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The Unbeatable Artificial Stock Market

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MG: The Lead Lag Report joining us for the hour here is Tony Nash of Complete Intelligence has found a lot of people that I respect following. Tony, I saw a few people saying they were excited to hear what Tony has to say. So hopefully we’ll have a good conversation here.

Tony for those who aren’t familiar with your background talk about who you are how’d you get involved in the data side of markets and forecasting in general. And what you’re doing with Complete Intelligence.

TN: Sure, Michael. First of all, thanks for having me. I have followed you for probably 10 or 15 years.

MG: I am very sorry for that I am very very sorry for that.

TN: But yeah so, I got involved in data way back in the late 90s when I was in Silicon Valley and I built a couple of research firms focused on technology businesses. I then took about probably eight years to become an operator. I did a turnaround in Asia of a telecom firm. I built a firm in Sri Lanka during the Civil War and then I started down the research front again. I was the Global Head of Research for the Economist and I was the Asia Head of Consulting for a company called IHS Markit which is now owned by S&P and then after that I started Complete Intelligence.

So, you know my background is really all about data but it’s also all about understanding the operational context of that data. And I think it’s very hard for people to really understand what data means without understanding how people use it.

MG: Okay. So that’s maybe a good direction to start with that point about context with data because I think part of that context is understanding what domains data is more appropriate for forecasting and others. Right? So, I always made this argument that there are certain domains in particular when it comes to, I would argue investing that have sort of a chaotic system element to them. Right? Where small changes can have ripple effects. So, it’s hard to necessarily to sort of make a direct link between a strong set of variables and the actual outcome because there’s always a degree of randomness. Whereas, something that’s more scientific right that doesn’t have that kind of chaos theory element is it’s clearer.

So, talk about that point about context when it comes to looking at data. And again, the kind of domains where data is more appropriate to really have more conviction in than others.

TN: Yeah. Okay. So, that’s a great place to start. So, the first thing I would say is take every macro variable that you know of and throw it out the window. It’s all garbage data 100 of it. Okay? I would never trade based on macro data.

We’ve tested macro data over the years and it’s just garbage. It doesn’t matter the country. You know we hear people saying that China makes up their data. Well, that may be true you can kind of fill in the blank on almost any country because I don’t know how much you guys understand about macro data. But it is not market clearing data. Okay? Like an equity price or a commodity price.

Macroeconomic data is purely academic made-up data that is a proxy for activity. It’s a second or third derivative of actual activity by the time you see, say, a CPI print which is coming out tomorrow. Right? And it’s late and it’s really all not all that meaningful. So, I wouldn’t really make a trade or put a strategy together based on macro data even historical macro data. Every OECD country revises their data by what four times or something.

So, you see, a print for CPI data tomorrow that’s a preliminary print and that’s revised several times before it’s put on quote-unquote actual. And so, you know, you really can’t make decisions using macroeconomic data beyond a directional decision. Okay? So, if you follow me on Twitter, you see I’m very critical macro data all the time. I’m very sarcastic about it.

I think the more specific you can get… You know if you have to look at say national data or macroeconomic data, I would look at very low-level data the more specific you can get the better. Things like household surveys or you know communist and socialist countries. Chinese data at the very specific level can be very interesting. Okay? Government data the high-level data in every country I consider it garbage data in every country. So, you’re looking at very low-level very specific government or multilateral data, that’s interesting.

The closer you get to market clearing data the better because that’s a real price. Right? A real price history on stuff is better and company data is the best. And of course, company data is revised at times but that really helps you understand what’s happening at the kind of firm level. And what’s happening at the transaction level. So, you know, those are the kind of hierarchies of data that I would look at.

MG: So, okay this is a great. That’s a great point you mentioned that it’s you said very these variables is macro variables they’re proxies for activity. Right? They’re really more proxies for narratives. Right? Because and that’s where I think… You mentioned sarcasm almost 99 of my tweets at this point are sarcasm because when Rome is burning, what else I’m not going to do except joke about it. Right? Because I can’t change anything. Right?

So, and to that point I share a lot of that cynicism around data that people will often reference in the financial media that sounds really interesting, sounds like it’s predictive but when you actually test it to your point, you throw it out because it doesn’t work. Right? There’s no real predictive element to it.

So, we’ll get into some of the predictive stuff that you talk about but I want to hit a little bit on this market clearing phrase you kept on using. Explain what you mean by market clearing.

TN: Data is where there is a buyer and a seller.

MG: To actual prices of some asset class or something like that.

TN: Yep. That’s right.

MG: Okay. So, that makes sense. Okay. Now again I go back to the certain domains that data is more clear in terms of cause and effect and getting a sense of probabilities the challenge with markets. As we know is that the probabilities change second by second because not only does that mean meaningless data change second by second but the market clearing data changes second by second. Right? Going back to that point.

So, with what you do with Complete Intelligence, talk us through a little bit. What are some of the variables that you tend to find have some predictive power? And how do you think about confidence when it comes to any kind of decision made based on those variables?

TN: Sure. Okay. So, before I do that let me get into why I started Complete Intelligence because if none of you have started a firm before don’t do it. It’s really really hard so…

MG: From the people in the back because I got to tell you I’m an entrepreneur, I’m going through. And all you got is people on Twitter kicking you when you’re down when it’s the small sample anyway.

TN: Absolutely. So, I was where I had worked for two very large research firms The Economist and IHS Markit. And I saw that both of them claimed to have very detailed and intricate models. Okay? Of the global economy industries, whatever. Okay? For all of the interior models. And I have never spoken with a global research firm a data firm that is different from this. And if I’m wrong then somebody please correct me. But at the end of that whole model pipeline is somebody who says “no that’s a little bit too high” or “a little bit too low” and they change the number. Okay? To whatever they wanted it to be in the first place. So, and I tell you 100% of research firms out there with forecasts today have a manual process at the end of their quote-unquote model. A 100% of them. Again, if there’s somebody else that doesn’t do that, I am happy to be corrected. Okay? But I had done that for a decade and I felt like a hypocrite when I would talk to clients.

So, I started Complete Intelligence because I wanted to build a 100% machine driven forecasts across economics, across market, across equities, across commodities, across currencies. Okay? And we’ve done that. So, we have a multi-phase, multi-layer machine learning process that takes in billions of data items. We’re running trillions of calculations every week when we reforecast our data. Right? Now the interval of our forecast is monthly interval forecast. So, if people looking at daily prices that’s not what we’re doing now. Okay? We will be launching daily interval forecasts. I would say probably before the end of the year to be conservative but we’re doing monthly interval forecasts now.

Why is everything I’ve said is meaningless unless we measure our error. Okay? So, for every forecast that we do. And if you log into our website, you can see whether it’s the gold price, the S&P 500, USD, JPY, molybdenum or whatever. We track our error for every month, for everything that we do. Okay? So, if you want to understand your risk associated with using our data it’s there right in front of you with the error calculations. Okay? It’s only fair, If I’m gonna say sell you a forecast, you should be able to understand how wrong we’ve been in the past, before you use that as a decision-making input.

MG: Well, maybe just add some framework on that because I think that’s interesting. So, what you call error I call luck. Right? Because luck is both good or bad. I always make that point that with any equation any set of variables you’re going to have that error is the luck component that you can’t control. And that doesn’t necessarily mean that the equation is wrong. Right? It’s just means that for whatever reason that error in that moment in time was higher or lower than you might otherwise want. Okay?

TN: There is no such thing as zero error. And anybody who tells you that they have zero error is obviously they’re an economist and they don’t understand how markets work. So, there is always error in every calculation.

So, the reason we track error is because that serves as a feedback loop into our machine learning process. Okay? And we have feedback loops every week as we and what we’re doing right now is every Friday end of day. We will download global data process over the weekend have a new forecast on Monday morning. Okay? And so all of that error whether it’s near-term error, short-term error or say medium-term error, we feed that all back in to help correct and understand what’s going on within our process. And we have like I said, we have a multi-phase process in our machine learning platform. So, error is simply understanding the risk associated with using with using our platform.

MG: Right, which is basically how apt is a thing that you’re forecasting to that error which is again luck good or bad. I’m trying to put in sort of a qualitative framework also because I think… Yeah, there’s errors in life obviously, too. Right? And so, when they’re good or bad. But you know those elements.

TN: Right. But here’s what I would and I don’t know, I don’t want to dispute this too much but I think there is. So, you use the word luck and that’s fine but I think luck has a bit to do with the human element of a decision. Okay? We’re using math and code there’s zero human interaction with the data and with the process. And so, I wouldn’t necessarily call it luck. I mean, it literally is error like our algorithms got it wrong. So, if you want to call luck that’s absolutely fine but I would say luck is more of a human say an outcome associated with a human decision. More than something that’s machine driven that’s iterating. Again, we’re doing trillions of calculations every week to get our forecasts out there.

MG: Yeah, no that’s fair and maybe for the audience, Tony. Explain what machine learning is now.

