The current and long-running debt and capital gains driven financial asset market has made everyone insane. If valuations were based on tangible cash flows (dividends paid out of profits) then the question of how much stocks are worth becomes far less insanely self-referential.
Real markets work on markup-based pricing and cash flow from profits. See Steve Keen's work[1]. In a world where the banking system can introduce nearly infinite leverage into any financial transaction (see student loans) what's the price of anything?
That is true, but I think the counter-argument is that the Fed does much of what it does to create price stability, not price accuracy.
Prices should be generally stable in the face of everything but supply and demand shifts brought upon by technological change and competition. If the price of a car rose/fell 20% "accurately" (per your comment) every few years people would have to hedge against the risks that those price fluctuations would cause. Our system reduces the need to worry about such risks b/c they are viewed as unnecessary noise.
In other words, when prices are generally stable we can plan for the future rather than hedging against the impact of random (though market-based) price volatility.
One thing I often wonder about is the value of Facebook stock. It has effectively no voting rights (Because of Zuckerberg's control), and I can't imagine Facebook issuing a dividend... so what rights does it really have? How do you value something like that?
Exactly. It's the modern world's equivalent of debating the number of angels capable of dancing on a pinhead. You have these chumps talking about future cash flows and dividend yield equivalents, all the while the board and executives march off with the actual cash.
The right thing is to eliminate the tax bias towards capital gains and make dividends tax-deductible for corporations, so they have incentive to pay profits out to, you know, the owners. Basically make them act like big LLCs, which is the fair thing: why should Grandma Jones pay the same corporate rate as Mitt Romney on her investments in Coca Cola before she sees a dime?
At some point it'll reverse, but it's been a good forty years, so... who knows?
In the future when Zuckerberg is finally willing to pay a dividend or he is replaced by a board willing to pay a dividend when he retires. It's hard to value that's why the value of Facebook stock is so volatile.
valuations are based not just on current cashflows, but future expected cashflows. That's why leverage is so crucial - it allows us to borrow against future cashflows so as to finance projects today. One way to think about, on this forum, we can agree that at some point in the future, we will have self driving cars, automated medicine, low cost energy etc. So, what we can do is borrow against that future to build stuff today
Yes, I understand productive debt and I have no beef with it (so long as no one is coerced into bailing out bad loans, which, of course, is constantly happening in the name of "systemic stability.")
If you think this has anything to do with the huge debt overhang in western societies... Well, I have an efficient market to sell you.
When doing any sort of theoretical work in finance or economics, academics often make the assumption that markets are rational and efficient. This assumption is almost universally false. They know that markets are neither: markets may or may not be rational (we can never realistically gather enough data to know thanks to information asymmetry) and they certainly aren't 100% efficient over most timelines.
Just because the assumption is wrong doesn't mean it's not useful: without it, you can't even build a reasonable model of financial markets. But because the assumption is core to financial modeling, this means that all financial models are wrong. In fact, that's exactly how my financial modeling professor opened up the first class: by explicitly stating that all models are wrong in some way. You have to understand the ways that the model can be wrong in order to make any substantive claims about it. The Black-Shoals options pricing model assumes market liquidity; it's not very useful if you're talking about an asset that isn't very liquid.
Misunderstanding when models should and should not be applied is one of the core reasons we got into an asset bubble in the mid-2000s. Banks were pricing financial products using models whose assumptions did not hold over the long-term. They didn't understand how their models were wrong until it was too late, and it ended up losing them (but ultimately the American taxpayers) a lot of money. But because the assumption that markets are always rational and efficient is wrong, it doesn't follow that markets are never rational or efficient. Markets can be rational and efficient, but it depends on the relative timeframe and the structure of the market involved.
It turns that it produces results that are close enough to reality to be good enough in lots of scenarios. (And, similarly to how you can ignore relativity in lots of scenarios, you can also ignore lots of other complexifying bits of physics some of the time -- plenty of mechanical equations will give you "good enough" results without taking into account air resistance, for example).
Yes, much of economics is like that. Economics is studying an inherently more complicated problem than simple billiard-ball mechanics, but the idea that economics has nothing useful to say about the real world is an agenda sold by people who don't like the implications of orthodox economics.
Economics is even more complex than physics in this regard, because in physics there is often a "right" answer. In economics, there may or may not be a right answer depending on the subject, but it's impossible to know if you've achieved it in most cases anyway.
And what investors try to do would be akin to asking a physicist to examine two cars before a Nascar race and tell you who was going to win the race.
You are attempting to measure the objective truth inherent in some model. Be careful with this (or at least be explicit about it).
Newtonian mechanics is pretty accurate to an astonishing degree[0]. To call it "wrong in all scenarios" is saying that because it is wrong in one scale, it is wrong in another. At which point every physics theory we have is "wrong in all scenarios". The standard model can't predict or talk about planck level physics, and thus, it is "wrong in all scenarios". GR can't talk about quantum gravity and thus is "wrong in all scenarios". Hell, the Schrödinger equation is "wrong in all scenarios".
If you believe in a fundamental truth inherent in some equation or model of some system, that is fine. But I think that is far from an accepted point of view.
