Tag: musing

Foreword

The world is ablaze with talk and speculation on Artificial Intelligence (AI), with untold billions of dollars poured into AI related companies.

As usual, a reminder that I am not a financial professional by training — I am a software engineer by training, and by trade. The following is based on my personal understanding, which is gained through self-study and working in finance for a few years.

If you find anything that you feel is incorrect, please feel free to leave a comment, and discuss your thoughts.

Words

Before we delve into the topic, a very brief history of AI and its related fields in the form of a mini glossary:

Artificial Intelligence (AI)

The term AI was originally used to mean intelligence that rivals that of the average human, and which was created by humans (i.e. not natural). The formal study of reasoning and how it can be applied via computer science began in the 1940’s, and over the decades, many have herald the imminent emergence of AI. Over the decades, many have been wrong about the imminent emergence of AI.

Machine Learning (ML)

ML is a term specific to computer science, and describes a field of study that encompasses many different technologies. ML is differentiated from AI in that while it aspires to human-level intelligence, it is mostly concerned with how to make machines (specifically computers) “smarter”, to learn and to improve.

Artificial Neural Network (ANN)

ANNs are an application of ML, i.e. a technology created as part of the study of ML. First invented in the 1970’s, ANN seeks to mimic the human brain by defining computing nodes akin to human neurons. These “neurons” are then connected to each other via a hierarchical layer structure. Each “neuron” operate independently of the others in the same layer, and all “neurons” in the same layer get input either directly or transitively from “neurons” of prior layers, with the final “neuron(s)” being the output of the entire network.

ANNs, and a close cousin Hidden Markov Models (HMMs), have been used very successfully over the decades in various applications, providing impressive results.

There are various specializations of ANNs, such as Deep Neural Networks (DNNs, aka Deep Learning), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), with each preferred for their own niches of ML.

Large Language Model (LLM)

Large Language Models (LLMs) encapsulate many of the features of the above, and a lot more not discussed, and have been extremely successful in producing almost human-levels of prose. Recent (circa 2023) developments in LLMs have resulted in the ability to generate not just text, but also other contents such as graphics and video. These latest developments are coined Large Multimodal Models (LMMs), and today, the terms LLM and LMM both generally refer to the latter.

The simplest (and somewhat crude) way of describing a LLM is that it is a statistical prediction model — given a stream of input such as words, what would be the most likely output (such as words of a prose, or pixels in a picture).

Grade inflation

Today, the term AI is used interchangeably with ML, or more specifically LLMs. This is despite the fact that most practitioners agree LLMs are not quite on par with human-level intelligence. To differentiate human-level intelligence and what LLMs are capable of today, a new term Artificial General Intelligence (AGI) was coined to refer to what AI used to mean.

I’m more of a traditionalist when it comes to definitions, and am not a fan of this marketing gibberish and rampant grade inflation, so for the rest of this post, I will be using AI to mean the original definition above.

State of the art

As mentioned above, as of this article, LLMs are extremely capable of producing prose and visual content that is at almost human level. In particular, many have found great success with using LLMs to produce code (also called Vibe Coding), where the user uses spoken English as input to the LLM in order to get it to produce (hopefully) useful source code, such as in C++ or Python.

However, as LLMs are not really thinking models, but rather statistical prediction models, they are prone to errors, sometimes called hallucinations. Nobody really understands what causes hallucinations or what they mean, but my personal take is that it is an artefact of the LLM being fed inputs that it simply hasn’t encountered in training before, and in such a way that its interpolation or extrapolation of the input results in output that is simply garbage.

There are techniques to working around hallucinations, such as feeding the output back into the LLM and asking it to rework or rethink through its response, effectively a mulligan. However, this is generally only applicable when the user can recognize that the output is wrong — if the user simply does not have the ability to evaluate the correctness of the output, then chances are the user will just end up believing the garbage.

Practical concerns

Despite the hype of the imminent emergence of AI, I am less optimistic. But even without AI, ML (and LLMs) can be of practical use. There are many areas where it is fairly easy for the user to verify the correctness of LLM output, such as when vibe coding (if the code compiles, and if the code does what the user wants), prose generation (user can read the prose) and graphics/video generation (look and see!), and in these respects, LLMs have proven to be huge time savers and productivity boosters.

Central to these use cases is the cost benefits analysis — if it is more cost effective to have a human produce the content, then it doesn’t make sense to have LLMs do it. Today, the cost to end-users of using LLMs is pretty cheap, with pricing around single to low double digit dollars per million tokens, where you can think of a token as somewhat like a “sub word” (some words cost one token, others may cost more). With the Lord of the Rings trilogy being less than 500k words, a million tokens is plenty!

And this is where hallucinations come in. The problem with the above paragraph is that while in the ideal scenario, you can almost produce the Lord of the Rings trilogy with a million tokens (~$10!), the reality is that you’ll likely end up with garbage — hallucinations tend to creep in pretty quickly, and without constant human supervision to read the output and force the LLM to redo/rework parts of the output, it simply won’t be very good.

In the end, in order to produce 500k words of actually useful and entertaining prose, the user may end up spending many millions of tokens, with most of them wasted due to hallucinations. More importantly, a lot of human time will be consumed reviewing and effectively redoing the LLM’s work.

Whether this is worth it depends a lot on the project at hand and the user’s circumstances. As an example, for someone who is not trained in computer science, being able to just produce any code would be a huge win. But for a trained and seasoned software engineer, the benefits can be much more dubious, especially when you are considering mission critical code where subtle bugs and security implications are not obvious and the inexperienced will simply not be able to even realize these issues need more thought/work.

My take

My personal take is that as things stand, LLMs are not going to lead to AI, absent dramatic changes to the fundamental basis of LLMs, such that whatever comes out will not look very much like the LLMs of today.

At their core, LLMs and all other related statistical prediction models are generally very good at predicting the “next thing given a series of things”, but are pretty terrible at innovation and making decisions based on novel situations — both critical components of intelligence as most would understand the word. Recall from the examples above, that a human supervisor is still needed to ensure correct output!

In my opinion, LLMs may very well form parts of AI, if and when it emerges, but won’t be the whole. Instead, I have been toying with the idea of a “meta model” — essentially a “thinking” and “decision making” layer on top of a bunch of other “doing” models.

For example, the “meta model” may decide that “for the problem at hand, we need to generate an essay about AI”, and hand the task off to a “doing” model that is based on an LLM. Or it may decide that in order to generate that essay, the LLM needs to be supervised by a more generalized RNN model that researches the output of the LLM to validate correctness in an iterative process — in this case, the RNN model, possibly with aid from the “meta model” will do the work of what the human supervision would be doing today.

This paradigm has benefits — the “doing” models can be specialized to various tasks for which they are best suited. Many tasks required of humans can be easily graded as “right” or “wrong”, and it is in these well defined problem spaces that LLMs thrive. Building bespoke models tailored to specific tasks has, historically, been an extremely successful approach in ML.

