Technical Analysis Debunked: 5 Reasons Why We Don’t Believe In Charting
The cult of technical analysis and day trading seems to grow and grow. The Web is crawling with technical analysis (TA). Tax changes have created a boom in spread betting, and hundreds of courses have sprung up to teach traders to read short term ‘technical’ chart set ups. All of this – coupled with the ongoing use of the terminologoy by market commentators and practitioners – may make you wonder whether technical trading rules are profitable and worth using in your own investing? Given its popularity, is there something to all this TA, basically?
The short answer is no, not really, at least not in developed markets like the US or the UK. This isn’t to say that there couldn’t be some technical indicator out there somewhere that might just possibly work consistently in some market. But if there is, it’s escaped the attention of any rigorous academic study on the topic that we’ve come across. Furthermore, most of the popular TA indicators that are bandied around are nonsense jargon and should be ignored as useless noise.
So what is Technical Analysis?
Technical analysis is the forecasting of market prices by means of analysis of data and charts generated by the process of trading. Its origins can apparently be traced to the seminal articles published by Charles H. Dow in the Wall Street Journal between 1900 and 1902. Technicians believe that certain chart formations and patterns will indicate market psychology about either an individual stock or the market as a whole at key turning points. This is based on three key assumptions:
- Market action discounts everything - A fundamental principle is that a market’s price already reflects all relevant information (including external drivers such as economic, fundamental and news events), so you just need to know the history of a security’s trading pattern to predict its future pattern.
- Prices move in trends - Technical analysts believe that prices trend directionally, i.e., up, down, sideways or some combination.
- History tends to repeat itself - Technical analysts believe that price action also tends to repeat itself because investors collectively tend toward patterned behaviour. Because investors collectively repeat the behaviour of the investors that preceded them, technicians believe that recognisable (and predictable) price patterns will develop on a chart.
With those assumptions under their belt, technicians use charts search for archetypal price chart patterns (e.g. the well-known head and shoulders or double top/bottom reversal patterns) and look for forms such as lines of support, resistance, channels, and more obscure formations such as cup and handle patterns. The idea is to try to find and profit from these patterns. Technical analysts also use market indicators, including up and down volume, and advance/decline data to assess whether an asset is trending, and if it is, the probability of its continuation.
Reason 1: Technical Analysis is a Moving Target
One of the issues we have with technical analysis is its highly subjective nature. Practitioners tend to define and use it according to their own beliefs. Different technical analysts can make contradictory predictions from the same data. The presence of certain shapes in historical price charts is often in the eye of the beholder. Coupled with that, the range of indicators is almost limitless and methods vary greatly.
This makes it difficult to refute technical analysis because once one indicator has been shown not to be predictive, it’s always possible for the technician to argue that, in current market circumstances, you should be looking at an entirely different indicator. As Karl Popper said, for a theory to be scientific, it must be falsifiable (i.e. it must make consistent predictions) and the vagueness around the meaning of the term ‘technical analysis’ isn’t helpful in this respect. This also creates massive scope for quot;revisionismquot;, selectively highlighting successes while ignoring failures. As Laszlo Biryani has noted:
quot;I would read in the newsletters quot;As we’ve been suggesting, the market has done X, Y, Z and I would go back and review the past newsletters and say, “I don’t see where you were suggesting this in the last four, five, or six newslettersquot;. They would be very vague in their commentary… A month or two later, after this or that company had risen, they would suggest that those were the exact companies they were recommendingquot;.
Reason 2: Empirical evidence for TA is negligible
Much of the faith in technical analysis hinges on anecdotal experience, not any kind of long-term statistical evidence, unlike value investing or other quantitative/fundamental methodologies we discuss on this site. Most of the statistical work done by academics to determine whether the chart patterns are actually predictive has been inconclusive at best. Indeed, a recent study by finance professors at Massey University in New Zealand examined 49 developed and emerging markets to see if TA added value. They looked at more than 5,000 technical trading rules across four rule families :
- Filter Rules – These rules involve opening long (short) positions after price increases (decreases) by x% and closing these positions when price decreases (increases) by x% from a subsequent high (low).
- Moving Average Rules – These rules generate buy (sell) signals when the price or a short moving average moves above (below) a long moving average.
- Channel Break-outs - These rules involve opening long (short) positions when the closing price moves above (below) a channel. A channel (sometimes referred to as a trading range) can be said to occur when the high over the previous n days is within x percent of the low over the previous n days, not including the current price.
- Support and Resistance Rules – These “Trading Range Break” rules involve opening a long (short) position when the closing price breaches the maximum (minimum) price over the previous n periods.