To put your point in other words, Newtonian mechanics isn't wrong. It's a system of measurement that is only useful at certain scales.
Imagine a yardstick that is only marked at full inches. You can use it to measure things in the scale of a couple of yards, a few feet, and many inches. You can't use it to measure anything smaller than an inch because it's not marked for that scale. You can't use it to measure anything more than a couple of yards because that's unwieldy. That doesn't mean that the yard stick is "wrong".
This is a flawed analogy, because the yardstick is accurate at the scales for which it is practical, whereas Newtonian mechanics is inaccurate at all scales, just less so in its sweet spot.
Of course. Nobody is arguing that Newtonian mechanics is not a phenomenal approximation at scales that are very useful for humans, but that doesn't make the analogy any less flawed.
Then I don't understand what you mean. You said the yardstick is accurate at some scales whereas Newtonian mechanics is always inaccurate, which to me implies that the yardstick is more accurate at some scales.
The yardstick analogy was set up by the parent to be 'because the yardstick doesn't have marks less than an inch'
Then yardstick is always an approximation that is useful within a particular domain, just as newtonian mechanics are always an approximation that is useful within a particular domain. So far we are in agreement.
Newtonian mechanics always produces an incorrect result, however when the error is small enough to be neglected, because our measurements are noisy or we have no requirement for greater precision, then we can say that they are accurate for our purposes. This is pretty much the definition of an approximation.
It also must be pointed out that in order to know whether our application falls within the domain of values for which Newtonian mechanics are accurate enough, we must also understand something about relativity and quantum mechanics.
Newtonian mechanics alone can't tell you anything about when it is grossly inaccurate, and when it gives you a value that is indistinguishable from experiment. You must understand its limits in order to use in in the general case. It is therefore not 'perfectly accurate', but merely a good approximation based on limited data.
Newtonian mechanics are not completely accurate at any time. However as you point out, the error in the model is often dwarfed by the error in the measurement.
In certain kinds of abstract thought experiments, it might not match.
If you're doing orbital calculations, it's often wrong.
In the vast majority of real world calculations, it's not approximate. It gives the same answer as relativistic calculations.
Oh, I have an analogy. It's like using 64 bit integers instead of bigints. Any time you input real world numbers, they never go above ten million. Both models give you the correct answer, no error.
No model ever captures the entire nuance of reality. Even a notion of speed is misleading when your objects contain heat. So don't peek inside the model and judge it based on how the pieces interact inside. Judge it based on the results. And Newtonian physics typically give you ideal perfect results.
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Or maybe I should put it a different way:
Newtonian physics are an approximation for the question "In a counterfactual thought experiment where my measurement had infinite precision in trailing zeroes, what would the answer be?"
Newtonian physics are exact for the question "What is the answer based on my measurement?"
The drawback in this reasoning is that with Newtonian mechanics alone you cannot know whether your measurements are in the domain for which it is accurate or not. For that you need relativity and quantum mechanics. Newtonian mechanics alone is seriously wrong, but if you know (because you understand relartivity and quantum mechanics) that you are in a domain for which it is sufficiently accurate, then you can use it to produce accurate results.
You seem to have completely missed my (and Asimov's) point about how there are degrees of wrongness.
Newtonian mechanics may not be 100% accurate but it's extremely accurate for a wide range of real-world, useful scenarios, to the extent that any discrepancy with reality is often beyond what can be measured. For example, the Pioneer Anomaly was a discrepancy of a few thousand kilometers in distance, over a total distance of more than a billion kilometers. This was still enough to prompt a lot of investigation, and the problem was finally solved by properly accounting for photon pressure from thermal emissions from the spacecraft itself. This is a discrepancy on the order of parts per million that could not only be detected but was considered extremely significant.
I get the impression that economists, on the other hand, are ecstatic if their theories are within even 1% of reality. That's a whole different class of wrongness.
The real problem with "market efficiency" arguments is that nobody is arguing about market efficiency. What people really want to argue is that they are smarter than the markets, or that they are capable of guessing what the markets are going to do better than the markets. This means that they or their intellectual fellow-travelers should therefore be given power over the market which they can use to make it do the "right" things by virtue of their insight.
Unfortunately, "the market is irrational" doesn't imply "you can name the ways in which it is irrational" nor does it imply even that there exists any single entity that is capable of correctly naming the way in which it is irrational. It is perfectly capable of having, and indeed almost certainly has, a chaotic, highly-information-packed, very fast-moving irrationality that is beyond any human or human institution's ability to understand, quantify, express, predict, control, harness, or "fix". I do not mean that figuratively or metaphorically... there's no reason to believe the market's irrationality is even remotely comprehensible.
So the argument shouldn't be phrased in terms of whether the market is efficient or not... everybody knows it isn't. So let's cut to the chase... why do you (for generalized values of "you") think you know better, and if you do know better, why aren't you using it to make a killing, or, alternatively, how about you write down some interesting predictions about what's going to happen in three or four years and demonstrate the rightness of your theories? Say something interesting and verifiable, and let's see how you do.
