Refactoring EMH – Thoughts following the latest market crash
post by Alex_Shleizer
the long run, the costs of analyzing public data in order to obtain alpha will be equal to the gained alpha.”
COVID-19 Crash and practical considerations
In a twitter thread, Eliezer Yudkowsky challenges the belief in EMH due to the seemingly illogical behavior of the markets with respect to the COVID-19. In the replies, Robin Hanson asks the obvious question: “what exactly is your better theory?”
I've been thinking a lot about EMH in recent years and would like to propose a different formulation of EMH - a one that I believe resolves some of the obvious issues with the current theory while preserving many of its conclusions and underlying logic at large. I will also suggest in which situations profit can be made and why the market was indeed inefficient in the COVID-19 situation.
Consider the following question:
If you feed a powerful AGI with almost unlimited computation resources all the public data we have regarding stocks and businesses - will it be able to beat the market?
If you believe in EMH your answer should be a “no.” But this answer seems wrong.
It’s hard to believe that a god-like AGI wouldn’t be able to find any competitive advantage in analyzing the data given practically unlimited resources. The underlying cognitive process of beating the market is the same as any other sufficiently hard cognitive task, a search in highly complex multidimensional space.
Having large amounts of computing power should help you to achieve better results, even without private information.
But yet we have those empirical results that show that traders having a very hard time to beat the market using public data. So I would like to suggest a different version of EMH that solves both of these issues. And the rule can be phrased in the following way:
“In the long run, the costs of analyzing public data in order to obtain alpha will be equal to the gained alpha.”
- Think of crypto mining as an equivalent, if you are able to mine crypto for less money than what it’s worth you can make a profit (equivalent to alpha). But your competitors will do the same – raising the difficulty of mining, so in the long run in a competitive market, you can expect the mining expenses will be very close to the value of the mined crypto.
- In a similar way, companies that invest in the market spend money on analysts and experts to find inefficiencies using public data and as long this is profitable the market should hire analysts to analyze data until the cost of analyzing data is equal to the alpha gained. So in an efficient enough market, you can expect that any marginal investment in data analysis will prove to be unprofitable.
This model is obviously somewhat of a spherical cow, the real world is much more complex and it’s worthwhile to discuss some of the special cases and complexities in the light of this model.
The first thing worth noting is that compared to the crypto mining algorithm which is usually standard across miners – meaning everyone needs to solve the same hash function. In the case of stock markets, the multidimensional search is more complex, domain-level knowledge and deep understanding of industries could be very helpful to get a competitive advantage while these factors don't exist in the crypto case. The practical implication means that someone might have a competitive advantage due to unrelated expertise which will grant him an ability to beat the market in a more cost-efficient way than its competitors.
Another important issue is the phenomenon of reflexivity, meaning the beliefs of the investors change the reality itself and by this creating very complex loops in the market. An interesting example could be the belief in EMH itself, as more people believe that it’s impossible to beat the markets the Ratio of dumb money (index funds) will become higher and will thus lower the data analysis costs you need to get alpha.
The last thing worth noting is that the rule is correct in the long run and on an average and stable situation, meaning when there are radical changes in the costs of data analysis due to rapid market changes it’s much easier to gain alpha. Returning to the crypto example, it would be equivalent to someone changing suddenly the hash functions to be better processed by GPUs instead of CPUs. So in the transition period, we can definitely expect players that have a competitive advantage in the ability to transition quickly to GPUs to make a profit.
COVID-19 Crash and practical considerations
In light of these points consider the COVID-19 market crash: the ability to predict the market suddenly shifted from the day to day necessary data analysis for stock price prediction that revolves more around business KPIs and geopolitical processes to understanding pandemics. How many of the Wall Street analysts are pandemic experts would you think? Probably very few. The rules have changes and prior data analysis resources (currently hired analysts) became suddenly very inefficient.
What players have a competitive advantage when the rules change?