TN: Sure.

MG: I once developed an app called “How Edition”. I was having dinner with the head developer once and he said he just came back from a conference about machine learning and he was just basically well, having drinks with me laughing and joking saying everybody use this term machine learning but it’s really just regression analysis. Right? So, talk about machine learning what is actual machine learning? How important is recent data to changes in the regression? Because I assume that’s part of the sort of dynamic nature of what you do just kind of riff on that for a bit.

TN: Okay. So, when I first started Complete Intelligence, I was really cynical about AI. And I spoke to somebody in Silicon Valley and asked the same question: what is AI? And this person said “Well AI is everything from a basic I say, quadratic equation upward.” I’m not necessarily sure that I agree that something that simple would be considered artificial intelligence. What we’re really doing with machine learning is there are really three basic phases. Okay? You have a preprocess which is looking at your data to understand things like anomalies, missing data, weird behavior, these sorts of things. Okay? So, that’s the first phase that we look at to be honest that’s the hardest one to get right. Okay?

A lot of people want to talk about the forecasting methodologies and the forecasting algorithms. That’s great and that’s the sexy part of ML. But really the conditioning and the pre-process is the is the hardest part and it’s the most necessary part. Okay? When we then go into the forecasting aspect of it, we’re using what’s called an ensemble approach. So, we have a number of algorithms that we use and let’s say they’re 15 algorithms. Okay? That we use we’re looking at a potential combinatorial approach of any individual or combination of those algorithms based on the time horizon that we’re forecasting. Okay?

So, we’re not saying a simple regression is the way to go we’re saying there may be a neural network approach, there may be a neural network approach in combination with some sort of arima approach. We’re saying something like that. Right? And so, we test all of those permutations for every historical period that we’re looking at.

So, I think traditionally when I look back at kind of quote-unquote building models in excel, we would build a formula and that formula was fairly static. Okay? And every time you did say a crude oil forecast you had this static formula that you set your data against and a number came out. We don’t have static formulas at all.

To forecast crude oil every single week we start at obviously understanding what we did in the past but also re-testing and re-weighting every single algorithmic approach that we have and then recombining them based upon the activity that happened on a daily basis in that previous week. And in the history. Okay?

So, that’s phase two the forecasting approach and then phase three is the post process. Right? And so, the post process is understanding the forecast output. Is it a flat line? Right? If it’s a flat line then there’s something wrong. Is it a straight line up? Then that there’s something you know… those are to use some extremes. Right? But you know we have to test the output to understand if it’s reasonable. Right? So, it’s really an automated gut check on the reasonableness of the outcome and then we’ll go back and correct outliers potentially reforecast and then we’ll publish. Okay?

So, there are really three phases to what we do and I would think three phases to most machine learning approaches. And so, when we talk about machine learning that’s really what we’re talking about is that that really generally three-phase process and then the feedback loop that always goes back into that.

MG: Yeah. No that makes sense. Let’s get…

TN: That’s really boring after a while.

MG: No, no, no but I think that’s it’s part of what I want to do with these spaces is try to get people to understand you know beyond sort of just the headline or the thing that is thrown out there. As a term to what does that actually mean in practice you don’t have to know it fully in depth the way the that you do. But I think having that context is important.

TN: I would say on the idea generation side and on the risk management side right now. Okay? Now the other thing that I didn’t cover is obviously we’re doing markets but we also do… we use our platform to automate the budgeting process within enterprises. Okay? So, we work with very large organizations and the budget process within these large organizations can take anywhere from say four to six months. And they take hundreds of people. And so, we take that down to really interacting with one person in that organization and we do it in say less than 24 hours. And we build them a continuous budget every month.

Once accounting close happens we get their new data and then we send them a new say 18-month forward-looking forecast for them. So, their FPA team doesn’t have to dig around and beg people for information and all that stuff. So, some of this is on the firm event could be on the firm evaluation side, as well. Right? How will the firm perform? Nobody’s using us for that but the firms themselves are using that to help them automate their budgeting process. So, some of that could be on this a filtering side and the idea generation side, as well.

So, we do not force our own GL structure onto the clients. We integrate directly with their SAP or Oracle or other ERP database. We take on their GL structure at whatever levels they want. We have found that there is very little deterioration from say, the second or third level GL to say the sixth or seventh level GL, in terms of the accuracy of our forecast. And when we started doing this it really surprised me. We do a say a team level forecast for 10, 12 billion organizations, six layers down within their GL. And we see very little deterioration when we go down six levels than when we do it at say two levels. Which is you know it really to me it speaks to the robustness of our process but would we consider Anaplan a competitor not really, they’re not necessarily doing the kind of a budget automation that we’re doing at least, that I’m aware of. I know that there are guys like Hyperion who do what we’re doing but again their sophistication isn’t necessarily. What we’re doing and they do a great job and Hyperion is a great organization. I think Oracle gave them a new name now but they’re not necessarily using the same machine learning approaches that we’re using. And our clients have told us that they don’t get the same result with using that type of say ERP originated or ERP add-on budgeting process.

Yep. So, I would say we can’t we can do company-specific information for a customer if that’s what they want. Okay? We don’t necessarily have that on our platform today aside from say individual ticker symbols. Okay? But we’re not forecasting say the P&L of Apple or something like that or the balance sheet of Apple. Something we could do in a pretty straightforward manner but we do that on a customer-by-customer basis.

So, what we’re forecasting right now are currency pairs, commodities about 120 commodities and global equity indices. Okay? We are Beta testing individual equity tickers and we probably won’t introduce those fully on the platform until we have our daily interval forecast ready to go to market. But those are still we’re still working some kinks out of those and we’ll have those ready probably within a few months.

MG: Okay. So, let’s talk about commodities here for a bit tonight. Obviously, this is where a lot of people’s attention has gone to. What kind of variables and I know you said you have a whole bunch of variables that are being incorporated here but are there certain variables in particular when it comes to oil and other commodities that have a higher predictive power than others.

TN: There are I think one of the stories that I tell pretty often and this really shocks people is when we look at things like gold. Okay? I’m not trying to deflect from your oral question but just to you know we’ve spoken with the number of sugar traders over the years. Okay? And so, we tell them that say the gold price and the sugar price there may not necessarily be a say short term say correlation there but there is a lot of predictive capability there and we talk them through why. And I think the thing that we get out of the machine learning approach and we cast a wide net. We’re not forcing correlations is that we’ll find some unexpected say drivers. Although drivers implies a causal nature and we’re not trying to imply causality anywhere. Okay?

We’re looking at kind of co-movement in markets over time and understanding how things work in a lead lag basis with some sort of indirect causality as well as say a T0 or current state movement. So, with crude oil you know there are so many supply side factors that are impacting that price right now, that I can’t necessarily point to say another commodity that is having an impact on that. It really is a lot of the supply side and sentimental factors that are impacting those prices right now.

MG: That makes a lot of sense. And I’m curious how did you mention it’s I think the intervals once a month. Right? So, given the speed with which inflation has moved and yields have moved how does a machine learning process adapt to sudden spikes or massive deltas in in variable movement. Right? Because there’s always a degree of randomness going back to error. Right? And you can make an argument that the larger move is the that may actually be more error but I think that’s an interesting discussion.

TN: So, I’ll tell you where we were say two years ago when 2020 hit versus today. Okay? So, in March of 2020, April 2020 everything fell apart. I don’t think there were any models that caught what was going to happen. It was an exogenous event that hit markets and it happened very quickly. So, in June, I was talking with someone who is with one of the largest software companies in the world and they said “Hey has your AI caught up to markets yet because ours is still lost” And you guys would be shocked if I told you who this was because you would expect them to know exactly what’s going to happen before it happened. Okay? I’ll be honest I think it was all of them but the reality is you know Michael you where you were saying that ML is just regression analysis.

I think a lot of the large firms that are doing time series forecasting really are looking at regression and derivatives of regression as kind of their only approaches because it works a lot of the time. Right? So, we had about a two-month delay at that point and part of it was because… So, by June we had caught up to the market. And we had started in February to iterate twice a month, we were doing once a month; I hope you guys can understand with machine learning two factors are we’re always adjusting our algorithms. Okay? We’re always incorporating new algorithms. We’re always you know making sure that we can keep up with markets because you cannot be static in machine learning. Okay? The other thing is we’re always adding capacity why? Because we have to iterate again and again and again to make sure that we understand the changes in markets. Okay?

So, at that time we were only iterating twice a month and so it took us a while to catch up. Guys like this major technology firm and other major technology firms they just couldn’t figure it out. And I suspect that some of them probably manually intervened to ensure that their models caught up with markets. I don’t want to accuse any individual company but that temptation is always there. Especially, for people who don’t report their error. The temptation is always there for people to manually intervene in their forecast process. Okay?