In the meantime, merely making the tedious observation that the market isn't "really" rational gets you no points, and I perceive no obligation on my part to change any of my political beliefs merely because "the market isn't rational". On the other hand, make correct predictions years in advance, especially if you can do so reliably, and I will sit up and take notice, I promise you. But, be warned, I'm very familiar with the process of bending a prediction to fit the data and that's not going to cut it... I demand that you be right, full stop. If you can't do that... welcome to the group of people who know the economy is irrational but can do nothing meaningful about it, population: entire rest of humanity.
> Unfortunately, "the market is irrational" doesn't imply "you can name the ways in which it is irrational" nor does it imply even that there exists any single entity that is capable of correctly naming the way in which it is irrational.
Absolutely. The number of players involved and the level of information asymmetry mean that it's not even knowable how the market is or isn't irrational.
You're basically arguing that "alpha" is an illusion borne out of the quirks of distributed probability. I (along with many leading minds in finance) also believe this to be true. What many hedge funds call "alpha" is often just dumb luck. In the few cases that it isn't, policy changes at central banks are often involved (this is why it's taken the fed several years to choreograph the wind-down of QE).
But the irony here is that the fact that everyone believes they can beat the market is what makes the market as efficient and liquid as it is. If everyone traded with the same strategy, you'd see massive swings in volatility and information asymmetry would be an even bigger issue than it is. Because there are so many different trading strategies built to hunt out arbitrage in certain pockets, the market as a whole is incredibly efficient - or at least more efficient than it would otherwise be.
Whether or not they are capable of guessing the direction of the markets is irrelevant: if you take a portfolio strategy and make equal bets on enough different assets, you'll end up with something that is probably close to the market rate of return anyway. Note that this only applies to the "professional investor" class (as defined by the SEC); everyone else is simply better off in mutual funds. Professional investors have access to classes of securities with a higher average rate of return than normal citizens do, so they likely can beat the market as long as they're also able to absorb big losses in the years they don't beat the market.
Problems occur when people expect the model to correspond 100% to reality, which it was never intended to do. It is confusing the map for the territory. Unfortunately most journalists have this tendency.
>Misunderstanding when models should and should not be applied is one of the core reasons we got into an asset bubble in the mid-2000s. Banks were pricing financial products using models whose assumptions did not hold over the long-term. They didn't understand how their models were wrong until it was too late, and it ended up losing them (but ultimately the American taxpayers) a lot of money.
I don't think this is true. Plenty of smart people who worked with various models knew their data wasn't solid and reflected just a few years of a benign market, and plenty of people understood the rather obvious idea of a Black Swan.
The reason they kept at it was systemic: they were paid to sit and cheer the market up, continue to sell product, and generally not question the business model.
The people who made a lot of money off the crash tended to be people who were able to sit out the upswing in some way, without being punished too hard. Hedge fund managers with either new funds or long track records, like Paulson and Andrew Lahde.
The crash was always coming. I remember going to a luncheon at GS where that was the major point. This was in 2006, and they predicted it would start in 2007 and culminate in 2008.
Some knew, but most didn't appreciate the risk correctly. Of course they knew about the idea of black swan events, but 2008 was not a black swan event. It was caused by a rather mild dip in real estate prices and a rather mild amount of mortgage defaults.
The problem was that models assumed some risks were uncorrelated and in 2008 they became highly correlated.
A lot of major wall street institutions got destroyed by it.
It was. Their models made the assumption that debt ratings were accurate, and they were substituting leveraged mortgage backed securities with AA ratings for risk-free debt. They also underestimated the effects that a liquidity crunch would have - any asset model backed by volatile assets assumes liquidity, but doesn't tell you what will happen in a period of non-liquidity.
Most models assume financial markets are a continuous function, when in reality markets are mostly continuous with periodic step functions. When you hit a period with a step function, the market is either being irrational or correcting prior irrationality. You can't predict these step functions with any accuracy; though many of the big investment banks knew it was coming a year or two in advance. But even if you can predict them, there is no good way to model them.
Goldman Sachs, for all its faults, is run by exceptionally competent people. They came out of the downturn stronger than they went in. They understand the models well because they built most of them. But there were plenty of large market players at Lehman, Merrill, Nationwide, etc. who were ignorant of the problems until it was too late. These are the people who just did what the model told them to because it was making them money, and they didn't understand the risk exposure they really had.
>Their models made the assumption that debt ratings were accurate, and they were substituting leveraged mortgage backed securities with AA ratings for risk-free debt. They also underestimated the effects that a liquidity crunch would have - any asset model backed by volatile assets assumes liquidity, but doesn't tell you what will happen in a period of non-liquidity.
Do you think all these guys with fancy degrees did not think about this? Come on, of course they did. You don't even need to know about any model in particular to understand that models rest on assumptions. It's a high school concept they teach you in any TOK class.
The reason GS was able to react is IMO they have a great network. The culture is geared towards finding out what other people are thinking. They're the only firm I've been out to dinner with where the guys cared to hear my opinion, in depth. The same salesguy at another shop reverted back to type.
So they found out through their feedback that many, many people did not think the models were correct and acted on it.
> Misunderstanding when models should and should not be applied is one of the core reasons we got into an asset bubble in the mid-2000s. Banks were pricing financial products using models whose assumptions did not hold over the long-term.
