You Learn From Good Businesses and Bad Trades

Plus! ByteDance, Clubhouse, China, Italy, Cloud, Facebook, more...

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You Learn From Good Businesses and Bad Trades

I’ve learned a lot from failure. Specifically, I’ve learned a lot  about how it’s incredibly unpleasant and should be avoided. From the  vantage point of the comfortably successful or the  not-directly-participating, the cost of a graceful pivot or a quick decision to cut  losses sounds like tuition. Actually experiencing it is a painful grind,  which is why I’m a big fan of learning from other people’s mistakes and missed opportunities.

But which? There’s no shortage of information out there, but time is  limited and you want to focus on the higher-order bits. The heuristic  I’ve found is:

Bad Businesses are Overdetermined

Annoyingly, there’s intense selection pressure in financial media for stories about bad businesses and good trades.

I’ve written about the business books:  they tend to get written when there’s a source and an axe to grind, and  they’re more entertaining when there’s a moral arc. A failure like  Enron or Theranos is a Greek tragedy, with the hero undone by hubris.  But a successful business is not a classical narrative: the standard Hero’s Journey doesn’t end with the hero as a middle-aged rich guy with a vacation home in Aspen.

Financial media report on big wins and big losses, but since the  target audience for financial media is people who want to get rich,  there’s more emphasis on the wins. (If you wanted to not-get-poor  instead of staying rich, you’d probably focus on your day job and put  your money in some kind of all-weather or 60/40 passive portfolio. If  you’re reading the Journal or FT, you probably have  more ambition.) One piece of evidence for this is that one of the worst  trades of all time was selling credit protection against AAA-rated  tranches of private-label mortgage-backed securities. And there are  writeups of this trade—about the winners.

Why Success In Business is Informative

A successful companies a) has the right idea, b) executes better than  the competition, and c) avoids every company-killing mistake. This is  practically tautological, but it’s a sort of Pigeonhole Principle for  narratives: if there were several companies trying to win in social,  search, streaming video, operating systems, CRM software, etc., but only  one company truly won in that space, then it probably avoided doing  something its competitors did wrong.

The idea is important, too. Idea value is paradoxical, because the market value  of ideas is low—you can’t sell one, and the closest you get to a market for ideas is patent  litigation. But ideas are a limiting reagent. Fortunately, founder-idea  fit tends to click early; a founder will try out a few disparate  businesses, but eventually one of them will take off, and in retrospect  it will be clear that the idea that worked was the cleanest version of  the thesis behind the failures. In the very early days, Facebook focused  more on a file-sharing product called Wirehog than on Facebook itself.  But the point of Wirehog was to connect people online, by way of the  files they had. As digital cameras got more common, the sharing loop  lost a step: instead of create, save, upload, the process was  create-then-share, and file-upload part was superfluous.

Google had a product before they had a business, but the core idea of  the product was that the structure of the Internet had latent  information about the meaning, relevance, and trustworthiness of every  single page. AdWords ultimately used the same logic: a second-price auction  is just a way to aggregate data on every company’s incremental pretax  profit from a click, and charge them an amount as close to that as  possible.

Amazon started with books, because they had few wholesalers, many  units, and plentiful metadata. Searching by title, author, genre, etc.  was a perfect application for the web circa 1994, but Amazon knew from  the beginning that books were the easiest place to start, but not the place to stop.

Successful founders like to talk about early failures and pivots, but  that’s mostly a feel-good exercise. Some companies were founded by  first-time founders, who may or may not have gotten lucky. Bill Gates  and Mark Zuckerberg both founded companies before the ones they’re known  for: Gates had a traffic analytics company called Traf-o-Data, and Zuck  had a music player called Synapse. These look like failures compared to  Microsoft and Facebook, but: both early failures were founded by high  school students and had some measures of validation (Traf-o-Data got  government contracts; Synapse turned down a million-dollar offer). If  that was all they had done, they’d look like failures compared to how  successful they are, but they’d have been minor legends at their  high schools.

A great company is a bit like a great novel or work of art: there’s an idea, followed by a lot  of effort to make the finished product reflect the original vision.  Sometimes, that requires backtracking and refining. But it’s with a  definite goal in mind.

