Although many investors rely on charts to time trades the actual evidence that this yields any kind of advantage is vanishingly small.  It's not zero, however, as we saw in Technical Analysis on Display, because there's a faint suggestion that human pattern recognition is so finely tuned that it may be able to extract information from the noise that machines cannot.

Intriguingly, though, research on Google search data suggests that there are exploitable trends in information signalled by interest in specific stocks.  Unfortunately merely publishing this fact almost certainly signals its demise as a useful prediction tool.  Exploit it while you can, because if you don't, someone else will, probably using a supercomputer.

Observational Problems

The basic idea behind charts is that they signal investor sentiment: investor sentiment may or may not tell us something useful about the fundamentals behind stocks and markets, but if enough people are buying or selling then this may not make any difference in the short-term.  The lack of hard evidence supporting this as an effective trading strategy doesn't put off hardened advocates of the method, who usually have their own personal observations to fall back on.

Typically, of course, personal observation is a very inefficient method of research.  It's entirely possible that one person's experiences may be atypical of the actual general situation: which is one of the main reasons we tend to rely on carefully designed research programmes and statistical evaluation, rather than individual beliefs. 

Illusory Patterns

There's a second key reason to be wary of personal experience, particularly when it comes to pattern recognition type problems.  We are incredibly good at perceiving patterns, but will often identify them when they don't exist – particularly in so-called conditions of uncertainty, such as those that regularly pertain in financial markets. 

These illusory patterns cause us to generate false hypotheses, which normally would fail because they're wrong and the actual evidence doesn't support them.  Unfortunately, markets being the random places that they are, you can often find examples that do match the observed patterns and, as it turns out, we don't need much reinforcement to maintain incorrect hypotheses in the face of otherwise overwhelming data.  Indeed, there's an argument that our false pattern matching algorithms are so powerful we should never rely on chart based data on its own, because it'll all too often be wrong.

Nowcasting

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