IMHO there's nothing wrong with economic bubbles, there's no economic law that I know of which says that economic growth should only happen linearly.
What was wrong with the financial bubble of 2008 was the way it was handled, starting with the Bear Stearns bailout in early 2008 (of which not many people speak anymore, is like everything started with Lehman Brothers in September).
Yes, banks had made some very wrong assumptions and they should have paid dearly for them, like in any free market. That didn't happen.
You don't; and most models don't even try because the causes behind market crashes are often complex, interdependent relationships that often aren't fully understood until after the crash has happened. But you can use other tools to perform a catastrophic risk analysis that basically asks questions like "Let's say our assumptions change; what does this model look like in a world where every mortgage-backed security drops to junk status?".
Problem is, the companies selling said securities have no interest in identifying the downside. Nor are they legally obligated to since they are selling these products to "professional investors" who ostensibly should know better.
It's pretty easy to guess how the EMH got started: economics professors tried to trade the markets and got their heads handed to them in thin slices. Their egos, thus battered, created an excuse. What they didn't understand is that trading is a competitive game of skill: mere intelligence is not sufficient, any more than it would be if they walked onto a tennis court with Roger Federer.
This is totally wrong; there's very little skill involved in trading. Any university student with a basic knowledge of calculus can learn all there is to know about the markets from a skill perspective in a year or less. In fact, this is exactly why the big investment banks prefer to hire kids fresh out of college.
Where companies really make a lot of money is on the relationships. Goldman Sachs doesn't make $500 million off an IPO because they're better at drafting SEC paperwork than anyone else; they make that money because they have thousands of clients with money to invest that they can call and convince to fund the IPO.
> This is totally wrong; there's very little skill involved in trading.
Not only are you badly misinformed, you are spreading information that could encourage someone to risk their money when they don't know what they're doing.
First off, something that requires a university education and a year of full-time training is not what most people think of when they hear the phrase "very little skill involved". When a member of the general public tries trading for the first time, they have far less than even that (and even economics professors have less of it than they think.)
Secondly, what you're describing is algorithmic trading, not discretionary trading. I guarantee you that that math whiz fresh out of college is not allowed to risk significant amounts of money on personal hunches. (I would guess they don't learn discretionary trading at all; they have their hands full with the algorithms.) I'm sure there's a lot of infrastructure to ensure that any strategy they come up with is well back-tested before going live. Again, the general public (including the professors) isn't doing algo trading and doesn't have access to that infrastructure and that support system.
Tennis was not the best analog. Trading is more like poker. First, they both have the property that the more you win, the bigger advantage you have, and conversely. Second, as they say, if you don't know who the sucker at the table is, it's you.
Except good poker players do tend to win lots of games, whereas studies repeatedly show that no trader ever consistently beats index funds, and all those skills have more to do with impressing gullible investors than with finance markets.
I can see how what I meant could be taken the wrong way. What I meant is that you can learn 90% of what you need to know about the mechanics of investment finance in a year. The rest of your career is spent cultivating relationships so that people allow you to handle large sums of money for them.
The "skill" involved in trading isn't really that difficult to learn - it's tradecraft, which you have to learn in any industry. And you're right; the math whiz fresh out of college doesn't get a ton of money to play with. He gets a small amount and if he does well, is progressively trusted with more. The bulk of this type of trading is about risk balancing. Institutional traders will make more money here simply because they trade using proprietary platforms that get simply them better prices on securities. The algo guys optimize the ordering systems such that the prop traders get first pick at everything.
Trading at high levels (billion dollar positions and above) is a lot like poker; but the bulk of securities trading is really no different than playing a video game. I used to know a guy who worked for a big HFT firm, and that's exactly how he described it. Humans are excellent at pattern recognition, and so the HFT guys use humans to make decisions about when to get in/out of an asset. Computer programs show patterns to the analysts, who issue a "yes/no" decision and who are judged solely on their performance.
But yes, you are also right that mere mortals like us have no chance because it's not an even playing field. I mean, the algo traders pay millions of dollars a year to get Reuters feeds 60 seconds early, so by the time news hits the market, the price has already adjusted. Hell, the trading platforms that are processing your orders sell access to their incoming order feed, and the algo traders love to pre-empt your trades to drive the price up.
A more apt definition would be that trading is like poker where the guys with huge piles of chips also have X-ray vision. And they get to skim the pot even when they lose. If you have X-ray vision and pot-skimming rights, then yeah, it's just poker. If you don't, then you should just go play Blackjack (i.e. buy-and-hold investment where you play against the market as a whole, not any individual players).
Indeed the article is poor. His beauty contest results are more plausibly explained by anchoring bias (start at 2 thirds of 100). EMH is about price dynamics so a single "run" of the beauty contest says nothing about it. I am willing to bet a significant amount that after a couple of iterations, the bids would go to zero.
I simply dont see The "paradox" the author thinks they have discovered. There is reams of empirical evidence that active fund management is GENERALLY a losing proposition for savers. Both parties to a trade can end up losers because trading costs are never zero. If you want to go quickly broke, just keep blindly buying and selling over and over. Even if you beat the spread, the fees will swallow your money.