The result? Using statistical methods to adjust for data snooping bias, the authors found:
quot;no evidence that the profits to the technical trading rules we consider are greater than those that might be expected due to random data variation.quot;
The paper looked at whether technical trading rules add more value in less developed (or efficient) markets. The authors found that technical analysis may work better in emerging markets than developed markets, but it was quot;not a strong result.quot;
Reason 3: What evidence there is probably data-mining
While very early academic studies did show limited evidence for technical analysis, and the odd study now pops up here and there, as Sullivan et al point out, this is likely to be survivorship bias in action. After all, over time, investors must have experimented with technical trading rules drawn from a very wide universe – in principle, thousands of parameterizations of a variety of types of rules. As time progresses, the rules that happened to perform well historically receive more attention and are considered ‘serious contenders’ by the investment community, while unsuccessful trading rules are more likely to be forgotten. After a long sample period, only a small set of trading rules may be left for consideration, and these rules’ historical track record will be cited as evidence of their merits.
quot;If enough trading rules are considered over time, some rules are bound by pure luck, even in a very large sample, to produce superior performance even if they do not genuinely possess predictive power over asset returns. Of course, inference based solely on the subset of surviving trading rules may be misleading in this context since it does not account for the full set of initial trading rules, most of which are likely to have under-performedquot;
In short, TA seems to be right up there with ghosts and UFOs on the evidence front.
Reason 4: A Technician is not a Quant
It is true that certain technical trading rules share a resemblance to momentum trading strategies, and academics have found some good evidence for momentum as a predictor. Does that mean there’s secondary evidence for technical analysis? Not really, the momentum effect is best explained by the findings of behavioural finance, none of which involves the other assumptions relied on by technical analysts. You may be able to make money from certain types of momentum investing, but this is a far cry from saying that it’s generally possible to outperform based on the kind of naive short-term technical trading packages being advertised on the Web.
The fact that someone is trading using trendlines and a few mathematical formulae doesn’t mean that they are trading using a quantitative strategy. A quant is someone who applies an empirically-tested and rules-based approach to exploit perceived market inefficiencies (e.g. by exploiting various systematic biases present in human behaviour, such as herding and overconfidence). Some technical indicators like MACD and Bollinger Bands do resemble statistical measures used by quants today (mean and standard deviation) and may even involve some statistical modelling. However, technicians mostly trade the market using relatively discretionary strategies and the techniques used are on a completely different plane of sophistication.
As someone has wisely noted, the relationship between Technical and Quantitative analysis can perhaps be likened to Astrology and Astronomy. One is close to superstition while the other is a science. Astrology came about due to the lack of sophisticated tools and theories, and it’s the same with Technical Analysis! People relied on charts because it was easier to analyse them than crunching data but, with the advent of faster and more powerful computers, this is no longer the case – lo and behold, astrology doesn’t really have the appeal it used to as an explanation of the Heavens…
Reason 5: The Hedge Funds are all over short-term trading
For the most part, technical analysis is a short- to very short- term trading strategy. Most technical traders won’t like to admit it, but the best PhDs in the world have been hired by the most sophisticated institutions to squeeze every pound they can out of short term price discrepancies in the market. Over the last 20 years, hedge funds and investment bank trading desks have invested massively in high frequency algorithmic trading designed to prey on the weaker hands in the market.
It’s madness to think individual investors can beat the City’s finest, armed with a laptop and a 30 day course on technical analysis. Hedge funds have all of these trades and more completely covered with complex algorithms and artificial intelligence that are well beyond a private investor’s ken. They are the ones taking the other side of the trade and, in doing so, are ensuring that the majority of short term traders go broke as those trades hit stops or margin calls.
The truth is that, while it is not the case that the market is always efficient, there are very few short term inefficiencies in the market, and that if you want to play that game, you are up against the best in the world. Would you tee up against Tiger Woods? Due to high frequency trading, the average length of time that a fund holds a stock before selling has fallen dramatically in recent years, and now stands at less than six months. The truth is that if you want to beat the market, you need to lengthen your investment timeframe and look beyond the immediate term that the hedge funds have so well covered (and that’s where value investing comes in).
But wait – it’s not all bad….
As you can tell, trading purely on the basis of technical analysis is a mug’s game. However, despite inconsistencies in predictive value, technical analysis may be a useful tool as part of a broader strategy for managing holdings (e.g. to help you time any investments that are decided on other, hopefully fundamentally-focused, criteria).
The fact is that many (misguided) market participants use technical analysis to drive their investment decisions. These collective actions result in tangible changes in asset values, so they need to be understood even by less mis-guided investors. A fundamental investor need not agree that a stock should be moving but it’s worth understand why a stock is nevertheless moving. As Birinyi, a research and money-management firm, noted in a research note:
quot;Technical approaches can and should be a useful adjunct to every investor’s — amateur and professional — arsenal, if and only if used properly and with understanding… Technicals detail and hopefully illuminate, but do not predict.”
In particular, one area where technicals may be useful is on the sell-side. We discussed recently William O’Neill’s stock selling rules and noted that very few of them involves changes in the fundamentals of a stock. His interesting explanation of this is that many big investors get out of a stock before trouble appears and if the institutional money is selling up in volume, individual investors don’t stand much of a chance. So while it’s very important to buy with heavy emphasis on fundamentals, he argues that this is not the right thing to focus on when selling:
quot;Many stocks peak when earnings are up 100% and analysts are projecting continued growth and higher price targets. Therefore, you must frequently sell based on unusual market action (price and volume movement)quot;.
Good investing is about managing your losses too, and here TA can be a useful tool to determine where best to place a stop-loss (given the number of TA practitioners out there that are likely to be anchoring around certain price points).
For these reasons, we’ll be reviewing a number of key technical indicators in a subsequent piece, along with the (mostly sparse to non-existent) evidence as to their predictive value.