i'm not smart enough to even understand it really.. for example, after reading the following, i could not in my own words explain it to someone else. lol can someone please explain this in simple terms? 
http://vader.brad.ac.uk/finance/tfp.html
specifically, i'd like to understand this part:
"In spite of the volumes of rubbish in classical economics texts, the Efficient Market Hypothesis is NOT TRUE. There is now overwhelming evidence to the contrary. Moreover, mathematical analysis of financial time series shows that the market is NOT normally distributed, it is more like a Pareto-Levy distribution. Unlike normally distributed time series which have computable moments, the market distribution is NOT well behaved - for example, the Pareto-Levy distribution has INFINITE variance! In other words, financial time series are NOT STATIONARY and they are NOT LINEAR. Consequently, EVERY filter you will ever find in any book on signal processing, be it a simple moving average or an advanced Kalman tracking filter, is about as much use as a chocolate padlock in financial applications! What is not written in two inch high letters on the first page of these books (but what is always tacitly assumed), is that the signals are STATIONARY and hence LINEAR filtering is applicable. This is generally true for signals in communications equipment but it is NOT true for signals arising from natural phenomena, especially the markets.
What is required for the markets is NON-LINEAR analysis. This is, of course, MUCH more difficult both theoretically and practically than the simple linear cases, so textbooks don't bother with non-linear stuff; it is just too difficult. Likewise, vendors of technical analysis software generally use what they can find in the literature, at most tarting up the algorithms a little here and there. But these algorithms don't work! Gauss proved three hundred years ago that the best estimator of a random process is its moving average. If the markets are random, then moving averages should make you money every time. But they don't. Now you know why."
http://vader.brad.ac.uk/finance/tfp.html
specifically, i'd like to understand this part:
"In spite of the volumes of rubbish in classical economics texts, the Efficient Market Hypothesis is NOT TRUE. There is now overwhelming evidence to the contrary. Moreover, mathematical analysis of financial time series shows that the market is NOT normally distributed, it is more like a Pareto-Levy distribution. Unlike normally distributed time series which have computable moments, the market distribution is NOT well behaved - for example, the Pareto-Levy distribution has INFINITE variance! In other words, financial time series are NOT STATIONARY and they are NOT LINEAR. Consequently, EVERY filter you will ever find in any book on signal processing, be it a simple moving average or an advanced Kalman tracking filter, is about as much use as a chocolate padlock in financial applications! What is not written in two inch high letters on the first page of these books (but what is always tacitly assumed), is that the signals are STATIONARY and hence LINEAR filtering is applicable. This is generally true for signals in communications equipment but it is NOT true for signals arising from natural phenomena, especially the markets.
What is required for the markets is NON-LINEAR analysis. This is, of course, MUCH more difficult both theoretically and practically than the simple linear cases, so textbooks don't bother with non-linear stuff; it is just too difficult. Likewise, vendors of technical analysis software generally use what they can find in the literature, at most tarting up the algorithms a little here and there. But these algorithms don't work! Gauss proved three hundred years ago that the best estimator of a random process is its moving average. If the markets are random, then moving averages should make you money every time. But they don't. Now you know why."
