Category: Economy

Posted on

Sveriges sak är inte vår

Rare post in Swedish: Det här är några utkast från min senaste krönika hos Vontobel. Läs resten där om du antingen hatar makro, älskar makro eller struntar i makro.

—

Följer du med i makrostatistiken? Varför? Är du en av alla de som noterar när siffror på konsumentpriser, BNP och räntor publiceras, och då funderar i några minuter över vad det kan betyda för börsen? Då är du en av alla makroturister som trampar gata upp och gata ner genom halvbekanta miljöer och tar hundratals fotografier som ingen någonsin kommer titta på.

—

Är BNP-statistiken något du kan använda i din aktiehandel? Inte direkt, men makroturister tar ändå gärna fram kameran ur magväskan och lägger några bortkastade minuter på att få en perfekt bild som ingen bryr sig om.

—

Skulder och arbetsmarknad leder oss till inflation och räntor. En sak är att hög sysselsättning ger ökad risk för fallande sysselsättning och BNP, men eftersom kopplingen till börsen inte är särskilt tydlig kan kanske de flesta ignorera statistiken. Men, hög sysselsättning medför ofta stigande löner och övriga priser — konsumentprisinflation.

—

Det är precis sånt här som kan få en makroanalytiker att tänka efter lite extra. Det var ekonomisk kris 1991-1993, 2001-2003, 2008-2009 och det sammanföll med hög inflation. Det gick 8-10 år mellan kriserna och nu är inflationen på gång igen 8-10 år efter den senaste krisen och perioden med inflation över Riksbankens mål. De flesta BNP-prognoserna för 2018 ligger på omkring 2,5% och för 2019 på strax under 2,0%. Riktningen är rätt, men som vanligt tar prognosmakarna nog i för lite (kul ändå att Ekonomistyrningsverket tror på så lite som 1,4% BNP-tillväxt 2019).

Läs resten här (japp, jag får betalt) och kolla in alla makrobilder på sysselsättning, inflation, skulder mm

Posted on

The first quadrillion dollar company won’t be Amazon, Apple or Alphabet

It’s not hip to be square

-popular commercial ca. 5000 BC

Do you remember when that round, friction-minimizing thingamajig was all the rage in the tech space? That was fun, albeit a bit slow moving in the beginning.

Of course, many were skeptical at first as always. But once Salpeter Steel invested in Squares With Supermany Corners, more and more Stoneage VCs showed interest. However, it wasn’t until the name change to We Have Efficient Enormous Load Relievers the business really took off.

After Peter Steel’s success in the WHEELR industry he turned his focus to the struggling start-up Hot As Hell But Still Good For Cooking And Scaring Wolves Away Inc.

“It doesn’t quite roll off the tongue all that easily”, he thought… not to mention the hassle carving it in stone entailed. He let his mind wander: “Four letter words are always popular. Maybe you should try something on F?”, he suggested to the founders Fred, Isla, Rose and Ember.

And on and on it went, until the famous: “Plastics” comment in the 1967 movie “The Graduate”. Little did they know plastics would soon be demonized by hippies and greens, while the real action would turn to semiconductors, computers, software, mobile phones and other information technology companies.

Topic: Hot technologies in the past, present and future; companies with names beginning on “A”

Discussion: The Singularity Is Near, but how should you invest on the way there?

Conclusion: One word: “Agents”. We could move away from P&P companies that own our data, our portals and more or less force products down our throats; to owning digital autonomous copies of ourselves (the company making those could become the largest in the history of corporations by several orders of magnitude) that finally relieve us of the paradox of choice without relinquishing control to Big Data corporations.

Railways and radios

There was a time when steam engine powered water pumps for coal mining were the only game in tech town. Railways and cars then stole the limelight, not to mention radios (now, that was crazy at a whole new level) and airplanes.

That was, however, just “technology”, not information technology. Once Turing set things in motion after deciphering nazi codes with his version of computers, and possibly indirectly contributed to solving equations underlying the first atom bomb, a whole new industry was born with IBM in pole position.

IBM’s president Thomas Watson had a vision of the future:

“I think there is a world market for maybe five computers.”

