Robots in Finance Could Wipe Out Some of Its Highest-Paying Jobs
bloomberg.com
bloomberg.com
For example, people like Elon Musk and Steve Jobs could pick up the phone and convince some very wealthy people to part with their cash on some investments that ML algos would consider stupidly risky.
But that's not the point here. The big money in investing isn't in beating the market, but charging fees, so fund managers only need to convince people to give them their money, they don't actually have to deliver results, provided they're working within the fund's stated goals.
7.7% real return with 17.7% vol vs 7.1% real return with 17.1% vol.
Sharpe for equal weight: 0.435; Sharpe for SPY: 0.415
Because everyone is doubling down on investing on the status quo, don’t be surprised if a disruption to the status quo has far more dramatic impact than it would have 20 years ago.
If the ETFs are incorrectly pricing assets because of blind buying, then an enterprising managed index fund could bet against it. Or, find some other arbitration mechanism. But of course, this doesn't actually happen, because price discovery _still happens_ even if ETFs account for a large percentage of trades (since price discovery can happen even with small number of trades).
And if ETFs is just betting on the market average, then the worst that can happen is they get average results. ETFs don't use leverage or debt.
If (when!) there is a correction, there will be analysis done on the cause (pick one or more of: student loan debt, sub-prime auto loans, trade war, something else) and then new investment strategies will be devised. And maybe passive investing will be shown to leave you vulnerable to a sea-change.
Maybe the machines won’t come for venture/angels but I certainly won’t bet on it.
It didn’t save them when the bots came.
That's an odd thing to say. If machines and programs are capital, which seems reasonable, the more automation there is, the more capital, which drives down the returns and makes labor relatively more expensive. Isn't this obviously happening in a world where savings accounts pay 0.01% but it costs $120/hr to hire a plumber?
Maybe it would be better to say that the extremely low returns on capital favor automation, which favors the people who make the highest wages. That's obviously? not the same as capital vs. labor.
This continues as technology gets more sophisticated until the only people left producing things and making money are those that own the tools that do the job.
It only costs $120/hr to hire a plumber because of artificial barriers to entry and a guaranteed market. A better example might be a typist who previously had a solid middle class job taking notes and dealing with faxes because their boss didn't have a cheaper way to do it.
That is what I mean by favoring. Eventually Bezos just owns a machine that runs everything he controls and needs no employees. Eventually. Don't doubt he would if he could.
Your statement contains the core of it though -- systems that favor the people who make the highest wages are in an unstable equilibrium where eventually very very few people make all the wages.
relationships were expensive (and thus valuable) when people had to cultivate them through temporal and spatial investment. relationships between computer systems are not bound by those constraints and are thus fast and cheap (via markets and networks).
but more to the point, the value of a relationship in finance is really the highly valuable non-public information you can glean from them.
knowing who has (or will have) the resources you want at or below the price you want to pay for them (or vice versa) is the hard part. people (and the algorithms they create) will actively try to hide that information because it maximizes revenue.
It’s not a ‘could’ in the future but something that’s already happening in significant amounts. The trading room floors of most the big banks are a tiny fraction of what they once were despite trading volumes skyrocking.
>many of whom will lose their jobs -- not necessarily because they are replaced by machines, but because they are not trained to work alongside algorithms,”
The story with technology is about adaptation, not replacement. Teams, departments, technologies have come and gone out from under me in about 15 years in the software field.
One could say I was replaced by software (or cheaper devs elsewhere) when I was laid off or when teams I was on were eliminated. But if I wanted to keep getting a paycheck, I had to adapt to something else in demand.
That’s life and these sensational articles generate clicks but it happens in almost every private industry.
You have to adapt and grow or else you’ll wither.
Agreed, but we now know that as people age it becomes increasingly difficult for them to learn new things. This is a basic function of biology and how the brain ages.
Due to automation, change is happening increasingly quickly, more rapidly than many people can adapt to. People are being left behind, which has pretty serious social and political implications.