Making fun of economists who assume efficient markets is a lot like making fun of physicists who assume frictionless motion. Or computer scientists who assume an infinite Turing tape. Simplifying assumptions can be very useful if you know their limitations.
It seems that in the author's beauty contest game, most people are unable to analyze this game correctly at the top of their head. Maybe some people don't even bother and just pick any random number. But assume that this game is played repeatedly and for real money (like in a stock market). I think people will notice that low numbers consistently win, which will cause a downward trend towards the Nash equilibrium in very few iterations.
precisely. In fact, you see it in the markets, even among quantitative Phds, only a very small fraction beats the market. It's broadly efficient, on long enough time scales. It's inefficient on extremely short time scales, which is why HFT works so well.
If physicists never turned "frictionless motion" assumptions into useful, accurate predictions, I see no reason not to make fun of them.
Physics has a long history of extreme success. Economics, on the other hand, often has more opinions than practitioners, and no physics-level accurate predictions in sight.
About three quarters of the way down, the author draws these conclusions from a classic 1980 paper:
- there is smart money in the market,
- the smart money can and is outperforming right now, but that smart money isn't YOU,
- so sit down and index.
I don't mean to be anti-intellectual, the various versions of the EMH and the responses to them are interesting, important concerns in the study of finance and economics. But the take away for the vast majority of the public should be the above. The market may or may not be efficient but you aren't going to beat it. And even if someone else can, he isn't going to use that ability for your benefit.
I think what it means any money can be "smart money"[1], but over a longer horizon, no money can be consistently smart.
[1] If you work in a company you're well placed than most in judging the value of the company. Yes there are regulations to prevent you from insider trading, but no it doesn't mean you can't invest in the company (or short it!) on the basis you know your colleagues better than others. You, however, have no advantage over the market over all other forms of investment.
When you do find an alpha, it tends to exist for some systemic reason. The financial system is organized in some way that systematically presents opportunities to make money. It may have to do with slowness of large players, information leakage from weak order placement systems, ideological disposition towards disregarding some phenomenon, etc.
The way to think about it is not some efficient calculating machine, but as an ecosystem. Can you imagine a tiger saying "hey, there's more prey here than I need to eat and reproduce. Must be something wrong with the system"? No, he just eats and shags. Evolution keeps things in check, meaning things don't go obviously out of whack, but now and again local imbalances occur that can be exploited. The fact that they are exploited also obscures their existence from a high level.
I've got a tangential question about efficiency that someone might be able to help with: The perceived wisdom is that capitalist systems result in efficient companies, because they beat the inefficient ones. So for example we have Apple making tons of money because they were better at what they were doing than (most) of their competitors. So the end result of a bunch of competition is an efficient company ... but how efficient was the process of getting to that end result? So for example once you count all the failed competitors in a given market, the bankruptcies, redundancies, people who invested their time and money in failing businesses. Has anyone measured the overall 'efficiency' of a competitive system whittling down the competitors to the viable ones? Is there a name for this concept in economics?
I'm wondering because efficient end result =/= efficient process, and the process itself must have a cost to society etc.
> the hypothesis that there is no information in the past history of share prices which can be used to predict the future
A belief in that hypothesis strikes me as particularly delusional. The strongest indicator of what a share price will be at `t+1` is what the share price is at `t+0`. Why would anyone delude themselves otherwise?
Maybe that would hold true if execution prices are never revealed (not even to the participants of a trade), but prices are streamed out constantly and are exactly what traders are interested in (how else can they measure their 'gains' and 'losses'?).
A market is weak-form efficient if you cannot use past share prices to predict the direction of future price changes. Or in other words, if you can't use past returns to predict future returns. There's no delusion.
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[ 315 ms ] story [ 2875 ms ] threadReal markets work on markup-based pricing and cash flow from profits. See Steve Keen's work[1]. In a world where the banking system can introduce nearly infinite leverage into any financial transaction (see student loans) what's the price of anything?
[1] - http://www.amazon.com/Debunking-Economics-Revised-Expanded-D...
Prices should be generally stable in the face of everything but supply and demand shifts brought upon by technological change and competition. If the price of a car rose/fell 20% "accurately" (per your comment) every few years people would have to hedge against the risks that those price fluctuations would cause. Our system reduces the need to worry about such risks b/c they are viewed as unnecessary noise.
In other words, when prices are generally stable we can plan for the future rather than hedging against the impact of random (though market-based) price volatility.
One thing I often wonder about is the value of Facebook stock. It has effectively no voting rights (Because of Zuckerberg's control), and I can't imagine Facebook issuing a dividend... so what rights does it really have? How do you value something like that?
The right thing is to eliminate the tax bias towards capital gains and make dividends tax-deductible for corporations, so they have incentive to pay profits out to, you know, the owners. Basically make them act like big LLCs, which is the fair thing: why should Grandma Jones pay the same corporate rate as Mitt Romney on her investments in Coca Cola before she sees a dime?
At some point it'll reverse, but it's been a good forty years, so... who knows?
If you think this has anything to do with the huge debt overhang in western societies... Well, I have an efficient market to sell you.
Read Keen. He explains it very well.
