Prices are pretty well modeled using Brownian motion. Most economists should know this while almost no one in the normal population will be aware of it. Sometimes people are just lucky, but overall the more trades you make the more you'll converge on the average return rate.
I would also like to note, that predicting price is different from predicting an overall increase in the value of the underlying security.
The assumptions underlying Brownian motion of prices have been disputed for quite a while now: the normality hypothesis can be rejected on most if not all historical financial returns series, as it turns out that most returns are actually fat tailed processes with very significant (and variable over time) correlations between distinct assets, which makes research around portfolio theory even harder to conduct.
Further, every time you trade, the overwhelming likelihood is that the counterparty to that trade is a financial professional with dramatically more access to company-specific research and information than you. This imbalance is minimized when you trade infrequently and maximized when you trade frequently.
As someone who has beat the market over a 17 year period, I attribute it to having made very few trades AND being lucky.
There are a lot of ways that a company can go under. All it takes is one mistake or one black swan event and what would have been an amazing investment 99.9999% of the time goes to zero.
On average, but prices come from the value of the company behind them long term which is not always brownian and so someone who knows the company can get in/out ahead of someone who trusts only brownian motion.
This reminds me a bit of a classic paper called "1/N". It compared a portfolio of putting equal money into each security, vs a bunch of fancier approaches. The 1/N almost always won.
This is widely known among practitioners, but there is a caveat -- a 1/N portfolio bears a much higher risk than, say, a cap-weighted portfolio or a risk-parity asset allocation. A 1/N portfolio receives an equal contribution in terms of volatility from each asset, meaning that very risky assets significantly increase the portfolio's volatility, while not necessarily contributing proportionally better returns, due to the nonlinearity and asymmetry of volatility's effects on prices. This way, 1/N ends up performing very poorly on a risk-adjusted basis while undoubtedly at the same time outperforming any other kind of allocation on the basis of return alone. This is rather unacceptable in a real world portfolio where the tail risks and emotions can lead an investor to ruin.
Sadly, the abstract doesn’t include the result, so here it is so you can decide if you want to read more:
> Our main result, which is independent of the market considered, is that standard trading strategies and their algorithms, based on the past history of the time series, although have occasionally the chance to be successful inside small temporal windows, on a large temporal scale perform on average not better than the purely random strategy, which, on the other hand, is also much less volatile.
Win % is a really useless metric in this business, try computing win % for something like long vol strategies (for example things like what Taleb did back in the day), it might come out to 5% or lower and still make money. And because every trade has a counterparty there's plenty of strategies that win 95% or more of the time but eventually lead to ruin. Returns pretty much never have a symmetric distribution.
Computing win % is akin to measuring software quality in terms of number of lines of code - only someone who has no first-hand experience would ever attempt to do that.
If any well known strategy was profitable presumably it will be used by people until it isn't, because knowledge of the strategy is already priced in to the relevant assets. That makes this result fundamentally unsurprising.
Doesn't the conclusion indirectly also indicate that day trading is a zero sum game?
If the answer is yes, then the only way you can make money from day trading is from commissions you earn performing day trade on behalf of other parties with money.
First they ran in simulation, not the real market. It may be that that act of being in the market changes the market enough to make your strategy work. (though typically it is the opposite - things work in simulation but applying them to the market makes them not work). As such this paper doesn't really tell us anything useful.
Even if we ignore the above, they only tested a few different strategies. That says nothing about any other trading strategy that someone might apply: any of them might work.
I still think day trading is a bad way to invest, but this paper doesn't prove anything even though it speaks to my bias.
If trading is a zero-sum game, which it is on a small scale, then random strategies are bound to be in the middle of the pack.
It is like rock-paper-scissors. A random player will win 50% of their games regardless of the other player strategy. When two non-random players play, one will successfully predict the other player moves and win more than 50% of the time, the other will fail and win less than 50% of the time.
So the ranking will always be 1. winning strategies 2. random 3. losing strategies, with as many winners as there are losers, and any number of randoms. So, random is more successful than half of the technical strategies.
Can you elaborate on what you mean by trading is a zero-sum game on a small scale? Without clarification that statement could be used to justify any conclusion.
Are you saying that someone who trades a small amount of capital is always winning an amount of money that is roughly equal (+/-) what the counterparty lost or vice-versa? That can be demonstrated to be untrue.
Are you saying that trades spanning a short period of time always win or lose an amount that sums up to zero for all participants? That also seems highly unlikely unless all participants are engaged in short term trading which is not true in practice.