It is the Low and Middle Income that gets squeezed if they are not already squeezed enough. And you will be surprised how many of them wont be able to adopt.
And I am not surprised it would be hard for anyone working in Tech to understand, because I have come to the conclusion most of the so called Middle Class Income group around the world mostly shrank to Technology Sector.
The whole, robots of tomorrow will be doing..... is like the flying car, everybody expects it soon and been that way for generations. People from the 1950's and 60's are still waiting for a robot to do all the housework.
Still you have to wonder how peoples trust in AI/Robots will play out, would an AI think that as humans don't trust me that I should not trust humans! Or would it go, the human reason for distrust is flawed, ego the human is wrong about not trusting me, however if the human has made that mistake, then I can not trust what the human says. It then applies the rules of Asimov and runs away never to be seen again.
It could happen, unlikely, but more likely to happen before all of the above.
But seriously - they are laying the foundations of the platform of mobility that will be built upon to do many things - yet like most things, military comes first.
I understand your sentiment, but the reality of just shedding a large fraction of those jobs is hard to even imagine. Unemployment in 2008-2009 peaked at around 10%.
>Robots in Government could wipe out just as many high paying jobs
I didn't know there were all that many high paying jobs in government. I can't think of many jobs in government where people make as much as investment bankers for instance?
Maybe he was talking about AI taking out consultants that contract with the government? (I know some of them make in the high 6 figures, and many can make 7 to 8 figures depending on what that consulting firm is doing.) Or maybe he has a different idea of what "high-paying" means? High 5 figures may be high-paying to a lot of people.
I am a series 3 (commodities) and quant. Take this example, at one point in time people were literally trading seats on the exchange, and a seat on the NYSE was as high as 3 million !!! While there may have been more people with those jobs than today, you had virtually no choice to be in a large institution.
Today, almost anyone can raise some money, use the simple Interactive Brokers API which sits on top of FIX protocol, and build a trading strategy. The main reason I think you don't see more competition from small new firms - even though there is a lot - is that the regulation is fierce and most funds already offer nice compensation.
Within those firms, yes there may be some decline in those jobs like a floor trader, but someone has to program and manage those algorithms.
It's also important to keep in mind that many firms are only replacing execution traders while directing traders through fundamental analysis, and if all firms ever go completely algorithmic you'll find firms trying to exploit these algos in ways unique to humans.
35 years ago, the number would have been 10's?
I laughed when I read this. 40 whole algorithms!
Why pick on the 40 algorithms?
I think this definition will be important in law because it is where we will define responsibility for actions of autonomous things.
That's how you write honestly for the target audience.
> Nasdaq runs more than 40 different algorithms, using about 35,000 parameters, to look for market abuse and manipulation in real time.
That is how you use big words on an audience not capable of seeing through their meaninglessness, and it's the responsibility of people who do see through it to call it out.
As someone else mentioned. The 35k parameters is skeptical. Taleb and Tversky and Kahneman have good evidence that most algorithms are better with less parameters. The more parameters, the more noise.
Interesting observation.
Do you have more specific references? Those three are so popular it’s hard to narrow to commentary on parameters and noise.
Basically, as your signals approach infinity, your chance of finding correlation approaches 1. Taleb argues your chance of finding causation is likely to decrease.
Click link:
> Nasdaq North America Surveillance team monitors 3 equity markets, 6 options markets and one futures market with real-time surveillance and post-trade surveillance of unusual market activity. The surveillance department is monitoring the markets for Insider Trading, Fraud and Manipulation, including manipulation through trading—pump and dump—and order book manipulation—spoofing and layering, as well as handling events in the market such as clearly erroneous transactions.
> The surveillance program today is using algorithmic coding to detect unusual market behavior running over 40 different algorithms in real-time, looking for market abuse and manipulation. The patterns have sophisticated algorithms that use approximately 35,000 parameters. In addition to real-time surveillance, there are over 150 patterns covering post trade surveillance, which are used to identify a wide range of potential misconduct.