By contrast, failures don’t tell you much, because too much went  wrong. Enron is my single favorite failure. Here’s a list of plausible  reasons they died:

Importantly, any one of these problems could have killed the  company. Enron had some good businesses, some bad ones, and some fake  ones. Even if they’d never actively cooked the books, or done so less  aggressively, the combination of leverage, growth, and intra-management  conflict would have eventually done them in, or a bad quarter’s trading  could have prompted a liquidity death spiral.

Good and bad businesses judgment are correlated: someone like Bill  Gates, who knew that there would be a computer on every desktop decades  before it happened, was also the sort of person to negotiate smart deals  with commodity PC companies, and also the kind of person who  would contribute code years into his tenure as CEO. And at Enron, a  chairman who would countenance levered high-risk investments in  politically-controversial developing world deals was also the sort of chairman who wouldn’t notice that a separate Enron division had sent California back to the dark age.

Short sellers like to say that accounting problems are like  cockroaches, and they’re right. The best predictor of a big accounting  scandal is a series of small ones (Enron had a smaller fraud in their  trading division in the late 80s). But that means that you never know every mistake they made, just the ones where the coverup was insufficiently thorough. In an interview with Tim Ferriss,  Peter Thiel describes failures as overdetermined: “You  will  think  it   failed  for  Reason  1,  but  it  failed  for  Reasons  1 through  5.   And  so  the  next  business  you  start  will  fail for  Reason 2, and  then for 3 and so on.” Reading about failures business failures is fun,  but successes are the ones that give you new information.

Bad Trades Are Bad Luck Compounded By Cognitive Bias

The informative nature of bad trades is another proof-by-negation:  you just don’t learn much from making money. A trade is a prediction,  and if your prediction comes true, that’s great! You saw the future! But  that also means that when you’re right, it’s because conventional  wisdom converged with your view, so now you believe the conventional  wisdom.

It’s a kind of strange feeling to get the trade right. You feel good,  and then you immediately feel like you’re back to zero; in the  double-entry sense, a profit is just shifting balance sheet entries from  “valuable insight” to “cash of equivalent value.” Finance is a domain where you can sell an idea, though that’s an iterated game. In a sense, the structure of a hedge fund is a form of DRM, a way  to sell IP (trading ideas) in a format that can’t be easily duplicated and shared.

Countless investors have talked about how easy the job is when they’re right

In Soros on Soros, George Soros says:

More generally, running an investment portfolio is not  work in the ordinary sense of the word. It is something else. It is risk  taking. The amount of work you need to do is inversely related to your  success. That is to say, if you are working at what is normally  considered work, if you are a salesman or a craftsman, your success is  directly related to the amount of work you put in. The more you hammer  away, the more goods you produce; the more customers you visit, the more  orders you are likely to take. There is a direct relationship.  When you are taking risks, if you make the right judgment, if you have  the right insight, then you don’t need to work very hard. But if you  make a mistake, and there is a divergence between your hypothesis and  the actual course of events, you need to do some really serious research  to find out what’s wrong. The less successful you are, the  more you’re going to have to work to correct the situation. If the  portfolio is doing well, you’ll have to work less. There is an inverse  relationship.

Here’s Buffett: “Time is the friend of the wonderful company, the enemy of the mediocre.”

And Phil Fisher: “[F]inding the really outstanding companies and  staying with them through all the fluctuations of a gyrating market  proved far more profitable to far more people than did the more colorful  practice of trying to buy them cheap and sell them dear.”

And Edwin Lefèvre, probably paraphrasing Jesse Livermore:

It never was my thinking that made the big money for me.  It always was my sitting. Got that? My sitting tight! It is no trick at  all to be right on the market. You always find lots of early bulls in  bull markets and early bears in bear markets. I’ve known many men who  were right at exactly the right time, and began buying or selling stocks  when prices were at the very level which should show the greatest  profit. And their experience invariably matched mine—that is, they made  no real money out of it. Men who can both be right and sit tight are  uncommon.

So there you have it. Of the four great investing traditions: macro, value, growth, and degenerate gambling, everyone agrees that holding on to winners is a good idea, which means that getting rid of bad investments is the hard part.

