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"...This is the sensational bit! You can use random
noise, smooth it, and generate nice looking, systematic effects. What
Slutzky did and what shocked the academic world at the time was to
mimic an actual trade cycle using only random noise..."
Burton Malkiel ("A Random Walk Down Wall Street") describes an
experiment where the outcome of coin flips (+1 for heads, -1 for
tails, and doing a running total) is displayed on a stock chart.
After a few hundred coin-flips, the resulting pattern of numbers looks
just like the activity of a "real" stock.
These are not just meaningless egg-head, academic thought
experiments. The implications are profound for traders/investors
using most kinds of TA, including moving averages. If your favored TA
method can't distinguish between randomly-generated data and the real
thing, is it really measuring what's going on in the market or is it
just measuring the characteristics of a data-set?
Since we know that most stocks travel together ("the rising tide
raises all boats"), can any indicator that ignores the activity of the
overall market really be valid? Stocks also rise and fall based on
earnings, dividends and valuations. Can any indicator that ignores
these factors be considered valid? What about economic factors?
Liquidity? Fed policy? Float size? Short interest? Volume?
I stand by my original point that massive historical back-testing
using the arbitrary mathematical formulas of the vast majority of TA
methods only produces unimportant coincidental correlations, and I
would welcome any logical argument or proof that this isn't the case.
Luck,
Sebastian
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