At any rate, while I've heard this claim repeated often, I've never heard anyone substantiate it and as far as I can tell it doesn't really make sense.
There are financial instruments that are zero-sum by their nature with respect to dollars, for example derivatives and currencies are by nature zero sum with respect to dollars, although they are not zero sum if you factor in risk. But that has nothing to do with short vs. long term though. Equities are not zero-sum, long term or short term.
Technical analysis sort of "works" in the same way that e.g. astrology "works", in that for any given plot of stock data, you can typically draw a of a number of technical patterns which seem to fit. I've never seen any convincing evidence to the contrary.
But one thing is for sure, if technical analysis works then a neural net will trivially pick up on existing strategies and although the cutting edge is always kept secret in the financial world, we probably would have heard of ML techniques rediscovering technical analysis by now if it were truly successful, since even an amateur could build and train a neural net from free data to learn technical analysis.
P.S. if simple technical analysis techniques ever worked, I also predict that they would quickly stop working as such arbitrages eventually disappear. You're not trading against news or patterns, ultimately, whether traders realize it or not, they are trading against mass financial psychology and HFT algos. Once neural net based training becomes the predominant tool, it will be interesting to see the collective patterns that emerge, likely totally disconnected from actual fundamentals. It may be chaotic, or it may be close to steady state, but it will definitely be in a state of flux as neural nets come online and constantly train on the latest patterns. It's a battle against the arrow of time.
The paper studies trades made on financial market indexes, so over the periods of time measured I wonder if the random strategy they used is about the same as investing in index tracker funds and spreading your buys / sells out in order not to time the market.
The technical strategies they compared it too are not strategies commonly used. It looks like they were chosen because they were simplistic and convenient to back test.
I love stuff like this. Pure comedy gold. It reminds me that someone can have all the knowledge, all the statistical tools in the world and still make huge mistakes (no, not explaining it, making too much money atm...maybe in a few decades). To the man with a hammer.
>Recently Taleb has brilliantly discussed in his successful books [15], [16] how chance and black swans rule our life, but also economy and financial market behavior beyond our personal and rational expectations or control. Actually, randomness enters in our everyday life although we hardly recognize it. Therefore, even without being skeptic as much as Taleb, one could easily claim that we often misunderstand phenomena around us and are fooled by apparent connections which are only due to fortuity. Economic systems are unavoidably affected by expectations, both present and past, since agents’ beliefs strongly influence their future dynamics. If today a very good expectation emerged about the performance of any security, everyone would try to buy it and this occurrence would imply an increase in its price. Then, tomorrow, this security would be priced higher than today, and this fact would just be the consequence of the market expectation itself. This deep dependence on expectations made financial economists try to build mechanisms to predict future assets prices. The aim of this study is precisely to check whether these mechanisms, which will be described in detail in the next sections, are more effective in predicting the market dynamics compared to a completely random strategy.
I think pundits, academics, experts etc. overestimate the randomness or unpredictability of markets and crowds. Consider this obvious thought experiment: given a choice between having to choose between a $10 bill or a $20 bill on the sidewalk, all else being equal, everyone will choose the $20.That is sorta how investing is. Quality beats crud. There is nothing mystical or unpredictable about it. Determining quality is subjective, but the FAANG index in which each company is worth at least $100 billion has pretty much beaten everything else since 2009.
Also a distinction should be made between fundamental analysis, quantitative analysis, and technical analysis (volume and chart patterns and readings). I think the the first is useful, as the out-performance of FAANG stocks shows. Quant strategies can also be very profitable. The alleged predictive power of technical analysis has long been debunked.
When investing in something like the FAANG index or Google you're not betting on how the companies will do. Predictions like "Google is going to do well in the future and continue to grow" are not useful for making investments.
You're making a bet that Google will do better than everyone else thinks it will. And even more than that. You're betting that it will do so by a wider margin and/or with a higher likelihood than the available alternative investments you could make with that same money.
And even more than that, you're betting that Google will do better than everyone thinks it will and that the market will
acknowledge this the way that you expect and the price will adjust accordingly in a time frame that is relevant for your investment goals and solvency.
>the FAANG index in which each company is worth at least $100 billion has pretty much beaten everything else since 2009.
In some sense I think this speaks more to the way that the US regulatory framework allows dominant players in a given market segment to retain and reinforce their dominance.
