I submitted this article because I was especially intrigued by the high-profile ex-Google ML Researcher hire (and then departure.) Anyone know what exactly the BlackRock ML group was doing? Was it supposed to be a competitor to Renaissance Technologies (https://www.bloomberg.com/news/articles/2016-11-21/how-renai...)? Was this an NLP-driven fund gone haywire?
I'm particularly interested as I was recruited for this group multiple times, so a bit relieved I didn't pursue it. I wonder if there are any lessons (about what to avoid) to be learned here about the way the group was structured...or if it was simple randomness gone wrong.
"It instructs the team to sell when losses become sizable, regardless of what its mathematical models say, according to a person with direct knowledge of the matter."
If I remember this correctly, research indicates that if the portfolio is being driven by a random walk process, a stop-loss always causes a reduction in returns (negative stopping premium), i.e., stop-losses are useful only if the return generating process can be shown to not be a random walk.
Also, they tend to mess with your mind (loss-aversion psychology, deep feeling of pain at losses) and make you exit and enter the trade multiple times, adding up to transaction costs, too.
All in all, a) Buy and Hold may actually be quite sensible and b) stop-losses are actually much more complicated than you'd think and seem to be a mathematical rabbit hole.
Stop-losses may make sense in, say, an intraday pair-trading strategy where an analysis of past return evolution clearly shows that winners keep winning and losers are hopeless beyond a point.
Exactly. Unless you can show that the price continues to move against your losing positions by more than their cost to liquidate, you shouldn't do it. Your screw-up was putting the bad trade on in the first place.
I'm especially surprised it's used in a model like this. Most quant funds use some type of portfolio risk management to allocate capital toward bets that with the highest expected return while controlling overall risk. That losing stock may be offsetting other risks in the portfolio.
As I mentioned in my other comment, a stop-loss is a very crude approach to risk management. It will help control the middle of your left tail, but the far left tail extreme events cannot be protected against by such a rule.
One. VCs structure their deals so that one breakout success is enough to return the fund. The smart ones never rely on lightning striking more than once.
Actually, Vanguard just strongly suggests that their customers do so (and provide nudges in the form of limiting sales and exchanges from certain funds to once per month). They don't get to make their customers follow a Buy and Hold strategy (and their ETF products have no restrictions), so I think it's more accurate to say that they do better (for their customers) due to their relentless push towards efficiency, with low turnover being an important part.
I feel like that's splitting hairs a bit. Sure, they can't prevent you from panic-selling or attempting to time the market, but their low expense ratios are possible because they, themselves don't do it. They just follow along with the markets.
I disagree. Since so much of their business is in their passive index funds, they/the fund managers, don't get to choose. Instead, they do their best to encourage and incent a set of customers towards low turnover.
They do a great job with VTI and their other funds to attempt to minimize the requirement of actually selling assets when liquidation requests come in, but that's not the same as saying that they, Vanguard (and more accurately the fund managers), practice a "Buy and Hold" strategy. Tracking an index well actually requires lots of buying and selling!
There's a distinction to be made between permanent losses and market driven / unbooked losses. If the original investment thesis stands, ignoring day to day market changes is perfectly reasonable.
> This is exactly what not should happen when investing longterm: Being driven by emotions rather than rational facts.
Putting a limit on one's potential losses is pretty rational, not emotional. Models are not perfect, and you do not want to find yourself with high paper losses when they fail.
Of course, if your models don't work well together with stop-loss limits, then that's a problem.
> Putting a limit on one's potential losses is pretty rational, not emotional. Models are not perfect, and you do not want to find yourself with high paper losses when they fail.
The BlackRock example plus the fact that many ETFs beat managed Funds in terms of performance seem to support the position that human interference is in most cases for the worse.
It gives a false sense of security in cases like this. The biggest losses you want to liquidate will always happen in cases where it's impossible or extremely costly to do so: company has merger announcement overnight while you're short, accounting scandal, bankruptcy, earnings surprise, fat finger order takes all liquidity out of the market. You will end up losing many multiples of your designated loss limit in the bad cases. The only way to prevent massive losses is to size your bets sensibly or insure against tail risk using options ahead of time, of course that comes at a cost.
By bugs and adverse selection I'm assuming you're talking about something like automated market making. For traders like that, a loss limit makes sense because of technology risk. If your order router has a bug like Knight's did, or your data feed gets stuck, you can lose a lot of money very quickly.
