Foreword
Some think of it as voodoo magic, others think of it as crayon drawings on price charts. But does technical analysis actually work? If so, why don’t more people do it?
As usual, a reminder that I am not a financial professional by training — I am a software engineer by training. The following is based on my personal understanding, which is gained through self-study and working in finance for a few years.
If you find anything that you feel is incorrect, please feel free to leave a comment, and discuss your thoughts.
Definitions
Before we dive deeper into the subject, let’s first define what technical analysis and fundamental analysis actually are.
The definitions are not particularly rigid and different people may have slightly different definitions, but to me, fundamental analysis is the attempt to value an asset, based on its fundamental metrics. For example, trying to value a delivery contract (e.g. futures contract) via the value of the to be delivered item, as well as the current risk free rate, or trying to value a share of a company based on the company’s revenues, expenses, growth rate, etc.
On the other hand, I take a somewhat more expansive view on technical analysis. To me, technical analysis is the attempt to determine whether the price of an asset is going to go up or down, and/or by how much, based solely on non-fundamental metrics. For example, looking at the price charts and trying to discern where prices are moving next, looking at trading volumes to determine investor sentiments and how that translates into price movements, etc.
Does it work?
There are many different types of technical analysis, due to the expansive definition above, and to be honest, most of them, I’ve found, do not work (at least, not any more). And even the ones that work often do so erratically. That is, technical analysis is not consistent.
To explain why, let’s consider a simple mean reversion model: We take the last 20 days of closing prices of a stock and compute the average closing price over those days. We then calculate the standard deviation of closing prices over those same 20 days. If the price moves above 2 standard deviations of the current average price, we short the stock hoping it’ll go down back towards the average price, and if the price moves below 2 standard deviations of the current average price, we buy the stock, hoping it’ll go up back towards the average price.
Astute readers will note that this is a simple Bollinger Bands trading system — the upper band is simply the average price + 2x standard deviation, and the lower band is simply the average price – 2x standard deviation, and we simply buy/sell if the current price moves outside of the bands.
Now, this model has a basis in statistics — In statistics, observations outside of 2 standard deviations of the mean in either direction are sometimes called outliers, i.e. these data points appear to be anomalous to the data series. And given that these data points are unexpected, then it somewhat makes sense that they will mean revert, thus restoring the “balance”.
To frame the model from a human perspective, let’s say we have a stock that normally trades somewhat calmly, moving a few points a day. However, on a particular day, it suddenly drops by 100 points, for no obvious reasons. Well, given that nothing untoward appears to have happened, traders are likely to think of it as either a fat finger mistake, or someone needing liquidity in a hurry, instead of something fundamentally wrong with the company. If so, then it makes sense that this is a good buying opportunity, and the buying generated by these traders would cause the price to go up towards the average price again.
Before we dig deeper into this case study, let me get these out of the way:
- This is an actual trading system, you can read a somewhat more modern take on this here.
- I can verify that this trading system somewhat works (more on this below) in the past, for certain assets.
- This trading system does not work for every asset today. It may not even work for any asset today (more on this below).
Regardless of if you approach this from a statistics point of view, or from the human behavioral point of view, you cannot escape a glaring conclusion — this trading system does not always work. That is, if you adhere strictly to the rules to buy and sell based on this trading system, you will suffer (at least) occasional losing trades.
From the statistics point of view, it should be clear that being, well, a statistical measure, this trading system must therefore be subject to tail events that fall outside of the model’s predictive powers. Which is to say, statistics does not guarantee that things will happen a certain way, it simply suggests that they are likely to.
From a human behavioral point of view, recall that we said “it suddenly drops by 100 points, for no obvious reasons“. The bolded part is key — just because we (or even the majority of traders in general) do not perceive some reason for the drop, doesn’t mean that there isn’t an actual reason! It could be that some bad news affecting the fundamentals of the company was leaked, and we simply are not privy to that information. If so, then the drop may be justified, in which case, the expected rebound of the stock price may not occur.
Another reason this trading system may no longer work is based on simple logic — this trading system was designed decades ago in the 1980s. That is a really long time ago in a competitive space like trading. If you knew that everyone knows of this system, and expects a large number of people to employ this system, what do you think should happen?
Well, firstly, as the system becomes more well known, more and more traders will start using it. As a result, it creates some sort of self-fulfilling prophecy — even if the drop was indeed due to some bad news, if enough traders are simply buying because the price dropped 2 standard deviations, then the price of the stock will go up, at least until the bad news is more widely known.