Just because the assumption is wrong doesn't mean it's not useful: without it, you can't even build a reasonable model of financial markets. But because the assumption is core to financial modeling, this means that all financial models are wrong. In fact, that's exactly how my financial modeling professor opened up the first class: by explicitly stating that all models are wrong in some way. You have to understand the ways that the model can be wrong in order to make any substantive claims about it. The Black-Shoals options pricing model assumes market liquidity; it's not very useful if you're talking about an asset that isn't very liquid.
Misunderstanding when models should and should not be applied is one of the core reasons we got into an asset bubble in the mid-2000s. Banks were pricing financial products using models whose assumptions did not hold over the long-term. They didn't understand how their models were wrong until it was too late, and it ended up losing them (but ultimately the American taxpayers) a lot of money. But because the assumption that markets are always rational and efficient is wrong, it doesn't follow that markets are never rational or efficient. Markets can be rational and efficient, but it depends on the relative timeframe and the structure of the market involved.
But it turns out that it's still a good idea to teach Newtonian mechanics -- it's a useful model, as long as you know its limitations.
Theories and models aren't just right or wrong. Some are more wrong than others. Isaac Asimov summed it up better than I could:
http://chem.tufts.edu/answersinscience/relativityofwrong.htm
Newtonian mechanics isn't so much wrong as it is incomplete. Is anything in economics like that? It doesn't seem so.
It turns that it produces results that are close enough to reality to be good enough in lots of scenarios. (And, similarly to how you can ignore relativity in lots of scenarios, you can also ignore lots of other complexifying bits of physics some of the time -- plenty of mechanical equations will give you "good enough" results without taking into account air resistance, for example).
Yes, much of economics is like that. Economics is studying an inherently more complicated problem than simple billiard-ball mechanics, but the idea that economics has nothing useful to say about the real world is an agenda sold by people who don't like the implications of orthodox economics.
And what investors try to do would be akin to asking a physicist to examine two cars before a Nascar race and tell you who was going to win the race.
You are attempting to measure the objective truth inherent in some model. Be careful with this (or at least be explicit about it).
Newtonian mechanics is pretty accurate to an astonishing degree[0]. To call it "wrong in all scenarios" is saying that because it is wrong in one scale, it is wrong in another. At which point every physics theory we have is "wrong in all scenarios". The standard model can't predict or talk about planck level physics, and thus, it is "wrong in all scenarios". GR can't talk about quantum gravity and thus is "wrong in all scenarios". Hell, the Schrödinger equation is "wrong in all scenarios".
If you believe in a fundamental truth inherent in some equation or model of some system, that is fine. But I think that is far from an accepted point of view.
[0]http://www.npl.washington.edu/eotwash/sites/www.npl.washingt...
Imagine a yardstick that is only marked at full inches. You can use it to measure things in the scale of a couple of yards, a few feet, and many inches. You can't use it to measure anything smaller than an inch because it's not marked for that scale. You can't use it to measure anything more than a couple of yards because that's unwieldy. That doesn't mean that the yard stick is "wrong".
The yardstick analogy was set up by the parent to be 'because the yardstick doesn't have marks less than an inch'
Then yardstick is always an approximation that is useful within a particular domain, just as newtonian mechanics are always an approximation that is useful within a particular domain. So far we are in agreement.
Newtonian mechanics always produces an incorrect result, however when the error is small enough to be neglected, because our measurements are noisy or we have no requirement for greater precision, then we can say that they are accurate for our purposes. This is pretty much the definition of an approximation.
It also must be pointed out that in order to know whether our application falls within the domain of values for which Newtonian mechanics are accurate enough, we must also understand something about relativity and quantum mechanics.
Newtonian mechanics alone can't tell you anything about when it is grossly inaccurate, and when it gives you a value that is indistinguishable from experiment. You must understand its limits in order to use in in the general case. It is therefore not 'perfectly accurate', but merely a good approximation based on limited data.
In 99.9% of use cases, Newtonian physics are completely accurate, not a decimal out of place.
Market explanations of 'efficiency'? Way out of whack.
If you're doing orbital calculations, it's often wrong.
In the vast majority of real world calculations, it's not approximate. It gives the same answer as relativistic calculations.
Oh, I have an analogy. It's like using 64 bit integers instead of bigints. Any time you input real world numbers, they never go above ten million. Both models give you the correct answer, no error.
No model ever captures the entire nuance of reality. Even a notion of speed is misleading when your objects contain heat. So don't peek inside the model and judge it based on how the pieces interact inside. Judge it based on the results. And Newtonian physics typically give you ideal perfect results.
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Or maybe I should put it a different way:
Newtonian physics are an approximation for the question "In a counterfactual thought experiment where my measurement had infinite precision in trailing zeroes, what would the answer be?"
Newtonian physics are exact for the question "What is the answer based on my measurement?"
Newtonian mechanics may not be 100% accurate but it's extremely accurate for a wide range of real-world, useful scenarios, to the extent that any discrepancy with reality is often beyond what can be measured. For example, the Pioneer Anomaly was a discrepancy of a few thousand kilometers in distance, over a total distance of more than a billion kilometers. This was still enough to prompt a lot of investigation, and the problem was finally solved by properly accounting for photon pressure from thermal emissions from the spacecraft itself. This is a discrepancy on the order of parts per million that could not only be detected but was considered extremely significant.