The first group is domain experts; a hobbyist investor pandemics expert can probably outcompete the market on average in this specific scenario.
I'm not a pandemics expert but as part of my undergraduate engineering degree, I worked for a year on a simulation model of a spread of pandemic flu, after reading about the COVID-19 I’ve sold all my stocks at the end of January because I believed I had a domain knowledge advantage over the market.
The second group is nimble generalists that can adjust and learn quickly while big players are much slower to move. Wei Dai who made 700% profit on his bet probably belongs to this category. The classic metaphor in these cases is thinking about turning quickly a small fishing boat vs a cruiser. In a normal scenario, we can expect the large players will outcompete the small ones due to size advantages, but when dealing with black swans that require quickness small players might have the advantage.
Comments sorted by top scores.
comment by romeostevensit ·
2020-02-28T19:51:33.466Z · LW(p) · GW(p)
I think one of the concrete takeaways from GJP and related classes of things is that it's possible to notice when experts are being stupid [LW(p) · GW(p)] in ways that make sense given the incentives and/or the environment changing from the training data the expert is operating off of and then make *measured* bets against said stupidity, since a lot of money seems to go with expert consensus. Calling this an EMH failure seems sort of like...underestimating the ability of intelligence to generate an information advantage? Like, you can in fact just sit down with a blank sheet of paper and gain an information advantage, that's what inventions are.Replies from: Alex_Shleizer
↑ comment by Alex_Shleizer ·
2020-02-29T08:53:50.306Z · LW(p) · GW(p)
EMH actually claims that sitting down with a blank sheet of paper and gaining an information advantage is impossible, the market prices already incorporated all the public data. I agree that this assumption seems wrong (and the goal of my post was to provide an alternative assumption)
comment by Mark Xu (mark-xu) ·
2020-03-17T03:42:08.044Z · LW(p) · GW(p)
I've been thinking about the EMH a lot, since it seemed to me that even after the initial COVID crash the market still was overvaluing everything. This turned out to be correct and now I have a lot more money than I did before (although far less because it takes 4 days to be approved for option trading).
My confusion was about half resolved by the following thought: since actors in the market have finite capital, it can be consistent that the value of an asset is X, everyone knows that it will be Y > X in the far-ish future, but no one buys X until it reaches value Y now because there are other things that you can do that will make you more money, i.e. exploit market volatility, short-term puts/calls, etc. In terms of COVID, everyone can know that a recession is coming, but think that you can make more money through exploiting short term volatility, e.g. panic buying/selling.
What I'm still a little confused about is the fact that recent market movement seems incredibly obvious to me (so obvious that I put literally all my assets into shorting positions), but hedge funds are going out of business and Bridgewater's Pure Alpha lost money. Dalio even thought about COVID significantly before the crash.
I only thought about COVID for like 5 days become becoming extremely confident that the market was going to drop. I wasn't an expert on pandemics. I don't think I'm a nimble generalist. I didn't even make a guesstimate model. I just looked at COVID, read LW comments and read the news. Hedge funds can read both the news and LW, so they should know all the things that I know. They also have more time to think and more incentive to think correctly, so they should be efficient relative to me. But they were not. I notice I am confused.
What do people at hedge funds even do all day? You would at least have a few people working full-time to think about COVID, right? And they can't all get it wrong? Is it really that important to be nimble?
And it didn't just drop once more to correct, it dropped like 3 more times? Something must have gone horribly wrong, but all of my explanations seem way too forced.
Replies from: Wei_Dai, Vaniver, Wei_Dai
↑ comment by Wei_Dai ·
2020-03-17T04:40:35.258Z · LW(p) · GW(p)
In a March 3rd post Dalio wrote:
Reactions to the virus (e.g., “social distancing”) will probably cause a big short-term economic decline followed by a rebound, which probably will not leave a big sustained economic impact. The fact of the matter is that history has shown that even big death tolls have been much bigger emotional affairs than sustained economic and market affairs. My look into the Spanish flu case, which I’m treating as our worst-case scenario, conveys this view; so do the other cases.
