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Speaker 1: Hello, and welcome to another episode of the Odd Blots podcast.

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Speaker 1: I'm Tracy Allaway and I'm Joe wisntal Joe. I don't

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Speaker 1: know if you've been following what's going on in Hong

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Speaker 1: Kong markets lately, but there is a very very big

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Speaker 1: event coming up. Well it's only a few weeks away now,

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Speaker 1: although I have to say I'm not exactly sure when

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Speaker 1: we're at leasing this episode, but in theory, there is

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Speaker 1: a very big event coming up, so much to choose from.

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Speaker 1: I feel like, you know, when you say something like, oh,

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Speaker 1: I'm not sure if you're following what's going on in

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Speaker 1: Hong Kong lately, my mind darts to, like, uh ten

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Speaker 1: different things. Yeah, okay, But in the market, there is

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Speaker 1: just one thing I think that everyone is focused on

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Speaker 1: at the moment, and that is the I p O

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Speaker 1: of Ant Group, which is the sort of offshoot of

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Speaker 1: Jack Ma's Ali Baba. The big thing about this particular

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Speaker 1: I p O is that I think they're aiming for

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Speaker 1: evaluation of something like at least two hundred eighty billion dollars,

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Speaker 1: which would easily make it the biggest I p O ever,

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Speaker 1: and the whole transaction again, it hasn't actually taken place yet,

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Speaker 1: but the whole transaction is so big that we're actually

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Speaker 1: seeing it affect things like demand for Hong Kong dollars.

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Speaker 1: We've seen liquidity in that market Titan. The demand for

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Speaker 1: ant shares is expected to be so high that they

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Speaker 1: haven't even bothered with Cornerstone investors, which is really unusual

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Speaker 1: for an I p O. We're also seeing brokers in

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Speaker 1: Hong Kong offer retail investors twenty times leverage on the

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Speaker 1: ant shares because they're so certain there's going to be

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Speaker 1: a pop on the first day of trading, and everyone

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Speaker 1: wants to get an allocation of the equity when it

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Speaker 1: first comes out. So it's a really really big deal,

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Speaker 1: and you can see that there's a lot of excitement

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Speaker 1: around at at the moment. Wow, those are some great stats.

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Speaker 1: And I knew it was a big deal, and I

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Speaker 1: knew it was a big company, but actually you sort

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Speaker 1: of just blew me away with all that stuff. I mean,

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Speaker 1: two quick questions for you. A what does AUNT do?

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Speaker 1: I mean, I know it's like this big finance thing,

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Speaker 1: but I actually have no concept of what the company

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Speaker 1: actually does. And be two billion dollars, I mean that's

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Speaker 1: got to make it. Whether you know, it's bigger than

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Speaker 1: any US financial institution by a laun shot, right, Yeah,

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Speaker 1: I think that's right. And if you think think back

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Speaker 1: to Saudi Aramco. I mean, there was so much talk

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Speaker 1: about Saudi Aramco when they listed on public markets for

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Speaker 1: the first time. I think we even did one or

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Speaker 1: two episodes about it that ended up being something like

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Speaker 1: twenty eight billion dollars raised, so much smaller than what

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Speaker 1: we're talking about now. Aunt grew Group. I'm sure I'm

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Speaker 1: not going to describe it right, and I'm sure some

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Speaker 1: people are going to take issue, but it's basically the

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Speaker 1: sort of um I think it's the fintech portion of

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Speaker 1: Ali Baba, including Alley Pay, which is one of the

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Speaker 1: big big payments providers in China along with we Chat.

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Speaker 1: So if you've ever visited China, you know that cash

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Speaker 1: is almost non existent at this point, and anywhere you go,

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Speaker 1: be it a Starbucks or a street side vendor, you

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Speaker 1: can pay using a digital wallet like ally Pay or

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Speaker 1: wee Chat. So that's one of the reasons people are

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Speaker 1: really excited about it. And of course it's a big

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Speaker 1: fintech giant in a very very large market in one

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Speaker 1: of the few markets that is really growing at the moment,

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Speaker 1: given China's purported economic strength. But of course when it

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Speaker 1: comes to this type of growth, particularly in China, there's

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Speaker 1: always a question mark over how you're actually measuring that

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Speaker 1: and whether those measurements or those numbers are accurate or not,

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Speaker 1: you know what I mean, absolutely so, So just to

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Speaker 1: step back, just to sort of conceptualize the company, um,

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Speaker 1: Ali Baba is this sort of right e commerce powerhouse,

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Speaker 1: extraordinary company doing all kinds of things, And so I

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Speaker 1: guess you could sort of imagine it is like say,

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Speaker 1: like Facebook or some of the US megacap internet companies,

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Speaker 1: they have their own like sort of payments apps and

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Speaker 1: payments messaging and stuff like that, and so this would

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Speaker 1: be this is Ali Baba's. It's gigantic, and they're essentially

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Speaker 1: spinning it out into its own publicly traded company that's

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Speaker 1: worth the insane amount of money. But as you say,

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Speaker 1: you know, with with so many of these UH companies,

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Speaker 1: I mean, they produce financials, but all kinds of um

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Speaker 1: difficulty really gating from the outside like a true like

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Speaker 1: size and scope of the business. Yeah, I guess you

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Speaker 1: could think about it as jen if Amazon also owned

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Speaker 1: Venmo or something like that, and everyone used Venmo to

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Speaker 1: pay for virtually everything in the US. Oh, and also

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Speaker 1: that Amazon had a gigantic money market fund that people

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Speaker 1: using Venmo could also invest in. That's that's kind of

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Speaker 1: Alley Pay in a nutshell. I'm excited now. Okay, So

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Speaker 1: for today's episode, we're going to do a deep dive

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Speaker 1: onto Chinese internet companies. We're going to get a better

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Speaker 1: sense of how they're actually accounting for the growth that

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Speaker 1: I just described, and I think we're going to get

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Speaker 1: a better idea of whether or not all that excitement

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Speaker 1: over future growth is necessarily justified. And to do that,

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Speaker 1: we're going to be speaking to Stephen Clapham. He's the

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Speaker 1: founder of Behind the balance Sheet and also runs an

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Speaker 1: investment and research training consultancy. He recently published a very

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Speaker 1: very detailed and expensive report into Chinese internet stocks, looking

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Speaker 1: at them from a forensic accounting perspective. So really the

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Speaker 1: perfect person to discuss all this with. I think, alright,

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Speaker 1: I can't wait. You got me super excited. Okay, all right, Steven,

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Speaker 1: welcome to the show. Thanks so much for coming on. Well,

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Speaker 1: thank you for having me. So I guess to begin with,

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Speaker 1: I'm curious what piqued your interest in Chinese internet stocks

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Speaker 1: in particular, because there's no shortage of companies that you

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Speaker 1: could be looking at with a forensic accounting background, and

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Speaker 1: you chose to look at these ones in particular. I

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Speaker 1: think you looked at five of the biggest ones. What

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Speaker 1: sparked your interest? Well, Tracy, you said that this report

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Speaker 1: was expensive, and in fact, I want to disagree articly

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Speaker 1: because I think this report is very cheap and the

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Speaker 1: reason I allude at these is five thousand dollars, right, honestly, Tracy,

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Speaker 1: five thousand dollars is a argon for this report. And

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Speaker 1: before you laugh, and let me explain why. So the

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Speaker 1: reason that I looked at this I was commissioned to

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Speaker 1: produce this report by a client, and one of my

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Speaker 1: training clients is particularly interested in the Chinese um internet

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Speaker 1: companies and was concerned that they hadn't been able to

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Speaker 1: get to the bottom of the way they were accounting

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Speaker 1: in various aspects when we talked about what those aspects were,

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Speaker 1: so they asked me if I could help, and I said, yeah,

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Speaker 1: of course, and foolishly I completely underestimated how long it

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Speaker 1: would take to to do the work. And when I

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Speaker 1: tell you that Ali Baba's accounts were a thousand and

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Speaker 1: seventy seven pages long, you can probably understand why it's

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Speaker 1: quite easy to underestimate how long it would take. And

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Speaker 1: so I went back to the clients halfway through the

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Speaker 1: work and I said, look, I'm having a better problems

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Speaker 1: here because it's taken me far, far longer than than

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Speaker 1: I was anticipating. I knew that it would be complicated,

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Speaker 1: but I didn't quite understand how much work would be involved.

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Speaker 1: And the clients, I know, look, no problem at all.