I get the impression that economists, on the other hand, are ecstatic if their theories are within even 1% of reality. That's a whole different class of wrongness.
Unfortunately, "the market is irrational" doesn't imply "you can name the ways in which it is irrational" nor does it imply even that there exists any single entity that is capable of correctly naming the way in which it is irrational. It is perfectly capable of having, and indeed almost certainly has, a chaotic, highly-information-packed, very fast-moving irrationality that is beyond any human or human institution's ability to understand, quantify, express, predict, control, harness, or "fix". I do not mean that figuratively or metaphorically... there's no reason to believe the market's irrationality is even remotely comprehensible.
So the argument shouldn't be phrased in terms of whether the market is efficient or not... everybody knows it isn't. So let's cut to the chase... why do you (for generalized values of "you") think you know better, and if you do know better, why aren't you using it to make a killing, or, alternatively, how about you write down some interesting predictions about what's going to happen in three or four years and demonstrate the rightness of your theories? Say something interesting and verifiable, and let's see how you do.
In the meantime, merely making the tedious observation that the market isn't "really" rational gets you no points, and I perceive no obligation on my part to change any of my political beliefs merely because "the market isn't rational". On the other hand, make correct predictions years in advance, especially if you can do so reliably, and I will sit up and take notice, I promise you. But, be warned, I'm very familiar with the process of bending a prediction to fit the data and that's not going to cut it... I demand that you be right, full stop. If you can't do that... welcome to the group of people who know the economy is irrational but can do nothing meaningful about it, population: entire rest of humanity.
Absolutely. The number of players involved and the level of information asymmetry mean that it's not even knowable how the market is or isn't irrational.
You're basically arguing that "alpha" is an illusion borne out of the quirks of distributed probability. I (along with many leading minds in finance) also believe this to be true. What many hedge funds call "alpha" is often just dumb luck. In the few cases that it isn't, policy changes at central banks are often involved (this is why it's taken the fed several years to choreograph the wind-down of QE).
But the irony here is that the fact that everyone believes they can beat the market is what makes the market as efficient and liquid as it is. If everyone traded with the same strategy, you'd see massive swings in volatility and information asymmetry would be an even bigger issue than it is. Because there are so many different trading strategies built to hunt out arbitrage in certain pockets, the market as a whole is incredibly efficient - or at least more efficient than it would otherwise be.
Whether or not they are capable of guessing the direction of the markets is irrelevant: if you take a portfolio strategy and make equal bets on enough different assets, you'll end up with something that is probably close to the market rate of return anyway. Note that this only applies to the "professional investor" class (as defined by the SEC); everyone else is simply better off in mutual funds. Professional investors have access to classes of securities with a higher average rate of return than normal citizens do, so they likely can beat the market as long as they're also able to absorb big losses in the years they don't beat the market.
Problems occur when people expect the model to correspond 100% to reality, which it was never intended to do. It is confusing the map for the territory. Unfortunately most journalists have this tendency.
I don't think this is true. Plenty of smart people who worked with various models knew their data wasn't solid and reflected just a few years of a benign market, and plenty of people understood the rather obvious idea of a Black Swan.
The reason they kept at it was systemic: they were paid to sit and cheer the market up, continue to sell product, and generally not question the business model.
The people who made a lot of money off the crash tended to be people who were able to sit out the upswing in some way, without being punished too hard. Hedge fund managers with either new funds or long track records, like Paulson and Andrew Lahde.
The crash was always coming. I remember going to a luncheon at GS where that was the major point. This was in 2006, and they predicted it would start in 2007 and culminate in 2008.
The problem was that models assumed some risks were uncorrelated and in 2008 they became highly correlated.
A lot of major wall street institutions got destroyed by it.
2008 was not the first time correlation shot to 1. Sure, it's hard to model, I'll admit that. But to think that nobody had considered this is wrong.
I saw a bunch of people who knew the models were wrong, but nobody had the balls to say so while the spreadsheets still worked.
Most models assume financial markets are a continuous function, when in reality markets are mostly continuous with periodic step functions. When you hit a period with a step function, the market is either being irrational or correcting prior irrationality. You can't predict these step functions with any accuracy; though many of the big investment banks knew it was coming a year or two in advance. But even if you can predict them, there is no good way to model them.
Goldman Sachs, for all its faults, is run by exceptionally competent people. They came out of the downturn stronger than they went in. They understand the models well because they built most of them. But there were plenty of large market players at Lehman, Merrill, Nationwide, etc. who were ignorant of the problems until it was too late. These are the people who just did what the model told them to because it was making them money, and they didn't understand the risk exposure they really had.
Do you think all these guys with fancy degrees did not think about this? Come on, of course they did. You don't even need to know about any model in particular to understand that models rest on assumptions. It's a high school concept they teach you in any TOK class.
The reason GS was able to react is IMO they have a great network. The culture is geared towards finding out what other people are thinking. They're the only firm I've been out to dinner with where the guys cared to hear my opinion, in depth. The same salesguy at another shop reverted back to type.
So they found out through their feedback that many, many people did not think the models were correct and acted on it.
