But there are many different agents who have different evaluations of what is considered a "high" price and a low price so the overall stock price should be built from a bunch of sin waves of different shapes. In math when we want to handle this kind of thing we use Fourrier analysis.
Here is a graph of Apple's stock price over a year.
See it looks very chaotic but when you run it through Fourrier analysis it comes out like:
Now the interesting thing is this above function shouldn't be too hard to find a function that matches it. In Maple I find:
I haven't put it through Fourier transform software to see how well this predicts the variation of the price over time yet, though.
The long and short of this whole thing is if you took 234^(1/f) and did the inverse Fourier transform on it, that should be a function that more or less looks like Apple's stock price over time.
The long and short of this whole thing is if you took 234^(1/f) and did the inverse Fourier transform on it, that should be a function that more or less looks like Apple's stock price over time.
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