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Speaker 1: I said, Look, the best way around this is I

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Speaker 1: don't want to do a bad job for you, but

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Speaker 1: equally I don't want to spend a huge amount of

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Speaker 1: time and I'm not getting rewarded for so the best compromises,

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Speaker 1: why don't I just sell the report once you were

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Speaker 1: finished with it and once you've done everything that you

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Speaker 1: want to do in the in the stocks. And they

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Speaker 1: said that's fine, and so we um, we just put

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Speaker 1: the report up on the website a few weeks ago.

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Speaker 1: There's been quite a bit of interesting we haven't actually

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Speaker 1: started to advertise it yet. Obviously, these are very, very

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Speaker 1: big companies, and they are incredibly complicated, and if you

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Speaker 1: think about a set of accounts, it's over a thousand

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Speaker 1: pages long. The most recent accounts are about half that,

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Speaker 1: but it's still a huge amount of time to go through.

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Speaker 1: And what we do is we go through word by words,

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Speaker 1: number by number, dissecting every element of it. And if

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Speaker 1: you're the average institutional investor, you just not have time

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Speaker 1: to do this detail work. So that's why five dollars

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Speaker 1: is Actually it's not nearly it's not nearly as much

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Speaker 1: as it sounds, because it's saved people a huge amount

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Speaker 1: of effort and there's a huge amount of effort involved

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Speaker 1: on our side and producing it. Okay, So setting aside

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Speaker 1: whether five thousand dollars is a fair value or not,

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Speaker 1: stepping back a little background, tell us your sort of

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Speaker 1: general work. You mentioned that this was originally produced for

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Speaker 1: a training client. What do you do for clients when

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Speaker 1: you say training clients? And what was the sort of

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Speaker 1: request that came in that ultimately led to this research? Oh? Sure,

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Speaker 1: so we I mean, our business has got three parts

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Speaker 1: to it. We've got our retail side, so we've got

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Speaker 1: an online training school for retail investors. So you can

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Speaker 1: go there and you can buy one of our courses

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Speaker 1: which helps you understand how to invest. And then on

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Speaker 1: the institutional side, which two things we do bespoke research

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Speaker 1: for people and we run training courses. So we've got

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Speaker 1: a forensic accounting course which we started in June and

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Speaker 1: we've done I think three hundred people have been through

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Speaker 1: that course in the last two and a bit years.

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Speaker 1: Obviously fewer people this year because it's a physical in

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Speaker 1: perison course, although we've been doing a little bit of

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Speaker 1: it on on Zoom. And on the bespoke research side,

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Speaker 1: people typically will come to us when they've got a problem,

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Speaker 1: they've got a piece of research that is either too

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Speaker 1: difficult for them to do in house, or too time

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Speaker 1: consuming for them to do in house, or sometimes too

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Speaker 1: controversial for them to do in has. Often what happens

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Speaker 1: is you own a stock and it goes down, and

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Speaker 1: the analyst that's involved in in looking after that position,

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Speaker 1: we'll do one of two things while they say he

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Speaker 1: or she, while they say, you know what, I think,

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Speaker 1: we think I'm in a mistake. We should just get out,

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Speaker 1: or they'll say I'm right, We've just got to be patient.

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Speaker 1: And often what happens is the portfolio manager doesn't feel

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Speaker 1: that the analyst is being able to make the decision

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Speaker 1: rationally without emotion, and he doesn't have enough time to

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Speaker 1: do the working staff, so they bring me in as

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Speaker 1: a sort of independent third party without any emotion attached

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Speaker 1: to the holding that can make a rational assessment of

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Speaker 1: where the risk and reward lies at this point after

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Speaker 1: the shares have fallen. And that's what we do. It

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Speaker 1: a little bit of that. And the other thing we

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Speaker 1: do is we do forensic accounting research. So people will say,

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Speaker 1: you know, I'm thinking of I'm thinking of buying a

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Speaker 1: stock and can you please have a look at it?

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Speaker 1: Or they'll say we own this stock, can you please

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Speaker 1: have look at it? In fact, it won't be a

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Speaker 1: surprise to you, Tracy sitting in Hong Kong that Hong

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Speaker 1: Kong listed or Chinese based companies are quite a popular

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Speaker 1: area for people to ask us to get involved. One

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Speaker 1: we did quite recently it was Hutchinson, so a client. Again,

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Speaker 1: I mean I do this obviously exclusively for Trump for

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Speaker 1: clients that our clients on the training side. I don't

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Speaker 1: get random people coming in off the street asked me

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Speaker 1: to do fends and accounting reports for them. I do

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Speaker 1: it for the people that know me and like my work.

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Speaker 1: And this particular client, very big two billion asset manager.

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Speaker 1: We're interested in Hutchson because it looked very cheap, and

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Speaker 1: he said, can you go and have a deeper dive

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Speaker 1: and tell us if it really is cheaper not? And

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Speaker 1: that's typically what we do, and this is exactly what

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Speaker 1: we did in the case of the fire Chinese internet

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Speaker 1: companies Ali Baba, ten Cent, JD, dot Com, Buy Do

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Speaker 1: and made To and Ding Dan King. There are those

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Speaker 1: five stocks we were asked to look at and to

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Speaker 1: look at on a series of I think it was

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Speaker 1: a dozen different elements, and there were things like is

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Speaker 1: the company flattering at sarnings? Is the company using investment

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Speaker 1: gains to boost tarnings? Is it carrying the value of

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Speaker 1: enlisted investments? Are they being carried at the correct values?

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Speaker 1: So it's a whole string of things that we were

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Speaker 1: that we were asked to the camp. That's a really

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Speaker 1: helpful description of what you do. I'm curious when it

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Speaker 1: comes to Chinese stocks though, are there particular challenges that

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Speaker 1: investors face when it comes to things like transparency or

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Speaker 1: you know, realistic accounts. I guess this is a criticism

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Speaker 1: that we hear about Chinese accounting and Chinese companies quite

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Speaker 1: a lot. There's all sorts of issues you face. I mean, clearly,

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Speaker 1: the first problem you've got is that the first of

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Speaker 1: all I've got is that I don't speak the language.

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Speaker 1: I can't read the language. So you're at a massive

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Speaker 1: disadvantage relative to looking at a company in the United States.

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Speaker 1: Are in the UK, where you've got that sort of

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Speaker 1: home country advantage and where you've got local pools of knowledge,

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Speaker 1: local sources of intelligence. And we've got some contacts in China.

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Speaker 1: But for example, one of the things that the client

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Speaker 1: asked us to look into was the role of the

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Speaker 1: audit firm and any connections between the partner involved in

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Speaker 1: the audit and the companies. And you know, we had

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Speaker 1: to say to them, look, we can't really do that

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Speaker 1: because we just don't have sufficient knowledge. We don't have

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Speaker 1: any feet on the ground in China um to be

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Speaker 1: able to assess if there are any hidden links between

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Speaker 1: the company and the auditor, whereas in the UK, for example,

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Speaker 1: that would be it wouldn't be easy to do. But

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Speaker 1: we've got various tools and techniques that we deploy in

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Speaker 1: order to make an assessment if that there, if there

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Speaker 1: could be that sort of connection. But it would be

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Speaker 1: impossible for us to look at something like that in China,

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Speaker 1: and we don't we just so we don't. We don't,

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Speaker 1: We don't even try. But there's there's a number of

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Speaker 1: issues with Chinese companies in general. Obviously the use of

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Speaker 1: these variable interest entities is a very common that these

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Speaker 1: companies are said, uh and these you know, that has

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Speaker 1: a whole set of other issues which we which we

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Speaker 1: don't go into detail in this report because they're generic.

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Speaker 1: You know, that's a generic thing. What are you buying

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Speaker 1: when you buy one of these businesses? And you know,

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Speaker 1: there's lots been been written about that, and people I

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Speaker 1: think have made their own judgments about whether they find

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Speaker 1: that an acceptable risk or an unacceptable risk. Instead, what

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Speaker 1: we did here was we drilled down into some of

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Speaker 1: the specifics. And many of the specifics are related to

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Speaker 1: the nature of these businesses, the fact that they are

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Speaker 1: internet businesses and that they're heavily involved in a whole

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Speaker 1: investment ecosystem, and so a lot of our work was

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Speaker 1: dedicated to that area that part of them. So to

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Speaker 1: do some compare and contrast. Obviously, we have our internet

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Speaker 1: giants here in the US, Facebook, Amazon, etcetera. Talk to

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Speaker 1: us about some of the general differences that are required

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Speaker 1: for someone to do serious accounting analysis of the books

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Speaker 1: or of the statements of US based Internet companies first

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Speaker 1: Chinese ones. Well, I mean some of these companies report

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Speaker 1: under US GAP. So there's you know that counting rules

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Speaker 1: are the same, um right, ten Cent reports sunder I

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Speaker 1: f r S, which I think is quite unusual because

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Speaker 1: ordinarily companies that are listed in the US generally have

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Speaker 1: gaps as their main language, mean accounting language, but that

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Speaker 1: counting rules are exactly the same. But what you've got

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Speaker 1: here is if you if you think of Ali Baba

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Speaker 1: and Amazon, they're being they're they're kind of similar in

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Speaker 1: in concept. I mean they're obviously they do different things

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Speaker 1: and their different score ten Cent, gd dot com you

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Speaker 1: would think of those in the same light as thinking

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Speaker 1: of an Amazon. But if I told you that the

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Speaker 1: Ali Baba cants went from over a thousand pages to

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Speaker 1: just under five hundred pages, do you know how long

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Speaker 1: that counts are for Amazon? Do you want to make

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Speaker 1: a guess? A good guess? So you're You're obviously practiced

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Speaker 1: at this game, because the Amazon accounts in twenty nineteen,

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Speaker 1: including Jeff Bezos's letter at the front, which is an

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Speaker 1: addendum to the actual pen K filing, was only eighty

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Speaker 1: seven pages, so their cants are much much simpler there

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Speaker 1: and an Amazon um I think, although I disagree with

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Speaker 1: some of it's some of the way it presents its numbers.

