Are random trading strategies more successful than technical ones?(journals.plos.org) |
Are random trading strategies more successful than technical ones?(journals.plos.org) |
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...
If the markets were efficient and equivalent to random brownian motion, Jim Simons wouldn't be producing 50%+ returns for decades non-stop in HFT.
His net worth is 25 billion dollars, and there are other countless billionaires, made from the "efficient" markets.
That's how much this fallacy is worth.
However, the problem with this strategy is, as Keynes put it: “The market can stay irrational longer than you can stay solvent.”
I think the market is dominated by front-running trades and randomness.
sounds like the economy to me :)
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.
> 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.
So while I agree the sentence is rather contorted, there is a sympathetic explanation. Especially as the authors claim no institutional affiliations.
I don’t think such a sentence would be justified coming from an institution in an English-speaking country.
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 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.
Can you elaborate? Is this because large flows of money eventually become the market? Insinuating that some strategies only work at low trade volume?
Zero-sums game are actually proven to have a winning strategy.
Chess is a zero sum game.
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.
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.
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.
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.
I fear I'm misunderstanding you. Are you saying despite having higher returns, the higher risk makes this strategy worse? That really feels like handwaving to me, since the only thing I care about is ROI. I understand nonlinearity and how it could tank your investment, but if it doesn't and you make more money then you're criticizing something that never happened. The higher risk is already baked into the ROI, because it includes the times that failed. The point is, in aggregate, you make more money - and most of the time that is the only thing I care about when investing.
Or am I misunderstanding you?
They compared equal weighting, but did they also check market-cap weighting?
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.
You are not betting on what will think in the future because market forces are bigger than will.
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?
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.
How many other companies are there that aren't IT related that you interact with on a daily basis? You might use your chair and toothbrush every day, but that doesn't require anything from the company that sold you the chair. Using Google does require Google's servers to respond though.
"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."
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.
Where's the experiment part?
Specifically to your Theranos point, Theranos was never public. Conning individual investors out of private investment money is somewhat different since the people who saw through the bullshit don't have an easy way of profiting off of that sense. The options were to buy in or opt out, and plenty of investors wisely decided to opt out but there was no play for them to profit off of the collapse.
In some less honest markets there are even cases where what the numbers say you can trade isn't possible because those offering the deal won't follow through, or will let a friend in ahead of you
A retail trader will usually be trading with some sort of market maker. That market maker will also trade with informed traders, and sets their bid-offer spread accordingly to offset this adverse selection. So the retail trader is paying a bid-offer spread that is the result of other informed traders in the market.
Of course, as a retail investor you may have access to a broker (e.g. Robinhood) that tries to exclude informed traders so it can set a lower spread.
In other words, reversing every choice in a mediocre sequence of binary choices yields another mediocre sequence of choices.
> it could tank your investment, but if it doesn't and you make more money then you're criticizing something that never happened
This way of reasoning is basically survivorship bias in a nutshell, and per my direct experience as a financial professional has brought down many investors who were too confident about their "strategy".
To bring this argument to the extreme: if you cared only about ROI, you could just go long some penny stock with exaggerate leverage and make big money "unless proven otherwise". In practice, what happens is you get euphoric for a couple days while you see the money shoot up to the sky, and then lose all of it to a margin call at the opening the very next day. I've seen it happen with my own eyes.
As a highly simplified, unrealistic example - let's say strategy A has an average return of 5%/year with max drawdown of 20%, and strategy B has an average return of 10%/year with a max drawdown of 50% (here max drawdown being a highly simplified proxy for risk). Theoretically, you could use leverage to go 2.5X long strategy A to achieve a return of 12.5%/year with a max drawdown of 50% (minus cost of the leverage - depending on how you do this, cost could be fairly small). This might do better, risk-adjusted, than just doing strategy B by itself.
There’s lots of metrics that try to balance the risk and reward. Often, the risk is based on the volatility of the asset. The common alpha metric does this by incorporating the assets volatility compared to the overall market volatility. There’s others like Sharpe ratio etc.
Factoring that volatility is particularly important in long-term investing so your choices don’t, as you say, tank your investment. So maybe you interested in cyclicals over the last nine months and your investments went gangbusters. Does that mean that same strategy will work in perpetuity? Probably not, because cyclicals tend to have high volatility. Risk -adjusted metrics attempt to quantify that risk.
And obviously there’s mathematical measurements where one tries to get the highest return per unit of risk. It’s possible that these returns might be higher but fall under the curve.
But all of those qualifiers I mentioned are needed, and that's a lot of qualifiers. If any of them are no longer true then there is not guaranteed to be a winning strategy.
In chess, it's possible to end the game in a draw, so Zermelo's theorem does not apply to it and OPs claim is wrong about chess.
I'm fairly certain one can trivially disqualify one of those criteria when it comes to financial markets as well.
Isn't it even assumed that a perfect game of chess is a draw. Once chess is solved, it'll be all draws.