion on equities in Japan? Are there any opinions maybe you think are commonly held that may be misguided?

Mio Kato (01:05:46): I mean I think the biggest one is probably I think it’s kind of weakened over time. But probably about two years ago, people had this real impression that the Japanese auto sector was really weak in terms of a lot of EV and electrification technologies. And that’s just so far from the truth that I found it mind-boggling that a lot of sophisticated investors could actually believe that because I don’t know.

I mean I think there’s a lot of kind of political issues in terms of the discussions with EVs, but also renewables because it’s just become an extremely emotional issue, and it’s morphed from we have this problem which we need to solve to a religious war. If you don’t say your sermons the right way, people just get mad.

And so, a lot of the Japanese companies, I think, were just taking the approach that there are various hurdles for EV penetration. And so, they’ll continue to basically try and develop everything especially Toyota and Honda. They were trying to develop battery EV technology, hybrids, fuel cell vehicles, more efficient gasoline vehicles.

And all of this stuff works towards the same goal of trying to reduce emissions. They have different aspects in terms of how much the maximum reduction potential is versus how quickly they can be deployed. But it’s not a bad thing to have a lot of different options. But I think the prevailing idea was if you try to do anything other than EVs, you’re just trying to slow down your wheels which is silly.

I mean the other thing is just that a hybrid is basically like a gasoline vehicle combined with an EV in a certain sense. It’s just that the battery’s smaller. So you need basically the same technologies. You can’t build hybrids without being able to build good motors, big good inverters, and even having battery technology.

The size is a lot smaller. So in a certain way, it’s easier to manage especially things like overheating and stuff like that. But you have to have a lot of same core technologies. But people had this idea that, oh my god, they’re 10 years behind, and they’ll never catch up. And I couldn’t understand how sophisticated investors especially were supposed to do fundamental analysis could have that sort of view.

And I used to speak to a lot of the companies. Even they couldn’t understand it because they’re like, “Don’t you explain to them you have the same technologies?” And they’re like, “Yeah, we tell them. But they don’t accept it.” Okay. Well, yeah. So I think since then, just with the progress of time, I think they’ve started to launch more battery-electric models.

And so, I think that some of that perception, I think, has dissipated. But there’s still this idea that they are kind of hesitant to move forward fully which they are, to a large extent. But I think that actually makes a lot of sense. People bring up a lot of different bottlenecks or hurdles that you need to pass for there to be real full deployment on EVs. Most of those are pretty solvable.

But the one thing that we’ve constantly been harping on is raw materials. About three years ago, I think we basically did some research. And we basically said, “Look. Lithium is going to be a huge problem.” And at that time, I think people are concerned about cobalt. But we weren’t that concerned about it because there was a lot of progress being made in terms of technology to reduce the proportion of cobalt used.

I think Panasonic is down to just a few single-digit percent. I don’t know, 5% or 2-1/2% or something like that. And they were looking at potentially eliminating it by probably the middle of this decade. So we never had that as such a huge concern.

But with lithium, there are serious problems because the amount of economically extractable lithium that’s available is probably only enough to replenish the entire global automotive a few times, like, maybe three or four times or something like that. And I mean that amount of extractable lithium grows over time as people sort of explore and better technology becomes available and things like that.

But that’s not enough of a margin of error to be really comfortable with that. So that was something that we always identified as an issue particularly because we’ve been looking for companies that can actually recycle lithium. And we haven’t found an example of any company that can clearly demonstrate that they can do it well. There are a lot of companies which claim to recycle lithium batteries and you can’t recycle various parts of it.

But the actual lithium in it, we speak to Dover Holdings which is one of the most advanced recyclers in the world. And they say that it costs something like three times the amount to actually recycle stuff in the lithium battery versus actually producing new which is not a good thing. And I think in terms of the lithium, it’s even worse.

So that sort of technology in terms of closing the loop still needs to really be developed before you can really say that battery EVs are really sustainable. And I’ve seen some projections that might be possible sometime around 2035 or something like that. Maybe, it happens earlier. But this is a real potential problem that people need to have some idea about how to solve before you really try to massively expand the deployment of EVs. But a lot of regulators don’t seem to be thinking about this at all.

So when you look at that and you look at the proportion of emissions from automobiles especially passenger vehicles versus normal power generation. I mean it’s pretty silly the amount of attention that’s being placed on this because to actually make EVs really environmentally friendly, you first need a very environmentally friendly power grid.

And that’s going to take a lot more time and a lot more investment because that trillions upon trillions of dollars of infrastructure. So, yeah, the discussion around this seems to deviate very far from reason. And I mean there’s kind of misinformation on both sides. Some people say EVs are more dirty than custom vehicles which isn’t true unless you intentionally make it like that.

But at the same time, there are a lot of people who use very optimistic assumptions about how much of an improvement there are with EVs right now. I think the FT said it was something like 15 to 30%. And then, I think they corrected it to something like 60% because someone gave them additional data.

And I need to actually go in and check exactly what the data says. But from what I know, my impression is in terms of what’s actually been done right now is probably close to the 30% figure because a lot of studies which get to 60% are generally looking at sort of US power generation mix, the European power generation mix which is a lot cleaner.

But the vast majority of batteries are made in China, Korea, Japan. So that doesn’t apply yet. I mean it’s a theoretical possibility. So obviously, it needs to be taken into account. But, yeah. You actually have to have an actual concrete plan. And to be able to demonstrate that, you can do this cost effectively because the vast majority of emissions in future are not going to saving emissions or reducing emissions. It’s not really about the US or Europe or Japan.