What, then, would be used to build this “meta model”? Honestly, I have no idea. If I were to guess, it’d be some hybrid of HMMs and ANNs. HMMs are good at predicting things based on internal, unobservable states and ANNs are good at predicting things based on historical correlations. But that’s just me.

Finance

What does any of the above have to do with finance? This is, after all, a finance blog! Well, a lot.

There are many avenues to invest in LLMs, from NVidia stock (still the gold standard of GPU chips, a critical component of building and running LLMs), to private investments in various LLM startups, to datacenter providers (GPUs need to be put somewhere), to hyperscalers that provide a more hands-off approach to dealing with the hardware.

The key to remember is that as of today, no LLM startup is profitable — all of them are burning money like there’s no tomorrow. Even more concerning, many businesses renting out the hardware for building and running LLMs are arguably in the red, with dubious progress toward profitability. Pretty much the only companies reaping outsized profits in this space are NVidia, and a handful of smaller energy and component suppliers. Actually running the hardware or LLMs has been extremely disappointing from a PnL perspective, despite the huge amounts of investments made.

No hope?

That’s not to say that there’s no hope. With enough research, it might be possible to pivot an LLM startup to actual profitable use, for example by focusing in a vertical where LLMs are particularly suited.

Also, somewhat more cynically, speculating in LLM companies can be profitable for investors if the companies get acquihired by larger players not so much for their products, but for their human talents, in a giant game of greater fools.

It is somewhat ironic that in the business of AI, the most valuable products thus far appear to be the humans.

Weekend video binge

To end, a trio of videos from notable people in the field discussing AI and LLMs. Enjoy!

Foreword

The world is in a bad place today — war rages on continental Europe, a phenomenon that hasn’t occurred for almost 80 years. The middle east is bathed in conflict, with Israel locked in fierce combat with its neighbors and Iran, while multiple less publicized conflicts wage across many parts of Africa. In total there are well over 100 armed conflicts currently in the world.

As usual, a reminder that I am not a financial professional by training — I am a software engineer by training, and by trade. The following is based on my personal understanding, which is gained through self-study and working in finance for a few years.

If you find anything that you feel is incorrect, please feel free to leave a comment, and discuss your thoughts.

Distasteful

Before we begin, I must admit that I find war distasteful. It is a depressing state of affairs, when one group of human beings thinks that the best, or only, way to resolve their grievances, is by the wanton destruction of properties, and the violent taking of lives of other human beings. It should not have to be this way!

But it is. Unfortunately, I do not have the power to unilaterally fix things and avoid war, so there is only the next best thing to do — plan and prepare for how it may affect me.

This is, primarily, a finance blog, and so we will focus on the finance and economics of war. Parts of this post may be distressing to some, and distasteful to many, but this post is only about the finance and economics of the situation. My apologies in advance.

USA

I also want to note that I am American, and so this piece is USA-centric. The USA enjoys many advantages that other countries simply do not — having control of the global reserve currency, having a large land mass relative to population, having a relatively large population, a vibrant economy, (relatively) stable politics, abundant natural resources, etc.

Some of the arguments below may not apply to other countries which do not enjoy these same benefits.

Recession

There has been a steady drumbeat of people calling for recession, and some of them point out to the various large scale conflicts in the world today, and how some of them are likely to drag (or have already dragged) the USA into them, and how these conflicts will drain the resources of the USA and lead to a recession.

The situation, I think, is far more complicated than that. (Un)fortunately, war is not always bad for the economy.

Take for instance War World 2. The USA officially entered the war in December 1941, and the war ended officially in September 1945. These are the graphs of the S&P500 and DJIA, with that time period highlighted (not exact, eyeball estimate), courtesy of Macrotrends:

As you can see, despite the rhetoric, the stock markets actually went up, and significantly during the period of time.

What gives?

As we’ve discussed early, war is terrible. It leads to the wasteful and mindless destruction of properties and lives. However, things need to be put in perspective.

During World War 2, the battles were almost entirely fought outside of American soil. As a result, other than Pearl Harbor and various military installations/equipment around the world, the USA actually did not suffer much property losses. Those countries that did suffer such losses, such as much of continental Europe, many parts of Asia, and parts of Northern Africa, did indeed see dramatic economic and financial suffering — their means of productions (power plants, factories, industrial vehicles, offices, etc.) were damaged or destroyed, and that naturally has a huge and negative impact on productivity. But the USA mostly escaped that fate.

Separately, the war effort needs to be financed. Soldiers need to be trained, equipped and paid, military hardware needs to be procured. All of these result in a transfer of wealth from the government to the private sector, in the form of payments for services or products, salaries to the soldiers, etc.

The fact of the matter is, a recession is, first and foremost, an unwillingness of participants in the economy to spend, leading to a dramatic drop in the velocity of money (i.e. how fast money gets spent, earned and then re-spent). If there is a large entity with effectively unlimited wealth, such as the US government, willing to spend huge sums of money, then how can a recession happen?

Another important thing to note is that after the war, the USA was the largest unscathed country. It thus naturally enjoyed the benefits of, almost literally, being the only country still able to produce in bulk many of the products needed to rebuild the rest of the world. Many academics have argued that in addition to the new factories largely financed by the US government during the war, this resulted in the beginning of the USA’s global financial and economic dominance.

To put it crudely, World War 2, despite its many human tragedies, was an economic and financial boon to the USA, pretty much from 1941 till today.

National debt

To be clear, the US government took on a lot of debt to finance its war efforts, and after the war, there was a period of adjustment during the late 1940’s to pay down that debt. Eventually, pain needs to be suffered if one incurs debt — either the debt is paid down slowly via small deductions over time, or all at once via severe austerity, or via default (which wipes the debt, but imposes many other penalties). Though as we have seen in more recent times, “eventually” can be very, very far off in the future.

While much of that money is wasted on destructive efforts, a lot of it was also spent on productive efforts, like the aforementioned new factories subsidized by government spending.

Germany and Japan

There are some who claim that war is profitable for the winners, and detrimental for the losers. That is also not something I agree with.

Two of the largest losers of World War 2 were Germany and Japan — Both were part of the Axis powers which lost the war. However, Germany is, and has been, the strongest economy in the European Union for many years now, and Japan was, briefly, a contender for the largest and strongest economy in the world in the late 80s, a mere 40 odd years after the war.

The key, I think, is again due to the destruction, or lack thereof — while the Axis powers inflicted much damage to the countries they invaded, they themselves suffered relatively mild property (and more importantly, productive assets) damages, as the end of the war was relatively swift compared to the length of it.

Not all fun and games

To be clear — I am definitely NOT advocating for war. It is, again, a senseless and horrific waste of resources, lives and treasures. However, I do not agree with many of the sentiments flying around that war is strictly bad economically or financially for the USA. As we can see from the largest war mankind has known to date, whether war is good or bad financially and economically, depends a lot on the circumstances.

Foreword

If you haven’t been hiding under a rock for the past ~5 months, you’ll know that there has been a bit of a kerfuffle in the regional banks, leading to 3 very dramatic bank failures, and a lot of uncertainty in regional bank stocks. What gives?