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Speaker 1: You know, I wrote a blog a little while ago

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Speaker 1: about the fact that it presents its free cash flow

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Speaker 1: in three different ways, all of them wrong in my view.

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Speaker 1: I've got a different definition, but at least it is

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Speaker 1: quite helpful in the way it presents its numbers. Some

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Speaker 1: of these Chinese companies are I would open it they're

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Speaker 1: less helpful than ANALYSM. Sorry, can you just dive into

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Speaker 1: that point? So how are the numbers less helpful? And

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Speaker 1: I'm aware that, you know, one of the criticisms of

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Speaker 1: tech companies all over the world is flattering their growth figures,

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Speaker 1: you know, how many users they have on their platform.

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Speaker 1: There's also, I guess, the use of non gap metrics,

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Speaker 1: the most famous being uh we work and community adjusted EBITDA.

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Speaker 1: Is that the kind of thing that you're that you

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Speaker 1: found in your report. Yeah, absolutely, I mean the use

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Speaker 1: of non gap. I mean this isn't a criticism solely

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Speaker 1: of these and Neese companies. I mean it's that it's

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Speaker 1: very prevalent throughout Less and Pole hundred as well. Um.

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Speaker 1: But you know these companies, the non gap numbers and

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Speaker 1: the gap numbers are there often miles apart, not so

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Speaker 1: much in the case of ten cents, but in the

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Speaker 1: case of all there for they're very significant differences. And

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Speaker 1: you know one of the main differences, which isn't a

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Speaker 1: criticism of the Chinese companies, more a criticism of the

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Speaker 1: way the South Side community treats the reporting these days.

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Speaker 1: But they all make huge adjustments for stock based compensation.

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Speaker 1: So I think in the case of Ali Baba, but

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Speaker 1: I remember correctly, it was something like five billion dollars

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Speaker 1: um and that obviously stock based compensation is a real

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Speaker 1: expense because if you didn't give people stock, you'd have

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Speaker 1: to pay them real money. And it's a real expense

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Speaker 1: because it comes at the expense of shareholders, shield shareholders

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Speaker 1: lucid to the extent that these shares are issued. So

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Speaker 1: it's daft. I think that analysts ignore this number when

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Speaker 1: they're calculating earnings because if they weren't, it wasn't stock based,

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Speaker 1: it'd be cash and they wouldn't ignore it. So I

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Speaker 1: think this is just you know, one aspect. But the

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Speaker 1: one of the big elements in the Chinese group which

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Speaker 1: is different from the US peers is the use of

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Speaker 1: investment gains to flatter profits. Do you see a lot

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Speaker 1: of gains on either the sale of investments or on

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Speaker 1: the revaluation of investments. And this isn't to say that

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Speaker 1: these companies are doing anything wrong because the counting rules

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Speaker 1: I think are slightly daft in in in this in

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Speaker 1: this respect, because what happens here is that I've got

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Speaker 1: an investment in company X. Company X is a young,

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Speaker 1: fast growing Chinese internet company. It's hungry for capital, so

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Speaker 1: it needs more capital, and it decides to bring in

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Speaker 1: an outside shareholder. When outside shareholder makes an investment, it's

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Speaker 1: likely to be at a higher price than the price

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Speaker 1: which I have invested, And accounting rules require you to

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Speaker 1: revalue the investment and book the game through the p now,

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Speaker 1: which it's obviously ludicrous. I mean, whoever thought up this

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Speaker 1: accounting standard, I don't know what planet they were that

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Speaker 1: they were wrong but I think it's a I think

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Speaker 1: it's a silly system. And I'm not saying that the

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Speaker 1: companies are doing anything wrong by adopting this. This is

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Speaker 1: what they're required to do, but it gives a misleading

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Speaker 1: representation of their profitability. So this should be a balance

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Speaker 1: sheet item as opposed to something that would flow down

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Speaker 1: to the bottom line basically, if we're trying to get

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Speaker 1: a true understanding of the company. Yeah, well, I mean

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Speaker 1: I would argue that it shouldn't even be adopted in

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Speaker 1: the balance sheet. You might want to note what the

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Speaker 1: what most recent valuation was, because obviously it's helpful for investors.

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Speaker 1: But to give you an example, in the case of

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Speaker 1: Ali Baba mean Ali Baba and had an investment in

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Speaker 1: a business and the investment was being reorganized and these

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Speaker 1: are almost invariably very complicated structuring. So this particular investment

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Speaker 1: was an investment that they held jointly without financial and

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Speaker 1: the business was restructured by being merged with another business.

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Speaker 1: And who should come in to make an investment in it?

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Speaker 1: But soft Bank. Now we all know that soft Bank

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Speaker 1: is not the most disciplined purchaser of assets, and that

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Speaker 1: they've been prepared to invest on the basis of a

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Speaker 1: longer term vision than most other more ordinary shareholders would

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Speaker 1: be prepared to say so, being prepared to pay very

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Speaker 1: high prices for some assets that I think are quite questionable.

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Speaker 1: So Ali Baba also owns it is also partly owned

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Speaker 1: by soft Bank, So soft Bank owns a big steak

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Speaker 1: in Ali Baba. So what you have is SoftBank coming

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Speaker 1: in paying a very high price for one of Alabama's assets,

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Speaker 1: and Ali Baba then revaluing the value of that asset

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Speaker 1: in its books and taking a difference to profit loss account.

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Speaker 1: What could go wrong? Well, um, just on that note,

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Speaker 1: could you maybe talk a little bit more about related

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Speaker 1: party transactions because this is something that also crops up

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Speaker 1: quite a bit with Chinese companies, where you often have

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Speaker 1: this sort of shadowy network of companies that are tangentially

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Speaker 1: related to each other and are sometimes, if not self dealing,

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Speaker 1: lending each other helping hand when it comes to things

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Speaker 1: like funding. What examples of that did you find? There

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Speaker 1: are a lot of related party transactions in in in

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Speaker 1: these companies. Ali Baba is actually not the not the

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Speaker 1: main culprit here. I think it was g D dot

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Speaker 1: Com had the largest exposure it's related party transactions and

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Speaker 1: the problem with the lated party transactions, and that we

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Speaker 1: we cover this in our forensic accounting course for our

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Speaker 1: institutional clients. We also cover it in online courses for

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Speaker 1: retail investors to say, look, one of the first things

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Speaker 1: that we look at when we open this out of accounts,

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Speaker 1: we look at the related parties, not because if there

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Speaker 1: are a string of related party transactions, you then have

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Speaker 1: to ask yourself, well, who's who's verifying this? How do

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Speaker 1: we know that these numbers are accurate? How do we

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Speaker 1: know that we're not being disadvantaged? And it's almost impossible

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Speaker 1: for the outside investor to make a rational judgment of this,

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Speaker 1: and equally, it's probably pretty difficult for the auditor to

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Speaker 1: make a real assessment of have the related party transaction

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Speaker 1: has been booked correctly in their cants? And so many

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Speaker 1: of the frauds that I studied when I was originally

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Speaker 1: building the forensic accounting course, I went to spent time

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Speaker 1: in the British Library and I poured through all sorts

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Speaker 1: of academic studies. I poured through lots of accounts. I

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Speaker 1: looked back at past frauds, and many of them. One

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Speaker 1: of the one of the key signals in advance was

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Speaker 1: related party transactions. And where you've got related party transaction,

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Speaker 1: you just don't know what the motivation is and you

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Speaker 1: don't know whether you are being fairly treated, and that's