"He also pointed to the group's longer-term track record of outperformance. [...] The firm doesn't disclose the peer groups or benchmarks it uses for comparison."
Stock prices screw up validation with back testing. I tried using stock prices to illustrate random walks and the superiority of naive forecasts with random walk data. Only problem: they aren't random walks. They are random sequences of predictable pattern. Frequently, a regression model, or Holt-Winters, or neural net will outperform the naive forecast on backtests, because it was a better model before now. The challenge, is that now it isn't.
"BlackRock inherited the three-decade-old quant business with its purchase of Barclays Global Investors in 2009. Initially, the group was a big success under new management, delivering outsize returns. More recently, things haven’t panned out quite as well." -- well you know those Barclay's Boys[1]
Its an interesting question about machine learning and market returns. But there is a remarkable number of people who seem to just step over the line of legality and pick up the extra profits they need which taints the whole industry.
BGI was basically a subsidiary and run independently. It was originally part of Wells Fargo (hence the SF HQ) - just pointing out that although it had the Barclays name, it was separate.
One of these days I may finally understand what was the problem we were trying to solve with all these increasingly complicated financial thingamajigs. And what exactly is the benefit we derive from them.
Not betting on it, though.
P.S. Looking for something simple, e.g. "If you have derivative trading, your GDP will be 5% larger, and the gain is spread across the population in proportion to income." The kind of things you have for engine efficiency, or compiler optimization ...
If I'm a big company, like Delta, I want to make sure my profits are based on my efficiency at my core business, like operating an airline, not some random thing like the fluctuations of oil prices. So, I'd like to buy a contract that insures me against high oil prices. To pay for that insurance contract, I'd sell a contract that gives away my excess profits that might accrue if oil fell. These contracts are derivatives of oil, not oil itself.
A healthy derivatives market helps businesses focus on producing useful things for society. Every human should buy health insurance. Every big business should buy commodities and currency insurance (they don't call it that).
An unhealthy derivatives market encourages business to gamble rather than produce. GE Capital, before it got shut down, was at one point a bigger business than the rest of GE.
Just FYI, GE Capital didn't get shut down. Some of it was sold off, but it was mostly spun off into a separate company: synchrony financial. But yes, because it was overshadowing the engineering core of GE.
I upvoted you because I don't understand the benefit of quant funds either.
With short selling, derivatives and high frequency trading, I can at least see the argument for increased liquidity and so forth... But how does society benefit when leading AI research minds spend their time building gambling models that try to predict the sentiment of other gamblers?
At the same time, I recognize that forcing any such social logic onto research is a slippery slope towards Soviet-style government-stunted research. So I guess I'll rather have machines gambling trillions of dollars.
It is – and similar things have been for the last decades– a waste of a generations' brightest minds in a high-stakes, zero-sum game with no benefit whatsoever for society.
It's a distinctively American quality to feel dirty for having these doubts and associating them with the Soviet system. Free markets often create perverse incentives, races to the bottom etc. and it's the genuine role of governments to counteract these: Outlaw it, tax it, or – what may be enough in these cases – make absolutely sure those standing to profit from these activities also bear the full brunt of their failures.
Not sure if anywhere else are public finances already quite as intertwined-with/invested-in "the cowboy Wall St financial system" if you will, as the American ones. All these current and incoming pensioners.. all the numerous social insurance schemes.. all these bonds that must be sold to same (effectively, behind the schematics) to fund current-day budgets.. remember the formers' contributions have already been "spent" (into "assets") and at collection time the entire scheme will have to remain just as fluidly operational as it was before. These quants and funds etc are not just managing some nobles' / oil sheikhs' / super-rich moneys after all --- perhaps not even primarily, in absolute numbers! This makes any and all regulations and reforms forever a major challenge in "realpolitik" terms.
Markets are only zero-sum on a transactional level, in isolation. Yes for every buyer there's a seller and for every winner over some time scale, there's a loser.
But in the broader context of the entire real world, a well-functioning market is valuable in itself. It enables people to transfer risk inexpensively, instantly, automatically, and at fair prices. This competitive pricing mechanism also provides valuable signals to the real economy so people can invest in areas that need it.
Imagine a market with 4 participants:
-A farmer who grows corn and wants to lock-in a price for next year's harvest so he can invest in a new tractor and refurbishing his grain elevator.
