One the best pieces I have read in quantitative finance - background information, analysis plus python code included as well - Hacking the Random Walk Hypothesis:
Relating this distinction back to the random walk hypothesis, we should be able to distinguish between the local and the global random walk hypothesis. The global random walk hypothesis would state that in the long run markets appear to be random whereas the local random walk hypothesis would state that for some minimum period time the market will appear to be random. This view of the world is, at least in my opinion, consistent with the empirical observations of anomalies such as the value, momentum, and mean-reversion factors especially when we acknowledge that these factors tend to exhibit cyclical behaviour. In other words, as with the sequence shown earlier, markets exhibit global randomness but during finite periods of time local randomness breaks down.
Unfortunately I have not seen this distinction made anywhere, it is my own opinion on how the empirical observations of individuals beating the market and the random walk hypothesis could be reconciled. Another distinction made by the algorithmic definition of randomness is that randomness is relative to information.
In the absence of information many systems may appear random, despite the fact that they are deterministic. In statistics this is known as a confounding variable. A good example of this is a random number generator which only appear random in the absence of the seed being used to produce that random sequence. Another, more interesting example is that market returns might appear to be random on their own, but in the presence of earnings reports and other fundamental indicators, that apparent randomness could break down and become non-random.
These two theories are impossible to prove but they are what I personally believe about the markets (in addition to my belief that they are not random, but rather appear random just like many other complex adaptive systems). The remainder of this article leaves the world of theoretical computer science, information theory, and economics behind and focuses instead on what can realistically be achieved, namely the application of statistical tests for randomness to markets in order to identify potential trading opportunities / attractive markets.
Relating this distinction back to the random walk hypothesis, we should be able to distinguish between the local and the global random walk hypothesis. The global random walk hypothesis would state that in the long run markets appear to be random whereas the local random walk hypothesis would state that for some minimum period time the market will appear to be random. This view of the world is, at least in my opinion, consistent with the empirical observations of anomalies such as the value, momentum, and mean-reversion factors especially when we acknowledge that these factors tend to exhibit cyclical behaviour. In other words, as with the sequence shown earlier, markets exhibit global randomness but during finite periods of time local randomness breaks down.
Unfortunately I have not seen this distinction made anywhere, it is my own opinion on how the empirical observations of individuals beating the market and the random walk hypothesis could be reconciled. Another distinction made by the algorithmic definition of randomness is that randomness is relative to information.
In the absence of information many systems may appear random, despite the fact that they are deterministic. In statistics this is known as a confounding variable. A good example of this is a random number generator which only appear random in the absence of the seed being used to produce that random sequence. Another, more interesting example is that market returns might appear to be random on their own, but in the presence of earnings reports and other fundamental indicators, that apparent randomness could break down and become non-random.
These two theories are impossible to prove but they are what I personally believe about the markets (in addition to my belief that they are not random, but rather appear random just like many other complex adaptive systems). The remainder of this article leaves the world of theoretical computer science, information theory, and economics behind and focuses instead on what can realistically be achieved, namely the application of statistical tests for randomness to markets in order to identify potential trading opportunities / attractive markets.
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