You can argue that these type of investments are "quality" or "safe", but the reasoning behind that label isn't going to be based on any kind financial analysis. There's no path to dethroning these giants or constraining them in any significant way, and as a result they're insulated from market fluctuations that might crash the price of a smaller player.
That's all without going into the feedback loop of safe investments -> more investors -> higher price (or price stability) -> upgraded safety rating -> algorithmic rebalancing of index funds -> higher price -> etc.
I don't have the impression that Taleb's thesis is anything like choosing between two known valued bills on the ground. Maybe it would be more like:
"if you were going to hunt for $20 bills on the sidewalk, which park would you go to? Central Park always does pretty well but if you were to play 'double or nothing' for tomorrow's find, you couldn't guarantee that you'd find a $20 bill there just because you found one there yesterday."
> Consider this obvious thought experiment: given a choice between having to choose between a $10 bill or a $20 bill on the sidewalk, all else being equal, everyone will choose the $20.
All else is never equal. If you change this experiment slightly, you'll get a more interesting result. If there is a $10 bill and a $100 bill on the sidewalk, and you have to choose one to take and one that will return to its owner, most people will choose the $10. The $100 seems suspicious and dangerous (in a "mystical and unpredictable" way.)
Quality is determined by experience and instinct. The personal valuation of a $100 bill might drop below $10 with no added information, other than that all treasure looks less like treasure than a lot of trash does.
Suffice it to say that if there were a market that accurately labeled the values of everything it sold, it wouldn't be a very interesting market.
> the FAANG index in which each company is worth at least $100 billion has pretty much beaten everything else since 2009.
That's because the politics are affected by size. To big to fail is real.
> Consider this obvious thought experiment: given a choice between having to choose between a $10 bill or a $20 bill on the sidewalk, all else being equal, everyone will choose the $20.
rubyn00bie|4 years ago
I would also like to note, that predicting price is different from predicting an overall increase in the value of the underlying security.
https://en.wikipedia.org/wiki/Brownian_model_of_financial_ma...
veeenu|4 years ago
stouset|4 years ago
jareklupinski|4 years ago
sounds like the economy to me :)
gitfan86|4 years ago
There are a lot of ways that a company can go under. All it takes is one mistake or one black swan event and what would have been an amazing investment 99.9999% of the time goes to zero.
bluGill|4 years ago
blitzar|4 years ago
cschmidt|4 years ago
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=911512
veeenu|4 years ago
gruez|4 years ago
They compared equal weighting, but did they also check market-cap weighting?
gumby|4 years ago
> Our main result, which is independent of the market considered, is that standard trading strategies and their algorithms, based on the past history of the time series, although have occasionally the chance to be successful inside small temporal windows, on a large temporal scale perform on average not better than the purely random strategy, which, on the other hand, is also much less volatile.
timmytokyo|4 years ago
anthony_r|4 years ago
Computing win % is akin to measuring software quality in terms of number of lines of code - only someone who has no first-hand experience would ever attempt to do that.
medvezhenok|4 years ago
sideshowb|4 years ago
habibur|4 years ago
If the answer is yes, then the only way you can make money from day trading is from commissions you earn performing day trade on behalf of other parties with money.
ZetaZero|4 years ago
bluGill|4 years ago
First they ran in simulation, not the real market. It may be that that act of being in the market changes the market enough to make your strategy work. (though typically it is the opposite - things work in simulation but applying them to the market makes them not work). As such this paper doesn't really tell us anything useful.
Even if we ignore the above, they only tested a few different strategies. That says nothing about any other trading strategy that someone might apply: any of them might work.
I still think day trading is a bad way to invest, but this paper doesn't prove anything even though it speaks to my bias.
JustFinishedBSG|4 years ago
Zero-sums game are actually proven to have a winning strategy.
Chess is a zero sum game.
Kranar|4 years ago
GuB-42|4 years ago
It is like rock-paper-scissors. A random player will win 50% of their games regardless of the other player strategy. When two non-random players play, one will successfully predict the other player moves and win more than 50% of the time, the other will fail and win less than 50% of the time.
So the ranking will always be 1. winning strategies 2. random 3. losing strategies, with as many winners as there are losers, and any number of randoms. So, random is more successful than half of the technical strategies.
Kranar|4 years ago
Are you saying that someone who trades a small amount of capital is always winning an amount of money that is roughly equal (+/-) what the counterparty lost or vice-versa? That can be demonstrated to be untrue.
Are you saying that trades spanning a short period of time always win or lose an amount that sums up to zero for all participants? That also seems highly unlikely unless all participants are engaged in short term trading which is not true in practice.