So, now, today if we look for example at how are we catching changes in markets. Okay? So, if I look at the S&P 500 for April for example, our error rate for the S&P 500 for April I think was 0.6 percent. Okay? Now in May it changed it deteriorated a little bit to I think four or six percent, I’m sorry I don’t remember the exact number offhand but it deteriorated. Right? But you know when there are dramatic changes because we’re iterating at least once a week, if not twice a week we’re catching those inflections much much faster. And what we’re having to do, and this is a function of the liquidity adjustments, is where in the past you could have a trend and adjust for that trend and account for that trend. We’re really having to our algorithms are having to select more methodologies with recency bias because we’re seeing kind of micro volatility in markets. And so again…

MG: So, kind of like the difference between a simple moving average versus like an exponential moving average. Right? Where you’re waiting the more recent data sooner.

TN: It could be. Yeah.

MG: Right.

TN: Yeah. That’s a very very simple approach but yeah it would be something like that, that’s right. Yeah. What so when we work with enterprise customers that level of engagement is very tight because when we’re getting kind of the full set of financial data from a client obviously, they’re very vested in that process. So, that’s different from say a small portfolio manager subscribing to RCF futures product where we’re doing forecasts and they have their own risk process in place. And they can do whatever they want with it. Right? But again, with our enterprise clients we are measuring our error so they can see the result of our continuous budgeting process. Okay?

So, if we’re doing let’s say, we launch with a customer in May, they close their mate books in June get them over to us redo our forecast and send it over to them and let them know what our error rate was in May. Okay? So, they can decide how we’re doing by department, by team, by product, by whatever based upon the error rates that we’re giving at every line item. Okay? So, they can select and we’re not doing kind of capital projects budgets we’re doing business as usual budgets so they can decide what they want to take and what they don’t want to take. It’s really up to them but we do talk through that with them and then over time they just start to understand how we work and take it on within their own internal process.

MG: So, back a little bit Tony. So, you mentioned you do this machine learning forecasting work when it comes to broad economics, markets and currency; of those three which has the most variability and randomness in other words which tends to have a higher error? Whenever you do any kind of machine learning to try to forecast what comes next?

TN: I would say it depends on the equity market but probably equity markets when there are exogenous shocks. So, our error for April of 2020 again, we don’t hide this from anybody it was not good but it wasn’t good for anybody. Right? And so, but in general it depends on the equity market but some of the emerging equity markets, EM equity markets are pretty volatile.

We do have some commodities like say rhodium for example. Okay? Pretty illiquid market, pretty small base of people who trade it and highly volatile. So, something like rhodium over the years our air rates there have not necessarily been something that we’re telling people to use that as a basis to trade but obviously, it’s a hard problem. Right? And so, we’re iterating that through our ML process and looking at highly volatile commodities is something that we focus on and work to improve those error rates.

MG: Here, I hope you find this to be an interesting conversation because I think it’s a part of the of the way of looking at markets, which not too many people are themselves maybe using but is worth sort of considering. Because I always make a point that nobody can predict the future but we all have to take actions based on that unknowable future. So, to the extent that there might be some data or some conclusions that at least are looking at variables that historically have some degree of predictive power. It doesn’t guarantee that you’re going to necessarily be better off but at least you have something to hang your hat on. Right? I think that’s kind of an aspect to investing here.

Now, I want to go a little bit Tony to what you mentioned earlier you had lived abroad for a while in Europe. And when I was starting to record these spaces to put up on my YouTube channel the first one, I did that on was with Dan Arvis and the topic of that space was around this sort of new world order that seemed to be shaping up. I want you to just talk from a geopolitical perspective how you’re viewing perhaps changing alliances because of Russia, Ukraine. And maybe even dovetail that a little bit into the machine learning side because geopolitics is a variable. Which is probably quite vault in some periods.

TN: Yeah, absolutely. Okay. So, with the evolving geopolitical order I would say rather than kind of picking countries and saying it’s lining up against x country or lining up with x country or what country. I would say we’ve entered an era of opportunistic geopolitics. Okay? We had the cold war where we had a fairly static order where people were with either red team or blue team. That changed in the 90s of course, where you kind of had the kind of the superpower and that’s been changing over the last say 15 years with say, China allegedly becoming kind of stronger and so on and so forth. So, but we’ve entered a fairly chaotic era with say opportunistic macroeconomic relation or sorry, geopolitical relationships and I think one of the kinds of top relationships that is purely opportunistic today is the China-Russia relationship.

And so, there’s a lot of talk about China and Russia having this amazing new relationship and they’re deep. And they’re gonna go to war together or whatever. We’ve seen over the past say three, four months that’s just not the case. And I’ve been saying this for years just for a kind of people’s background. Actually, advised the Chinese government the NDRC which is the economic planning unit of the central government on a product or on an initiative called the belt and road initiative. Okay? I did that for two years. I was in and out of Beijing. I never took a dime for it. I never took expense reimbursement just to be clear, I’m not a CCP kind of pawn. But my view was, if the Chinese Government is spending a trillion dollars, I want to see if I can impact kind of good spend for that. So, I have seen the inside of the Chinese Government and how it works and I also in the 80s and 90s spoke Russian and studied a lot on the Russian Government and have a good idea about how totalitarian governments work.

So, I think in general if we thought America first was offensive in the last administration then you really don’t want to learn about Chinese politics and you really don’t want to learn about Russian politics because they make America first look like kindergarten. And so, whenever you have ultra-ultra-nationalistic politics, any diplomatic relationship is an opportunistic relationship. And I always ask people who claim to be China experts but say please tell me and name one Chinese ally. Give me one ally of China and you can’t, North Korea, Pakistan. I mean, who is an ally of China there isn’t an ally of China.  There is a transactional opportunistic relationship with China but there is not an ally with China.

And so, from a geopolitical perspective if you take that backdrop looking at what’s happening in the world today it makes a whole lot more sense. And a lot of the doomsayers out there saying China is going to fall and it’s going to have this catastrophic impact. And all this other stuff, the opportunism that we see at the nation-state level pervades into the bureaucracy. So, the bureaucracy we hear about Xi Jinping. And Xi Jinping is almost a fictional character. I hate to be that extreme on it but there is the aura of Xi Jinping and there is the reality of Xi Jinping, just a guy, he’s not Mao Zedong. He doesn’t have the power that supposed western Chinese experts claim that he has. He’s just a guy. Okay?

And so, the relationships within the Chinese bureaucracy are purely transactional and they are purely opportunistic. So again, if you take that perspective and you look at what’s happening in geopolitics, hopefully you can see things through a different lens.

MG: Now, I’m glad you’re framing that in those terms because I think it’s very hard for people to really understand some of these dynamics when it’s almost presented like a like the story for a movie. Right? For what could be a conflict to come by the media because and it’s almost overly simplified. Right? When you hear this type of talk. So again, I want to go back into how does that dovetail into actual data. Right? Maybe it doesn’t at all. When you have some of these dynamics and you talk about market clearing data, you’re going to probably see mark movement somewhat respond off of geopolitical changes. Talk about anything that you’ve kind of seen as far as that goes and how should investors consider geopolitical risk or maybe not consider geopolitical risk?

TN: Yeah, I think, well when you see geopolitical adjustments today all that really is… I don’t mean overly simplified but it’s a risk calibration. Right? So, you know Russia invades Ukraine, that’s really a risk calibration. How much risk do we want to accept and then what opportunities are there? Right?

So, when you hear about China, you have to look at what risk is China willing to accept for actions that it takes? Keeping in mind that China has a very complicated domestic political environment with COVID shutdown, lockdowns and all of this stuff. So, having worked with and known some really smart Chinese bureaucrats over the years, these guys are very concerned with the domestic environment. And I don’t although there are idiot you know generals and economists here and there who say really stupid stuff about China should take over TSMC and China should invade Taiwan, these sorts of things. My conversations over the years have been with very pragmatic and professional individuals within the bureaucracy.

So, do I agree with their policies? Not a lot of them but they are well thought out in general. So, I think just because we hear talk from some journalist in Beijing who lives a very sheltered life about some potential thing that may happen. I don’t think we necessarily need to calibrate our risk based on the day-to-day story flow. I think we need to look at like… so there’s a… I’m sure you all know who Leland Miller is in China beige book like?

MG: Yeah, he’s not too long ago.

TN: Yeah. He has a proxy of the Chinese economy and that’s a very interesting way to look at an interesting lens to look through China or through to look at China or whatever. But so, I think that the day-to-day headlines, if you follow those, you’re really just going to get a lot of volatility but if you try to understand what’s actually happening, you’ll get a clearer picture. It’s not necessarily a connection of a collection of names in China and the political musical chairs, it’s really asking questions about how does China serve China first. What will China do to serve China first and are some of these geopolitical radical things that are said do they fit within that context of China serving China first? So, that’s what I try to look at would I be freaked out if China invaded Taiwan? Absolutely. I think everybody would right but is that my main scenario? No, it’s not.