This is consistent with my earlier guess [LW(p) · GW(p)]:
Global equity markets may have underestimated the economic effects of a potential COVID-19 pandemic because the only historical parallel to it is the 1918 flu pandemic (which is likely worse than COVID-19 due to a higher fatality rate) and stock markets didn’t drop that much. But maybe traders haven’t taken into account (and I just realized this) that there was war-time censorship in effect which strongly downplayed the pandemic and kept workers going to factories, which is a big disanalogy between the two cases, so markets could drop a lot more this time around. The upshot is that maybe it’s not too late to short the markets.
I also find it curious that unlike past major market drops (such as the 2008 financial crisis) no money manager has become famous from predicting it ahead of time and making money from it. The only one that comes close is Michael Burry but he is apparently just managing his own money now and it sounds like he just had a general bearish bet that wasn't specific to the coronavirus.
↑ comment by Vaniver ·
2020-03-17T03:54:23.878Z · LW(p) · GW(p)
Dalio even thought about COVID significantly before the crash.
I think he thought about the reference class of pandemics, more than he thought about COVID. I think the key details in this becoming as bad as it is are mostly missing from that post.
↑ comment by Wei_Dai ·
2020-03-17T04:44:50.636Z · LW(p) · GW(p)
although far less because it takes 4 days to be approved for option trading
Did you try calling them to expedite the request or figure out what caused the delay? I've applied for options trading at multiple brokerages and they all approved in 1 business day.
Replies from: mark-xu
↑ comment by Mark Xu (mark-xu) ·
2020-04-20T03:56:07.897Z · LW(p) · GW(p)
I misspoke. It wasn't actually getting approved that cost time, it was money I transferred taking time to be approved for options trading. I was led to believe that 4 days was standard and relatively unavoidable.
comment by Jalex Stark (jalex-stark-1) ·
2020-03-01T01:41:57.836Z · LW(p) · GW(p)
I feel like this version of EMH is already a consensus replacement (of the EMH found in textbooks) among the finance people that I know, but it is possible that I am speaking from within a very small bubble. Anyway, I think your formulation is well-stated and useful.
Replies from: Alex_Shleizer
comment by jmh ·
2020-02-28T20:49:07.492Z · LW(p) · GW(p)
I would suggest that one week -- or even a couple of months in the context of COVID-19 -- of market price behavior really tells us little about EMH. A lot there probably depends on just which version of that theory you want to apply.
Quick follow up regarding your last point about small nibble-large slow. I just saw this bit on CNBC. You might need to wait and see what the quarterly income reported from the investment banking/trading side of the house for these large financial houses.Replies from: ioannes_shade
↑ comment by ioannes (ioannes_shade) ·
2020-03-01T04:25:28.877Z · LW(p) · GW(p)
I would suggest that one week -- or even a couple of months in the context of COVID-19 -- of market price behavior really tells us little about EMH.
Doesn't the EMH imply that small active investors shouldn't be able to beat the market / compete with Goldman?Replies from: jmh
↑ comment by jmh ·
2020-03-01T21:03:21.092Z · LW(p) · GW(p)
Depending on both your assumptions about the degree of information asymmetries and version of EMH, EMH can be seen as saying the retail investor and GS cannot beat the index so both should achieve the same investment outcomes. That would be one extreme.
I think the reality is that GS out competes the little guy on two factors: a) ability to actually do the research and make better assessments of the publicly known information (Give me the GS database and I will still not know what the GS analytical team knows, and definitely not in a useful time frame) and b) institutional factors that allows GS relevant but non-public information (e.g., order flow) and they also generate information that is relevant (new product type things).
