Speed is not the solution to Generative AI

What is it about human thought and creativity that cannot be captured by a computer?

I have always said that humans are smart but slow, while computers are dumb but fast. A human plus a computer is very powerful.

Generative AI is all about making computers think for themselves. Despite the doomsday warnings and related fantasy, computers remain a dumb workhorse. Researchers have decided to buy ever more computer power to try to emulate human creativity. But this hasn’t worked,  nor do I think it will, because no matter how much a computer can access information, this is probably not the key to creativity.

It is worth emphasising here that there is a difference between knowledge and intelligence.  Providing a computer with as much memory and information as possible will definitely make it knowledgeable but will it make it intelligent?  Elon Musk famously diverted a shipment of Nvidia processors from Tesla to SpaceX to provide the computer power to know everything.  But Musk is wrong in believing that this will trip the intelligence switch.  A dumb computer with access to a faster processor and all the information in the world is still a dumb computer. Knowing what has failed historically is valuable in so far as you can avoid repeated failure but it does not stimulate creative success.

Why are humans slow thinkers? Rather than this being a weakness, perhaps this is a creativity trait? Humans do not know everything but given a limited dataset can conjure up brilliant solutions. Rather than combing through billions of potential combinations, could it be that restricting knowledge to a limited set can stimulate a leap of thought that creatively bridges the gap? Asking questions is a human trait and the first step toward finding a solution…

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Elon “MAC” Musk

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AI: A statistics package in the hands of monkeys

There are two ways to approach an empirical question.

1. Formulate an hypothesis based on some physical, structural or behavioural model.  Test the hypothesis using a relevant dataset.  Interpret the results relative to the model predictions.

2. Dump a bunch of data into a bucket, stir and implement the correlations that appear.

I have been intrigued by the aura-ed veil with which AI distinguishes itself from statistics.  One journalist poses the question aptly “If machine learning is a subsidiary of statistics, how could someone with virtually no background in stats develop a deep understanding of cutting-edge Machine Learning concepts?“[Joe Davison, Medium, June 28 2018 ‘No, AI is not just glorified statistics’].  The journalist’s intent argues that AI is fundamentally different to statistics – that understanding the statistical algorithms is not necessary. I disagree.  I question whether a ‘deep understanding’ of these cutting edge concepts was ever attained.

Let me explain the dangers of a statistics package in the hands of monkeys.  Recently we were visited by an AI-person asking us to invest millions in his stock picking model.  Stock picking  has been a fascination of statisticians for centuries so there is an established literature of acceptable practice amongst researchers to prove their success or otherwise.  Measures such as information ratios, Sharpe ratios, Sortino’s, benchmark relatives etc etc are standard.  But this AI-person was out and about seeking investors without any of this supporting evidence.  When quizzed about how he chose his investment universe and the subset of securities that made up his portfolio the response was that the ‘AI chose it’.  Add to this, where he did calculate some measures of success, the calculations were wrong (an information ratio of 27 without a benchmark!) Financial datasets are notoriously unstable with outlying observations driving inference and spurious correlations galore.  Ceding control of your dataset to an AI algorithm is hardly comforting…

…to me.  However, this seems to be standard AI practice.  The bigger the data bucket, the more crunchtime that is needed, the better the story it seems.  AI practice is to specify, say, a linear relation between some X-variables as they affect a Y-variable, calculate the coefficients and just use them.  It is a fact, however, that the optimal coefficient estimates from a multivariate regression are functionally related to the variance and covariances in the data.  The response coefficients are positively related to covariances of X with Y, and negatively related to the variance of X.  It is also a fact that these coefficients will be exactly the same irrespective of whether the user identifies themselves as an AI data-scientist or a statistical researcher.  Clearly, where the end-result is the same and the method for estimating the relations are the same, then the same thing is being generated.  So who are the monkeys here?

It seems to me that the AI-field fails to distinguish itself from the other users of  the mathematical techniques that are used in their model-building.  The monkeyness shows up as a lack of thinking about the datasets and the problem that they are trying to solve.  One of the first things that you are told is to ‘look at your data’ but most people start calculating means and variances when they come into possession of a new dataset.  The AI-types just stick the data in a bucket and stir!  They dont want to know about where the data came from, how it is calculated or what it represents.  They just want results – means and variances and covariances – that may repeat themselves or not.