MG: In terms of the data inputs on the machine learning side how granular is the data meaning? Are you looking at where geographically demand might be picking up or is it simply this is what the price is and who cares the source? Because again with hindsight if you knew that the source of China and kind of had a rough sense of the history of Russia-Ukraine maybe that could have been an interesting tell that war was coming.

TN: Yes or No. To be honest it had more to do with the value of the CNY. Okay? And I’ll tell you a little bit about history with the CNY. We were as far as I know, the only ones who called the CNY hitting 6.7 in August of 2019 with a six-month lead time. And so, we have a very good track record with USD-CNY and I would argue that China’s buying early in 2022 had a lot more to do with them from a monetary policy perspective needing to devalue CNY. So, they were hoard buying before they could devalue the CNY and I think that had a lot more to do with their activity than Russia-Ukraine. Okay? And if you notice they’ve made many of their buys by mid-April and once that happened you saw CNY, go to 6.8. Right? It’s recovered a little bit since then but China has needed to devalue the CNY for probably at least nine months. So, it’s long overdue but they’ve been working very hard to keep it strong so that they could get the commodities they needed to last a period of time. Once they had those commodities, they just let the parachute go and they let it do value to 6.8 and actually slightly weaker than 6.8.

MG: The point of the devaluation is interesting. I feel if I had enough space but we were talking about the Yen and what’s happened there. And this observation that usually China will start to devalue when they see the end as itself going through its own devaluation.

How does some of those cross correlations play out with some of the work that on machine learning you’re doing? Because there’s a human element to the decision to devalue a currency. Right? So, the historical data may not be valid I would think because you might have kind of a more humanistic element that causes the data to look very different.

TN: Well, they’re both export lab economies. Right? And we’ve seen a number of other factors dollar strength and we’ve seen changing consumption patterns. And so, yes when Japan devalues you generally see China devalue as well but also, we’ve seen a lot of other activities in on the demand-pull side and on the currency side especially with the US dollar in… I would say over the last two quarters. So, yes, that I would say that the correlation there is probably pretty high but there are literally thousands of factors that contribute to the movement of those of those currencies.

MG: Is there anything recently Tony in the output that machine learning is spitting out that really surprises you? That you know… And again, I understand that there’s a subjective element which is our own views on the world and of course then the pure data. But I got to imagine it’s fascinating sometimes if you’re sitting there and seeing what’s being spit out if it’s surprising. Is there anything that’s been kind of an outlier in in the output versus what you would think would likely happen going forward?

TN: Yeah. You know, what was really surprising to me after we saw just to stick on CNY for a minute because it’s the first thing that comes to mind, when we saw CNY do value to 6.8. I was looking at our forecast for the next six months. And it showed that after we devalued pretty strong it would moderate and reappreciate just a bit. And that was not necessarily what I was hearing say in the chatter. It was kind of “okay, here we go we’re going to go to seven or whatever” but our data was telling us that that wasn’t necessarily going to happen that we were going to hit a certain point in May. And then we were going to moderate through the end of the year. So, you know we do see these bursty trends and then we see you know in some cases those bursty trends continue for say an integer period. But with CNY while I would have on my own expected them. I expected the machines to say they need to keep devaluing because they’ve been shut down and they need to do everything they can to generate CNY fun tickets. The machines were telling me that we would you know we’d see this peak and then we would we would moderate again and it would kind of re-appreciate again.

So, those are the kind of things that we’re seeing that when I talk about this it’s… Oh! the other thing is this: So, in early April we had a we have people come back to us on our forecast regularly who don’t agree with what we’re saying and they complain pretty loudly.

MG: So, what do you say I talk when I hear that because whenever somebody doesn’t agree with the forecast, they are themselves making a fork.

TN: Of course. Yeah. Exactly. Right? Yeah, and so this person was telling us in early April that we’re way wrong that the S&P was going to continue to rally and you know they wanted to cancel their subscription and they hated us and all this other stuff. And we said okay but the month’s not over yet so let’s see what happens this was probably a week and a half in April. And what happened by the end of April things came in line with our forecast and like I said earlier we were like 0.4 and 0.6 percent off for the month. And so that person had they listened to us at the beginning of the month they would have been in a much better position than they obviously ended up being in. Right? And so, these are the kind of things that we see on a… I mean, we’ve got hundreds of stories about this stuff but these are the kind of things that we see on a regular basis. And we mess up guys I’m not saying we’re perfect and but the thing that we when we do mess up, we’re very open about it. Everything that we do is posted on our on our website. Every call we make, every error we have is their wars and all. Okay? And so, we’re not hiding our performance because if you’re using our data to make a trade, we want you to understand the risk associated with using our data. That’s really what it comes down to.

MG: It reminds me of back in 2011 and in some other periods I’ve had similar situations, where I was writing and I was very adamant in saying the conditions favored a summer crash. Right? I was saying that for the summer and the market should be going up and people would say oh where’s your summer crash and I would say this summer hasn’t started. Like it’s amazing how people, I don’t know, what it is, I don’t know if it’s just short-termism or just this kind of culture of constantly reacting as opposed to thinking but it is it is remarkably frustrating.

Going back to your point at the very beginning being entrepreneur don’t do it, that you have to build a business with people and customers who in some cases are just flat out naïve.

TN: That’s all right though. That’s a part of the risk that we accept. Right?

MG: Yeah, the other thing right now that happens with every industry but from the entrepreneur’s standpoint. It’s what you’re doing the likely outcome of your product of your service. You’re trying to communicate that to end clients but then in the single role of the die the guy the end client who comes to you exactly for that simply because they disagree with you know the output, now says I want out.

TN: Oh! Yeah! Well, your where is your summer call from 2011 the analogy today is where is your recession call. Right? So, that’s become the how come you’re not one of us calls right now. So, it’s just one of those proof points and if you don’t agree with that then you’re stupid.

So, I would say you never finish with that there is always a consensus and a something you’re you absolutely, must believe in or you don’t know what you’re talking about.

MG: Yeah, well, thankfully. What you’re talking about so appreciate everybody joining this space Tony the first time you and I were talking. I enjoyed the conversation because I think it said on investing and I encourage you to take a look at Tony’s firm and follow him here on twitter. So, thank everybody. Thank you, Tony and enjoy.

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China’s Belt And Road Has Failed. TONY NASH In Conversation With Daniel Lacalle

Tony Nash joins Daniel Lacalle in a discussion on the rise of the machines in a form of AI and machine learning and how Complete Intelligence helps clients automate budgeting with better accuracy using newer technologies like now casts. How GDP predictions are actually very erroneous yet nobody gets fired? And how about China’s GDP as well, and why it’s different from other economies? All these and so much more in markets in this fun discussion.

 

The video above is published by Daniel Lacalle – In English.

 

Show Notes

 

DL: Hello everyone and welcome to this podcast. It is a great pleasure to have somebody that you should actually follow in social media on Twitter, Tony Nash. He is somebody that you definitely need to need to look for because it has very very interesting ideas. Tony, how are you?

 

TN: Great, thanks Daniel. Thanks so much for having me today.

 

DL: It’s a tremendous pleasure as I said I was very much looking forward to to have a chat with you. Please introduce a little bit yourself. A little bit to our audience and let us know what is it that you do.

 

TN: Sure, thanks Daniel. My name is Tony Nash. I live in Houston, Texas. I’ve spent actually most of my life outside of the U.S. I spent most of my 20s in Europe, North Europe, the UK, Southern Europe and from my 30s to almost the end of my 40s I was in Asia. And so you know being in the U.S., Europe and Asia has really given me personally an interesting view on things like trade economics markets and so on and so forth.

 

During that time I was the global head of research for the economist out of London, I was based in Singapore at the time. Led the global research business. I moved from there to lead Asia consulting for a firm called IHS Markit which is owned by S&P now.

 

And after that I started my current firm Complete Intelligence which is a machine learning platform. We do global markets currencies, commodities, equity indices, economic concepts. We also do corporate revenue and expense forecasting so we’ll automate budgeting for large multinational firms.

 

DL: Wow! amazing. Truly amazing. You probably have a very interesting viewpoint on something that a lot of the people that follow us have probably diverging views. Know the situation about the impact of algorithms in the market the impact of high frequency trading and machines in markets.

 

We had a chat a few months ago with a professor at the London School of Economics that he used to invite me to his year-end lectures to to give a master class. And he mentioned that he was extremely concerned about the almost the rise of the machines. What is your view on this?

 

TN: I think so an Algo is not an Algo, right? I mean, I think a lot of the firms that are using Algo’s to trade are using extremely short-term algorithmic trading say horizons. Okay? So they’re looking at very short-term momentum and so on and so forth. And that stuff has been around for 10 plus years, it continues to improve. That’s not at all what we do we do monthly interval forecasts, Okay?

 

Now, when you talk to say an economist they’re looking at traditional say univariate and multivariate statistical approaches, which are kind of long-term trendy stuff. It’s not necessarily exclusively regression, it gets more sophisticated than that.