Part of that is relevant to trading activities but part is going to impact long term, investment results.
comment by Nick R ·
2020-03-06T23:12:19.649Z · LW(p) · GW(p)
I'm afraid the argument was a straw man from the start. Only the so-called "strong form" EMH says that prices are always right. Most economists and even more practitioners have long understood that there are inefficiencies in the market--after all, the market is efficient because departures from efficiency are exploited, driving prices toward efficiency. For most of us, the inefficiencies are too small or too fleeting to be exploitable. For a few, those who are either much smarter than everyone else, or got there earlier, or have the most efficient trading algorithms, there do exist exploitable inefficiencies. It's no coincidence that mispricings used to be much, much larger in the past than they are now, as trading costs have declined and computer algorithms of increasing speed and sophistication have been deployed. Replies from: Alex_Shleizer
↑ comment by Alex_Shleizer ·
2020-03-09T20:24:21.371Z · LW(p) · GW(p)
Strong form doesn't mean that the prices are "always right" the idea of strong form EMH is that the prices also already incorporate private information (meaning you can't get alpha even if you had insider information) compared to Semi-strong that claims that the markets don't account for private information.
The definition of EMH (from wikipedia) is:
The efficient-market hypothesis (EMH) is a hypothesis in financial economics that states that asset prices reflect all available information. A direct implication is that it is impossible to "beat the market" consistently on a risk-adjusted basis since market prices should only react to new information.
There was no straw man, I have specifically reflected on this definition.
comment by bfinn ·
2020-04-29T13:39:35.791Z · LW(p) · GW(p)
An example: some years ago a friend of mine worked for a startup that had the idea of arbitraging between sports betting markets (like Betfair), since they often show different odds and anyone can lay odds.
But only after developing software to do this - which worked - did it dawn on them that the liquidity was too low (e.g. not enough people laying odds). They had no trouble making revenues, but only tens of dollars per hour, not thousands. No possibility of profits.
So they closed the business. And presumably that market remains inefficient.
comment by ioannes (ioannes_shade) ·
2020-03-01T04:23:31.560Z · LW(p) · GW(p)
Relatedly, from Seeing the Smoke [LW · GW]:
The coronavirus was identified on January 7th and spread outside China by the 13th. American media ran some stories about how you should worry more about the seasonal flu. The markets didn’t budge. Rationalist Twitter started tweeting excitedly about R0 and supply chains.
Over the next two weeks, Chinese COVID cases kept climbing at 60%/day reaching 17,000 by February 2nd. Cases were confirmed in Europe and the US. The WHO declared a global emergency. The former FDA commissioner explained why a law technicality made it illegal for US hospitals to test people for coronavirus, implying that we would have no idea how many Americans have contracted the disease. Everyone mostly ignored him including all major media publications, and equity markets hit an all time high. By this point several Rationalists in Silicon Valley and elsewhere started seriously prepping for a pandemic and canceling large social gatherings.
On the 13th, Vox published a story mocking people in Silicon Valley for worrying about COVID-19. The article contained multiple factual mistakes about the virus and the opinions of public health experts.
On February 17th, Eliezer asked how markets should react to an obvious looming pandemic. Most people agreed that the markets should freak out and aren’t. Most people decided to trust the markets over their own judgment. As an avowed efficient marketeerwho hasn’t made an active stock trade in a decade, I started at that Tweet for a long time. I stared at it some more. Then I went ahead and sold 10% of the stocks I owned and started buying respirators and beans.
By the 21st, the pandemic and its concomitant shortages hit everywhere from Iran to Italy while in the US thousands of people were asked to self-quarantine. Most elected officials in the US seemed utterly unaware that anything was happening. CNN ran a front page story about the real enemies being racism and the seasonal flu.
Finally, the narrative couldn't contain the sheer volume of disconfirming evidence. The stock market tumbled 10%. The Washington Post squeezed out one more story about racism before confirming that the virus is spreading among Americans with no links to Wuhan and that’s scary. Trump decided to throw his vice president under the coronavirus bus, finally admitting that it’s a thing that the government is aware of.
And Rationalist Twitter asked: what the fuck is wrong with everyone who is not on Rationalist Twitter?