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Speaker 1: a huge it's always a huge risk. So what I

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Speaker 1: say to the to my retail clients is that if

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Speaker 1: you open their hands and there's a page related party transactions,

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Speaker 1: it's probably it's probably not worthwhile pursuing that as a

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Speaker 1: potential investment because it could take you a huge amount

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Speaker 1: of time to verify, and even then you may not

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Speaker 1: know that you've got the right answer. So what else

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Speaker 1: is in there? I mean, you mentioned that the ali

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Speaker 1: Baba accounts are five hundred pages, the Amazon accounts are

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Speaker 1: eighty seven pages. What else is found in that four

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Speaker 1: hundred and thirteen page gap that fundamentally makes the analysis

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Speaker 1: of an Ali Baba more complicated and more work than

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Speaker 1: the analysis of Amazon. But I mean, it's just this

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Speaker 1: year's scale volume, number of transactions that they're doing, apart

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Speaker 1: from anything else. You know, when we when we look

431
00:26:49,320 --> 00:26:52,359
Speaker 1: at companies that are doing a lot of acquisitions and disposals,

432
00:26:52,960 --> 00:26:58,520
Speaker 1: it's very difficult to determine what's actually going on, because

433
00:26:58,560 --> 00:27:01,240
Speaker 1: where you've got a lot of acquisitions, and even where

434
00:27:01,240 --> 00:27:05,440
Speaker 1: you've got disposals, the underlying cash flows can be obscured.

435
00:27:06,040 --> 00:27:09,880
Speaker 1: Just they explain what I mean using perhaps a simpler

436
00:27:09,880 --> 00:27:14,760
Speaker 1: example would be you know, you often see rolloups platform

437
00:27:14,800 --> 00:27:18,199
Speaker 1: companies where they're making acquisition after acquisition. One of the

438
00:27:18,240 --> 00:27:20,879
Speaker 1: things that we worry about when we see these is

439
00:27:20,920 --> 00:27:23,920
Speaker 1: when you look at the operating cash flow in any year,

440
00:27:24,640 --> 00:27:27,080
Speaker 1: you don't know how much of the cash flow has

441
00:27:27,119 --> 00:27:31,199
Speaker 1: been generated from the business itself and how much of

442
00:27:31,200 --> 00:27:34,840
Speaker 1: the cash flow has been generated from improving, for example,

443
00:27:34,920 --> 00:27:38,360
Speaker 1: the working capital in the businesses you acquired last year.

444
00:27:38,880 --> 00:27:41,399
Speaker 1: And you often see this in rollouts where they'll do

445
00:27:41,480 --> 00:27:44,480
Speaker 1: a lot of acquisitions of of mom and pop businesses.

446
00:27:45,400 --> 00:27:47,720
Speaker 1: They buy the mom and pop businesses which maybe didn't

447
00:27:47,760 --> 00:27:51,359
Speaker 1: have an independent credit controller, and they'll enforce very strict

448
00:27:51,480 --> 00:27:55,080
Speaker 1: terms and their customers, and immediately they get a cash

449
00:27:55,160 --> 00:27:59,280
Speaker 1: return in that they're working capital shrinks, so they're operating

450
00:27:59,320 --> 00:28:03,119
Speaker 1: cash flow looks much better than it really would would

451
00:28:03,160 --> 00:28:06,680
Speaker 1: do were it not continuing to make acquisitions. And that's

452
00:28:06,720 --> 00:28:08,760
Speaker 1: fine as long as you carry on making more and

453
00:28:08,800 --> 00:28:12,240
Speaker 1: more acquisitions and and repeating the formula, But as soon

454
00:28:12,280 --> 00:28:16,359
Speaker 1: as you stop, what often happens with these businesses is

455
00:28:16,400 --> 00:28:19,760
Speaker 1: the cash flow on wines and it's the exact same

456
00:28:20,119 --> 00:28:23,360
Speaker 1: thing is true, but on a different scale for these

457
00:28:23,480 --> 00:28:28,000
Speaker 1: Chinese companies because they're making loads and laws of acquisitions

458
00:28:28,000 --> 00:28:32,280
Speaker 1: and disposals each year, and so trying to divorce what's

459
00:28:32,320 --> 00:28:35,400
Speaker 1: going on in their core business with what's going on

460
00:28:35,920 --> 00:28:39,280
Speaker 1: as a result of acquisitions is extremely difficult. Now, they

461
00:28:39,280 --> 00:28:43,960
Speaker 1: would probably argue that they're investing in young, immature businesses,

462
00:28:44,360 --> 00:28:47,600
Speaker 1: and to the extent that they're making acquisitions, they're probably

463
00:28:47,640 --> 00:28:51,320
Speaker 1: having to invest in the working capital, so that the

464
00:28:51,920 --> 00:28:55,400
Speaker 1: actual cash flows are worse than they would otherwise be.

465
00:28:56,080 --> 00:29:00,160
Speaker 1: But you're seeing um also a change in the coversation

466
00:29:00,200 --> 00:29:03,160
Speaker 1: of the businesses. So more of these businesses are going

467
00:29:03,240 --> 00:29:08,320
Speaker 1: to subscription type models in which customers are paying in advance.

468
00:29:08,800 --> 00:29:12,080
Speaker 1: So obviously the opposite is true, and their cash flows

469
00:29:12,280 --> 00:29:15,200
Speaker 1: would then be enhanced relative to what they would have

470
00:29:15,240 --> 00:29:19,720
Speaker 1: been if they hadn't been making acquisitions. So the most

471
00:29:19,840 --> 00:29:24,080
Speaker 1: complicated part of this is the fact that there's lots

472
00:29:24,120 --> 00:29:28,360
Speaker 1: and lots of moving parts and they're all moving very quickly,

473
00:29:28,800 --> 00:29:31,680
Speaker 1: and that's what makes the makes the job of understanding

474
00:29:31,680 --> 00:29:50,880
Speaker 1: the business much much more complicated. So your description of

475
00:29:51,160 --> 00:29:55,480
Speaker 1: add backs just reminded me very much of Valiance roll

476
00:29:55,600 --> 00:29:58,080
Speaker 1: up strategy back in the day, and I remember one

477
00:29:58,120 --> 00:30:00,920
Speaker 1: of the issues with the roller was that you ended

478
00:30:00,960 --> 00:30:04,080
Speaker 1: up getting a lot of add backs where the company

479
00:30:04,200 --> 00:30:10,280
Speaker 1: would add back line items for acquisitions. So, for instance,

480
00:30:10,400 --> 00:30:13,560
Speaker 1: if it expected a transformational m and a deal to

481
00:30:14,000 --> 00:30:17,520
Speaker 1: really add to its bottom line, it would add back

482
00:30:17,720 --> 00:30:21,680
Speaker 1: extra revenue or extra profit into its accounts. Is that

483
00:30:21,840 --> 00:30:25,360
Speaker 1: something that you see with Chinese companies and does that

484
00:30:25,440 --> 00:30:29,760
Speaker 1: also impact they're funding because again going back to the

485
00:30:29,840 --> 00:30:34,000
Speaker 1: Valiant example, I remember that the ad backs basically made

486
00:30:34,080 --> 00:30:38,840
Speaker 1: Valiant appear a lot less leverage than it otherwise would

487
00:30:38,880 --> 00:30:42,000
Speaker 1: have appeared, which allowed it to tap the debt market

488
00:30:42,160 --> 00:30:45,280
Speaker 1: relatively cheaply for a very long time and keep buying

489
00:30:45,480 --> 00:30:50,080
Speaker 1: extra companies. Yeah. I mean Valiant is a very curious

490
00:30:50,200 --> 00:30:55,600
Speaker 1: example because there was such a massive difference between the

491
00:30:55,680 --> 00:30:59,280
Speaker 1: numbers that analysts were focusing on and the numbers which

492
00:30:59,280 --> 00:31:02,960
Speaker 1: were being report that you would have imagined that people

493
00:31:03,000 --> 00:31:06,880
Speaker 1: would have spent more time focusing on that. I think

494
00:31:06,920 --> 00:31:12,520
Speaker 1: the case of these Chinese companies is slightly different. If

495
00:31:12,560 --> 00:31:16,400
Speaker 1: I told you that if you looked at the last

496
00:31:16,520 --> 00:31:21,240
Speaker 1: five years for these five companies, um and I told

497
00:31:21,280 --> 00:31:26,560
Speaker 1: you that they had made investments in the Chinese, predominantly Chinese,

498
00:31:26,560 --> 00:31:29,720
Speaker 1: and they made a very small number of acquisitions, very

499
00:31:29,760 --> 00:31:33,600
Speaker 1: small volume of acquisitions overseas. But they basically they've invested

500
00:31:33,840 --> 00:31:39,320
Speaker 1: in a huge range of Chinese venture capital. And if

501
00:31:39,360 --> 00:31:43,720
Speaker 1: I told you that they had spent nearly three trillion,

502
00:31:44,840 --> 00:31:48,920
Speaker 1: that's trillion, remember in the last five years. So what's

503
00:31:48,960 --> 00:31:54,920
Speaker 1: that that's like five vision funds, these five companies in

504
00:31:55,040 --> 00:31:58,200
Speaker 1: five years. I like, how we measure everything in vision

505
00:31:58,280 --> 00:32:00,800
Speaker 1: funds nowadays? Yeah, it's a great it's a great unit

506
00:32:00,800 --> 00:32:04,280
Speaker 1: of I think it's a useful it's a useful unity.

