ALL THINGS BIG AND SMALL 2.0

  Next week’s 2,10,30-year Treasury yields may determine whether we will see the straw that will break the camel’s back, the snowflake that will trigger the avalanche, the spark that will set off the powder keg. Refer to image above for my 8 July post for a checklist of what will happen.   This week's models were run against a backdrop of extraordinary macro turbulence compressed into the final two trading sessions of July. On July 29, the Federal Reserve held its benchmark rate at 3.5–3.75% in a divided 9-3 vote — three members dissented in favour of an immediate hike — sending a hawkish signal that drove the 30-year nominal Treasury yield to 5.28%, its highest level in 19 years. The following day, the Japanese Ministry of Finance conducted stealth yen-buying intervention (estimated at $74bn YTD), sending USD/JPY down almost 3% to 158 before a partial recovery. The combined effect: the DXY briefly fell below 100 — its lowest since mid-June — while nominal Treasury yields...

  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...