 

When we talk to people about machine learning, they assume we’re using exclusively those kind of algorithms. It’s not the case. There’s a mix we run what’s called an ensemble approach. We have some very short-term approaches. We have some longer-term traditional say econometric approaches. And then we use a configuration of which approach works best for that asset or that revenue line in a company or that cost line or whatever for that time.

 

So we don’t have let’s say, a fixed Algo for gold, Okay? Our algorithm for the gold price is continually changing based upon what’s happening in the market. Markets are not static, right? Trade flows economics, you know, money flows whatever they’re not static. So we’re taking all of that context data in. We’re using all of that to understand what’s happening in currencies, commodities and so on, as well as how that’s impacting company sales. Down to say the department or sub department level.

 

So what we can do with machine learning now. And this is you know when you mentioned should we fear the rise of the machines. We have a very large client right now who has hundreds of people involved in their budgeting process and it takes them three to four months to do their budgeting process. We’ve automated that process it now takes them 72 hours to run their annual budgeting process, okay? So it was millions of dollars of time and resources and that sort of thing. We’ve taken them now to do a continuous budgeting process to where we churn it out every month. So the CFO, the Head of FP&A and the rest of the say business leadership, see a refresh forecast every month.

 

Here’s the difference with what we do, compared to what a lot of traditional forecasters and machine learning people do, we track our error, okay? So we will as of next month have our error rates for everything we forecast on our platform. You want to know the error for our gold price forecast, it’ll be on there. You’ll know the error for our Corn, Crude, you know, JPY whatever, it’s on there. So many of our clients use our data for their kind of medium term trades so they have to know how to hedge that trade, right? And so if we have our one, three month error rates on there, something like that it really helps them understand the risk for the time horizon around which they’re trading. And so we do the same for enterprises. We let them know down to a very detailed level to error rates in our forecast because they’re taking the risk on what’s happening, right? So we want them to know the error associated with what they’re doing with what we’re doing.

 

So coming out of my past at the economist and and IHS and so on and so forth. I don’t know of anybody else who is being transparent enough to disclose their error rates to the public on a regular basis. So my hope is that the bigger guys take a cue from what we’re doing. That customers demand it from what we’re doing. And demand that the larger firms disclose their error rates because I think what the people who use information services will find is that the error rates for the large firms are pretty terrible. We know that they’re three to seven times our error rates in many cases but we can’t talk about that.

 

DL: But it’s an important thing. What you’ve just mentioned is an important thing because one of the things that is repeated over and over in social media and amongst the people that follow us is well, all these predictions from the IMF, from the different international bodies not to the IMF. Actually the IMF is probably one of the one that makes smaller mistakes but all of these predictions end up being so aggressively revised and that it’s very difficult for people to trust those, particularly the predictions.

 

TN: Right. That’s right.

 

DL: And one of the things that, for example when we do now casts in our firm or when with your clients. That’s one of the things that very few people talk about, is the margin of error is what has been the mistake that we have made in the in that previous prediction. And what have we done to correct it because one might probably you may want to expand on this. Why do you think that the models that are driving these now cast predictions from investment banks in some cases from international bodies and others? Are very rarely revised to improve the prediction and the predictability of the of the figures and the data that is being used in the model.

 

TN: It’s because the forecasters are not accountable to the traders, okay? One of the things I love about traders is they are accountable every single day for their PNO.

 

DL: Yeah, right.

 

TN: Every single day, every minute of every day they’re accountable for their PNO. Forecasters are not accountable to a PNO so they put together some really interesting sophisticated model that may not actually work in the real world, right? And you look at the forward curves or something like that, I mean all that stuff is great but that’s not a forecast, okay? So I love traders. I love talking to traders because they are accountable every single day. They make public mistakes. And again this is part of what I love about social media is traders will put their hypothesis out there and if they’re wrong people will somewhat respectfully make fun of them, okay?

 

DL: Not necessarily respectfully but they will.

 

TN: In some cases different but this is great and you know what economists and industry forecasts, commodity forecasters these guys have to be accountable as well. I would love it if traders would put forecasters up to the same level of criticism that they do other traders but they don’t.

 

DL: Don’t you find it interesting? I mean one of the things that I find more intellectually dishonest sometimes is to hear some of the forecasters say, well we’ve only made a downgrade of one point of one percentage point of GDP only.

 

TN: Only, right. It’s okay.

 

DL: So that is that we’ve grown accustomed to this idea that you start the year with a prediction of say, I don’t know three percent growth, which goes down to below two. And that doesn’t get anybody fired, it’s sort of like pretty much average but I think it’s very important because one of the things. And I want to gather your thoughts about this. One of the things that we get from this is that there is absolutely no analysis of the impact of stimulus packages for example, when you have somebody is announcing a trillion dollar stimulus package that’s going to generate one percent increase in trendline GDP growth it doesn’t. And everybody forgets about it but the trillion dollars are gone. What is your thoughts on this?

 

TN: Well, I think those are related in as much as… let’s say somebody downgraded GDP by one percent. What they’re not accounting for, What I think they’re not accounting for is let’s say the economic impact kind of multiplier. And I say that in quotes for that government spending, right? So in the old days you would have a government spending of say you know 500 billion dollars and let’s say that was on infrastructure. Traditionally you have a 1.6 multiplier for infrastructure spend so over the next say five years that seeps into the economy in a 1.6 times outs. So you get a double bang right you get the government spending say one-to-one impact on the economy. Then you get a point six times that in other industries but what’s actually happened.

 

And Michael Nicoletos does some really good analysis on this for China, for example. He says that for every unit of say debt that’s taken out in China, which is government debt. It takes eight something like eight units of debt to create one unit of GDP. So in China for example you don’t have an economic multiplier you have an economic divisor, right?

 

DL: Exactly.

 

TN: So the more the Chinese government spends actually the less GDP growth which is weird, right? But it tells me that China is an economy that is begging for a market. A real market, okay? Rather than kind of central planning and you and Europe. I’m sure you’re very familiar with the Soviet Union. I studied a lot of that in my undergrad days very familiar with the impact of central planning. China there’s this illusion that there is no central planning in China but we’re seeing with the kind of blow-ups in the financial sector that there is actually central planning in China.

 

And if you look at the steel sector you look at commodity consumption, these sorts of things it’s a big factor of china still, right? So but it’s incredibly inefficient spending. It’s an incredibly inefficient way and again it’s a market that is begging for an open economy because they could really grow if they were open but they’re not. They have a captive currency they have central planning and so on and so forth.

 

Now I know some of the people watching, you’re going to say you’ve never been to China, you don’t understand. Actually I have spent a lot of time in China, okay? I actually advise China’s Economic Planners for about a year and a half, almost two years on the belt and road initiative. So I’ve been inside the bureaucracy not at the high levels where they throw nice dinners. I’ve been in the offices of middle managers for a long time within the Chinese Central Government so I understand how it works and I understand the impact on the economy.

 

DL: Don’t you think it’s interesting though that despite the evidence of what you just mentioned. And how brutal it has been because it’s multiplied by 10. How many units of debt are required to generate one unit of GDP in a little bit more than a decade? Don’t you find it frustrating to read and hear that what for example the United States needs is some sort of central planning like the Chinese one. And that in fact the the developed economies would be much better off if they had the type of intervention from from the government that China has?

 

TN: Sure, well it’s it’s kind of the fair complete that central bankers bring to the table. I have a solution. We need to use this solution to bring fill in the blank on desired outcome, okay? And so when central bankers come to the table they have there’s an inevitability to the solution that they’re going to bring. And the more we rely on central bankers the more we rely on centralized planning. And so I’ve had so many questions over the last several years, should the us put forward a program like China’s belt and road program, okay?

 

We know the US, Europe, the G20 nobody needs that, okay? Why? Because Europe has an open market and great companies that build great infrastructure. The US has an open market and although European infrastructure companies are better. The US has some pretty good companies that build infrastructure in an open market. So why do we need a belt and road program? Why do we need central planning around that? And we can go into a lot of detail about what’s wrong with the belton road and why it’s not real, okay? But that type of central planning typically comes with money as the as kind of the bait to get people to move things. And so we’re already doing that with the FED and we’re already doing that with treasure with money from the treasury, right?

 

And if you look at Europe you’re doing it with the ECB. You’re doing it with money from finance ministries. The next question is, does the government start actually taking over industries again? And you know maybe not and effectively in some ways they kind of are in some cases. And the real question is what are the results and I would argue the results are not a multiplier result they are a divisor result.

 

DL: Absolutely. Absolutely it is we saw it for example. I think it’s, I mean painfully evident in the junk plan in Europe or the growth and jobs plan of 2009 that destroyed four and a half million jobs. It’s not easy to to achieve this.

 

TN: You have to try to do that.

 

DL: You have to really really try it, really try.