507
00:32:04,680 --> 00:32:06,680
Speaker 1: It's absolutely great. You want to look at you want

508
00:32:06,760 --> 00:32:11,280
Speaker 1: to scale something ridiculous, And that's so you know, on average,

509
00:32:12,480 --> 00:32:15,120
Speaker 1: there's a vision fund every year, or a vision fund

510
00:32:15,120 --> 00:32:16,560
Speaker 1: every company to any how you look at it. But

511
00:32:16,640 --> 00:32:23,000
Speaker 1: that's basically what we're saying. And you then have to decide, okay,

512
00:32:23,200 --> 00:32:29,360
Speaker 1: unlike perhaps the vision fund, have they made sensible investments

513
00:32:29,480 --> 00:32:35,640
Speaker 1: and that the issue here then becomes, okay, well, what

514
00:32:35,800 --> 00:32:39,400
Speaker 1: is the carrying value that they're that they're showing in

515
00:32:39,480 --> 00:32:43,360
Speaker 1: their in their books for these investments, and to be

516
00:32:43,480 --> 00:32:47,960
Speaker 1: fair to them, they vary. The disclosures do vary from

517
00:32:48,000 --> 00:32:51,040
Speaker 1: company to companies. Some are better, some are worse. But

518
00:32:51,440 --> 00:32:54,400
Speaker 1: you know, there are so there are enough disclosures at

519
00:32:54,480 --> 00:32:56,600
Speaker 1: some of the companies that you can do some quite

520
00:32:56,680 --> 00:33:00,680
Speaker 1: detailed analysis and you can come up within assessments that

521
00:33:00,760 --> 00:33:04,240
Speaker 1: whether you think the numbers are sensible, and that's and

522
00:33:04,280 --> 00:33:07,400
Speaker 1: that's what we've done. A little spoiler for you. They're

523
00:33:07,400 --> 00:33:10,480
Speaker 1: not all sensible. So there are some you know, there's

524
00:33:10,480 --> 00:33:13,840
Speaker 1: some numbers in there that if we were the finance

525
00:33:14,000 --> 00:33:18,000
Speaker 1: director we would be rather uncomfortable about the carrying values

526
00:33:18,000 --> 00:33:21,760
Speaker 1: of those businesses. But the real point here isn't that

527
00:33:22,320 --> 00:33:26,400
Speaker 1: you know, I can sit here in London trying to

528
00:33:26,520 --> 00:33:32,800
Speaker 1: value an investment in an unimportant, unlisted, no public information

529
00:33:32,960 --> 00:33:38,400
Speaker 1: Chinese venture. I mean, obviously that's extremely difficult and would

530
00:33:38,440 --> 00:33:40,400
Speaker 1: be impossible for me to do, to do the whole thing.

531
00:33:40,920 --> 00:33:44,200
Speaker 1: But the question I've got is how can the auditors

532
00:33:44,280 --> 00:33:51,680
Speaker 1: do that? Because it's not easy, right, and I mean

533
00:33:51,920 --> 00:33:56,800
Speaker 1: just the scale of these investments is phenomenal. So what

534
00:33:56,880 --> 00:33:59,880
Speaker 1: you would have to ask yourself is how likely is

535
00:34:00,040 --> 00:34:05,280
Speaker 1: it that the auditors would have found all the right

536
00:34:05,360 --> 00:34:09,399
Speaker 1: bands that were required and made the companies take those

537
00:34:09,480 --> 00:34:12,080
Speaker 1: right nows. And I can tell you that there right

538
00:34:12,200 --> 00:34:14,040
Speaker 1: there are There have been right gunds, not saying that

539
00:34:14,080 --> 00:34:16,520
Speaker 1: haven't there's never been any right downds, but the right

540
00:34:16,600 --> 00:34:21,000
Speaker 1: bunds are on a somewhat lower scale than division funds.

541
00:34:21,239 --> 00:34:23,560
Speaker 1: So what does that tell you, Well, it tells you

542
00:34:23,719 --> 00:34:30,200
Speaker 1: either that they're phenomenally successful at making these investments or

543
00:34:30,320 --> 00:34:34,920
Speaker 1: that they can justify carrying values because they found somebody

544
00:34:34,920 --> 00:34:37,680
Speaker 1: else to come in at a higher price. Which that

545
00:34:37,719 --> 00:34:41,440
Speaker 1: doesn't mean to say that the values are accurately disclosed

546
00:34:41,440 --> 00:34:43,279
Speaker 1: in the balance sheet, but it does mean to say

547
00:34:43,320 --> 00:34:47,880
Speaker 1: that they've conformed with their counting rules right. Or it

548
00:34:47,920 --> 00:34:51,439
Speaker 1: could be that they've invested a lot of money and

549
00:34:51,480 --> 00:34:55,200
Speaker 1: obviously the value valuations of these stocks has gone through

550
00:34:55,200 --> 00:34:57,920
Speaker 1: the roof in the last few years, and they've carried

551
00:34:57,920 --> 00:35:03,600
Speaker 1: on investing throughout. So is it possible that these companies

552
00:35:03,680 --> 00:35:06,239
Speaker 1: will be forced to take some significant right hands in

553
00:35:06,239 --> 00:35:10,360
Speaker 1: the next few years if the valuations don't hold firm

554
00:35:10,480 --> 00:35:15,239
Speaker 1: and they've invested in a range of I mean, you know,

555
00:35:15,280 --> 00:35:18,160
Speaker 1: a very wide range of products. It's not like they're can,

556
00:35:18,360 --> 00:35:22,480
Speaker 1: you know, confined to a single vertical. They're all trying

557
00:35:22,520 --> 00:35:27,279
Speaker 1: to expand across a whole range of different activities, many

558
00:35:27,320 --> 00:35:31,960
Speaker 1: of them completely divorced from their core business. So we

559
00:35:32,040 --> 00:35:35,560
Speaker 1: started this conversation talking a little bit about ant Financial

560
00:35:35,719 --> 00:35:40,520
Speaker 1: and the upcoming I p O valuation target is something

561
00:35:40,560 --> 00:35:45,760
Speaker 1: like two billion dollars. If someone is buying a share

562
00:35:46,000 --> 00:35:50,360
Speaker 1: of ant Financial, what do you think they're buying exactly?

563
00:35:52,040 --> 00:35:54,040
Speaker 1: And Financial wasn't one of the stocks that we were

564
00:35:54,080 --> 00:35:56,279
Speaker 1: asked to look at because when we were asked to

565
00:35:56,280 --> 00:36:00,160
Speaker 1: do this work, the filings weren't available, and you know,

566
00:36:00,400 --> 00:36:02,839
Speaker 1: perhaps the client will ask us to come back and

567
00:36:02,840 --> 00:36:07,200
Speaker 1: and have another look at at that one. The difficulty

568
00:36:07,320 --> 00:36:12,360
Speaker 1: with these sorts of flotations, and you know, it's particularly

569
00:36:12,360 --> 00:36:14,239
Speaker 1: true in emerging markets, and I've seen a number of

570
00:36:14,239 --> 00:36:17,719
Speaker 1: times in Asia, is that when there's a buzz, and

571
00:36:17,800 --> 00:36:21,919
Speaker 1: particularly when there's a large retail component to the deal,

572
00:36:22,680 --> 00:36:25,239
Speaker 1: the institutions will just follow in because it be that

573
00:36:25,440 --> 00:36:28,919
Speaker 1: not too so these things. You can create your own

574
00:36:28,960 --> 00:36:33,400
Speaker 1: success by producing a big enough buzz about the story

575
00:36:33,560 --> 00:36:35,440
Speaker 1: and then as long as the numbers appear to be

576
00:36:35,440 --> 00:36:39,640
Speaker 1: going in the right direction, everything everything is fine. It's

577
00:36:39,680 --> 00:36:44,440
Speaker 1: only if things stop and you find that they've been

578
00:36:44,480 --> 00:36:47,600
Speaker 1: sold for more than their otherwise worth. I always think

579
00:36:47,640 --> 00:36:51,840
Speaker 1: with with I p O s, I used to specialize

580
00:36:52,360 --> 00:36:54,720
Speaker 1: one of my specialties when I was at the hedge funds.