As usual, a reminder that I am not a financial professional by training — I am a software engineer by training, and by trade. The following is based on my personal understanding, which is gained through self-study and working in finance for a few years.

If you find anything that you feel is incorrect, please feel free to leave a comment, and discuss your thoughts.

Banks, why’d it have to be banks?

If you follow me on StockClubs (1), you’ll know that I basically never buy bank stocks. There is a fairly straight-forward reason for this — banks are weird. Everything about them is weird.

Because of the nature of their business, they have very different accounting rules compared to regular companies. They are also allowed ridiculous amounts of leverage. And finally, because banks are run by, well, bankers, and bankers are really good at finance, it sometimes (frequently?) occurs that the financial statements put forth by banks are, shall we say, less than transparent.

Which is to say, a bank’s books and financial statements are clear… as mud.

And because I don’t like buying things I don’t understand (2), I generally don’t buy bank stocks.

Drama

Which brings us to the happenings since the start of the year. Or really, since around the middle of last year. As discussed in “Safest Money“, essentially all banks have been taking it on the chin on their portfolio of bonds and loans, due to the rapidly increasing interest rates. This is particularly true for regional banks, because they tend to hold the loans on their books.

Holding loans with 2-3% interest rates on your books are good when you are a bank and can get capital (via deposits or via borrowing from the Fed) for 0-1%. You pay out 0-1%, you take in 2-3%, and the difference you pocket.

Holding those loans when interest rates are at 4-5%, however, is not quite as good, as Silicon Valley Bank, then Signature Bank and, more recently, First Republic Bank found out to their detriment.

At the same time, the news outlets have been having a blast, blaring out scary headlines about the blow by blow of which banks are having trouble, what the Fed/FDIC officials are talking about behind the scene, and which banks may be next. Accordingly, the stock prices of essentially all regional banks have been behaving like the electrocardiogram of someone having multiple heart attacks, at the same time. All very not-pretty.

Banking

Throughout all this drama, it’s natural to ask — are banks safe?

My personal opinion is that, as a whole, the banking industry should be fine. Most people simply do not pull money out of banks and stuff them in their mattresses. Instead, most of the money withdrawn are, almost immediately, shoved into another bank — there simply isn’t enough physical cash in circulation for everyone to withdraw.

What about those folks who take their money to buy assets like stocks, bonds or money market funds, you ask?

Well, every transaction has a buyer and a seller. The buyer gets the asset, and gives the money to the seller. And the seller (generally) promptly puts the money into… a bank.

So, collectively, it is very hard for money to escape the banking system (3).

That said, while banks, as a whole, should be fine (4), individual banks may drop like flies as we’ve witnessed in the past few months, because…

Irrational

… the situation really isn’t about rationality anymore. If you have under $250k in a bank account, the probability of you losing money is essentially 0. Even if you have more than that, the FDIC has shown that they are willing to go to extraordinary lengths to prevent depositors from losing even $1. While that guarantee is not set in stone and they may relent at any time, it seems that providing the safety net, at least in the short term, may be the only way to prevent contagion, so short of some really dramatic changes to the circumstances, I don’t think the implicit guarantee is going away anytime soon.

Which is to say, most of the action in terms of bank deposit flight, seems to be mostly due to irrational fear.

As for banks’ stock prices, the past few days have been a whirlwind of crazy — it simply doesn’t make sense for (at least on paper) profitable banks’ stocks to drop 60% and rebound 100% in the span of 24hours; Either the sellers are wrong, or the buyers are wrong. They can’t both be right.

So, while depositors should mostly be OK (4), shareholders may find their shares of various regional banks turning into donuts overnight, and often with very little fundamental reasons.

Systemic?

The thing to watch out for, is if the situation becomes systemic. As some may remember, the 2008 Great Financial Crisis was essentially a banking crisis, and it was not pretty.

That said, be very careful about the scary headlines. It is true that the 3 banks that just went down were the 2nd, 3rd and 4th largest banking failures ever in US history. But that fact sounds more ominous than it really is. The fact of the matter is that the US banking system is dominated by 4 very, very large banks, and thousands of, well, not very large banks. According to Bankrate, Wells Fargo, the 4th largest bank is more than 3 times larger than the 3 failed banks combined, and JPMorgan Chase, the largest bank in the US, is almost twice the size of Wells Fargo.

To put it simply, while it certainly isn’t good that medium size banks are failing, the situation isn’t quite as dire as some are making it out to be.

Even more importantly, the underlying problems in 2008 were bad loans — back then, many banks made loans to folks who simply could not possibly pay back the loans. As a result, there was a serious solvency crisis, as well as a serious confidence crisis — nobody knew which loans were good or bad, so nobody knew who was going to take massive losses, and thus nobody trusted anybody, resulting in a deep freeze of the financial system.

This time around, the problematic loans in question are mostly Treasury bonds, literally the safest security on Earth. The issue isn’t one of solvency, nor one of confidence — every bank’s holding of Treasury bonds are reasonably well documented and nobody is really worried of a Treasury hard default. The issue is one of liquidity — the affected banks simply do not have enough cash on hand if there is a severe bank run.

Given that the underlying assets backing the banking system are mostly safe, the installation of new federal backstop via the BTFP, and the willingness of larger banks to scoop up rivals at a huge discount (5), the chances of a systemic crisis seems slim. Though, again, shareholders of some banks may find a lump of coal in their stockings in the next few months.

Footnotes

1. Disclaimer: I am an investor in StockClubs. Also, as of publication time, only 1 (out of 10+) of my brokerage accounts are linked to the app.
2. They make me feel inadequate.
3. There are some exceptions, for example reverse repo operations with the Fed, etc. But those are generally not available directly to retail investors. Also, according to the Fed, the amount of money parked in reverse repos hasn’t really moved that much since the start of the year.
4. To be clear — banks (collectively) should be fine as long as the current situation remains a liquidity and duration mismatch crisis borne of interest rate risks. If this morphs into a insolvency crisis because of bad loans, then things will be very different.
5. JPMorgan Chase’s CEO, Jamie Dimon, was noted to be bragging about the sweetheart deal to his shareholders.

Foreword

In which I come up with a possibly crazy idea….

As usual, a reminder that I am not a financial professional by training — I am a software engineer by training, and by trade. The following is based on my personal understanding, which is gained through self-study and working in finance for a few years.

If you find anything that you feel is incorrect, please feel free to leave a comment, and discuss your thoughts.

Capital Stack

As discussed before in Capital Stack, there are different ways that a company can finance itself. In particular, bonds (i.e. debt) are generally structured such that as long as the company makes periodic payments to the bond holder (and possibly making a balloon payment at maturity), the bonds remain as bonds. However, if the company fails to make payments, in most cases, the equity holders are generally wiped out (or at least lose a substantial portion of their investment), while the bond holders now become the equity holders, while the bonds themselves are generally canceled.

In a sense, bonds are just “safer equity”, or equity with a deductible — as long as the deductible is not met (the company does not do too poorly), you remain a bond holder. But once the deductible is met (the company does so poorly it cannot afford its periodic bond payments), then bond holders become equity holders.