Now, let’s say you are avant-garde trader living on the edge. How would you exploit this phenomenon? For one thing, you can simply try to sell your position a bit earlier — if there is indeed bad news, you want to sell before the bad news becomes widely known, and if there isn’t bad news, you want to sell before others start selling which would drag down the price somewhat.
So one person starts selling earlier, and they make a consistent profit. Other traders notice, and so they too start selling earlier. To counteract this, our enterprising trader decides then to sell even earlier, to which the response from the other traders is to sell even earlier. This continues until everyone is selling so quickly that the price doesn’t really have much time to move, and nobody really makes a profit from this trading system.
Which is to say, for most technical based trading (in case this is not clear, this includes quantitative trading models), eventually the efficacy of the model fades as more and more people know about it and try to work around it, thus competing away the potential alpha of the model.
So.. it doesn’t work?
While it is true that most trading systems based on technical analysis eventually get watered down due to competition, it doesn’t mean that they don’t work.
Going back to our Bollinger Bands example, we noted that eventually “nobody really makes a profit from this trading system”. So if you aren’t making a profit from this trading system, why bother? And so, traders will eventually stop using it.
But the only reason the trading system doesn’t work anymore is because too many people are using it, and if people stop using it, it should work again… right?
This is a contradiction that has multiple possible (un)stable equilibriums. In some cases, certain technical analysis models have a low capacity, which is to say, as more money gets put into trading the model, the model stops working relatively quickly — it has a low capacity for the marginal speculative dollar before it stops working. Models like this tend to be cyclical — the model works for a while, it becomes overheated and traders start losing money, so everyone stops using it, and so it works again. This on again and off again nature of the model repeats over time as new traders start using the model and abandoning it.
A possible stable equilibrium is that the model because incorporated into some other, larger and more comprehensive model, and so it more or less stops working permanently.
Yet another possible stable equilibrium is that the model gets just enough usage from traders that it works well enough just for those traders, who manage to somehow dissuade others from employing it (for example by keeping it a secret). The model works decently, well enough for its traders to be sufficiently profitable, but not well enough for other traders to spend the effort to try and decipher it.
Practical technical analysis
It should be clear by now, that a large part of the job of quantitative trading is to try and figure out which models work, which don’t, and when. Successful quantitative traders need to be able to not just encapsulate the trading system, but also a risk management system that monitors when your trading system stops working and makes adjustments as necessary. Alternatively (or in addition to), you could have a macro overlay, which tries to predict when the trading system will stop working, before it stops working.
For example, if we are trading the Bollinger Bands setup as described above, a simple risk management system would be if the price moves from beyond 2 standard deviations of the average price to beyond 3 standard deviations, we simply close the position at a loss. This is based on the guess that if the price is moving strongly in a direction, there’s a good chance that there’s something we don’t know yet which may justify the move.
And a macro overlay could be as simple as “do not trade a stock using this system within the 3 trading days after its earnings report” — this is based on the very obvious observation that earnings report days are more likely to feature fundamental changing news, so we want to ignore any large moves shortly after an earnings report because the moves may be justified by fundamentals.
Technical analysis for the enthusiast
For those of us who aren’t doing this for a living, and cannot afford to spend the massive amounts of resources to build out a full quantitative trading system, technical analysis can still be of use.
Recall that technical analysis is just a way of trying to quantify the sentiments around the price movements of an asset. To put it crudely, you’re just trying to use some sort of formal or semi-formal method to try and guess what other traders are thinking and how they may react.
To that end, I’ve seen some success using various technical analysis measures to try and give me a “feel” of the market, so as to try for better entry/exit points. Which is to say, I may use fundamental analysis to guess whether the asset’s price will go up or down in the medium to long term (say more than 1 month). But once I’ve already decided to buy or sell an asset, I can then use technical analysis to try and guess whether the asset’s price will go up or down in the short term (say under 1 week), to decide when to actually effectuate that trade.
Final word
The way I think of it, technical analysis tries to gauge what the market is thinking and thus try to predict what the market may do. Fundamental analysis tries to quantify the value of the asset, and thus try to predict what the market should do. Both have their pros and cons, but technical analysis tends to lend more towards speculative endeavors, while fundamental analysis tends to lend more towards investing efforts.
Janky Thoughts 