ALL THINGS BIG AND SMALL 2.0

  Data used: Boeing Co. Data as of 17 April 2025 It is true that short-term modeling and predictions of stock prices using just price data is valid and useful. We do not need fundamental data as input variables for short-term predictions. But there are inherent flaws in traditional Technical Analysis (TA). The inherent flaws of traditional technical analysis indicators, such as RSI, MACD, Bollinger Bands, all assume that the relationship between market variables is linear and that data distributions are Gaussian (Normal). But it is well-known that financial markets exhibit non-linear dynamic characteristics with distributions that are not Normal i.e. have more than 1 peak, are highly skewed and have long fat tails (kurtosis). And that the relationship between market variables is highly non-linear.  However, these short-term linear relationships can be modeled with Linear Regression . The Table below shows that Linear Regression, particularly its Boosted version produces the sm...

Autocorrelation characteristics of Exxon Mobil [XOM] Autocorrelation characteristics of Petroleo Brasileiro [PBR.A] Autocorrelation characteristics of Statoil [STO] Autocorrelation characteristics of Google [GOOG] * click on images for full-size and details. It is a fact that a stock's past price affects its current price. i.e. A stock's price is correlated with itself for any given period of lag. A less technical explanation of this phenomenon would be that investors look to a stock's past price as a guide for future prices. The 'collective memory' of a stock's investors thus manifests itself as its autocorrelation characteristic. The degree of autocorrelation [AC], and the total effect over time varys greatly with each stock. In the context of ValuEngine's Valuation model, a high autocorrelation level indicates high persistence in over or undervaluation, and a long period to correct the mispricing. If we know the AC characteristics of a stock, it can be...