> The activity is monitored across equity and options markets, with some market-specific alerts and some alerts encompassing data from all markets.
So they have 40 different predictive models running in production (a lot!), and 150 hand-written (or single-pattern) rules. They also employ active learning with experts and this reduces their training data needs with 95%. 40 models using 35000 features, is 875 features per model on average. All this bickering is typical.
Btw, I think some of this is correct. It still amazes me that anyone thinks the CFA is relevant (it is largely done by Chinese/Indian students who will never work in finance). But there will always be a place for fundamental investing (i.e. you should be aware that not everything in life can be measured quantitatively).
So it is actually very rare. I have probably come across less than 10 managers who are +EV, and the majority don't manage any public money (again, economics of the business).
But it is straightforward: are they doing actual research (the majority of fund managers don't)? Are they turning over their portfolio frequently? Do they say dumb shit (i.e. constantly use buzzwords)? It isn't magic.
It isn't hard. As I said, the first thing you have to understand is that 99% of managers cannot outperform and are not trying to.
If you look at who is saying this, and follow the money (not disclosed here, but a short search away), you'll notice that this is a butcher grading his own meat. Without blinking he further suggests that the U.S. government hold tournaments on anonymized data to crowdsource market manipulation detection.
The bias in ML sections are all mealy-mouthed and elitist. Just because you hold a PhD in a technical field does not mean you understand economics, AI futurology, public policy, or ethics. It would be career suicide to stray from the path of the popular safe opinion. So you get language-imperialist terms like "Latinx" instead of "South Americans" to describe Brazilians, claims that automation will favor taking jobs from minorities, that the government should promote women in leadership positions at private companies, and that especially women of color have no access to study computer engineering.
Then again, there are few kitchen jobs that solely consist of flipping burger patties.
That's pretty steep compared to the price of the labour.
The thing is, I (and many other lefties) would welcome a world in which robots do all the work while humans are free to do whatever they want - the problem is that in current capitalism, the burger will cost the same for the customer while the costs implode (most cost in restaurants is staff!)... meaning the extra profit goes to the owner class, not the worker class which has to fight for the few jobs that remain.
> Contrary to traditional assumptions, high-level reasoning requires very little computation, but low-level sensorimotor skills require enormous computational resources.
Simple scripts are already wiping out many high paying developer jobs.
My girlfriend can't get her Squarespace website to work.
I could not understand the conceptual underpinning of it all. What goes where and why do some things appear on the page...
Smart ethereum contracts based on chainlink data are going to be the new financial instruments. You are already seeing the fertilization of this with stuff like makerdao.
I know it’s annoyingly to talk about the blockchain all the time, but unfortunately I think I may have become a true believer. I don’t think human “financial advisors” are going to be able to outperform these sorts of funds.
Also: deregulation (or difficult to enforce regulation) is going to allow a LOT of innovation to happen. How long until some 17 year olds create a token which represents the consumer side of a fund? And then how long until the funds strategy is codified into a smart contract and runs on its own?
It’s going to be a wild time.
I don’t think it’s a flavor of the month. Maybe I’m wrong, but neither I nor apparently the market thinks I am.
Groupon debuted at a $17.8 billion market cap with a great deal of fanfare.
It's now at $1.6 billion.
Coinbase's last round's investors valued the top-of-the-stack preference they were given at a price per share that, if multiplied across the cap table, comes to $10bn.
That valuation printed when crypto hype was near its peak. It's reasonable to conclude it may be stale. Given the preference, enterprise value would fall faster than the value of those most-recent shares.
Valuation is a poor sole measure of success. It's easily manipulated, determined by a few people and often stale.
I think this is moot, because human financial advisors are not trying to beat anything, they are trying to spend your money. Read "Where Are the Customer's Yachts?" By Fred Shwed
Also Jack Bogle used to talk about the S&P 500 would be most people trading individual stocks (sorry I need to dig up the source for this).
Point being: you may not need fancy algorithms to outperform humans at the market, if your goal is only to outperform most humans. You may need something fancy to outperform all humans.