581
00:36:54,760 --> 00:36:56,080
Speaker 1: One of the things that I used to do is

582
00:36:56,120 --> 00:36:58,480
Speaker 1: I used to look for I p o s that

583
00:36:58,560 --> 00:37:03,080
Speaker 1: were either really unpopular. So a company coming to the

584
00:37:03,120 --> 00:37:07,120
Speaker 1: market which was deemed unattractive for whatever reason, needed to

585
00:37:07,120 --> 00:37:09,560
Speaker 1: come to the market, needed to raise the capital, would

586
00:37:09,600 --> 00:37:13,279
Speaker 1: often come at a ridiculously cheap price. And on the

587
00:37:13,280 --> 00:37:14,920
Speaker 1: other side, what we used to do was we used

588
00:37:14,920 --> 00:37:18,640
Speaker 1: to look for these overhyped stocks coming to the market

589
00:37:19,040 --> 00:37:21,600
Speaker 1: and we used to shore them. And it was an

590
00:37:21,640 --> 00:37:25,520
Speaker 1: incredibly profitable strategy because the issue with an I p

591
00:37:25,680 --> 00:37:29,319
Speaker 1: O is that you've got very level playing field. Very

592
00:37:29,400 --> 00:37:31,720
Speaker 1: very few people in the stock market have got history

593
00:37:31,800 --> 00:37:38,520
Speaker 1: with the company, so everybody's equal, and if you spend

594
00:37:38,560 --> 00:37:42,560
Speaker 1: more time than your competitors and understand the company better,

595
00:37:43,200 --> 00:37:45,680
Speaker 1: there's It's one of the few areas in the stock

596
00:37:45,719 --> 00:37:50,239
Speaker 1: market where you can deploy an information advantage legally. You know,

597
00:37:50,320 --> 00:37:53,680
Speaker 1: sometimes there's an information advantage, but you've got inside information.

598
00:37:53,840 --> 00:37:56,360
Speaker 1: Obviously you can't deploy that. But this is one of

599
00:37:56,400 --> 00:38:00,360
Speaker 1: the few areas where a fund with a bigger research

600
00:38:00,440 --> 00:38:06,120
Speaker 1: department with more resources can effectively deploy these resources to

601
00:38:06,160 --> 00:38:10,360
Speaker 1: gain an information advantage. Actually, I have a sort of

602
00:38:10,400 --> 00:38:14,279
Speaker 1: curveball question. Here's something I've wondered about, but you mentioned

603
00:38:14,880 --> 00:38:17,600
Speaker 1: earlier on that. You know, you give advice to people

604
00:38:17,719 --> 00:38:20,920
Speaker 1: that if they see a whole page of related party

605
00:38:20,960 --> 00:38:24,279
Speaker 1: transactions and a filing, that's probably a red flag to

606
00:38:24,600 --> 00:38:27,600
Speaker 1: steer clear. Are there any you know, when you when

607
00:38:27,600 --> 00:38:32,480
Speaker 1: people think about forensic accounting, they think about uncovering a

608
00:38:32,520 --> 00:38:37,640
Speaker 1: good short or maybe sort of justifying long position. Have

609
00:38:37,800 --> 00:38:41,000
Speaker 1: you done or a people doing any work on sort

610
00:38:41,040 --> 00:38:45,960
Speaker 1: of quantitative uses of this stuff? So for example, just

611
00:38:46,600 --> 00:38:49,800
Speaker 1: you know, short all the companies with lots of related

612
00:38:49,840 --> 00:38:53,120
Speaker 1: party transactions go along all of the parties all of

613
00:38:53,120 --> 00:38:55,359
Speaker 1: the companies that don't have them to some sort of

614
00:38:55,400 --> 00:38:58,640
Speaker 1: market neutral strategy. Are there any sort of approaches to

615
00:38:58,880 --> 00:39:01,759
Speaker 1: investing in the work that you do that don't try

616
00:39:01,800 --> 00:39:04,160
Speaker 1: to drill down in one company, but just take a

617
00:39:04,200 --> 00:39:07,200
Speaker 1: few accounting rules and then do a big balance diversified

618
00:39:07,239 --> 00:39:10,560
Speaker 1: portfolio based on these items. Yeah, I mean, I think

619
00:39:10,560 --> 00:39:13,239
Speaker 1: it's a very good question, Joe, and I think that

620
00:39:13,440 --> 00:39:19,560
Speaker 1: actually most of the popular tools have been all gold

621
00:39:19,640 --> 00:39:22,759
Speaker 1: out of existence. So there's a very good paper by

622
00:39:22,840 --> 00:39:28,880
Speaker 1: research affiliates looking at gross profitability, which was a subject

623
00:39:28,920 --> 00:39:31,880
Speaker 1: of an academic paper in came up with some very

624
00:39:32,000 --> 00:39:35,440
Speaker 1: very good results. The fact is that all the algorithms

625
00:39:35,480 --> 00:39:40,680
Speaker 1: have been using the findings of that paper. And what's

626
00:39:40,719 --> 00:39:44,440
Speaker 1: happened is those stocks have been re rated, um, and

627
00:39:44,480 --> 00:39:47,400
Speaker 1: they've done well, but they've done well because they've been rerated.

628
00:39:47,480 --> 00:39:50,200
Speaker 1: And I think most of these most of these issues

629
00:39:50,360 --> 00:39:55,120
Speaker 1: are most of the accounting issues have been arbitraged away.

630
00:39:55,480 --> 00:40:00,640
Speaker 1: And in fact, in my book which comes out next month, UM,

631
00:40:00,680 --> 00:40:04,640
Speaker 1: we talk about the fact that algorithms have been kind

632
00:40:04,640 --> 00:40:09,480
Speaker 1: of the enemy of the the analysts because algorithms can

633
00:40:09,480 --> 00:40:12,799
Speaker 1: do all this stuff much better than the analyst. And

634
00:40:13,000 --> 00:40:15,880
Speaker 1: the one area which I'm I'm quite intrigued about but

635
00:40:16,080 --> 00:40:21,520
Speaker 1: slightly helpless about is is natural language processing. So we've

636
00:40:21,680 --> 00:40:27,000
Speaker 1: on some basic textual analysis in this report, looking at

637
00:40:27,320 --> 00:40:32,160
Speaker 1: the words and trying to interpret whether there has been

638
00:40:33,520 --> 00:40:37,719
Speaker 1: an unusual amount of obfuscation but by these companies relative

639
00:40:37,840 --> 00:40:43,080
Speaker 1: to an Amazon, for example. And you can't do this

640
00:40:43,160 --> 00:40:46,920
Speaker 1: without a computer and there's all sorts of things areas

641
00:40:47,080 --> 00:40:51,360
Speaker 1: in the in the area of understanding text, understanding words

642
00:40:51,640 --> 00:40:54,439
Speaker 1: that computers can do much better. To my knowledge, there

643
00:40:54,480 --> 00:40:58,600
Speaker 1: isn't anybody that can analyze dissect a computer that can

644
00:40:58,640 --> 00:41:02,120
Speaker 1: analyze and dissect the related parties notes, because obviously they're

645
00:41:02,239 --> 00:41:05,400
Speaker 1: very complicated, very specific, and I think that's one of

646
00:41:05,480 --> 00:41:08,320
Speaker 1: the area's what you do need a human being. Fortunately,

647
00:41:08,360 --> 00:41:11,880
Speaker 1: it's still some areas what you do need humans, just

648
00:41:12,000 --> 00:41:16,920
Speaker 1: on the subject of natural language text. Looking at Chinese accounts,

649
00:41:16,960 --> 00:41:20,640
Speaker 1: one of the surprising things, or one of the things

650
00:41:20,640 --> 00:41:23,719
Speaker 1: that I think is surprising to people who aren't familiar

651
00:41:24,000 --> 00:41:27,440
Speaker 1: with this particular market, is sometimes you read the accounts

652
00:41:27,480 --> 00:41:30,480
Speaker 1: and you get a lot of mentions of what the

653
00:41:30,600 --> 00:41:36,400
Speaker 1: company is doing to benefit China or to benefit the

654
00:41:36,480 --> 00:41:40,120
Speaker 1: Communist Party and president she and things like that. I

655
00:41:40,160 --> 00:41:44,279
Speaker 1: remember one of the I guess the most amusing examples

656
00:41:44,320 --> 00:41:46,920
Speaker 1: that I found it that recently was there was I

657
00:41:46,960 --> 00:41:52,239
Speaker 1: think it was a bank and someone, probably an old

658
00:41:52,280 --> 00:41:56,400
Speaker 1: lady in China, had microwaved her bank notes in order

659
00:41:56,480 --> 00:42:01,160
Speaker 1: to get rid of the virus um on them. She

660
00:42:01,239 --> 00:42:05,560
Speaker 1: thought COVID might be transmitted through banknotes, and she brought

661
00:42:05,600 --> 00:42:09,080
Speaker 1: these destroyed banknotes into the bank and they had helped

662
00:42:09,080 --> 00:42:13,400
Speaker 1: her piece them back together or salvage them in some way.