The monkey that visited us seeking capital (and who I described above) is a classic case of someone who is destined to make the mistakes of statisticians several centuries before him and really doesn’t know how naive he is.  He will patch up his model when it fails to deliver actual investment results and arbitrarily impose constraints to improve historical performance, while not improving anything in the future.  He will lose money for investors and not know why.

It does seem odd that AI researchers have embraced the Finance industry without bothering to learn from the mistakes that have gone before them.  Do they genuinely believe that they are the first to apply mathematical techniques to these datasets?  A monkey with a statistics package is a dangerous combination.

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Silicon Voodoo Bank and the managed funds industry

So what is the big deal with the failure of SVB?  The story goes that SVB’s depositor base was a bunch of tech startups.  The asset’s went down in price, chewing up the Bank’s capital and then some.  This triggered a classic ‘bank run’ where depositors tried to withdraw their deposits at a price that was higher than the true value that remained in the bank.   Put simply, due to bad investment decisions, the price of a deposit had fallen from $1 to, say, $0.87, yet the bank was paying out deposits at $1 until assets were seized and SVB put into administration.

Shocking as this may be, there were/are some simple ways to avert such a collapse.  The financial press has worked over the SVB crisis quite thoroughly so there is no reason for me to list them here.  But one aspect that has attracted little attention, which strikes me as the most important issue, is mark-to-market. The managed funds industry lives by this principle.  That is, the withdrawable amount of any investment is the daily NAV of a fund.  NAV is calculated by taking assets, subtracting liabilities, and dividing by the number of shares on issue.   Were this principle applied to SVB, the NAV would have been $0.87 and there would have been no incentive to withdraw in a panic.

Now banks and managed funds work on a different premise.  The bank deposit is supposed to be a safe, no brainer for investors with no brains.  $1 in, $1 out.   From a social standpoint there are classes of investors that go to banks for transaction services alone and do not need to think about investment risk they are bearing. The bank’s capital is supposed to cushion the investment risk for depositors. Money market funds provide a similar service.  However, the mark-to-market principle enables a demonstrably larger industry with much less capital to operate effectively.  The mark-to-market principle shifts the investment risk from a bank intermediary to the investors directly.[1]  Socially this is seen as involving requiring too much knowledge from investors.  But does it?

The idea that investors are better off bearing investment risk directly and earning the spread themselves rather than suffering the rarer but debilating catastrophe of a bank run, is considered ‘radical’ in policy circles.  The concept echoes the relation between Defined Benefit and Defined Contribution pension plans where the former uses the company balance sheet to pay pensions in the future while the latter places the investment ownership and risk on the individual beneficiary.   In economies where DB has been replaced by DC plans, there is ample evidence of non-panicked and intelligent investor behaviour under DC plans which are marked-to-market and for which there is substantial investment risk variation.  Even relatively unsophisticated investors can cope with investment risk.

The above makes the case for direct ownership of assets and mark-to-market to avert the problem of SVB.  Ironically, however, the regulatory authorities in the US have done exactly the opposite.  The Federal Reserve has suspended the mark-to-market principle lending on 100% of initial cost of bank investments despite those securities trading in the 80’s.  The managed fund industry tends to avoid the widespread problems of traditional banking and this should be embraced.  The traditional bank model is a dinosaur in need of a comet.  Isn’t it better for society if, during a financial crisis, many investors wake up to find their bank balances decline by a few pennies rather than a smaller number of investors discovering their balances have been completely erased?

Endnotes

[1] In the US there is a strange rule that requires MMF’s to only publish a lower NAV is the daily mark-to-market is less than half a percent.  This provides the feeling that the MMF is a bank deposit so long as it doesn’t ‘break the buck’.

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Respect age (of the system)

When the missionaries arrived at Aitape in Papua at the turn of the century they were appalled to see the villagers carry there boats from the mountain settlement to the beach each day, fish and return to the hills each evening boat in tow.  No-one knew quite why so the missionaries decided to resettle the village on lower ground near the beach to make the fishing process more efficient.  In July 1998 an undersea earthquake caused a tsunami that killed 2200 Aitape residents.  The system of boat carrying to higher ground that had evolved over centuries was there for a reason.

