# Proving Random Walk Hypothesis in Stock Market

Given the time series for a particular stock market, what are the statistical weapons one can bring on to prove, or disprove that random walk hypothesis?

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As a note, most stock markets tend to increase over time as they compete w/ interest. Or are you talking about a "biased" random walk? –  barrycarter Feb 10 '11 at 16:00
@barrycarter, I'm talking about random walk with a constant drift term, like Wiener process –  Graviton Feb 10 '11 at 16:03

Test your historical time series for both randomness and independence. Understand that a time series may be random and independent; non-random and independent; random with dependencies; and non-random with dependencies. A mistake would be to limit dependency tests to autocorrelation. The most general test I know of is called the differential spectrum by Sherry, which works like this:

• histogram the price changes in your time series
• if the price changes are independent, they should be symmetric about 0
• use Pearson's $\chi^2$ test with one sign as "observed" and the other as
"expected" for a quantitative measure of symmetry.

However, when you find something with this test, you've still got to hunt for the dependency. But at least the test can tell you if you've got a dependencies or not.

The most important point is that whatever tests you work with, that market can change in the future. So I wouldn't say a market "is" or "isn't" a random walk. Rather, it may phase in and out of random-walkiness for indeterminate amounts of time.

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Hi I don't know what it's worth but as it seems quite easy to implement you might try this one : arxiv.org/abs/1007.4259 –  TheBridge Feb 10 '11 at 13:41
Thanks Bridge! Looks cool. I'll see how it performs. –  Pete Feb 10 '11 at 16:23