Trading Bonds

Now, the problem with trading bonds, is that trading bonds are hard. And annoying. Unlike shares, where every share (of the same class) is identical, companies tend to issue different “series” of bonds, and each series tend to have its own slightly different rules. As a result, the number of “identical” bonds tend to be fairly large, compared to the number of types of shares, and as a further result, corporate bonds tend to have fairly poor liquidity.

Which is to say, trading corporate bonds is hard and annoying. Especially if you are not splashing 10’s of millions in a single trade… at least.

Cash

For those who follow me on StockClubs (which I have invested in), you’ll notice that pretty much since Sep 1, 2021, the portfolio I share on StockClubs is almost entirely in cash. While this is just 1 of 10+ brokerage accounts I hold, it is pretty representative of my entire stocks portfolio (1).

Instead of buying stocks, I have bought money market funds, which have been yielding from 3-5% (pretax) in the past year or so. As noted in Safest Money, right now, money market funds are one of the safest way to hold US dollars, and they provide an almost decent yield.

Puts

At the same time as holding a lot of cash (via money market funds), I’ve also been selling puts on a few select stocks that I am interested in. These are generally stocks that I feel will do well during inflationary periods.

If the stock price falls below the strike price of the put, then I will have to buy the stock at the strike price. But if the stock price is above the strike price at expiry, then the premium I collect is mine to keep with no other consequences (other than taxes, always the taxes…). If you think about it, this is, in a hand-wavy way, sort of like a bond. A synthetic bond based on the stock price instead of company cash flows, sure, but since stock price tends to follow performance, it’s close enough for me.

If I happen to get assigned, then I will just switch to selling calls against the stock position, effectively creating a synthetic put. Yes, a synthetic put that I’m using as a synthetic bond. We must go deeper!

Now, to be clear — I don’t generally oversell puts. In fact, I generally undersell puts, i.e. if the puts are assigned, I have more than enough cash to pay for the stocks — I simply have to sell off some of the money market funds. Overselling puts is equivalent, somewhat, to using leverage, but selling cash secured puts is not.

All together now

Put together, this setup means that:

1. I get a steady stream of income from the money market fund.
2. Every month or two, I sell puts/calls with expiry in the next 1-2 months on stocks I like.

The money market fund yields me 3-5% a year, while options play yields between 6-8% annualized (depends on the stock, the expiry and the strike, but I generally aim for about 5-10% out of the money).

The plan is to continue doing this, until the market makes up its mind whether it wants to go up or down, and then revisit the decision. While this strategy limits my upside (I can’t earn more than the option premiums + money market fund yields), I have almost the full downside of owning stocks outright — if the stock prices drop far enough I’ll be holding the shares at potentially large unrealized losses.

But if my guess is right — that stocks will mostly not go anywhere for a while, then there’s no upside in owning stocks outright anyway, but I still get to keep the yields from the fund and the option premiums.

Fingers crossed, let’s see where this leads us.

Footnotes

1. To be clear, this is just representative of my stocks portfolio. I also have a real estate portfolio which is entirely in… real estate. Duh.

Foreword

In the last few days of last week (week of March 6, 2023), Silicon Valley Bank (SVB) suffered a bank run. On Thursday alone, $42B of deposits fled the bank and on Friday, the bank was taken over by the FDIC. While not one of the big 4 banks, SVB was still a pretty big bank, somewhere in the top 20. In such a climate, where can we keep our money and be safe?

As usual, a reminder that I am not a financial professional by training — I am a software engineer by training, and by trade. The following is based on my personal understanding, which is gained through self-study and working in finance for a few years.

If you find anything that you feel is incorrect, please feel free to leave a comment, and discuss your thoughts.

Definitions

Before we go forward, let’s get a little bit of definitions out of the way. While many people use the words “money” and “cash” interchangeably, they are actually not the same things. “Money” refers to the entire spectrum of assets that can be used to pay for stuff. Depending on the type of money, each unit of it may or may not be worth what it says it is worth.

At one end of this spectrum, is “high powered money” or “base money”, which is money that is generally assumed to be always worth its face value — $1 is $1 is $1. A small subset of high powered money is “cash”, which is generally taken in academia to be actual physical bank notes, coins, etc.

At the other end of the spectrum, is “broad money”, which is money that is only worth its face value over time. For example, a 2year(4) Treasury bond is a form of broad money — it is worth a portion of its face value (depending on current interest rates). edit: However, when the bond matures, it will be worth 100% of its face value (assuming no defaults), regardless of interest rates, so as the bond heads towards maturity, it becomes closer and closer to narrow money.

In high finance, where large sums of money are transferred regularly, broad money is often used to settle transactions, a very stark difference to what retail users are used to, on a day to day basis, where narrow money (cash) is more common.

Silicon Valley Bank

There is more than enough coverage of SVB in the media, so I will just touch on the bits relevant here.

SVB took deposits from a lot of customers and promised the customers that the money is available on demand (i.e. demand accounts). When the money changed hands from the customer to SVB, it is transformed — if you hand over $100 in bills to a bank, you are giving the bank cash, and getting in return a claim on the bank to the tune of $100. Similarly, if you transfer $1m from one bank to another bank, you are exchanging a claim on the first bank for a claim on the second bank, both to the tune of $1m.

The FDIC provides insurances to all bank accounts per account holder to a maximum of $250k. That is, assuming the federal government does not go bankrupt, the first $250k you have in a checking account at SVB is high powered money — it is (transitively) backed by the faith and credit of the US government, and so is effectively narrow money. However, anything above that limit is technically no longer high powered money at all times. Amounts above the FDIC insurance limits is high powered money only as long as the bank itself is operating, because those limits are now a “claim on the bank”, i.e. debt owed to you by the bank.

So, in our first example, the $100 in cash you hand over is high powered money. The $100 in claim on the bank you get in return is high powered money only if it is under $250k at the bank, or if the bank is operating normally.

SVB typically takes deposits from its customers and uses those deposits to invest in US Treasuries, loans to customers, or non-government bonds (1). A large portion of these investments are long term debt, i.e. broad money. And as we know, the value of broad money is generally as a discount to its face value, and is often influenced by interest rates.

And interest rates have been rising, so the value of broad money has been declining. By a lot.

As a result, the mark to market value of SVB’s assets is below the value of SVB’s liabilities (the money it owes its customers). In scenarios like this, if a lot of customers decide to pull money out quickly, SVB will be forced to sell its assets at a huge loss, and since there’s not enough assets to cover all liabilities, if all customers want their money now, some of them will not get all their money back.

Fallout

Because of SVB’s forced insolvency, all customers with balances at or below $250k should get all their money back — the FDIC is promised that the money will be accessible on Monday (3/13) in full. However, because SVB is insolvent (i.e. assets < liabilities), there is a good chance that anyone with accounts more than $250k will lose some of the money above $250k, if they want the money now.

As far as I can tell, SVB did not commit fraud, so the money isn’t missing, it’s just a paper loss due to the change in interest rates. So, with time, as the investments SVB made matures and the debts are paid back, SVB should end up making a profit — that is, assuming all customers are willing to lock up their money until all the investments mature, then everyone should get 100% of their money back, and SVB would have made a small profit as well.