Thomas Watson, president of IBM, 1943

A companies

The IT industry has progressed through mainframes and minis to Personal Computers, separating and celebrating hardware vs. software, and a whole stack of layers of operating systems, databases, applications etc. The workload has shifted from central (mainframe) to local (PC) to central (minis) to local (PC, laptops) and central (mobiles vs. cloud) again. The stock market has shifted its focus (a.k.a. ‘hype’) from semiconductors to computers to operating systems to applications to databases to business intelligence to browsers to search engines to network equipment (from data to voice and back to data again), to phones and minimalistic small applications known as applets or apps, not least social media apps.

Where is it all headed? Let’s just take a look at a few randomly selected companies in alphabetical order: Alibaba, Alphabet, Amazon and Apple. The first and most obvious conclusion is that names on ‘A’ are more successful than other companies. But we’ve known about that since Salpeter Steel’s first service business back in 5000 B.C.: AAA Wheeler Tow and Sons.

Jokes aside, the secret sauce is knowing your customer and having access to his attention and wallet, as well as products to sell. The best companies have tons of intelligence on its customers for crafting the perfect pitch, and an addictive portal to control the flow of products and services:

It’s all about the platform and the pitch

During the Mad Men era in the 1960s, a pitch consisted of convincing customers your bland and commoditized product was better than the competition’s. Today deep learning algos instead tease your core preferences from your largely unintended data radiation and satisfy your every want and need perfectly.

Alphabet’s search engine Google controls your attention and sells it to the highest bidder. Amazon knows about everything you buy, when and in what combinations — it controls both the platform and the pitch. Apple does the same, albeit in the form of a handy little gadget that enables recording and sharing as well, and that is placed one step before Amazon and Google. In China, WeChat is even more dominant with a billion Chinese users spending 5 hours a day on the platform.

What’s next? AR contacts, 3D printers, robotic companions?

The highest valued businesses harvest your data, sell it or reverse engineer your utility function to pitch increasingly addictive products. The actual manufacturers of most products and services have taken a back seat to the “portal” companies.

At the risk of predicting the equivalent of the Internet collapsing under its own weight within a year, or nuclear powered vacuum cleaners, here goes some of my thoughts about the remainder of the 21st century in tech.

Contact lenses and bionic limbs

Analyzing and hooking clients will only grow in importance, but the portals will morph into something quite different. Mobile phones will become increasingly mobile/wearable and gradually fuse with the body, perhaps in the form of contact lenses enabling seamless Augmented Reality and Virtual Reality experiences; perhaps through neuronal interfaces pioneered by the prosthetics industry. Nota Bene that there’s already touch feedback bionic limbs available, not to mention rudimentary AR contacts. There are even eye implants that restore some sight to the blind.

Will Apple be able to hold its own when the “phone” hardware becomes so different from today’s fragile glass bars? A robotics or biotech company could very well be better equipped to take the lead in that scenario — or Apple could try to take them over.

The HMI era, the portal of the future

In any case, Human Machine Interface technology will be crucial whatever form it takes. Today’s crude Finger And Voice input methods won’t last long, except for particular situations that don’t require precision.

Intention Readers, Emotion Detection Systems, Autocorrect Deduction Devices (that combine gestures, voice (words + tone), facial expressions, blood flow, heart beat, breathing etc. to guess and anticipate your desires) and so on will replace keyboards and touch screens. All these technologies already exist by the way.

What about existing search and retail platforms? Hard to say, it depends on what the H-M Interface companies decide. They could choose to connect directly to the end products, or they could uphold the status quo and go through Google and Amazon.

—

There is a whole different set of solutions to the HMI problem: digital and real world agents, wholly owned by you, that gradually mimic your every trait (don’t worry, you’ll be able to edit out unwanted evolutionary mismatched psychological biases). Rather than letting Facebook, Cambridge Analytica, Alphabet, Netflix, Amazon, WeChat, Alibaba and others know everything about you and abuse that information, you can elect complete anonymity but let your own proprietary agent know exactly everything and in effect turn into an exact copy of you. Your agent could over time assume more and more responsibility, from booking tables at restaurants to shopping for groceries and clothes.

In the beginning your agent might merely suggest a few alternatives, and as its precision improves you allow it to only show the single best one, then make preliminary bookings and finally just hand you the goods, reservations and tickets: “Your Uber will arrive at 7:48 tomorrow morning. The alarm is set for 7:19. Your face and iris scan are valid as your flight ticket. You’ll be staying at your usual hotel”

William Gibson wrote about such agents (eventually becoming self aware) in his epic book Neuromancer from 1984. I see the development of such artificial “helpers” as all but inevitable, leaving us ample time to explore both our inner and outer worlds and experience the human condition to its fullest.

