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One of the details I always comment on when I am reading articles is the inclusion of 'technical analysis' or other types of short-term price prediction based on patterns. It takes a lot of different forms, some as simple as merely looking at charts (i.e. past prices of the stock graphed with time on the x-axis), and others slightly more sophisticated (read: 'mathier'). I have seen citations including Fibonacci retracement, Bollinger Bands, and of course the common 50 day and 200 day moving averages, all interpreted to derive many a different conclusion.
Typical technical analysis mumbo-jumbo
Moving averages can be informative for one's first cold look at a stock: comparing them to the security's current price more or less gives you the market's impression of the company or security. This information can also be generally gleaned from the price relation to 52 week highs and lows and stock analyst buy/sell ratings.
Beyond that, however, I see technical analysis as market tomfoolery. It is an attempt to see patterns in short-term price movements, that depending on which theory you subscribe to, can be more or less random.
The random walk theory is the classic economic perception that because markets are efficient, prices are going to follow a random walk. Here is some analysis that attempts to separate the efficient market theory from the random walk theory. In essence, the idea is that prices are explained by successive random steps in any given direction. The third link above is important because I think it reconciles the fact that a random walk can only coincide with efficient market theory if the random walk is to be short-term noise while eventually leading to the efficient market price. This of course would not support a very rigorous version of the efficient market hypothesis (EMH).
While I subscribe to a weak version of EMH, I do not necessarily believe in the random walk theory, at least in the long term. I do believe that it could explain bubbles and short term market mispricings, but in my opinion these could be better explained with behavioral finance or simply by variance around an 'accurate' price. I find behavioral finance to have more explaining power due to a presumption by a great deal of the statistical analysis in investing that markets are 'cold' and that decisions are being made by efficient automatons and not by humans (Nudge: Improving Decisions About Health, Wealth, and Happiness provides an excellent framework for understanding the difference, except where I use 'efficient automatons' they use 'econs'). Behavioral finance provides a way of explaining mispricing in the market based on human emotion and the way we perceive.
Does that mean that market participants using statistics have not been successful? Of course not. One of the best success stories I have heard of is the hedge fund Renaissance Technologies, which according to their wikipedia page has averaged a 35% annual return after expenses and as far as I know has never had a losing year. Because hedge funds are such black boxes, it is difficult to understand how Renaissance is truly making money and I am not sure how to address them as a phenomena in the short-term trading sphere. Note: there are also a large number of high-frequency trading (HFT) rigs that use some sort of fundamental or technical indicator, but I have yet to see conclusive evidence that technical trading like this can make money over a longer time period.
The question will always be whether it is not the pattern but rather some fundamental idea being observed through the pattern that is what is making money. Instead of simply finding more complicated mathematical techniques to observe the pattern as it is seen through market prices, perhaps a more effective methodology would be to understand what is causing that event.
On the frontier of simply statistical analysis, Bruce Babcock suggests that over the longer term markets trend after you have looked past short-term noise, and suggests using chaos theory to understand it. This to me seems more plausible than patterns in the short-term, however once again I think it could be better explained by behavioral finance.
Considering the relatively small amount of market data we have to pull from to make statistical assertions lends one to reject a 'patterns for the sake of patterns' investment style. Sample size limitations and a lack of fundamental reasoning for why prices should behave in any given pattern leads me in the end to reject the notions provided by Babcock. While it could be successful as a trading strategy, without any underlying reasoning why prices should adhere to a given pattern might suggest that any given 'trend' he is observing could be better explained and modeled using some other methodology. I am of the persuasion that behavioral finance, while still in its infancy, offers the best methodology for explaining long-term price variance and that the way human emotions interact with capital markets would be the only effective way of attempting to predict future market prices.