Which is to say, if the customers with accounts more than $250k are willing to wait and let the FDIC slowly unwind the investments, they should be able to get back all their money, though it may take a while, possibly a few years.

The problem is that, currently, you can invest money in short term US Treasuries (one of the safest investments around) for 3.5-5% annual returns, so why would you leave money in SVB for long periods of time just to break even? Also, a large number of customers probably need the money now to make payrolls or pay their own debts, so leaving that money with the FDIC until everything is wound down orderly may not be feasible.

This dilemma is what led to the bank run, and also what likely leads to at least some customers taking a haircut on their money. I have no real insights on how bad the haircuts could be, but I’ve heard estimates from 0-50% (the FDIC is promising to make available at least 50% of money above $250k available on Monday as well).

Crypto

No modern day discussion on money can be done without touching crypto (sadly), and so I will just say this:

SVB, a bank with over $100B went bust in an disorderly manner. Anyone with accounts <= $250k will not lose a cent, but will be inconvenienced for a few days while the FDIC is working to take over. Anyone with accounts greater than $250k will get ~50% of the amounts above $250k back, and may or may not get more back over time.

FTX, Celsius, Voyager, all bank-like crypto entities went bust in the recent past. Pretty much all their customers lost access to all their money, there are various lawsuits taking place to try and recover the assets, and so far it seems like most customers will lose more than 50% of their assets, and that’s after likely a few more years of legal wrangling.

USDC, a stablecoin administered by Circle, a US crypto entity, broke its peg and is now trading at 96c to the dollar, previously having reached as low as 85c. This is simply because for reason I cannot fathom (see below), they decided to keep over $3B (BILLION) of their cash at SVB, and a large chunk of that money is now locked up by the FDIC. This is despite the fact of numerous claims of being “audited”, and that their assets are “safe”. Before the blow up, SVB’s equity base was around $12B, and USDC’s deposit $3B of unsecured assets, earning basically 0% interest, at SVB represents 25% of that equity base, alone. That $3B also represents about 7% of all USDC in circulation before the blowup. How that is “safe” is beyond me.

BTC, the original cryptocoin that did not go bust, lost ~70% of its value in the past year or so. All users maintain access to their assets, though at the new much lower valuation.

Decide for yourself which case you prefer, certainly none of it is good.

Diversifying money

It is frustrating to me, that many companies with large treasuries keep a large part of their treasury(2) in bank accounts. The most basic job of a company’s treasurer (or CFO), is literally to keep the treasury safe. I want to say a “good treasurer/CFO”, but really, any treasurer/CFO that is not a teenager playing pretend should take into account basic things like counterparty risk, diversification of assets, etc.

For both the individuals and the corporate, there are fairly simple things you can do to diversify your liquid holdings to reduce the chance of a serious, crippling financial disaster if some counterparty goes under.

The following is purely US-centric, because I’m based in the US. Also, all the efforts noted below only reduces the risk — there is no way to entirely eliminate all risk. For example, if aliens invade the Earth and just nukes the planet to little bits, there’s really not much any of the following can do to help you. Tough.

Bank accounts vs brokerage accounts

The first thing to note, is that there are 2 types of accounts you can hold liquid assets in. The first is bank accounts, including checking, savings, CDs, etc. These accounts are administered by a bank, and deposits in these accounts represents a claim against the bank (i.e. the bank owes you the money you put in). As noted above, the FDIC provides insurance to all bank accounts up to a limit of $250k.

So, for money deposited into a bank account, as long as you are under the $250k limit, you are pretty safe — the worst thing that generally can happen, is if the bank goes under in a bad way, and the FDIC needs more time to sort things out. In that case, your money may be stuck for a few days (maybe even a few weeks!), but you should get everything back reasonably quickly.

Brokerage accounts, on the other hand, are not insured by the FDIC. Instead, they are insured by the SIPC. SIPC insures all brokerage accounts up to $500k, though only up to $250k of that can be cash. You can get around the $250k cash limit by buying a money market fund (more below), because these are securities and covered up to the $500k limit, or by buying other short term US Treasury ETFs.

Note: When opening a new banking or brokerage account, be sure to check that:

• The bank/brokerage is legitimate. There have actually been cases of scammers pretending to be small banks/brokerages and then running off with the money.
• That the account is insured and under which plan (FDIC or SIPC). Some banks actually have brokerage arms, while some brokerages have bank arms, and it’s not always obvious which arm your assets are put under from a legal perspective.
• Some brokerages provide 3rd party insurance on your assets above the FDIC/SIPC insurance limits. The details vary based on the brokerage, and for the most part, these insurances have not really been tested before. So while it’s better than nothing, these schemes may not end up protecting you 100%.

Now, in general, absent fraud or heavy losses (like SVB), the assets will be at the bank/brokerage even if it fails. So, the FDIC/SIPC will generally be able to return you all your assets, once they have time to untangle the whole mess, even above their insurance limits. The FDIC/SIPC isn’t going to make off with the excess assets once they pay out their insurance limits, don’t worry.

In the case of brokerages, the assets are actually held at a depositary institution (DTCC), which provides another layer of security — the brokerage going down just means that the SIPC needs to talk to the DTCC to get your assets back, and then go through the brokerages’ books to figure out who owns what. Again, this works only if there is no fraud — if the brokerage is secretly selling your assets to buy beanie babies or magic beans, then you’ll likely be out of luck. Note that DTCC only holds securities — cash you hold at your brokerage is generally held by the brokerage itself, or whatever bank arm it has.

Money market funds

If you hold your assets at a brokerage, then you have a separate choice to make — how do you keep the assets? While bank accounts only let you keep the assets in cash, brokerage accounts offer you the option of keeping it in cash or buying securities. If you want to maintain the liquidity of your cash, one good option is to buy a money market fund.

Money market funds are offered by many financial entities, including brokerages. You may have heard of, for example, the Schwab Money Fund (SWVXX), which currently has a 7-day yield of 4.48%, much higher than what most bank accounts offer. Note that just because you hold cash at Schwab, does not mean that your idle cash will be invested in SWVXX — you have to make the conscious decision to buy SWVXX!

In general, most funds (including mutual funds, money market funds, ETFs [exchange traded funds], private equity funds, etc.) have 3 components — there is the fund itself, which is a separate company and separate legal entity. There is a sponsor (also called general partner, administrator, manager, managing partner, etc.) who manages the fund, but does not actually hold the assets (i.e. they are not legally allowed to use the money for their own purposes), and finally there are the investors (also called limited partners, partners, investors, shareholders, etc.). In the case of our example (SWVXX), Schwab is the sponsor, SWVXX itself is the fund, and whoever buys shares of SWVXX are the investors.

Because of the sponsor vs fund setup, and again, absent fraud, the assets in the fund are typically safe even if the sponsor goes bust. In particular, money market funds are, by law, only allowed to invest in certain very safe short-term assets, so the chance of them breaking the buck is extremely low (in all of history, I believe only 2 funds have ever done that). Also, money market funds are not allowed to use leverage, making them even more safe.