-A baker who wants to lock-in his price for his corn purchases next year so he can invest in new ovens for his plant.
-A speculator who monitors price trends and takes risk intermediating between buyers and sellers.
-An exchange that provides a meeting place for these parties.
On Monday, the farmer places his orders with the exchange to sell his 2018 corn harvest. The baker is on vacation until the next day, so the farmer can't transact. However, the speculator knows bakers tend to buy corn around this time of year, so he buys the contracts from the farmer at a slight discount. The exchange also takes a fee from the farmer, and from the speculator.
The farmer goes off to the tractor dealer, and gets to work refurbishing his grain elevator.
On Tuesday, the baker comes to the exchange. No farmers are around, just the speculators who bought from them yesterday. Today is a lucky day for the speculators who can now sell to the bakers at a slight premium. Sometimes they misread the market and take losses instead. Again, the exchange takes a fee from the baker, and from the speculators.
The baker is comfortable funding purchases of his new ovens, knowing that he isn't at risk of corn prices rising next year, which would cut into his profit margins and make his plant unprofitable.
So far, the exchange has made money for providing a meeting place, and the speculator has made money for matching up buyers and sellers who arrive at different times. The farmer and baker each lost a bit of money by trading against the speculator--his profits are their losses, and vice-versa when he's wrong.
2018 rolls around. The cost of corn has risen due to import tariffs. The baker made money on the contracts and the farmer lost an equal amount. The exchange and speculator each made some money. Transactionally, this is actually a negative-sum game, because the exchange makes money no matter what, but it let everyone involved focus on their particular business and insulated them from risks they didn't want to bear.
I wasn't denying that– it's quite obvious that capitalism and its market-mechanisms are the best form of organising an economy. At least among those that we know.
The criticism was directed at the ever-expanding high end of financial markets where it seems to me the benefits it produces in the "real world" are marginal at best and in no way proportional to the money earned in the sector and the brain capacity it utilises. Some deep learning outfit with 200 quants basically putting pressure on the "speculator" in the scenario you sketched out, that may or may not create some small marginal improvement in the market, but could also just serve to better capture any residual utility the farmer and baker may have had from participating in the first place.
When push comes to shove these things determine the allocation of resources in society. How many people does a company making spoons gets to hire or should we put more of them on webforum moderation ? Same for cars/glass/shoes/...
The idea is that letting algorithms decide will result in better/more productive allocation of resources, which will result in more and better everything.
Of course, is that reality ? I would point out, however, that stock markets resource allocations are far better than royalty/governments allocating resources.
> When push comes to shove these things determine the allocation of resources in society.
John Bogle, founder of Vanguard and renowned investor, seems to disagree with you: "The stock market has nothing—n-o-t-h-i-n-g—to do with the allocation of capital. All it means is that if you’re buying General Motors stock, say, someone else is selling it to you. Capital isn’t allocated—the ownership just changes. I may be an investor, you may be a speculator. But no capital goes anywhere. This is basically a closed system. You have new IPOs and whatnot, but they’re very small compared to this vast thing we call a market, which is now around $24 trillion. The allocation of capital? That’s just nonsense."
I think you are mixing up the concept of a market economy with a specific kind of market, the "stock" market.
That sounds like an exaggeration in order to make an unrelated point.
Why would people sell equity in their company except for cash, and why would you pay cash for ownership in a company except for the stream of income it represents and the secondary market for that ownership?
How come people decided it is illegal to trade ivory from an elephant killed a couple hundred years ago? Was it made illegal to possess child pornography that was already created only because it is morally toxic, or does said consumption also induce demand for additional victims?
Even though debt markets have a lot more to do with day to day financing of corporations, equity markets have a great influence on terms. Furthermore, the unavoidable importance of secondary markets for debt and their derivatives can be understood by considering the importance of secondary markets with respect to prices of homes, automobiles, or any other large consumer purchases.
I didn't mean to imply that stock markets were not important, just that "allocating resources" in the sense that parent meant, e.g. "How many people does a company making spoons gets to hire or should we put more of them on webforum moderation ?", being its primary purpose seems like a stretch (except for the occasional new IPO / stockholders' meeting). If anything, venture capital and as you mentioned, debt markets are closer to this idea of allocating resources.
My main point really was that the examples parent gave seemed much closer to examples of market economics in general and would hold true, with or without stock markets.