If you rise to the top of a sales and trading business at Goldman Sachs or JPMorgan you have displayed incredible trading, sales, personal and political skills.  You may also have computer skills beyond Microsoft Office but this is unlikely.  Whatever your background you inherit ‘the system’ which has been created and honed over many generations of traders and years of experience.  You have probably been frustrated with the system in your own dealings but there is nothing you can do about it.  The system predates you by decades.  It is version 26.14 of the modern computerised system, built upon the trading ledger that at one point was kept manually but migrated to COBOL in the 60’s.  While it is a dinosaur it integrates a trade order system, risk management and collateral management around a core accounting system.  It works and when it says ‘no’ there is nothing you can do to overide.

When you start work at a new crypto trading platform you have a ‘clean sheet of paper’ to do really cool stuff.  Luca Pacioli may have written the defining tome of double-entry book-keeping in 1494, but what does he know?  Accounting is something that can be easily constructed from the trading records so we want to trade first.  Anyway, protocol sillycoin.leverage50x.check.collateral.dump will protect the platform from losing money.  And no we don’t want to spend $20 per month on Xero to keep track of our customer deposits, revenues and liabilities because that is boring and uncool and so 1494.  We are a crypto trading platform that is so new that it can be hacked and robbed and backdoored by management.  The ‘system’ wont say ‘no’ because there is nothing in it stopping unusual trades or capital movements.  A clean sheet of paper can be dirty as…

The FTX collapse is becoming an ageist story about inexperienced 30 somethings creating a house of cards that eventually collapsed.  I think this is unfair.  If you get promoted to a senior role in Goldman’s or JPMorgan you haven’t spent decades in one role and your experience is possibly limited to one aspect of the game.  There is not much difference between 30 and 40 somethings in actual fact when it comes to creating a new trading platform.  The issue is that there was no system that the FTX people inherited containing all the procedures, checks and balances that established financial institutions have evolved with.  It is not a question of how old the people are, it is a question of how old the system is. Old systems that work are to be respected.

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What happened to the cow?

In the classic fairytale Jack and the Beanstalk, Jack is charged with the important job of taking the family’s cow to market to sell for some much needed money to put food on the table.  Jack, however, gets stopped by some scammers and is convinced to sell the cow for a handful of ‘magic beans’.  The rest of the tale is well known and works out well for Jack.  However, the gaping hole in the story is what happened to the cow?

FTX is a similar fairytale where related parties have exchanged valuable assets for the equivalent of a handful of magic beans.  The beans are fiat tokens that FTX can simply create from nothing.  But what happened to the valuable assets, the cow, that FTX was taking to market?

Suppose you deposit $1 with a broker.  The money is in a hard currency and can be exchanged for securities like Tbills or shares, or just sit there in a bank account.  Alternatively, the broker can change the basis of the deposit, presumably with your permission, for a token that it values at $1.   The deposit then becomes a token and the $1 then goes to the broker.  If you want your money back, the exchange reverses, and the $1 goes back to you in exchange for the token.

The transfer of $1 back and forth using the token, in this example, does not change the core asset’s value which is the original $1.  The only difference is who gets to control the $1 until the withdrawal takes place.  If the broker doesn’t do anything with the original $1 after the temporary exchange of a token then, by and large to a first approximation, the token is fully backed by the $1 and he can make good on the claim.

The now collapsed FTX crypto exchange did not do this.  It seems that the exchange transferred the $1 to itself for its own token, FTT, and then whisked the $1 away to spend on something else.  That something else became worth less than $1 and the exchange collapsed because they could not redeem the FTT tokens.  This is a classic bet-gone-wrong situation that many financial actors have suffered over the centuries.  But what must have happened is a bet on something that didn’t work well for FTX causing a loss.  The FTT token is not the problem here and its decline in value reflects the loss rather than itself being the difficulty.  I say this because a cheap FTT token that lays claim to something worth more is an opportunity for arbitrageurs to make a buck.  Indeed, that seemed to be the role that Almeda was supposed to play in the FTX empire where they would be a fund to buy assets cheap when they presented themselves.  However, Almeda seemed to have been the repository for FTT tokens to extract a lot of $1 from.  Where did this money go?  What happened to the cow?