When choosing a money market fund, be sure to pick one with reasonable yields (some have low yields because they are administered badly, others have low yields because they are tax exempt, so be sure to pick one that makes sense for you), and be very careful to pick one sponsored by a reputable sponsor. Shifty G may sound like a really cool guy, but I wouldn’t necessarily buy a fund that they are sponsoring.

Money market accounts

One thing to be very careful of, is the distinction between money market funds and money market accounts. Money market funds are separate legal entities as described above, from their sponsors. Money market accounts are typically bank accounts that invest in money market instruments (i.e. the same stuff as money market funds). So while the assets held by the money market accounts and money market funds are themselves pretty safe, money market accounts are subject to the $250k FDIC insurance limit and all the caveats discussed above, instead of the more generous SIPC $500k limits. Not to mention that because money market funds are separate legal entities, they have an additional layer of protection against the sponsor going bust.

Exchange traded funds (ETFs)

If for whatever reason you cannot invest in a money market fund in your brokerage account, you can also buy short term US Treasuries ETFs. ETFs, by their fund nature, share the same sponsor vs fund vs investor legal separation discussed above, so the assets are generally quite safe.

However, because ETFs are traded (money market funds are not traded, they are bought/sold directly with the fund), their price fluctuates. While a money market fund may have paper losses on a day to day basis, the fund generally keeps its per share value at $1, and absent fraud or serious financial issues, you will be able to redeem your shares for $1 per share. ETFs, however, are traded, and so every paper gain or loss is reflected immediately in the share price.

In general, this is fine — for a ETF that invests in very short term US Treasuries, the chance of a permanent loss is small, and the chance of a large gain or loss on a daily basis is also very small. What you’ll generally see, is that the ETF’s per share value goes up slowly over time, and then drops suddenly. Don’t panic — these drops generally are due to the ETFs paying out dividends, so the share price is decreased by the value of the dividend.

Exchange traded notes (ETNs)

If you have a brokerage account, you may have come across something called an ETN. Collectively, ETFs + ETNs = Exchange traded products (ETPs). But other than being exchange traded, ETFs and ETNs are very different beasts.

A note, in finance nomenclature, is a debt instrument — so if someone borrows money from me, one way we can denote that debt is for them to issue a note to me, indicating the amount owed. In other words, notes are a form of broad money.

And that is the clue — ETNs, unlikely ETFs, are generally NOT separate legal entities from their issuers. Instead, an ETN represents a debt that the issue has to you. So, while ETNs are generally subjected to the same $500k SIPC insurance limit, they do not really protect you very well if the issuer goes bust.

I don’t know if there are ETNs reflecting short term US Treasuries, but given the wide availability of money market funds and ETFs, I wouldn’t go anywhere near these ETNs.

WDJBD

Given this wide array of choices, what does JB do?

If you follow me on Stockclubs (disclaimer: I’m an investor in this app), you’ll know that a large chunk (over 95%) of my portfolio on display is in a money market fund. For reasons I may get to in the future, I am currently remaining liquid with some smallish option trades on the side.

That account represents 1 (out of 10+) of my brokerage accounts — In total I have 5 checking accounts, 2 savings accounts and 10+ brokerage accounts. This allows me to keep well below the various insurance limits in each of the accounts, and still remain very liquid for my purposes.

In each brokerage account, excess cash is generally held in a tax advantaged money market fund (in taxable accounts) or a regular money market fund (in tax deferred accounts), while cash in the checking/savings accounts are reduced to only just what I need to ensure I don’t miss my bills.

In effect, I have partitioned my liquid assets into multiple tiers of liquidity (for the computer science folks, think of it as multi-layer caching) — my checking accounts hold the cash that I expect to need to pay my bills due this month, plus a little bit of buffer for unexpected stuff. My savings accounts hold the cash that I expect to need for the next ~6months. The brokerage accounts hold the cash that I expect to need for investments or for the next ~12 months.

This setup gives me flexibility, while ensuring that every dollar of asset(3) is covered by applicable insurance limits. It does make things a little complicated to manage, so a good system of bookkeeping is definitely required (I use Quicken).

Footnotes

1. While both loans and bonds are debt, they are not quite the same thing. All bonds are loans, but not all loans are bonds. The difference is similar to the difference between options and warrants or shares and units — bonds, options and shares are types of loans, warrants, units with well defined properties that are enshrined in either contracts or regulatory rules. Because of this standardization, each bond of the same tenure from the same issuer, each option of the same expiry and strike of the same underlying, and each share of the same class from the same company are fungible, and thus can be traded on a public exchange.
2. A company’s treasury is its financial assets, managed by a treasurer (or CFO).
3. Note that I also have private equity investments, which are not covered by any insurance at all. Can’t have it all, I guess.
4. The first copy of this post used 30year Treasuries as an example of broad money. I was later informed that most (all?) academic endeavors generally stop at 2year for the definition of broad money. Obviously, I prefer a definition that is broader, and readers can draw their own conclusions about the narrowness of that thinking in academia. Just kidding… I made a mistake, it’s fixed. ;p

Foreword

Some people are born with a silver spoon in their mouths, others inherit healthy trust funds or major companies and never need worry about money in their lives. Yet others strike the lottery, or stumble upon buried treasures in their backyards. How is any of these fair?

As usual, a reminder that I am not a financial professional by training — I am a software engineer by training, and by trade. The following is based on my personal understanding, which is gained through self-study and working in finance for a few years.

If you find anything that you feel is incorrect, please feel free to leave a comment, and discuss your thoughts.

Conversations

For the past 10 odd years, perhaps more, millennials (1) have been complaining of the short end of the stick they’ve been given, especially by the baby boomers. The gist of the complaint goes along the lines of baby boomers in power have rearranged rules and laws, such that they have benefited unfairly, for example the housing boom (which benefited boomers the most), the financial boom (again, boomers were the main beneficiaries), etc. At the same time, millennials complained that boomers have left us a pile of trouble, including global warming, geopolitical tensions, divided politics in the USA, etc.

Talking to my software engineering compatriots, many of them are completely gloomy about their future prospects, with some predicting they’ll never be able to afford a home, never be able to afford kids, get married, retire, etc. Most of them are convinced the only real way they can ever get ahead in life is to win the lottery or otherwise cheat in a “rigged game”.

Childhood

A long time ago, when I was little, around 6 or 7, my family visited a small island in Indonesia, then a third world country, and fairly poor by most standards. Upon arrival, we were swarmed by a swarm of kids, most around my age, more than a few younger. One of them made a particularly strong impression on me, an impression I remember to this day:

The kid, who was probably around 4 or 5, was running with a shallow wooden drawer strapped to his neck and balanced on his belly, filled with various sundries. He was going around, clearly asking anyone to buy something from him.

I had no need of the goods he was peddling, and I had no money anyway. But for some reason, I felt sad that this was the life of someone who otherwise was so similar to myself, and I offered him the only thing of value I had at the time — a sticker of the Teenage Mutant Ninja Turtles (2) that I was particularly proud of, and carried around with me all the time.