I can see the limitations in equating stock price directly with GM retooling a factory for a new model year, but it is a far more accurate depiction of what is going on, even if a little abstract, than to say that they have nothing to do with each other and even spelling it out letter by letter. Saying that it is basically a closed system is even more bizarre.
John Bogle is either clearly incorrect, or the quotation may be taken out of context and may be unrelated (I don't know); however, the price that the seller sells at is usually not the price that he originally bought at. This difference, specifically the change in dollar value for the same object being sold, is how capital enters and leaves that system. Note that this is specifically about money-capital, and not capital based on other resources -- so if Bogle was talking about some other kind of value, he could easily be correct; although from that quotation alone, it really looks like he's talking about stock-trading money, and even values the market at $24T, so... I'm guessing he just didn't think it all the way through.
> The idea is that letting algorithms decide will result in better/more productive allocation of resources
Is it? I thought the idea behind HFT was to make a shedload of money for the firms with the most effective algorithms. To be clear, I have no problem with that, and I'm not close to the industry. But it would surprise me to hear that those in it conceive of themselves as optimizing social resource allocation, rather than, say, setting themselves up to retire at thirty with all the money they'll ever need.
There isn't just one argument. The liquidity argument is focused on why would we allow HFT, instead of limiting transactions to traditional investors only.
The algorithms talked about here are not HFT algorithms, but investment algorithms. Things like "if it's down for 3 days in a row, allocate 5% in the stock" type programs, written using machine learning. They generally will not change orders rapidly. They are about more efficient/effective investment, and the argument I gave is more focused on what the function of a stock market and investing is.
Because these algorithms are much more like traditional investors than HFT.
Can't you say that about any profession, especially other forms of middlemen? Does it really matter if your grocer's motivation is having a nice house and good schools for his kids vs. feeding people and providing a market for farmers?
Yes. A grocer with illicit intent can poison the food that they serve because some God told them to do it. We need to ensure that they both are sane and correctly motivated. This isn't simply about providing welfare for people; it also concerns the identification of malicious intent and the avoidance of harm. There may be a few professions where this doesn't matter, but by and large I'd say an individuals motivation for choosing a profession does really matter (especially with middlemen).
The investment side of that equation is tiny compared to the demand side of that equation. GM's stock has very little to do with GM's actions. Much like the most profitable approach when a companies tax rate is 10% is effectively the same approach as when there tax rate is 20%.
GM's stock price has very much to do with GM's actions. GM can do countless things which will impact its equity value.
Also, changing the effective|marginal tax rate by 50% has material implications on your business model and capital structure, e.g. debt (and tax shields).
10% to 20% is a 100% increase not a 50% increase. Now sure, paperwork changes, but the price of your widget or the layout of the factory etc don't really care about the taxes outside of extreme cases.
Anyway, profitable companies like GM can issue stock to raise capital or buy back stock. Buybacks don't really depend on the stock price as it's not an investment it's another form of dividend. Issuing stock is an inefficient way to raise capital better to issue bonds or not issue dividends.
Now sure, there are second order effects of stock price such as stock options. But again +/- 10% to stock price on a given day does not do much.
PS: Consider Microsoft if the tax rate where to increase to say 40% what would they change?
...and 20 to 10 is 50%. I chose the smaller % as it's still substantial.
For your widget company, it does matter! ;-) What widgets you make, your price, your price relative to competition, market share protection, pricing power, where you build your factory, PP&E decisions, and more all depend on your tax rates. I promise, and want to compete against firms that overlook these parameters.
For GM, you're overlooking other (mis)management decisions that will show up in the share price.
Right now, MSFT is facing just such an issue re: re-patriating money from overseas. If there were a one time foreign tax holiday as floated by Obama, MSFT would choose very different decisions than the status quo in terms of buy backs and R&D spend. In terms of operations changing, I guarantee they'd re-think their debt and their product mix within their 3 reporting segments. Some products wouldn't be profitable enough to sell if the profit margin shifted 2-3%.
I don't nessisarily disagree with you examples. But, I think you are misssing the forest for the trees.
I have had several successful CEO's all tell me to ignore taxes. That does not mean you install or don't install solar panels based on tax breaks. It can be very important, but it's generally premature optimization. Further, it's not the top tax rate that is important it's differential tax rates aka A @X or B @ less than X.
MSFT's re-patriating money is a good problem to have. They may see a tax holiday in the next administration or they may not. But again, it's getting to that point is the issue.
