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.<p>I would also like to note, that predicting price is different from predicting an overall increase in the value of the underlying security.<p><a href="https://en.wikipedia.org/wiki/Brownian_model_of_financial_markets" rel="nofollow">https://en.wikipedia.org/wiki/Brownian_model_of_financial_ma...</a>
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.<p><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=911512" rel="nofollow">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=911512</a>
Sadly, the abstract doesn’t include the result, so here it is so you can decide if you want to read more:<p>> 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.<p>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?<p>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.
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.<p>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.<p>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.
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.<p>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>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.
<a href="https://markets.businessinsider.com/news/currencies/hamster-trading-cryptocurrencies-rigged-cage-goxx-bitcoin-price-ether-doge-2021-9" rel="nofollow">https://markets.businessinsider.com/news/currencies/hamster-...</a><p>I think so
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.<p>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.<p>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.