At any rate, while I've heard this claim repeated often, I've never heard anyone substantiate it and as far as I can tell it doesn't really make sense.
There are financial instruments that are zero-sum by their nature with respect to dollars, for example derivatives and currencies are by nature zero sum with respect to dollars, although they are not zero sum if you factor in risk. But that has nothing to do with short vs. long term though. Equities are not zero-sum, long term or short term.
twofornone|4 years ago
But one thing is for sure, if technical analysis works then a neural net will trivially pick up on existing strategies and although the cutting edge is always kept secret in the financial world, we probably would have heard of ML techniques rediscovering technical analysis by now if it were truly successful, since even an amateur could build and train a neural net from free data to learn technical analysis.
P.S. if simple technical analysis techniques ever worked, I also predict that they would quickly stop working as such arbitrages eventually disappear. You're not trading against news or patterns, ultimately, whether traders realize it or not, they are trading against mass financial psychology and HFT algos. Once neural net based training becomes the predominant tool, it will be interesting to see the collective patterns that emerge, likely totally disconnected from actual fundamentals. It may be chaotic, or it may be close to steady state, but it will definitely be in a state of flux as neural nets come online and constantly train on the latest patterns. It's a battle against the arrow of time.
Gormisdomai|4 years ago
oraoraoraoraora|4 years ago
I think so
unknown|4 years ago
[deleted]
marcrosoft|4 years ago
unknown|4 years ago
[deleted]
oraoraoraoraora|4 years ago
[deleted]
hogFeast|4 years ago
paulpauper|4 years ago
I think pundits, academics, experts etc. overestimate the randomness or unpredictability of markets and crowds. Consider this obvious thought experiment: given a choice between having to choose between a $10 bill or a $20 bill on the sidewalk, all else being equal, everyone will choose the $20.That is sorta how investing is. Quality beats crud. There is nothing mystical or unpredictable about it. Determining quality is subjective, but the FAANG index in which each company is worth at least $100 billion has pretty much beaten everything else since 2009.
Also a distinction should be made between fundamental analysis, quantitative analysis, and technical analysis (volume and chart patterns and readings). I think the the first is useful, as the out-performance of FAANG stocks shows. Quant strategies can also be very profitable. The alleged predictive power of technical analysis has long been debunked.
myownpetard|4 years ago
You're making a bet that Google will do better than everyone else thinks it will. And even more than that. You're betting that it will do so by a wider margin and/or with a higher likelihood than the available alternative investments you could make with that same money.
And even more than that, you're betting that Google will do better than everyone thinks it will and that the market will acknowledge this the way that you expect and the price will adjust accordingly in a time frame that is relevant for your investment goals and solvency.
na85|4 years ago
The companies that have seen the largest growth, amid the longest bull market in history, have beaten everything else?
Isn't that pretty much a tautology?
Root_Denied|4 years ago
In some sense I think this speaks more to the way that the US regulatory framework allows dominant players in a given market segment to retain and reinforce their dominance.
You can argue that these type of investments are "quality" or "safe", but the reasoning behind that label isn't going to be based on any kind financial analysis. There's no path to dethroning these giants or constraining them in any significant way, and as a result they're insulated from market fluctuations that might crash the price of a smaller player.
That's all without going into the feedback loop of safe investments -> more investors -> higher price (or price stability) -> upgraded safety rating -> algorithmic rebalancing of index funds -> higher price -> etc.
chillacy|4 years ago
"if you were going to hunt for $20 bills on the sidewalk, which park would you go to? Central Park always does pretty well but if you were to play 'double or nothing' for tomorrow's find, you couldn't guarantee that you'd find a $20 bill there just because you found one there yesterday."
pessimizer|4 years ago
All else is never equal. If you change this experiment slightly, you'll get a more interesting result. If there is a $10 bill and a $100 bill on the sidewalk, and you have to choose one to take and one that will return to its owner, most people will choose the $10. The $100 seems suspicious and dangerous (in a "mystical and unpredictable" way.)
Quality is determined by experience and instinct. The personal valuation of a $100 bill might drop below $10 with no added information, other than that all treasure looks less like treasure than a lot of trash does.
Suffice it to say that if there were a market that accurately labeled the values of everything it sold, it wouldn't be a very interesting market.
> the FAANG index in which each company is worth at least $100 billion has pretty much beaten everything else since 2009.
That's because the politics are affected by size. To big to fail is real.
maybelsyrup|4 years ago
Where's the experiment part?