663
00:42:13,480 --> 00:42:17,400
Speaker 1: And the bank had written a footnote in its accounts

664
00:42:17,440 --> 00:42:20,319
Speaker 1: about this particular incident, and it went into a lot

665
00:42:20,360 --> 00:42:23,719
Speaker 1: of detail just to say that it was doing its

666
00:42:23,880 --> 00:42:29,000
Speaker 1: part to help people during the coronavirus outbreak. So that's

667
00:42:29,040 --> 00:42:31,520
Speaker 1: a long winded way of me asking you how much

668
00:42:31,600 --> 00:42:38,239
Speaker 1: does politics enter into Chinese accounting corporate accounting? Well, and

669
00:42:38,640 --> 00:42:43,280
Speaker 1: for these companies, I think their main they're looking westward. Really,

670
00:42:43,680 --> 00:42:47,319
Speaker 1: they're looking at the investors in all right. Maybe it's

671
00:42:47,320 --> 00:42:51,040
Speaker 1: not so true today, but going back a few months,

672
00:42:51,440 --> 00:42:55,120
Speaker 1: they were looking at Western investment. And I think, you know,

673
00:42:55,200 --> 00:42:59,560
Speaker 1: talking too much about how they're engineering themselves to help

674
00:42:59,560 --> 00:43:04,080
Speaker 1: the Chinese government doesn't endear them to the average marginal

675
00:43:04,120 --> 00:43:06,960
Speaker 1: investor in New York. So I don't think there was

676
00:43:07,000 --> 00:43:10,600
Speaker 1: as much of that as you might see in a

677
00:43:10,680 --> 00:43:16,120
Speaker 1: domestic Chinese issuer. But there's some interesting I mean, some

678
00:43:16,200 --> 00:43:20,520
Speaker 1: of them have got some quite interesting observations about what

679
00:43:20,560 --> 00:43:23,520
Speaker 1: they're doing to help the Chinese consumer, what they're doing

680
00:43:23,520 --> 00:43:27,239
Speaker 1: to help their employees, and and and those sorts of

681
00:43:27,520 --> 00:43:29,879
Speaker 1: sorts of things. I don't think we saw too many,

682
00:43:30,520 --> 00:43:34,800
Speaker 1: too much discussion about microwave banknotes or anything along those lines.

683
00:43:35,000 --> 00:43:39,480
Speaker 1: I don't I don't recall recall one of those. One

684
00:43:39,560 --> 00:43:43,279
Speaker 1: last question for you, Stephen, what's your I guess your

685
00:43:43,400 --> 00:43:49,320
Speaker 1: number one tip to any investor who's trying to survey

686
00:43:49,520 --> 00:43:53,120
Speaker 1: the accounts of a company that they're considering putting money in,

687
00:43:53,560 --> 00:43:57,359
Speaker 1: whether it's Chinese or from somewhere else in the world.

688
00:43:58,120 --> 00:44:00,799
Speaker 1: But I'm glad you I'm glad you asked asked me that.

689
00:44:00,880 --> 00:44:02,600
Speaker 1: And if I can just put in a plug for

690
00:44:02,680 --> 00:44:06,879
Speaker 1: my book The Smart Money Method where you got where

691
00:44:06,880 --> 00:44:09,440
Speaker 1: you talking about three pillars. So there's three things you

692
00:44:09,440 --> 00:44:11,560
Speaker 1: should do when you look at a stock to make

693
00:44:11,600 --> 00:44:14,920
Speaker 1: sure you're not buying a fraud. And you might not

694
00:44:15,000 --> 00:44:17,120
Speaker 1: be looking to avoid a fraud. You might just be

695
00:44:17,120 --> 00:44:19,760
Speaker 1: looking to avoid a company that's not going to perform well.

696
00:44:19,960 --> 00:44:24,359
Speaker 1: And you know all the studies tell you that the

697
00:44:24,400 --> 00:44:28,759
Speaker 1: majority of stocks do badly, the majority of stocks underperform.

698
00:44:29,520 --> 00:44:34,239
Speaker 1: So if you do these three simple checks, then you

699
00:44:34,280 --> 00:44:38,319
Speaker 1: will protect yourself and reduce the odds that you're buying

700
00:44:38,320 --> 00:44:40,920
Speaker 1: a loser. The first check is to look at the

701
00:44:40,920 --> 00:44:46,840
Speaker 1: working capital ratios companies that have rising days of receivables

702
00:44:47,719 --> 00:44:52,359
Speaker 1: rising days of inventory tend to be well. They can

703
00:44:52,400 --> 00:44:54,880
Speaker 1: be frauds, but they tend not to be as good investment.

704
00:44:55,120 --> 00:44:58,240
Speaker 1: And the reason is very simple. If your customers aren't

705
00:44:58,280 --> 00:45:02,120
Speaker 1: paying you, that probably isn't a good thing. If your

706
00:45:02,160 --> 00:45:05,200
Speaker 1: stocks are rising, it usually means your customers don't want

707
00:45:05,239 --> 00:45:09,600
Speaker 1: what you're trying to sell. The second tip is always

708
00:45:09,760 --> 00:45:14,200
Speaker 1: do a comparison of the margins today with the past

709
00:45:14,239 --> 00:45:18,200
Speaker 1: and with the peer group. Every single fraud I studied

710
00:45:18,640 --> 00:45:22,400
Speaker 1: for my freensic handing course had margins which were higher

711
00:45:22,400 --> 00:45:27,440
Speaker 1: than peers and usually unexplainable. And the third thing is

712
00:45:27,840 --> 00:45:30,360
Speaker 1: don't just look at the earnings, look at the cash flow.

713
00:45:30,960 --> 00:45:35,400
Speaker 1: Is the company generating as much cash as it's reporting

714
00:45:35,440 --> 00:45:38,720
Speaker 1: in earnings, And if there is a trend in which

715
00:45:39,200 --> 00:45:42,439
Speaker 1: earnings have carried on going up and cash hasn't, that's

716
00:45:42,480 --> 00:45:45,400
Speaker 1: usually assigned to stay away. So those three simple tricks

717
00:45:45,560 --> 00:45:50,480
Speaker 1: working capital, margin comparisons and cash persus earnings will keep

718
00:45:50,480 --> 00:45:53,600
Speaker 1: you out of trouble. That was really great. Thank you

719
00:45:53,640 --> 00:45:56,920
Speaker 1: so much for coming on our boots of great conversation.

720
00:45:57,320 --> 00:45:59,200
Speaker 1: Oh thank you so much for having me. I mean,

721
00:45:59,360 --> 00:46:02,239
Speaker 1: I'm absolutely you want to do. That was fantastic. I'm

722
00:46:02,239 --> 00:46:04,600
Speaker 1: really excited about reading the book now. Thank you so much,

723
00:46:23,920 --> 00:46:37,880
Speaker 1: ye Joe. I really enjoyed that conversation. I think it

724
00:46:37,920 --> 00:46:41,920
Speaker 1: was a useful bit of critical analysis to offset some

725
00:46:42,080 --> 00:46:45,560
Speaker 1: of the optimism and excitement that we've seen lately, not

726
00:46:45,640 --> 00:46:48,680
Speaker 1: just around the AT I p O, but around tech

727
00:46:48,719 --> 00:46:52,120
Speaker 1: stocks really all over the world. I feel like we

728
00:46:52,160 --> 00:46:56,600
Speaker 1: could just do episode after episode with UM with accountants

729
00:46:56,640 --> 00:46:59,759
Speaker 1: and particularly forensic accounts. It's just like it's always so

730
00:47:00,000 --> 00:47:01,680
Speaker 1: interesting and every time we do or like we got

731
00:47:01,680 --> 00:47:04,520
Speaker 1: to do more accounting episodes and then we forget. But

732
00:47:04,600 --> 00:47:07,319
Speaker 1: I always remember after these conversations, like why they're like

733
00:47:07,520 --> 00:47:11,279
Speaker 1: so interesting and uh, he was like a great sort

734
00:47:11,320 --> 00:47:15,600
Speaker 1: of articulator of some of the issues that make corporate

735
00:47:15,640 --> 00:47:20,000
Speaker 1: analysis so difficult and interesting. Yeah, I can't even imagine

736
00:47:20,040 --> 00:47:23,080
Speaker 1: going through a thousand pages of accounts, but I thought

737
00:47:23,080 --> 00:47:26,239
Speaker 1: the point about, for instance, related party transactions was a

738
00:47:26,280 --> 00:47:29,719
Speaker 1: really good one. If there are pages and pages of

739
00:47:30,040 --> 00:47:34,400
Speaker 1: complex footnotes and related party transactions, then that's probably a

740
00:47:34,480 --> 00:47:37,560
Speaker 1: red flag right there, or at least I don't know.