The classic movie Dumb and Dumber is useful here where Jim Carrey and Jeff Daniels realise that there is $1m in a briefcase and go on a spending spree – ferraris, hotel suites in Aspen, fancy tailors – all accounted for with IOUs that they are ‘good for’.  FTX’s IOU’s are just FTT tokens with an artificial value previously administered by FTX itself.  Whereas Jim and Jeff’s spending spree is transparent in knowing where the money went, we are not so fortunate to know if FTX stole or lost the money in the markets.  If it’s the former, then $9Billion is hard to hide.  If it’s the latter then there are some traders on the opposite side of the ledger who are being very quiet.  Most probably its a mix of both bad deals (paying a negative spread on yield farming ventures) and outfight theft.

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ALM, LDI and CSA spells trouble for the BOE

I cannot remember the number of times I walked into meetings to discuss Asset – Liability Matching (ALM) with pension funds and insurance companies.  Insurers fell into two buckets – the savvy ones running sophisticated matched portfolios and the others with serious mismatches.  Pension funds, alternatively, either lacked instruments to reduce the mismatch or fell into the trap of treating equities as ‘long duration’ proxies for their liabilities.  I had some success with insurers but none with pension funds.

Liability Driven Investing (LDI) is the same as ALM.  LDI had an amazing following in the UK amongst pension funds.  The idea is that defined benefit plans have actuarially predictable liabilities with long duration profiles.  Typically a fund would buy equities and hold short term bonds to generate a return sufficient to reduce the cost of the pension liability but this often meant there was a mismatch between assets (short-term) and liabilities (long-term).  As interest rates fell the liabilities increased more than the assets leaving a hole in the fund that required additional funding by the corporate.  LDI promised to plug this hole.

Aided by derivatives, it was possible to create long dated assets to match liabilities.  A 10 year Gilt future has about a 7 yr duration.  To match a 21 year liability all you need to do is buy 3 times the number of futures per dollar liability and , voila, you are matched.  But while every problem has a solution, every solution has a problem…

Margin calls.  Cash Settlement Agreements (CSAs) are methods for ensuring that credit risk is diminished in derivative markets.  As prices fluctuate, actors are expected to contribute and settle up balances owing or receivable during the life of a contract.  For LDI portfolios, however, only the asset side of the trade is subject to CSA or margining.  Liabilities move in value equivalently to the assets but do not fund the margins.  Therefore, for instance, if Gilt futures fall in price by GBP10 per contract, the cash margin requires GBP10 to be contributed by the pension fund per contract which in the example above would be 3 times higher to extend the duration.  Liabilities would increase in value but generate no CSA.  Therefore there is an interim cash crunch where there is insufficient cash to meet margins despite the net value of Assets minus Liabilities being unchanged.  This nightmare scenario occurred  last week in the UK where a perfectly reasonable risk controlled strategy had to be unwound to meet margins.

The unwinding caused a sell off in Gilts that brought the market to its knees triggering Bank of England (BOE) intervention.  The main culprit for all this sits with the Accounting rules that do not recognise the Asset-Liability match and, secondarily, the CSA margining rules.  Marking to market a liability stream creates accounting difficulties so these are sometimes left off-balance sheet or held constant at actuarial valuations from several years previously.  Assets, on the other hand, are marked every second with margining often daily but sometimes at longer intervals.  So even though the A’s and L’s are M’ed there is no way to use the gains on the L’s to meet the margins on the A’s losses.

This leaves the LDI industry in a conundrum and the BOE stymied.  The basic idea is sound and therefore the market regulator cannot be critical of the practice.  Accountants have always done their own thing no matter how wrong or intransigent.  The derivatives markets rightfully want their credit risk controlled.  What LDI needs is a natural hedger with the opposite liability profile to the pension funds to deal together so that they bypass the CSA’s.  The natural long dated borrower is the BOE.

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The markets are not scared of inflation, they are scared of the Fed

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The inflation playbook 4 decades later

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Australians all let us rejoice, for we love money and mischief

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