Purely digital agents might be the end station. They would receive input from our every move and interaction with the world. The internet of things guarantee we are always recognized, our activities gauged, categorized and the corresponding data securely transmitted to our digital copies roaming the net hunting down optimally tailored experiences for us. A simple RFID implant could do some of the tracking, but otherwise every single item we face would be the eyes, ears, LIDAR, X-ray vision, Ultrasound etc. of your agent’s.

Quadrillion dollar co.

What happens if you own your own data yourself, and your agent doesn’t need the “help (prying eyes)” of search engines and entertainment suggestion algos to sift through billions of choices? Amazon gone? Apple gone? Alphabet gone? Would end product companies stage a comeback, based on highest quality and best price/performance rather than highest portal visibility and most nefarious data scraping abuse?

And, will the Agent company become the first quadrillion dollar company?

Runway to sublimation (a popular post Singularity state)

In David Simpson’s most recent book, The Dawn Of the Singularity, Simpson envisions more or less every household buying or leasing humanoid robots; androids that are quite similar in function to Gibson’s digital agents, albeit in physical form.

Four billion robots at a clip of 1000 USD/month for the basic version and upgrade subscription can turn into serious money over time, in particular valued at 5 times sales. Higher priced versions, upgrades, and using the robots themselves as portals for other goods and services easily increase the numbers by a factor four, and voilà!

I can definitely imagine such robots as both part of the input function for reverse-engineering their owners, and as platforms for showing off your wealth (complementing your car and boat). Once household androids become useful enough, just picture the “Joneses” pitching their robots against each other in terms of best finish, speed, balance, range of functions, intelligence, model and not least price.

“Mom, why is our android so slow and old?”

“I just got back from the Joneses, and they’ve just bought the HuBot2028 LAL. Maybe it’s about time we upgraded ours too”

Summary – what to do?

Biotechnology, artificial biology, active nanotechnology (molecular replicators and molecular-sized computers and robots) in contrast to today’s inert nano materials; strong general artificial intelligence (and its current predecessor, deep learning), robotics, quantum computing, bionic limbs, AR/VR and various forms of entertainment etc. are all promising tech areas today.

Add in the potential of immersive computer games, sex robots, designer drugs — or a combination of all three and it’s easy to imagine an interesting near future. The question still remains, however, which companies will emerge as winners in this race. On the one hand, IBM, Alphabet, Amazon, TenCent, Alibaba, Apple and Netflix all have interesting AI software and quantum computing embryos, but on the other, all that research money doesn’t stop history from repeating with altogether new start-ups making the crucial inventions.

I would bet some money on each and every one of all the mentioned companies, but I would be even more ready to invest in new, truly innovative robot and AI companies, if I get a chance before they sell out to the FANGs.

Fortunately, you don’t have to get rich betting on the right digital agent company. The future will be bright enough just having access to them as a consumer; just as standard shipping containers have made us all rich without any of us ever having owned the company that invented them.

Talking about investments, wouldn’t it be cool if our agents could perform financial analysis? They could find out everything, and, if allowed, talk to other agents in as large groups as we grant authority. Thus we would actually know the sales and likely profits, thus enabling optimal investments. Brokers, gone! Portfolio managers, gone!

Interesting you say, but: bah, no robots, no agents, I just want to see what next year’s iPhone looks like.

Posted on

Without precise definitions you end up in forecast hell

Topic: Imprecise definitions lead to useless models and conclusions

Discussion: If you’re performing macroeconomic research, which inflation are you talking about, which growth, which interest rate? The answers to those questions can be of crucial importance for your eventual investment outcome.

Length: Short — maybe 5 minutes reading time

Teaser: It’s easy to predict the weather. Not to mention stock market returns

PODCAST TIP: listen to my latest podcast episode (#6) on Future Skills with philosopher Alexander Bard. We talk about definitions of infantile grown-ups and much much more. Check it out on iTunes here, or your favorite Android app here.