It’s hard for typical cognitive-bias reasons: most people are averse  to taking losses, and anxious to get to breakeven. And past a certain  point, more information leads to higher confidence rather than better  judgment. Combine these two factors, and someone who makes a bad trade  is psychologically primed to justify it and factually well-prepared to do so.

At the start of this year, I had a large (for me) position in  European dividend futures. It did not go well, and I exited the trade at  a substantial loss. I did a fair amount of research beforehand,  got reasonably confident that it was a sensible risk/reward decision,  and plunged in. The futures represent a bet on a single year’s dividends  from an index of European stocks; when the future matures, it pays out  at that dividend—so while it can be mispriced, you at least know when  the gap between expectations and reality will go away.

For a while, things went well: the general cadence of the trade is  that the dividend futures gradually appreciate over time, but drop by  more than they fundamentally should when the market declines. So I could  follow a fairly lazy strategy: buy and hold, and buy the dip. I listed a  bunch of specific risks to the trade in my writeup, but there was one  risk I alluded to as a side comment and totally ignored in practice: “A  lot more can go wrong in Europe by year-end 2021 than year-end 2020.” As  it turns out, this was not just an obvious point about the fact that  time moves in one direction: it was also an admission that a single catastrophic event could cause dividends in one year to suddenly collapse.

So I learned something from that trade: think about the exact scenario you’re betting on, and think about the precise  ways that scenario could change. “Dividend futures” are an abstraction,  but any one year’s contract is a bet on what happens in that specific  year. Now that we live in 2020, The Year Of Rescaled Y Axes, I’m aware  of that.

A more concrete lesson is to use stop-loss orders. A stop-loss is an  order to sell something you own if it drops or to buy something you’re  shorting if it rises. I’m deeply philosophically opposed. If you thought  the stock was cheap at $20, and you precommit to automatically selling  your position at $18, that’s totally irrational. What I’ve realized is  that stop losses are irrational in a fixed, known quantity; you’re  committing to buy high and sell low. But anyone with a losing position is arbitrarily irrational. A stop loss order is a put option on your own self-destructive insanity.

A stop-loss is not a portfolio-management tool; it’s a psychological management tool. What you should do with every position is ask yourself if you’d  size up the trade at the current price, and exit if the answer is no. In  practice, that’s a hard question to answer accurately when you’re down.  Many of the best investors do sell their losers, but usually because  the stock has dropped in response to fundamentals and they’ve recognized  this. (Anchoring bias is real, and momentum effects persist: stocks are  a little more likely to underreact than overreact to important news, so  the stock that looked cheap at $20 might be expensive at $18.) Value  investors generally don’t use stop losses, but the difference between  bad ones and great ones is that the great ones don’t keep adding to  positions when they don’t work out; a bad value investor is someone who  ends up with 100% of their money in Kodak, which had a low P/E all the way  down to zero.

Looking at my trading records, the really surprising thing to me is  that I mostly didn’t do anything as futures prices plummeted. It would  have been more intellectually honest to buy some, and lose even more  money, but I didn’t do that. So that’s a lesson I’ll be remembering: for  every trade I have on, I should ask myself if, given an unrelated cash  windfall, I’d be adding to it right now. If the answer is no, time to  exit the trade.

That’s easy to type because I’ve heard it before many times. But now I really know it.

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Elsewhere

I recorded another podcast  with Erik Torenberg of Village Global. Topics include: MMT,  institutional trust, journalism, pensions, and Bitcoin. And on Coindesk,  some thoughts on Paul Tudor Jones' Bitcoin trade.

ByteDance

The Information has a great writeup on TikTok parent company ByteDance, with a focus on their advertising plans. ByteDance has been an incredible platform for creators:

And that growth comes from ably matching content to viewers—exactly the sort of skill that applies directly to targeting ads.

Clubhouse

Meanwhile, the US can still produce novel social apps. Clubhouse  has a tiny userbase unless you measure it by Twitter followers or  assets under management. It looks like a goofy novelty, but it might be  something more important: there’s been a persistent pattern in social  media of tighter feedback loops between posting content and getting  feedback. Static sites have a very slow feedback loop; blogs with  subscription tools are faster; Twitter is faster than that, but Snapchat  is close to instantaneous since it makes sense for users to get a push  notification every time there’s something new for them to see. Clubhouse  found a way to make it even faster, by making it real-time only. You can get  last night’s Snap or last year’s tweet, but Clubhouse content expires  immediately.

