ALL THINGS BIG AND SMALL 2.0

LATEST UPDATE OF CHERNOFF FACES: 31 JAN 2011. According to the Chernoff faces, the DJIA (and by implication the general market, with a bias towards big cap bluechips) remains still much the same. Size of face is proportional to valuation +/- % and if you are looking for undervaluation, look for the small faces. e.g. Alcoa and BAC. But then there are reasons why BAC is undervalued. Alcoa is not so squinty-eyed anymore, due to a combination of improvements in its Expected EPS, volatility and Beta. Chernoff faces of the DJIA as at 31 January 2011 Chernoff faces circa 15 Dec 2010 Above is a Chernoff Face glyphplot of the DJIA stocks. For explanation of Chernoff Faces see my post at http://www.technifundamentals.com/2010/12/merry-xmas-edition-fun-with-statistics.html . The 3D visualizations below are derived from the 30 constituent stocks of the DJIA, and their fundamentals e.g. P/E, Beta, 12-month Return, Earnings Per Share Forecast, Earnings per Share Actual etc - 15 selected variables ...

1. A Self-Organizing Map of 2800 US stocks' Beta. Any area can be selected with mouse to Open up and identify the stocks in it. 2. A Daubechies 4 Wavelet; 5 Level decomposition of the Hang Seng Index. 3. Using a Wavelet to De-Noise the Hang Seng Index:more efficient than a Moving Average, with no lag. (click on image to see how the Wavelet 'hugs' the time series closely, yet gets rid of daily noise) 4. A 1-D complex continuous wavelet of the Straits Times Index in Jet Color Mode. Self-similar patterns are indications of fractals i.e. the Index is not totally random but has its own 'memory'. 5. A 1-D Continuous Wavelet of the Straits Times Index in Prism Color Mode. Again we see patterns of sorts. But it takes a skilled Wavelets practioner to interpret, Unfortunately, I'm just beginning to understand Wavelets. Financial markets historical data are essentially the same as digital signals. And therefore they can be processed and analyzed with the tools used in...

1. Self-Organized clusters of S&P500 stocks, plus screened Growth, Quality and Value stocks 1. Statistics of the self-organized clusters shwoing the model variables of the screened stocks 3. Short/Long Areas of the self-organizing map. Though generated for serious analysis of the U.S. stock markets, these colorful self-organizing maps are attractive in an artistic way. Taken from my financial markets Blog, these images were generated using Self-Organizing Map technology [SOM], which is a form of Artificial Intelligence. In essence the map clustered elements according to their degree of similarity. Imagine it as a matrix with each row representing a stock, and each column filled with data on fundmental and technical characteristics of the stock e.g. its P/E, the last 5 years Return, the Sharpe Ratio, the estimated Earnings Per Share etc. All in there were about 30 variables for each stock. The SOM took into account all the variables of all the stocks simultaneously when plotting...