It’s about China. It’s about India. And if it’s not affordable to consumers in India and China, you’re not going to achieve significant reductions in greenhouse gas emissions which is why a lot of the kind of negative invective about hybrids, I think, is extremely silly because in terms of the premium, it adds on price. It’s like a thousand, $2000 which is not nothing. But it’s a lot cheaper than the step up to battery vehicles. And it is a transition technology.

I mean it’s not something that’s going to last forever. But if you need to use it for 10 years to make sure that everybody in India can go from traditional gasoline vehicles to something cleaner, it makes a lot of sense to do it. So this whole thing of only-battery vehicles is if there’s a massive advantage to it, and it’s cost effective, fine, that makes sense. But right now, it seems to be more of a religion than anything else.

And it’s a very silly use of resources, I think, because you have to probably also subsidize a lot of the transition to the power generation mix. And that’s not going to be cheap either. So, yeah, stuff like that kind of it gets on my nose because it’s like people aren’t trying to actually reason things through in the real world. It’s kind of like, “I want this to be true.”

So if people disagree with this and the possibilities of what theoretically can be done, they’re just not going to listen. Yeah. It only looks at sort of a very narrow band of consumers of like very wealthy Californians or people in Norway whereas that’s just not the reality. I mean you have to make sure that a lot of people in emerging markets can make a similar positive decision to at least have some sort of impact because there are a lot of them.

Kalani Scarrott (01:15:42): I think that’s probably been the story of the last year and a half. I want this to be true. So it is.

Mio Kato (01:15:46): Yeah. The world seems to be going crazy. But-

Kalani Scarrott (01:15:51): It is what it is. What can you do? Yet for my final question to wrap up this amazing conversation, what is the most undervalued life experience that you think university-age students don’t give way to? What’s an underrated skill or maybe an experience that you think they should have?

Mio Kato (01:16:04): I think that’s really tough. I mean I think probably in the current environment for the social media generation, I don’t know if I’d call it a life experience. But I think in terms of a mentality or something to be aware of everything especially younger people need to be more focused on the internal versus external balance.

I think that because of social media and how it affects your brain, I mean I think there’s a lot of media and news right now about sort of how it affects your dopamine function and kind of trains your behavior. And it kind of makes you extremely obsessed with external approval and things like that.

And I think it’s very easy to get carried away with that. But if you lose that balance, I think that you tend to really go astray. And I think there’s not enough focus on sort of people developing like an internal sense of what fulfills them and what they’re actually trying to achieve in life.

I think it’s very easy to just get swept away in all the noise around you. And I think that it’s very easy to do. And it’s difficult to realize that it’s happening to you. So I think, yeah. My advice would be to be very conscious of exactly what you’re doing. And I kind of get it that it’s very easy to be affected by this.

I’m in my 40. It doesn’t bother me. But if I was 18, obviously, what everybody’s saying about me every five minutes, I could see it happening. So I think it’s kind of natural. And everybody is drawn to that.

But if you don’t figure out a way to kind of insulate yourself from that and have a very strong kind of core personality or core set of ideas and values, I think you can end up wasting a lot of time which is very valuable especially in your youth in terms of developing a lot of like core skills and your core kind of approach to life and resiliency because you can end up just getting dragged along with whatever is the hot fad at the moment. And that might be fine while you’re 18.

But if you keep doing that for five, 10 years, when you don’t develop the skill set and gain the kind of experience that you need to actually pursue whatever career or whatever you want to spend your time doing, basically, you’re not going to end up particularly happy with yourself. I think that’s very difficult to do. But increasingly, I think that’s extremely important. Yeah, I think that’s one area which people should invest time in even though it’s kind of difficult.

I think the other thing would be in terms of experience or skills, I mean, I obviously have a bias towards quantitative or numerical disciplines. But I do think data is going to be extremely important. I would actually suggest that maybe things like programming and those sorts of tech skills are probably being overvalued right now.

So I personally would advise people who are kind of think you’re going to programming or IT because the opportunity is large rather than because they enjoy it to have a real think about that because there’s going to be a lot of people doing that. And at the same time, you have a lot of things like people trying to automate actual programming and coding.

And so, the supply demand balance isn’t necessarily going to be what it is now in five years’ time when you graduate 10, 15 years’ time when you’ll be getting into the prime of your career. So, yeah. if you love doing it, I mean you still make a great career for yourself. But if you’re doing it for the money, it may not work out exactly how you think.

So I think that when you’re at that age, I think it’s very easy to get caught up in what’s been doing very well. But I think you need to think about, so, 10, 20 years ahead. And that can be very difficult at that sort of age. But I think stuff in the area of data, if you’re very quantitatively focused, I think it’s very new. And I think there’ll be a lot of opportunities to kind of develop.

If you’re not quantitatively focused, I don’t know. I think behavior of finance, behavioral psychology, those sorts of areas are extremely interesting. And I think especially considering the way the world has gone mad, I think, yeah. You might have lots of people needing therapy in 10 years’ time. So that could be another opportunity. But, yeah, I mean I think that’s a real difficult question.

Maybe, the best advice I can give to people is don’t believe all the stuff you see in LinkedIn which is a lot of very positive, but not necessarily very accurate advice. So, yeah. I think just try to have a long-term perspective. And I mean it’s very simple advice. But sort of be yourself. Make sure you understand what that actually means to be yourself, I think, is very important.

Kalani Scarrott (01:21:53): Well, I love that answer, Mio. And, yeah, such a unique answer. And I love this conversation. So for sure, I’d love to have you back on again. But thank you so much.

Mio Kato (01:22:00): Yeah. I mean it’s a pleasure. Yeah. Thanks very much for having me on and for letting me rumble.

Kalani Scarrott (01:22:07): If you enjoyed this podcast episode, be sure to check out the website, compoundingpodcast.com. On the website, you’ll find every episode complete with transcripts, show notes, and other related resources. Either way, links to all content mentioned will be in the description below.

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