 

I think that you mentioned a very important factor which is that central banking brings central planning because central banks present a program of monetary easing of monetary policy. And they say well we don’t do fiscal policy but they’re basically telling you what fiscal policy has to be implemented to the point that their excuse for the lack of results of monetary policy tends to be that the that the transmission mechanism of monetary policy is not working as it should. Therefore because the demand for credit is not as much as the supply of money that have invented. They say, well how do we fill in the blank? Oh it has to be government spending. It has to be for planning. It has to be so-called infrastructure spending from government.

 

You just mentioned a very important point there is absolutely no problem to invest in infrastructure. There’s never been more demand for a good quality infrastructure projects from private equity, from businesses. But I come back to the point of of central banks and a little bit about your view. How does prolonging easing measures and maintaining extremely low rates affect these trends in growth and in these trends in in productivity?

 

TN: Well, okay, so what you brought up about central banks and the government as the transmission mechanism is really important. So low interest rates Zerp and Nerp really bring about an environment where central banks have forced private sector banks to fail as the transmission mechanism. Central banks make money on holding money overnight, that’s it. They’re not making money necessarily or they’re not doing it to successfully to impact economies. They’re not successfully lending out loans because they say it’s less risky buying bonds. It’s less risky having our money sit with the Fed. It’s less risky to do this stuff than it is to loan out money. Of course it’s less risky, right? That’s goes without saying.

 

So you know I think where we need to go with that is getting central banks out of that cycle is going to hurt. We cannot it… cannot hurt, well I would say baby boomers in the West and and in Northeast Asia which has a huge baby boomer cohort. Until those guys are retired and until their incomes are set central banks cannot take their foot off the gas because at least in the west those folks are voters. And if you take away from the income of that large cohort of voters then you’ll have, I guess I think from their perspective you’ll have chaos for years.

 

So you know we need to wait until something happens with baby boomers. You tell central banks and finance ministries or treasuries will kind of get religion and what will happen is behind baby boomers is a small cohort generally, okay? So it’s that small cohort who will suffer. It’s not Baby Boomers who will suffer. It’s that small cohort who will suffer. It’s the wealth of that next generation that Gen x that will suffer when central banks and finance ministries get religion.

 

So we’re probably looking at ten more years five more years of this and then you’ll see kind of… you remember what a rousing success Jeff Sax’s shock therapy was, right?

 

DL: Yeah.

 

TN: So of course it wasn’t and it’s you know but it’s gonna hurt and it’s gonna hurt in developed countries in a way that it hasn’t hurt for a long time. So that kind of brings to the discussion things like soundness of the dollar, status of the Euro that sort of thing. I think there are a lot of people out there who have this thesis. I think they’re a little early on it.

 

DL: Yeah, I agree.

 

TN: So economists you know these insurance people see it from a macro perspective but often they come to the conclusion too early. So I think it’s a generational type of change that’ll happen and then we start to see if the US wants the dollar to remain preeminent. They’re going to have to get religion at the central bank level. They’re going to have to get religion at the fiscal level and really start ratcheting down some of the kind of free spending disciplines they’ve had in the past.

 

DL: Yeah, it’s almost inevitable that you’re in a society that is aging. The net prison value of bad decisions for the future is too positive for the voters that are right now with the middle age, in a certain uh bracket of of age. Me, I tried the other day my students I see you more as the guys that are going to pay my pension than my students. So yeah…

 

TN: But it’s you and me who will be in that age bracket who will pay for it. It’s the people who are 60 plus right now who will not pay for it. So they’ll go through their lives as they have with governments catering to their every need, where it’s our age that will end up paying for it. So people our age we need to have hard assets.

 

DL: Absolutely.

 

TN: You know when the time comes we have to have hard assets because it’s going to be…

 

DL: That is one of one of the mistakes that a lot of the people that follow us around. They they feel that so many of the valuations are so elevated that maybe it’s a good time to cash in and simply get rid of hard assets, I say absolutely the opposite because you’ve mentioned a very important thing which is this religious aspect that we’ve that we’ve gotten into. And I for just for clarity would you care to explain for people what that means because…

 

TN: I say get religion? I mean to become disciplined.

 

DL: I know like you because that is an important thing.

 

TN: Yes, sorry I mean if anybody but to become disciplined about the financial environment and about the monetary environment.

 

DL: Absolutely because one of the things that people tend to believe when you talk about religion and the the state planners religion and and central bank’s religion is actually the opposite. So I wanted to write for you to very make it very clear. That what you’re talking about is discipline you’re not talking about the idea of going full-blown MMT and that kind of thing.

 

TN: No. I think if there is if there is kind of an MMT period, I think it’s a I don’t think it’s an extended period. I think it’s an experiment that a couple of countries undertake. I think it’s problematic for them. And I think they try to find a way to come back but…

 

DL: How do you come back from that because one of the problems that I find when people bring the idea of well,  why not try. I always, I’m very aware and very concerned about that thought process because you know I’ve been very involved in analyzing and in helping businesses in Argentina, in Hawaii, in Brazil and it’s very difficult to come back. I had a discussion yesterday with the ex-minister of economy of Uruguay and Ignacio was telling me we started with a 133 percent inflation. And we were successful in bringing it down to 40 and that was nine years.

 

TN: Right. So, yeah I get how do you come back from it look at Argentina. look at Zimbabwe. I think of course they’re not the Fed. They’re not you know the EU but they are very interesting experiments when people said we’re going to get unhinged with our spending. And we’re going to completely disregard fundamentals. Which I would say I would argue we are on some level disregarding fundamentals today but it’s completely you know divorced from reality. And if you take a large economy like the US and go MMT it would take a very long time to come back.

 

DL: Absolutely.

 

TN: So let’s let’s look at a place like China, okay? So has China gone MMT? Actually, not really but their bank lending is has grown five times faster than the US, okay? So these guys are not lending on anything near fundamentals. Sorry when I say five times faster what I mean is this it grew five times larger than the bank lending in the US, okay? So China is a smaller economy and banks have balance sheets that are five times larger than banks in the US. And that is that should be distressing followers.

 

DL: Everybody say that the example of China doesn’t work because more debt because it’s growing faster what you’ve just said is absolutely critical for for some of our followers.

 

TN: Right, the other part about China is they don’t have a convertible currency. So they can do whatever they want to control their currency value while they grow their bank balance sheets. And it’s just wonderland, it’s not reality so if that were to happen there are guys out there like Mike Green and others who look at a severe devaluation of CNY. And I think that’s more likely than not.

 

DL: Yeah, obviously as well. I think that the the Chinese government is trying to postpone as much as it can the devaluation of the currency based on a view that the imbalances of the economy can be sort of managed through central planning but what ends up happening is that you’re basically just postponing the inevitable. And getting a situation in which the actual devaluation when it happens is much larger. It reminds me very much. I come back to the point of Argentina with the fake peg of the peso to the dollar that prolonging it created a devastation from which they have not returned yet.

 

TN: Right. And if you look at China right now they need commodities desperately, okay? Metals, they need energy desperately and so on and so forth. So they’ve known this for months. So they’ve had CNY at about six three, six four to the dollar which is very strong. And it was trading a year ago around seven or something like that. So they’ve appreciated it dramatically and the longer they keep it at this level. The more difficult it’s going to be on the other side. And they know it these are not stupid people but they understand that that buying commodities is more important for their economy today because if people in China are cold this winter and they don’t have enough nat gas and coal then it’s going to be a very difficult time in the spring for the government.

 

DL: And when you and coming back to that point there’s a double-edged sword. On the one side you have a currency that is out to free sheet are artificially appreciated. On the other side you also have price controls because coal prices are limited by the government. And therefore you’re creating on the one hand a very big monetary hole and on the other hand a very big financial hole in the companies that are selling at a loss.

 

TN: That’s true but I would say one slight adjustment to that things like electricity prices are controls. When power generators buy coal, they buy that in a spot market, okay? So coal prices have been rising where electricity prices are highly regulated by the government this is why we’ve seen blackouts and brownouts and power outages in China. And why it’s impacted their manufacturing base because they’re buying coal in a spot market and then they’re having to sell it at a much lower price in the retail market.

 

And so again this is the problem with central planning this is the problem with kind of partial liberalization of markets. You liberalize the coal price but you keep the electricity price regulated and if you don’t have the central government supporting those power plants they just blow up all over the place. And we’ve seen the power generators in the UK go bankrupt. We saw some here in Texas go bankrupt a couple years ago because of disparities like that and those power generators in the UK going bankrupt that’s the market working, right? So we need to see that in China as well.

 

DL: Yeah, it’s a very very fascinating conversation because on the other hand for example in Europe right now with the energy shortage we’re seeing that a few countries Spain, France, etc. are actually trying to convince the European Union, the European Commission to try to get into a sort of intervened market price in the in the generation business. Which would be just like you’ve mentioned an absolute atrocity very very dangerous.

 

TN: This creates a huge liability for the government.