The child stared at me like I was mad, shook his head and eventually ran off. I couldn’t explain to him that it was a gift, that I wasn’t expecting to trade it for something — he spoke no English and I spoke no Indonesian, and that was that.

To put things in context, the most expensive thing on his drawer was a 25c (rough equivalent of local currency). I paid a friend almost double that for the sticker.

Blessings

For the vast majority of folks who were born in the USA, Canada, western Europe, the richer countries in Asia (Japan, South Korea, Singapore, etc.), the raw truth is that you’ve already won life’s first mini lottery. If you don’t have to worry about clean running water, if you can reasonably trust your doctors and leaders, if you can step out of the house in the middle of the night without reasonable fear of being harmed, then compared to the majority of the world’s population, you already have it pretty good.

If you further were born in one of the first or second tier cities, or at least in the suburbs of one, then you’ve won the second lottery. Access to life’s opportunities are disproportionately available to those who live near the centers of finance, typically the tier one and two cities.

Finally, in addition to all the above, if you were afforded the chance to attend K-12 schooling, or even better, if you had attended college, then you’ve pretty much struck the lottery. As long as you do reasonably well in school, you are almost guaranteed a decent selection of jobs.

Software engineers

Rounding back on my software engineering compatriots — I’m fairly certain that all of them make a 6 figure salary, and most make $200k or more a year, with more than a few going much higher. Even if many of them live in the San Francisco Bay Area, infamous for being one of the most ridiculously expensive places to live on Earth, it is instructive to note that those in the same area not so fortunate to work for a large tech company will be lucky to see a 6 figure salary (3).

So, yes. Life is unfair, and some people just have it easier in life. Welcome to Earth, blah, blah, blah.

But maybe let’s not rub it in other people’s faces?

A bit of light heartedness

And with that, I leave you with a little bit of light heartedness. Happy New Year!

Stocks, why’d it have to be stocks

Because this is, after all, a finance blog. On the topics of being broke, for those who want to see what crazy shenanigans I’ve been up to, and how fast I’m going broke in my brokerage account, you can follow 1 (out of 10+) of my brokerage accounts on StockClubs, an app I’ve invested in.

Footnotes

1. Disclaimer: I’m a millennial.
2. Don’t judge. TMNT was hot stuff back then, and TMNT stickers and Ghostbuster stickers were basically money to kids in my school.
3. Per capita income in San Francisco Bay Area in 2021 is just under $80k. Source.

Foreword

There are many models of how stocks are priced, dealing with different aspects of pricing, and different pricing models. Intuitively, if 2 things are the same, then they should trade with the same price. But empirically, we know that they don’t always do that (see here and here). Why not?

As usual, a reminder that I am not a financial professional by training — I am a software engineer by training, and by trade. The following is based on my personal understanding, which is gained through self-study and working in finance for a few years.

If you find anything that you feel is incorrect, please feel free to leave a comment, and discuss your thoughts.

A = B = C

Let’s say we have 3 different securities, which may be stocks, futures, options, or whatever, A, B and C, which are essentially the same thing, modulo some constants (such as interest rates, trading fees, etc.). For example, SPY, ES, VOO, IVV, etc. The conventional wisdom is that the price of A, B and C should be the same — again, modulo some constants — since these are constants, we can model them out and just assume each of A, B and C are trading sans these conditions.

In practice, we know that SPY, ES, VOO and IVV don’t trade exactly in lockstep. Yes, they trade pretty closely, and divergences don’t last for very long. But in some short-ish timeframe (which may be on the order of seconds or even milliseconds), their prices can diverge.

Arbitrage

Assuming A, B and C are convertible, i.e. we can convert any of A, B or C into any of A, B or C, then there is arbitrage — if the price of A and B, say, get out of sync, someone can short the more expensive one, buy the cheaper one, and convert from the cheaper one to the more expensive one to close the short, thus making a “risk free” arbitrage profit.

The Efficient Market Hypothesis likes to assume that all these happen instantly, so there are no risk free profits to be made. But Wall St’s legions of market makers, liquidity providers and ETF arbitragers say otherwise. The fact that these companies are massively profitable suggests then that price dislocations can happen, and these companies close the price dislocations, getting us closer to (but never really reaching!) a perfectly efficient market, while making a profit for their troubles.

Not so perfect information

Now, in the perfect world, everyone knows all publicly available information at all times (i.e. information dissemination is instantaneous). In practice, that is clearly not true and can never be true — physics teaches us that nothing can move faster than the speed of light, including information. Since light moves at a finite speed, entities further from the source of information need necessarily get the information at a later time than entities closer to the source of information.

So, let’s take that a bit further, and assume that not everybody knows about the fact that A = B = C. Let’s say some segment of the market knows that A = B, another segment know that B = C and a third segment knows about A = C. Now these segments can overlap, but they are not the same, i.e. some people may know more than one of the 3 sub-equations, but not everyone knows all the sub-equations. Further, we use “know” here loosely — it’s possible that an entity actually does understand the 3 sub-equations, but for whatever reasons, decide that they only want to trade one or two of the 3 sub-equations. For example, an entity may only be a market maker in A and B. So while they can easily trade the A = B equation, they may not be able to trade the B = C or A = C equations as effectively, and so they don’t.

A moment in time

Let’s say some large entity, E, decides, for whatever reasons, they want to buy our security, and in large quantity. E can choose to buy A, B or C, and they fully understand the A = B = C relationship. However, E is a practical, real world entity, and they are not a market maker — they are a regular investor, and more interested in the productive earnings of the security, than the temporary price dislocations. So, for practical purposes, E will only buy one of the 3 of A, B or C, as it makes bookkeeping easier.

Now, even if E does the smart thing, and buy the cheapest of the 3 right now (and for the sake of argument, let’s say that’s A), if E decides at some future date to add on their position, and in order to maintain the “only one symbol on my books” rule, they will, at that future time, continue buying A. This may be true, even if A is not be the cheapest of the 3 at that point in time. In effect, at some arbitrary point in time, E may, for perfectly rational, though not financial, reasons, decide to buy A, even if A is not the cheapest.

Next, let’s say E is expecting to deploy large sums of money. We know that a trade is always between a buyer and a seller. We have a seller in E, but who’s selling? Unless there is another entity (or set of entities) that are willing to sell at least as much as E is buying at the current market price of A, E‘s buying will necessarily push the price of A up, at least in the short term. And since at any particular point of time, it is impossible to guarantee that you can always trade such that someone else is always willing to take the other side of the trade from you at the current market price, especially if you are trading in large sizes, we come to the conclusion that at least in the short term, E‘s trades will necessarily push the price of A up.

As the price of A increases, arbitragers will start to do their work by shorting A and buying B or C to convert into A to close their short. This will push the price of A down, while simultaneously pulling the prices of B and C up. However, since it’s generally not possible that the number of entities in the segments arbitraging A = B and A = C to be exactly the same, one of B or C will move up faster than the other. Let’s say for our discussion that B moves up faster than C, so we arrive at A > B > C, again, in the short term.