Microsoft (and other international companies) go to great lengths to optimize and defer taxes into the future. As the US already has among the highest corporate tax rates in the world not much would change as all international revenue is being held overseas anyhow. There would be changes in how they allocate their resources but only a CFO might be able to tell you what those would be.
Because it's a static, well-known, open problem on which to focus research and public attention, and then that work can be generalized onto something useful.
A more efficient engine might be as good a metaphor as many others. Five percent, equivalent to about a quarter percent faster growth over 20 years isn't a bad starting point either, depending on just how constrained the simplified capital markets would be. The increased friction that would be created by simplifying financial instruments across the board would probably facilitate a lot of rent extraction for people working in finance, but only up to a point, since the less efficient allocation of resources would also eventually leave a smaller pot for them to extract their share.
Like an engine, generalizations about quantities of reagents and thermodynamics aren't going to be violated by the real world, yet the engineers designing engines will understand the dynamics of low level interactions that are unexpected by someone with only a top level view. That is, in general more funding and more instruments that fine tune allocations of resources and risk really do accelerate growth. And yet, the details within the financial industry that can pervert incentives or corrupt signals do undermine some of the gains in funding efficiencies that are to be had. Given a fixed amount of design work a simpler engine might be more reliable than a complex one, but it won't be as efficient as one with more features and sufficient engineering to make them reliable.
> The increased friction that would be created by simplifying financial instruments across the board would probably facilitate a lot of rent extraction for people working in finance
You can have rent extraction through complexity as well as through friction. It's not obvious to me that a more complex, deregulated system is more efficient than a regulated, simpler system. I know that is the dichotomy that is usually talked about - a balance between efficiency and morality, but the sort of marginal increases in GDP we're talking about could easily be wiped out by other factors, for instance complexity that the clients can't understand, and greater instability in the general economy.
I agree that regulation is an important part of controlling information asymmetries, obfuscation of risks etc, and that regulation can mean better signals and be more efficient than only relying on the reputation of institutions.
What I was saying though, is that a blunt approach of declaring that financial instruments can only have some specific measure of complexity would eventually limit the resources available to new projects and businesses. The tangent about rent extraction was a distracting and motivated by my suspicion that a lot of people simply resent incomes in the finance industry, and want to see them make less money regardless of net impact on the economy. However, just as a baker counter intuitively makes more when the price of flour increases, less fine-tuned and efficient financial markets could ultimately make more money for the people working in finance.
I think that there is a belief that complex financial instruments inefficiently insert extra steps into the flow of finance and extract rent from those manufactured inefficiencies. Yet, a better reading is that they circumvent some inefficiency elsewhere in the financial system, and the people who implement that gain in efficiency are able to extract a little of the decreased waste until they too are circumvented.
If the quant programs duplicate a manager's strategies it will be useful. The manager may have forgotten some steps in making a good decision. The programs also will be helpful if you change the markets, like from US to Saudi. The programs can identify the differences in legislation, etc. that may cause your strategy to fail and you don't need to completely reformulate strategy which was successful.
I've always been puzzled how it could be possible for a machine to predict stock markets, even on an purely hypothetical basis. To me the idea seems to generate the same kind of paradoxes as time travel or oracles.
I mean, imagine there is a machine that is capable of predicting the markets with 100% reliability. Then soon enough, the machine will acquire a reputation and everyone will follow its advice. The machine says "the price of X will go up", everybody would buy X, so the price of X would indeed go up, but this price surge would have only been initiated by the machine statement and we'd have a self-realizing prophecy. Other situations would be more complicated I suppose, but in every case the machine would have to take into account its own prediction, which would be some kind of a mise en abyme.
In the end just like antic oracles, the machine would have to make cryptic predictions that would make sense only with hindsight.
Try out your thought experiment again with a machine that can predict markets with 55% reliability, or 50.1% reliability . This more likely reflects reality.
If someone had a machine that could predict the stock markets with 100% reliability, or even 55% reliability, do you think they would make those predictions known to the public? Or would they start a hedge fund and become one of the wealthiest individuals of all time?
As far as I can see, making the information available to the public isn't even a choice. There is no market information you can make available to the public that will make everyone richer. The only people who get richer are the ones who act before it becomes public knowledge. When they are done, asset prices reflect the now public knowledge, and there is no longer a profit to take.