 

DL: It creates a massive liability for the government. This is a key point that people fail to understand the debate in the European union is that, oh it’s a great idea because France has this massive utility company that is public. And therefore there’s no risk it had to be bailed out twice by the taxpayers. People tend to forget that you’re paying for that.

 

TN: But again this is what’s that block of voters who doesn’t really care about the impact 10 or 20 years down the road. That’s the problem. There’s a huge block of voters who don’t really care what the cost is because the government’s going to borrow money long-term debt. And it’s going to be paid back in 10 or 20 years and the biggest beneficiaries of this and the people on fixed incomes they actually don’t care what the cost is.

 

DL: Yeah, yeah exactly, exactly. There’s this fantastic perverse incentive to pass the bill to the next generation. And that obviously is where we are right now. Coming back to the point of the infrastructure plans and the belt and road plan. What in your view are the the lessons that we must have learned or that we should be learning from the Belgian road initiative?

 

TN: So here’s a problem with the Belton road and I had a very candid discussion with a senior official within China’s NDRC in probably 2015 which was early on, okay? And this person told me the following they said the Belgian road was designed to be a debt financed plan. What’s happening now, and again this was six or seven years ago, very early on in the in the belts and road dates. They said the beneficiary countries are pushing back and forcing us to take equity in this infrastructure, okay?

 

Now why does that matter well the initial build out of infrastructure is about five percent of the lifetime cost of that asset, okay? So if you’re if China is only involved in the initial build out they’re taking their five percent, it’s a loan and they get out. If they’re equity holders in that let’s say they’re 49 equity holders in an Indonesian high-speed rail then they become accountable for part of that build-out. And then they have to maintain the other 95 of the cost for the next 30 to 50 years. So they thought they were going to be one and done in and out. We do this infrastructure we get out they owe us money and it’s really clean what’s happened is they’ve had to get involved in the equity of those assets.

 

And so I’ve since had some uh government officials from say Africa ask me what do we do with the Belton road with china? Very simple answer force them to convert the debt to equity, okay? They become long-term involved on a long-term basis. They become involved in those assets and then they’re have a different level of interest in them in the quality maintenance and everything else but they’re also on the long-term basis accountable for the costs.

 

So they don’t just build a pretty airport that and I’m not saying this necessarily happens but they don’t just build a pretty airport that falls apart in five years, okay? They then have to think about the long-term impacts and long-term maintenance costs of that airport, right? And so but you know the original design of the Belton road was debt financing. Mobilizing workers and so on and so forth what it’s become is a mix of debt and equity financing. And that’s not what the Chinese government has wanted.

 

So I’ve been telling people for three or four years the Belton road is dead, okay? And people push back me and say no it’s not, you know think tank people or whatever. But they don’t understand the fundamental fact of how the Belton road was designed it was designed as a one-and-done debt financed infrastructure build out it’s become a long-term investment all around the world. So it’s a different program. It’s failed, okay?

 

They’re not going to make the money they thought yes they’ll keep some workers busy but they’re not going to make the money they thought. All of those assets, almost all those assets are financed in US dollars, okay? So they’re not getting their currency out. It’s not becoming an international unit like they had hoped. They’re it’s not they’re not clean transactions and so on and so forth. So this is what’s happened with the Belgian road. So the lesson learned is they should have planned better. And they should have had a better answer to you become an equity owner. And uh

 

I think you know if any western governments want to have kind of a belt and road type of initiative. They’re going to have to contend with the demand from some of these countries that they become equity owners. And I think that’s a bad idea for western governments to be equity owners in infrastructure assets so you know this is this is the problem.

 

Japanese have taken a little bit different because of where the Yen is and because of where interest rates are in Japan. Japanese have basically had kind of zero interest or close to zero interest on the infrastructure they’ve built out. And so they haven’t gone after it as aggressively as China has. They’ve had a much cleaner um structure to those agreements. And so they’ve been, I think pretty successful in staying out of the equity game and staying more focused on the debt financing for their infrastructure initiatives.

 

DL: Oh, absolutely big lesson, big lesson there because the we see now that the vast majority of those projects are impossible to the debt is impossible to be repaid. There’s about 600 billion dollars of unpayable debt out there. And we also have the example from from the internationalization of the French, Spanish, Italian companies into Latin America that they fell into the same trap. They started with a with a debt-financed infrastructure build type of clean slate program that ended up owning equity. And in some cases with nationalizations hopefully that will not…

 

TN: And watch for debt to equity conversions in these things. It’s good. There’s going to be huge pressure because the Chinese say the exit bank the CDB. A lot of these organizations are going to be forced to convert that debt to equity and then unload it on operating companies in China. They’re not going to want to do it but we’re going to start to see more and more pressure there over the next couple of years.

 

DL: Great! Well I’m absolutely convinced that will happen. Tony, we’ve run out of time so it’s been an incredible conversation lots of things that are very very interesting for our followers. We will give all the details to follow you and to get more information about your company in the details of the of the video. And thank you so much for your time. I hope that that we will be able to talk again in a not too distant future.

 

TN: Thank you Daniel. Anytime. Thank you so much.

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Supply Chain Innovation, Transformation, and Sustainability

How can leaders and finance teams enable business growth, innovation, and resilience through supply chain management (SCM) and digital transformation? And, how does sustainability affect supply chains? To answer these questions, we spoke with Jon Chorley, Chief Sustainability Officer and Group Vice President of Oracle, and Tony Nash, CEO & Founder of Complete Intelligence.

 

This video interview first appeared and originally published at https://www.cxotalk.com/video/supply-chain-innovation-transformation-sustainability on April 17, 2021.

 

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The conversation includes these topics:

 

Jon Chorley is group vice president of product strategy for Oracle’s supply chain management (SCM) applications and leads the team responsible for driving the business requirements and product roadmaps for these applications. Chorley is also the chief sustainability officer for Oracle.

 

Tony Nash is the CEO and Founder of Complete Intelligence. Previously, Tony built and led the global research business for The Economist and the Asia consulting business for IHS (now IHS Markit).

 

 

Show Notes

 

Michael Krigsman: We’re discussing supply chain innovation and transformation and sustainability with Jon Chorley of Oracle and Tony Nash of Complete Intelligence. Jon, tell us about your role at Oracle.

 

Jon Chorley: I run the supply chain management strategy group at Oracle, responsible for our overall investment priorities and directions for our supply chain solutions. I also have the chief sustainability officer role at Oracle where I help coordinate all of our sustainability policies and practices for the Oracle Corporation and help drive some of those ideas and thoughts into the products and services we deliver to the market.

 

Michael Krigsman: Tony Nash, tell us about the focus of your work.

 

Tony Nash: Complete Intelligence, we’re a globally integrated and fully automated artificial intelligence platform for cost and revenue proactive planning. We do forecasting for enterprises and markets in areas like continuous cost budgeting, continuous revenue budgeting, automation of certain, say, forecasting tasks. We also offer agile budgeting and forecasting.

 

We measure our error rates, so that’s important that someone is planning, especially around supply chain. We’re trying to help people reduce the risks around their future costs.

 

Supply chains are very complex: time, cost, quality, all sorts of considerations. Our focus is on the cost element of it, and there are many other things and why we’re working with Oracle. They have so many other things to bring to the table that try to complement them on that side.

 

Michael Krigsman: You met Jon through the Oracle startup program. Just briefly tell us about that.

 

Tony Nash: Oracle for Startups program is a fantastic way for early-stage companies to integrate with the Oracle ecosystem. There is the Oracle technology product side of it, but there is also meeting people like Jon, meeting people like his colleagues, and the Oracle marketing team, Salesforce, and product teams. Amazing opportunities to understand how an organization like Oracle works and how a company like Complete Intelligence can come alongside them and enhance Oracle’s end customer experience for the better.

 

 

How did supply chains function during the disruptions of 2020?

 

Michael Krigsman: Jon, during the last year, supply chain became a household topic for pretty much everyone.

 

Jon Chorley: Yes.

 

Michael Krigsman: What did the last year tell us about the nature and the reality of supply chains?

 

Jon Chorley: Well, that they’re central to everything that makes the modern world. When you see an empty shelf and realize it’s an issue with the supply chain. Or you see a run on a product as some shortage or some challenge in some way. People now understand that the complicated infrastructure that brings those products to them is the supply chain.

 

As we’ve gotten into the more recent months where we’re looking at the vaccine distribution, people understand that yes, it’s a technical problem to produce the vaccine, but it’s also a supply chain problem to get it in people’s arms.

 

All of those things, I think, have helped take the supply chain from the back office, from the folks like Tony and I who work in it day-to-day, into the board room, which I think is very important. But also into the dining room. People now understand the importance and centrality to efficient supply chains.

 

Michael Krigsman: Jon, give us some insight into the kinds of weaknesses that this last year exposed in how we handle supply chains.

 

Jon Chorley: I think that there are a couple of areas there that I’d point out. One is we had a very uncharacteristic demand shock. There was a real change in short-term demand.