741
00:47:37,600 --> 00:47:39,760
Speaker 1: I kind of I wonder how many investors are actually

742
00:47:39,800 --> 00:47:43,880
Speaker 1: going through all of those I can't imagine it's many.

743
00:47:44,560 --> 00:47:48,359
Speaker 1: You know, it's interesting. I thought your question was really

744
00:47:48,360 --> 00:47:51,360
Speaker 1: great at the end about politics and the connection between

745
00:47:51,440 --> 00:47:54,880
Speaker 1: the corporate leadership, particularly these big Chinese companies, and the

746
00:47:55,000 --> 00:47:59,040
Speaker 1: government in China. Just in general, it feels like that

747
00:47:59,239 --> 00:48:02,440
Speaker 1: is such a sort of factor in understanding both like

748
00:48:02,520 --> 00:48:06,319
Speaker 1: how the economy works and how specific businesses work. I

749
00:48:06,360 --> 00:48:09,920
Speaker 1: was thinking back to our our conversation with Tom Rlick,

750
00:48:10,400 --> 00:48:13,120
Speaker 1: the Bloomberg economist who has the book about the China

751
00:48:13,200 --> 00:48:18,800
Speaker 1: bubble and so much so many misconceptions of analyzing China,

752
00:48:19,040 --> 00:48:22,200
Speaker 1: at least in or leaks view, sort of stemmed from

753
00:48:22,239 --> 00:48:25,719
Speaker 1: the idea that Chinese, the Chinese government by control of

754
00:48:25,760 --> 00:48:28,040
Speaker 1: the banks, can sort of get any outcome at wants,

755
00:48:28,080 --> 00:48:30,960
Speaker 1: and it can forestall a bubble or constall bubbles from

756
00:48:31,000 --> 00:48:33,720
Speaker 1: crashing as long as it wants, And so thinking about

757
00:48:33,760 --> 00:48:37,919
Speaker 1: like some of these sort of the non financial companies

758
00:48:38,320 --> 00:48:40,480
Speaker 1: and the investments they make, and how they mark their

759
00:48:40,520 --> 00:48:43,680
Speaker 1: investments and whether they get whether they have a um,

760
00:48:43,960 --> 00:48:46,319
Speaker 1: whether they can find an entity to invest in an

761
00:48:46,400 --> 00:48:49,759
Speaker 1: investment that will continue to increase the value, it just

762
00:48:49,800 --> 00:48:53,799
Speaker 1: seems like it's got to be pretty tough to analyze

763
00:48:53,840 --> 00:48:56,920
Speaker 1: that from any sort of like outside sort of like

764
00:48:56,960 --> 00:49:00,239
Speaker 1: typical Western standards, when you have so much of the

765
00:49:00,440 --> 00:49:05,239
Speaker 1: large industrial leadership of a country so tightened with the government. Yeah,

766
00:49:05,320 --> 00:49:07,800
Speaker 1: I think that's absolutely right, and that's probably why Chinese

767
00:49:07,840 --> 00:49:13,920
Speaker 1: political analysis is probably a growing area of opportunity in finance.

768
00:49:14,480 --> 00:49:16,279
Speaker 1: I mean it is one of the benefits of a

769
00:49:16,320 --> 00:49:21,720
Speaker 1: command economy, right, you can direct capital and control industries

770
00:49:22,560 --> 00:49:25,880
Speaker 1: to some degree. And there are examples in China of

771
00:49:26,000 --> 00:49:30,080
Speaker 1: companies that have basically based their entire business model on

772
00:49:30,280 --> 00:49:33,600
Speaker 1: figuring out what it is the Chinese leadership wants. So

773
00:49:33,719 --> 00:49:37,960
Speaker 1: the best example of that is China Evergrand, which I

774
00:49:38,000 --> 00:49:40,240
Speaker 1: don't even know how to describe it. It's a Chinese

775
00:49:40,239 --> 00:49:43,960
Speaker 1: property company, but it also makes electric cars because the

776
00:49:44,040 --> 00:49:47,680
Speaker 1: Chinese government was really into that, and it also runs

777
00:49:47,719 --> 00:49:51,520
Speaker 1: some hospitals because healthcare is important. At one point, it

778
00:49:51,600 --> 00:49:55,600
Speaker 1: got into soccer teams or football because she was really

779
00:49:55,640 --> 00:49:59,399
Speaker 1: into sports. It's a it's a that's probably the most

780
00:49:59,400 --> 00:50:03,920
Speaker 1: extreme example of companies trying to tow the party line,

781
00:50:03,960 --> 00:50:07,080
Speaker 1: but it's one of the most interesting. Let's do a

782
00:50:07,520 --> 00:50:10,879
Speaker 1: ever Grand episode. Yeah, you know what I love, like, Hey,

783
00:50:10,960 --> 00:50:13,279
Speaker 1: we should do an ever Grand episode. But also I

784
00:50:13,320 --> 00:50:16,200
Speaker 1: love how many times I feel like when it comes

785
00:50:16,239 --> 00:50:20,600
Speaker 1: to a Chinese, big Chinese company, like there's a really

786
00:50:20,640 --> 00:50:25,080
Speaker 1: big challenge with how to describe it, right, It's always like, Okay,

787
00:50:25,120 --> 00:50:27,840
Speaker 1: it's good. There's it's almost like there's no analog that

788
00:50:27,880 --> 00:50:30,360
Speaker 1: we can think of, at least among Western companies, for

789
00:50:30,400 --> 00:50:33,520
Speaker 1: the sort of the range of business lines that some

790
00:50:33,600 --> 00:50:37,120
Speaker 1: of these you know, Chinese giants are in. Yeah. I

791
00:50:37,120 --> 00:50:40,920
Speaker 1: always find myself reaching for American parallels and then saying

792
00:50:41,440 --> 00:50:45,320
Speaker 1: like Amazon but much bigger, or like Apple but much bigger,

793
00:50:45,440 --> 00:50:48,880
Speaker 1: or like Google but also with a random money market

794
00:50:48,920 --> 00:50:52,680
Speaker 1: fund and things like that. I love it. Well. One

795
00:50:52,680 --> 00:50:56,799
Speaker 1: other things, Um, we gotta start using vision fund as

796
00:50:56,840 --> 00:51:00,799
Speaker 1: a unit of Yeah, the U s economy by too

797
00:51:01,280 --> 00:51:06,080
Speaker 1: soft bank vision funds this year. Yeah? Perfect, All right,

798
00:51:06,520 --> 00:51:09,400
Speaker 1: should we leave it there? Let's leave it there. Okay.

799
00:51:09,480 --> 00:51:12,480
Speaker 1: This has been another episode of the All Thoughts podcast.

800
00:51:12,520 --> 00:51:15,200
Speaker 1: I'm Tracy Alloway. You can follow me on Twitter at

801
00:51:15,239 --> 00:51:18,160
Speaker 1: Tracy Alloway and I'm Joe Why Isn't All? You could

802
00:51:18,200 --> 00:51:21,560
Speaker 1: follow me on Twitter at the Stalwart. Follow our guest

803
00:51:21,680 --> 00:51:25,520
Speaker 1: on Twitter. He's Steve Clapham and his handle is at

804
00:51:25,560 --> 00:51:30,000
Speaker 1: Steve Clapham and follow our producer, Laura Carlson at Laura M. Carlson,

805
00:51:30,320 --> 00:51:33,880
Speaker 1: followed the Bloomberg head of podcast, Francesco Levie at Francesca

806
00:51:33,960 --> 00:51:36,840
Speaker 1: Today and check out all of our podcasts under the

807
00:51:36,880 --> 00:51:40,799
Speaker 1: handle add podcast and check out Steven's new book, The

808
00:51:40,880 --> 00:52:01,400
Speaker 1: Smart Money Method. Thanks for listening to to