What the avocado said, you only need to be better than random chance, even a small bit and you can slowly grow a principal investment over time.
It isn't a whole lot different than an FFT which pickts apart frequency components, HFT algorithms amplify correlation and dampen noise. Then they trade on the correlated signal against the affected securities in an anticipation of the correlated signal's effect.
If correlation was equal to causation they would be 100% effective at making lots of money, but that isn't. So to the extent that the algorithm identifies a correlation that is close enough, it makes money. But it can be crazy like 'sunny days in Paris' is correlated with higher shopper volume in at 'The GAP' and as the sunny days begin to pile up the algorithm buys GAP shares and as the sunny days decline it can sell off GAP shares.
That example we can imagine that people think about buying new clothes when it is sunny but the algorithm doesn't care what the correlation is, just that it has a strong probabilistic link to the changing value of a security. Nothing magical about it.
I've seen this manifest itself in various forms. In some ways, it's like the classic pump and dump scam that used to plague the Internet stock forums back in the late 90's. Tokyo Joe, I think his name was, became infamous for using his track record to front run his mostly illiquid OTC stocks. Because people thought he was this great trader, they would plow into his announced picks, creating a surge of volume in these thinly traded stocks, in which Tokyo Joe would sell out and cause a frantic collapse.
Nowadays, though, you see this sort of behavior in legal and more respectable ways. However, I think the purported results are highly disingenuous. What do I mean? Let's use a well known investment advisory service known as AAII.
AAII has a model shadow stock portfolio with fairly well known and transparent stock selection criteria. However, they often announce stock picks and stock sells to their followers within a day, supposedly, of their actual transaction. I've been studying the behavior of their stock pick/sells announcements, and on average, stocks are trading +5% to -5% (depending on whether it was a buy or sell) within the day following the announcement. This is HUGE for many reasons:
1) They often have multiple transactions on the portfolio in any given year.
2) The performance on the portfolio is boosted by, on average, 5% for any buy or sell. Compounded, this makes a big statistically significant difference on their purported shadow stock portfolio's CAGR and strategy.
3) They do not disclaim this behavior as a caveat emptor.
4) Though their strategy is transparent, their data feed / source is not. No one will be able to replicate their stock picks/sells on their own with their own live data source.
Quant shops look for patterns in the data to try to predict how the stock market moves and codify this in the model. If everybody is looking for the same pattern then the pattern becomes worthless. The patterns are therefore a closely guarded trade secret.
Quant shops have to continually research to detect new patterns to get ahead of the competition for this reason.
Then they also have to account for what other quant shop models are doing. Something something brightest minds of our generation playing algorithm battles. It's sad to me that such pursuits generate massive amounts of money while not benefitting anyone in the slightest.
To my understanding simple regression models worked pretty well the first time they were used. My laymans understanding is that the prediction of a simple model of the system like a stockmarket becomes unusable once there is a feedback loop from the model to the system. I.e. enough users using the same model that the model starts to affect prices.
Nobody can know with 100% certainty what the weather will be like tomorrow but you would be a fool not to look at forecasts. Especially if you are are farmer or are planning a picnic. The mental shift to statistical thinking is something that takes a lot of time and effort. I don't think it is valid to cook up so called paradoxes using arguments from non probabilistic domains.
> The Quran says that the jinn were created from a smokeless and "scorching fire", but are also physical in nature, being able to interact in a tactile manner with people and objects and likewise be acted upon. The jinn, humans, and angels make up the three known sapient creations of God. Like human beings, the jinn can be good, evil, or neutrally benevolent and hence have free will like humans.
I see no connection of that religious book and these particular Arab beliefs based on the myths from around the 7th century with the article about the BlackRock Inc.
I guess you're attracted by the part "can be good, evil, or neutrally benevolent" but it's too little for my taste, the rest is more problematic to be related. And can't most things "be good, evil, or neutrally benevolent" anyway?
If you clicked the first link given by GP, you would have seen that BlackRocks investment platform is called Aladdin, who of course has a strong link to the jinn (genie). Thus the connection.
Maybe the connection was that the platform was going to be the one that "took the genie out of the bottle"? Any other alternative is more tenuous, I think.
I'm particularly interested as I was recruited for this group multiple times, so a bit relieved I didn't pursue it. I wonder if there are any lessons (about what to avoid) to be learned here about the way the group was structured...or if it was simple randomness gone wrong.