Dream Warriors

Are you dreaming about making perfect economic forecasts, and using them for producing amazing equity investment returns? How does this sound to you:

Weather and production bottlenecks in combination with monetary policy induced growth are starting to cause higher commodity prices. Inflation is already showing in their wake. People worry about rising interest rates, just take a look at OIS spreads. Some central banks are turning less dovish. Higher interest rates means less funds for investments, lower growth, lower profits and lower share prices. Higher interest rates mean lower bond prices, higher borrowing costs, lower real estate prices among other things. Higher inflation means money loses its value. And this time it’s at a time you can’t hide in stocks or bonds. You could hide in gold. One bar of gold is always one bar of gold. Maybe that’s why the gold price in dollars is rising (despite obvious manipulations and jaw-boning from various authorities).

Does the above fit your view? Higher commodity prices => higher interest rates => sell stocks, bonds and real estate and use cash to buy gold and soft commodities, until the cycle turns again?

Well, hold your horses for just a little while.

What’s your definition of a boombastic jazz style?

Which commodity prices are you talking about exactly, when you say their prices are rising? Wheat, hogs, orange juice? Iron ore, coffee, cacao? Silver, cobalt? Platinum, palladium?

Similarly, which interest rates are you referring to? The Fed funds rate? Treasury bills, longer term bond market rates? Corporate bond rates, bank lending rates (to consumers, to corporate clients, to house builders?), peer to peer lendning rates? Intrabank market swap rates?

Oh, I almost forgot, “inflation” you said. Would that be the (ever manipulated and ever changing) CPI measure? Or the PPI gauge? Input our output PPI? How about house price inflation numbers? Or energy price inflation? Avocado prices?

My point in this post is that if you don’t clearly define exactly what variable you are talking about it becomes exteremly difficult to make any kind of coherent analysis, not to mention draw any practical conclusions whatsoever from the exercise. Macroeconomic research is difficult enough as it is without averaging everything together, whether it be “the inflation”, “the interest rate”, “the oil price”, “the stock market” or “GDP”.

Take that last one, e.g., GDP. What does Gross Domestic Product really tell you? What conclusions can you draw from it even if you knew its exact trajectory going forward a few quarters? How about nominal GDP vs. real GDP (using which deflator measure?), or GDP per capita? Then there are data series for wages, wage growth, hours worked, hourly wages, lost jobs, added jobs, seasonal adjustments (many orders of size larger than the actual net number), employment (measured in at  least three different ways depending on, e.g., how to define somebody without a job, based on whether he’s searching for a job or doesn’t care).

And what’s so special about GDP growth by the way? There’s zero useful correlation between real GDP growth and stock market returns. How about a house price recession like the one that began in 2006, several years before the ‘actual’ recession. Don’t even let me begin to talk about the NBER’s definition of a recession (no it’s not “two quarters of contracting real GDP”

 

“a significant decline in economic activity

spread across the economy, lasting more than a few months,

normally visible in real GDP, real income, employment, industrial production,

and wholesale-retail sales.”

 

No matter the problems of making macroeconomic models work at all, you don’t want to make it even harder by using impractical and vague definitions. My message in this post is that you need to make sure your definitions are practical and at least theoretically can lead to better decisions.

After that we can talk about the ephemeral character of macroeconomic causations and correlations, not to mention their flimsy associations with actual stock market behavior.

For now take this with you: Knowing what you know and knowing what you don’t know, is paramount in uncertain environments. And the financial markets are as uncertain and stochastic as they come.

Thus, make sure you do define all concepts and ideas about their connections precisely. That’s your only chance of keeping track of what you know and what you don’t. In addition, it’s your only fair chance of creating a feedback loop of increasing knowledge and strategy adaption.

Such directed or deliberate practice is in similar fashion your only chance of coming out on top in the arguably most competitive sport known to man (and yet untrained newbies gladly step into the ring and bet their life saving’s on themselves).

Today’s advice holds true for everyday life as well. I don’t know how many arguments with friends I could have saved, had we only defined the concepts and words precisely at the outset…

Stock market forecasts coming up

I’ll soon write a follow up on this article, where I’ll explain how stock market returns can be reliably forecast in much the same way as the weather can be accurately forecast. For now this teaser will suffice:

Just as I know there’ll be snow in the middle of Sweden on quite a few days every year between December and February, and almost completely certainly no snow 99 per cent of the time between June and August; a highly priced market will produce very low rates of return, and a lowly priced will produce high rates of return over the coming decade or so. But more detailed notes about next time and the exact implications for the current situation. Stay tuned.

FUTURE SKILLS: Don’t forget to check out the super energetic conversation with Alexander Bard on Future Skills Ep. #6! You’ll find it on iTunes here, or your favorite Android app here.