This also has privacy implications, at least for the everyday threat  model of having something you said taken out of context long after the  fact. Unless someone else in the room is recording you, your Clubhouse  comments vanish as soon as you’re done talking.

What’s China Up To?

It’s always sad when a beloved compounder pulls guidance: China is not setting a GDP growth target for 2020.  This makes perfect sense. There’s no reason for them to stretch to hit  the number when much of what they need to do won’t positively impact  GDP.

That said, there are some interesting datapoints coming out of China recently. For example, the FT reports that China is streamlining its iron ore import process.  Iron is an input into infrastructure, and infrastructure spending is  China’s classic approach to stimulating the economy during demand  shortfalls. They used a lot of rebar in 2009 to keep unemployment low.

But in post-industrial-revolution terms, they still have catching up to do: Semiconductor Digest notes that China is well behind its Made In China 2025 goals  for semiconductor production. And, more damning: China-headquartered  firms are only responsible for 39% of semiconductor production in China.  This is an ironic reversal of Shenzhen’s enduring importance. China is  hard to displace as a manufacturing hub because their human and  institutional capital in that area is tied to big machines that are hard  to move; if you duplicated every factory in Shenzhen and put them  somewhere else, you’d have no hope of competing, because you wouldn’t  have the managers, purchasing agents, and engineers who keep the system  optimized. The same force may make it hard for China to succeed in  semiconductors: they can certainly supply the capital, but if the right  engineers are in Hsinchu and Hillsboro, that’s just a stranded asset.

The Cloud

At a time when capital and free time are abundant, and out-of-home  entertainment is hard to come by, cloud computing is a very good bet.  The FT has an overview  of cloud competition in Asia. One notable takeaway: outside of China,  four of the top five cloud computing companies in Asia are American.  Within China, four of the top five are Chinese, and one is an Amazon  joint venture.

Cloud economics are ideal for the panic-then-relax cadence of  Covid-19. They’re the fastest way to get to arbitrary scale for a surge  in demand—but a forgotten S3 bucket or EC2 instance is basically an  annuity. Cloud spending is a complement to engineering spend, but has  the added benefit that when layoffs happen, companies lose the one  person who knows whether the data they’re paying to store is essential  or not. So cloud spending is not just a bet on Internet usage, but a bet  on volatility within the Internet sector.

Meanwhile, overall IT spending trends look bad, but have stopped getting worse.

Facebook Goes More Remote

Facebook has announced  that it’s adding hubs in cities like Dallas and Atlanta, and that the  company could be 50% remote in a decade. I maintain that residential  real estate in superstar cities will be less impacted than other cities  by this: it’s harder for two-earner couples to leave, and proximity to  the main office still matters even if it’s a bit further away.

Facebook also plans to adjust salaries based on cost of living. Since  Facebook is still hiring, they’re a price-setter for engineering  talent, meaning that tech workers will be teleported back about half a  century, to a time when your standard of living was the same whether you  lived in the Midwest or California, but you could choose CA because the  weather was better.

Italy’s Financial Engineering

The government of Italy plans to make equity investments in major Italian companies.  Can they afford to? In a sense: “Under European accounting rules, such  financial holdings have no impact on countries’ budget deficits.” As it  turns out, the EU’s rules set countries up for some very interesting  rogue trading. They can fund themselves by borrowing from retail investors,  whose savings are up due to typical recession saving patterns, and up  more because so many spending venues are unavailable. They can transfer  this money to companies, which will keep workers on payroll, and those  workers will continue to save a larger share of their incomes, which  they can reinvest in more government debt. The payoff function for the  Italian government is favorable: if Italy needs a bailout, the size of  the bailout will be based on the size of the fiscal hole they’re in. If  they don’t need a bailout, the investments will appreciate.  Looking at the transactions, this policy is funded by Italian savers.  But looking at the outcomes, it’s funded by France and Germany. (Who are  both willing to cut checks.)