Since B and C diverged, our last segment of arbitragers will step in, shorting B and buying C. This has the effect of pushing C up faster, but notice how it work against the efforts of the A = B arbitragers!

Big picture

Stepping away from the instantaneous snapshots of the prices of A, B and C caused by E‘s trades, we arrive at a well known scenario described in mathematics as a “converging recurrence relation”. E‘s trades immediately pushes up the price of A, and the efforts of the arbitragers, over time, tries to spread that “information” (here the price of the security represented by A, B and C), so that all of A, B and C all reflect the same price.

Notice that we did not talk about fundamentals! It maybe that E is too optimistic, and pushes the price of A (and eventually B and C) up too high. But that doesn’t matter to the arbitragers — they are simply making risk free profits by arbitraging the equation A = B = C. So, in the short term, it’s perfectly possible that the prices of A, B and C are dislocated from fundamentals.

More interestingly, for the prices of A, B and C to converge to a final single, identical value, will require many rounds of arbitragers shorting/buying different pairs of the 3, but since the relationship is convergent, we can assume that over time, the absolute magnitude of dislocations will reduce, and the prices will eventually settle down (assuming no one else is trading these symbols other than the arbitragers).

Most interestingly, it should be noted that we cannot actually predict the final stable price of A, B and C! Without knowing the relative numbers of arbitragers in each of the 3 sub-equations, and without knowing their relative trading speeds and aggressiveness, it’s generally not possible to figure out whether the A = B arbitragers will be more successful pulling up the price of B to meet the price of A, or that the B = C arbitragers will be more successful pushing DOWN the value of B, which eventually results in A and C catching down to B.

Liquidity

One thing to note, though, is the need for liquidity. For the arbitragers to work, there needs to be external sellers and buyers in each of A, B and C. Given that we know A = B = C, then objectively, even if not everyone knows the full equation, we can generally expect that the number of arbitrage buyers in A will be less than the number of arbitrage sellers in A, whenever A is overvalued compared to B and C.

For securities which are very liquid, such as the SPY, ES, VOO, IVV, etc., the arbitragers have a deep pool of external traders to trade against, so the convergence of the prices can be fairly quick, sometimes in the order of milliseconds.

But for securities which are very illiquid, such as certain ETFs, the arbitragers may not be able to find willing buyers/sellers to take the other side of their operations, and in those cases, the convergence can take a much longer time. In some cases, illiquid ETFs have been known to diverge from their underlying for days or even weeks at a time.

Final words

Clearly, the above does not perfectly describe every security. In fact, it does not perfectly describe any security (or set of securities). It is a model to think about how prices move, out of many, many different models that try to explain subtle nuances of different parts of the market.

While it is sort of true in practice, there are a number of assumptions made which are not realistic in practice — such as no external buyer/seller willing to move prices other than E.

However, hopefully a discussion of the model, even in our made up world, is a useful exercise in thinking about how prices move in practice.

Foreword

Beginning as early as Q1 2021, there was a lot of consternation from employers about an unusually large number of employees resigning. Coupled with a seemingly general lack of available candidates to fill in those empty roles, it seemed for the past year or so that a large number of workers just simply decided to stop working altogether.

As usual, a reminder that I am not a financial professional by training — I am a software engineer by training, and by trade. The following is based on my personal understanding, which is gained through self-study and working in finance for a few years.

If you find anything that you feel is incorrect, please feel free to leave a comment, and discuss your thoughts.

I quit!

The Great Resignation seems to have started around Q1 2021, though it really picked up steam around mid 2021 through the end of 2021 and into early 2022. A lot of ink has been spilled about potential causes, amongst which include job dissatisfaction, fear of Covid, as well as being unable to work due to Covid.

Left unsaid was how those folks were going to support themselves, especially since a large number of those who quit were relatively young, and thus not eligible for various retirement/social security schemes.

Anecdotally speaking…

From my personal experience, it seems like the enabling factor for many of these folks to essentially retire early, was a booming, speculative market, especially in crypto. Talking to various people in different forums, the common theme I’ve found, was that those who have decided to retire early all fell into one (or more) of the following categories:

• Got into crypto early, and at least at the end of 2021, had a large crypto portfolio.
• Began day trading stocks and/or crypto, especially since early 2021, and made semi-stable income from the trading.
• Made a lot of money speculating on options, especially during the Gamestop craze of Q1 2021.

Employment effects

The effects of the Great Resignation was easily predictable — as most of these folks seem to skew towards people in tech related companies, there was an acute shortage of tech employees, especially around Q3/Q4 2021. Many tech companies were offering double digit percentage increases in sign on bonuses as well as annual compensation — I’ve personally had multiple recruiters approach me with 7-figures annual compensation packages.

… and it’s gone

However, something broke in late 2021. When the Fed started talking about raising interest rates in Q4 2021, stocks and crypto started stalling. It was no longer possible to make ridiculous daily returns just by buying random short term calls.

At the start of 2022, this accelerated with dramatic (for that time) drawdowns in all 3 major US indices, cumulating today, with the NASDAQ composite down about 30%, and S&P 500 down about 20% year to date. Crypto fell anywhere between 70% to 100%.

And suddenly, instead of hearing friends and colleagues talking about early retirement, I start hearing about folks who had retired earlier starting to look for jobs.

Triple whammy

While some folks are starting to look for jobs again, the crypto market devastation resulted in multiple brokerages, funds and “banks” running into serious financial trouble. Three Arrow Capital was forced to liquidate, and rumor was it that their inability to settle their margin debts resulted in the insolvency of Celsius, BlockFi, Voyager, amongst others. Even Coinbase, previously seen as a bastion of stability in the US crypto market was not spared.

The stocks market drawdown(1) also has the market spooked, with many openly talking about impending recession, resulting in many companies, even large blue chip stocks, freezing hiring or even laying off employees.

These actions seem to be resulting in a surplus, at least temporarily, of tech workers, resulting in a triple whammy for those looking for jobs:

• Portfolio losses
• Increased competition from retrenched workers
• Reduced job openings due to companies tightening

Short term

In the short term, it seems like there’s going to be pain all around in the form of higher prices, higher unemployment, lower wages(2), lower consumer demand, lousier economy.

On a lark, I’ve initiated some small sized, short term, short positions(3) — you can follow the trades (made on 6/29) by following me on StockClubs(4). Let’s see how that goes!

Footnotes

1. Note that I’m only calling it a “drawdown”, as opposed to “meltdown” or other more bombastic terms as others have used. Because, honestly, 20-30% isn’t that big a deal. If this gets really bad, then we ain’t seen nothing yet.
2. To be clear, it seems like wages are still going up, though slower than inflation. Also, I was referring specifically to tech workers.
3. As always, this is not financial advice. I’m playing with a very, very small portion of my portfolio here, and it’s more gambling than anything else.
4. Full disclosure — StockClubs is an app founded by a friend, and I have made a small investment in that company. I am definitely conflicted with regards to the success of the app.