IMHO there's nothing wrong with economic bubbles, there's no economic law that I know of which says that economic growth should only happen linearly.
What was wrong with the financial bubble of 2008 was the way it was handled, starting with the Bear Stearns bailout in early 2008 (of which not many people speak anymore, is like everything started with Lehman Brothers in September).
Yes, banks had made some very wrong assumptions and they should have paid dearly for them, like in any free market. That didn't happen.
Problem is, the companies selling said securities have no interest in identifying the downside. Nor are they legally obligated to since they are selling these products to "professional investors" who ostensibly should know better.
Where companies really make a lot of money is on the relationships. Goldman Sachs doesn't make $500 million off an IPO because they're better at drafting SEC paperwork than anyone else; they make that money because they have thousands of clients with money to invest that they can call and convince to fund the IPO.
Not only are you badly misinformed, you are spreading information that could encourage someone to risk their money when they don't know what they're doing.
First off, something that requires a university education and a year of full-time training is not what most people think of when they hear the phrase "very little skill involved". When a member of the general public tries trading for the first time, they have far less than even that (and even economics professors have less of it than they think.)
Secondly, what you're describing is algorithmic trading, not discretionary trading. I guarantee you that that math whiz fresh out of college is not allowed to risk significant amounts of money on personal hunches. (I would guess they don't learn discretionary trading at all; they have their hands full with the algorithms.) I'm sure there's a lot of infrastructure to ensure that any strategy they come up with is well back-tested before going live. Again, the general public (including the professors) isn't doing algo trading and doesn't have access to that infrastructure and that support system.
Tennis was not the best analog. Trading is more like poker. First, they both have the property that the more you win, the bigger advantage you have, and conversely. Second, as they say, if you don't know who the sucker at the table is, it's you.
The "skill" involved in trading isn't really that difficult to learn - it's tradecraft, which you have to learn in any industry. And you're right; the math whiz fresh out of college doesn't get a ton of money to play with. He gets a small amount and if he does well, is progressively trusted with more. The bulk of this type of trading is about risk balancing. Institutional traders will make more money here simply because they trade using proprietary platforms that get simply them better prices on securities. The algo guys optimize the ordering systems such that the prop traders get first pick at everything.
Trading at high levels (billion dollar positions and above) is a lot like poker; but the bulk of securities trading is really no different than playing a video game. I used to know a guy who worked for a big HFT firm, and that's exactly how he described it. Humans are excellent at pattern recognition, and so the HFT guys use humans to make decisions about when to get in/out of an asset. Computer programs show patterns to the analysts, who issue a "yes/no" decision and who are judged solely on their performance.
But yes, you are also right that mere mortals like us have no chance because it's not an even playing field. I mean, the algo traders pay millions of dollars a year to get Reuters feeds 60 seconds early, so by the time news hits the market, the price has already adjusted. Hell, the trading platforms that are processing your orders sell access to their incoming order feed, and the algo traders love to pre-empt your trades to drive the price up.
A more apt definition would be that trading is like poker where the guys with huge piles of chips also have X-ray vision. And they get to skim the pot even when they lose. If you have X-ray vision and pot-skimming rights, then yeah, it's just poker. If you don't, then you should just go play Blackjack (i.e. buy-and-hold investment where you play against the market as a whole, not any individual players).
The answer is: the mean gradually approaches zero.
I simply dont see The "paradox" the author thinks they have discovered. There is reams of empirical evidence that active fund management is GENERALLY a losing proposition for savers. Both parties to a trade can end up losers because trading costs are never zero. If you want to go quickly broke, just keep blindly buying and selling over and over. Even if you beat the spread, the fees will swallow your money.
It seems that in the author's beauty contest game, most people are unable to analyze this game correctly at the top of their head. Maybe some people don't even bother and just pick any random number. But assume that this game is played repeatedly and for real money (like in a stock market). I think people will notice that low numbers consistently win, which will cause a downward trend towards the Nash equilibrium in very few iterations.
Physics has a long history of extreme success. Economics, on the other hand, often has more opinions than practitioners, and no physics-level accurate predictions in sight.
[1] If you work in a company you're well placed than most in judging the value of the company. Yes there are regulations to prevent you from insider trading, but no it doesn't mean you can't invest in the company (or short it!) on the basis you know your colleagues better than others. You, however, have no advantage over the market over all other forms of investment.
The way to think about it is not some efficient calculating machine, but as an ecosystem. Can you imagine a tiger saying "hey, there's more prey here than I need to eat and reproduce. Must be something wrong with the system"? No, he just eats and shags. Evolution keeps things in check, meaning things don't go obviously out of whack, but now and again local imbalances occur that can be exploited. The fact that they are exploited also obscures their existence from a high level.
I'm wondering because efficient end result =/= efficient process, and the process itself must have a cost to society etc.
A belief in that hypothesis strikes me as particularly delusional. The strongest indicator of what a share price will be at `t+1` is what the share price is at `t+0`. Why would anyone delude themselves otherwise?
Maybe that would hold true if execution prices are never revealed (not even to the participants of a trade), but prices are streamed out constantly and are exactly what traders are interested in (how else can they measure their 'gains' and 'losses'?).