 

Some of that was upside. A lot of charcoal sold to power the grill. A lot of toilet paper.

 

Some of it was downside. Restaurants challenged, hospitality, and so on.

 

Those demand shocks forced people to look at different ways to look at their traditional forecasts. That is supportable by the kind of technology Tony and I can help deliver, but it does require people to look carefully at how they’re forecasting their demands. That’s one angle.

 

Another angle, I would say, is the overall concern about resiliency. A lot of folks looked at ways of single sourcing, for example. Maybe relying on goods out of Western China, for example.

 

All of those things had a lot of challenges, and that forced people to look at, was the single-sourcing strategy driven by cost only the right answer? Did they need to look at A) maybe simplifying their product lines a little bit, so they had more flexibility, and B) looking at alternate sources of supply? I think resiliency came a lot more to the fore.

 

Tony Nash: We’ve had even companies like semiconductor companies (who have been based in Asia) start to build facilities in the U.S. so that they can regionalize some of those supply chains and de-risk the downturn impacts of future shocks like this. Electronics manufacturers, other people who are assembling goods, or even some primary goods, are regionalizing their supply chains so that they don’t see huge impacts or any future issues like COVID or other shocks.

 

There’s at least a little bit of a buffer by region, which saves. It’s greener in terms of saving on the sea freight fuel and that sort of thing, but it also helps cushion any shocks on the supply side so consumers can get what they need when they need it.

 

 

Challenges associated with overseas manufacturing operations

 

Michael Krigsman: Jon, I’ve heard you talk in the past about the inherent challenge of manufacturing goods overseas (in China, for example) and the timeliness of getting them here in the U.S.

 

Jon Chorley: It has a lot of advantages in terms of costs, scale, and so on. But it does bake into your supply chain a certain fixed amount of time. That is fine if you have predictable demand. But if you have variable demand, it becomes a lot trickier to manage.

 

The same is true really of the innovation cycles. The speed with which you may want to innovate can be constrained by working those things from points of consumption (let’s say Europe, North America) and points of production (let’s say the East, China, Vietnam, and so on). Those are factors folks are considering.

 

I think, in some areas, certainly advances in things like automation and technologies like 3D printing, rapid prototyping, those things are changing the equation a little bit in terms of what constitutes the most cost-effective or the most efficient, or the most responsive approach to manufacturing. I think you’re going to see those factors gradually have more and more of a play as people develop new ways to balance those equations.

 

Tony Nash: Michael, that’s interesting because, as we look at how the history of supply chains have evolved from keeping POs on 3×5 notecards 30 years ago to the digitization of that, it started with EDI (electronic data interchange) from, say, the ocean lines and the airfreight firms so that you knew where your package was, all the way down to today where you have everything kept, let’s say, in a bill of material within an ERP system or a supply chain system.

 

What people have been doing for the past few years is really bill of material versioning, where you’re running scenarios on the same product configuration, of bill of materials for multiple locations, to understand where they should make a certain good. Those considerations are allowing people flexibility. They can make the time and cost tradeoffs to look at when they can have goods in a market, whether it’s seasonality or whether it’s some disruption or whether it’s some demand pop for some reason people may not know. Allowing people to run multiple bills of material or versions of bills of material allows them the flexibility to identify what they should produce where and what it should be made of.

 

Michael Krigsman: It sounds like this is a data and analytics problem.

 

Tony Nash: It is, and the way things have been done typically is, as a manufacturer, you sign a longer-term agreement for your raw materials with a vendor. They provide that for you to a certain point. You make it in factory A somewhere and then ship it out. Of course, there is not necessarily a single factory for any large company, but it’s a well-worn path.

 

We’ve had an atomization of that with mini manufacturing, or regional manufacturing, flexible manufacturing, so people can have localized versions or, like I said, seasonality. These sorts of things. Manufacturing and finance teams can only make those types of decisions with data and with automation. It’s a simpler way on how to make better business decisions.

 

 

Digital tansformation and sustainability in supply chain

 

Michael Krigsman: You need clarity around the goals and the strategy. You need the right kinds of data. Then you need the cultural willingness to innovate and do things differently. Is that an accurate way of summarizing?

 

Jon Chorley: I agree. I think you need to have some idea of where you’re going. Although, that probably is going to change. But you need to have that idea. You need to have the information, as Tony has discussed, that helps you navigate that path.

 

Then you need to be able to course-correct because we live in the real world, and nothing quite goes the way you expect it to. You need to be able to constantly course-correct.

 

Like I say, if you have a great set of headlights, you can see what’s coming. You’re coming to a cliff. If you have no brakes and no steering wheel, it’s a huge problem you’d rather not know.

 

The ability to course correct is like having brakes and a steering wheel. You need to be able to make those adjustments as things change around you. That means flexible systems, flexible processes, a willingness to look at new ways of doing things, cultural changes. All of those things become important.

 

Michael Krigsman: Tony, I have to imagine you spend a lot of time thinking about the sources of data as well as the machine learning models and other types of models that you create.

 

Tony Nash: I get excited about things like data governance, but most people don’t. I get excited about it because I understand that it helps to have much better forecasting applications and tools to make those decisions.

 

Yes, we’re thinking about the granularity, the frequency, the level of detail people have. Are they using the data that they have to make decisions today because it’s not just, let’s say, a cultural change of let’s rely on automation of things like forward-looking views or forecasting or proactive planning? It could also be a cultural change: are we looking at our data to make our decisions? How much of our data are we looking at? Are we looking at maybe the error rates of the way we plan? Are we looking back on that from time to time?

 

Although that may seem mundane and small, it’s actually very big for things like digital transformation because you have to take inventory of what you’re doing today so you can plan where you’re going tomorrow. As Jon said, it’s never going to go exactly to plan – never. I wish it would, but it never does. You have to understand yourself well today so that you can identify what’s possible.

 

Michael Krigsman: Jon, we’ve been talking about the complexities of supply chain. Let’s shift gears slightly and talk about the complexities of sustainability. How does sustainability intersect supply chain?

 

Jon Chorley: Most people would agree that supply chains are about making and moving physical goods around the world. That is a huge part of what’s impacting the environment. It’s a huge impact on sustainability.

 

The way we design those supply chains, historically, has been what I would call a linear supply chain. Which is we make a product, we sell a product, we forget the product. We then make another product, sell that product, and forget that product. It’s a fire and forget mentality, if you like – to some degree.

 

If we want to be sustainable, we need to think about the full lifecycle of those products and how they get recycled back into the forward supply chain. As we progress into the future and start thinking about these things more — and we’re required to by the markets, by regulations (potentially), and by what constitutes good business — we will increasingly move towards adjusting our supply chains to be more circular. That is, looking at the full lifecycle of the product.

 

That begins with how you design it. That’s going to be a fundamental change in the way we think about all supply chains.

 

Advice on supply chain transformation for business leaders

 

Michael Krigsman: As we finish up, Tony, can you offer advice to business leaders and finance teams who are listening to this who say, “Yes, we want to change, transform our supply chain, but where do we even begin? It’s such a daunting challenge.”

 

Tony Nash: I would say, really start with the easy stuff. Get some successes. Do a pilot. Then you can accelerate it very quickly.

 

Data scales very quickly. Technology scales very quickly. But your team may be uncomfortable with digital transformation, especially around supply chains. Help them see some quick wins and then push forward as quickly as possible after that.

 

Michael Krigsman: Jon, you discussed earlier the cultural dimensions of supply chain transformation. It’s really important, so just share some further thoughts on that and advice that you have for folks who are listening.

 

Jon Chorley: I think any change is at least as much cultural as it is technological, and the people who implement those changes are key to its success. I think part of what’s needed is a willingness to understand that the way you did things in the past may not be the way you need to do things in the future.

 

Quite often companies, for example, feel that they have a certain special way of doing a process that’s absolutely required, and they hold onto that even though there is really no business differentiation for them to do it that way. They’ll invest a lot of time and energy to duplicate that on a new platform.

 

We always encourage people to step back a little bit and leave behind some of those preconceptions. Not everything is your secret sauce. Your secret sauce is a little bit on the top. It’s not stuff on the bottom.

 

Leave behind those preconceptions. I think that’s probably the single biggest cultural shift.

 

Then the other point we mentioned earlier is board support. I think that’s top-down. Having that support from the upper levels of the business is critical to any large-scale transformation.

 

I think the great thing, if there is a great thing from 2020, is that boards are aware now of the criticality of supply chains in their business and are probably more open to those kinds of conversations. Those difficult conversations from supply chain professionals with their board. Now is the time. The folks that make the investments now are the folks who are going to benefit from the uptick that we all hope is coming.

 

Michael Krigsman: Jon Chorley and Tony Nash, thank you both for sharing your expertise with us today.

 

Jon Chorley: All right.

 

Tony Nash: Thanks, Michael.

 

Jon Chorley: Thank you so much. Great talking with you all.

 

Tony Nash: Thank you.