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5-Day U.S. Market Forecast
And Sector Analysis
Current Price
Data as of
Requires Javascript Enabled In Browser
[No canvas support]
SectorCallCall Date
TechnologyBearish2015-05-07
EnergyBullish2015-01-28
MaterialsBullish2015-01-23
IndustrialsBullish2015-04-24
UtilitiesBullish2015-04-16
Consumer CyclicalBullish2015-01-28
FinancialsBullish2014-09-29
Consumer StaplesBullish2015-01-23
MetalsBullish2015-04-22
Based on Relative Price Change, RSI and MACD

How It Works

These are Gaussian matrix elimination models.

This model uses the stocks from the S & P 500. I create three groups of 5 stocks from 15 highest correlating stocks for each symbol forecasted. I build "submodels" based on periods of the stocks' price changes with a similar price pattern that matches the symbols' changes (something similar to a Baysian condition). I call these patterns "Fingerprints." So if the correlating symbols all are positive then the fingerprint is "+++++". Using the submodels that provide the closest (read best) there is a test for best historical performance. Historical performance is based on the up to 10 best submodels; best meaning they generate price changes that are closest to the historical day's price change of the current date range of 1 1/2 years.

From each of the 3 groups the model uses (up to) 10 resulting vector with the previous day's price. Or if the application is run during the day the current price.

Finally two numbers are displayed for each future date:

  • a simple average of the 3 results is computed.
  • the median number from the list of calculated 30 numbers.

If no matching fingerprint submodels are available then all submodels from all 3 groups are used, calculated, totaled and the average is computed. The average is then used as the predicted price change.

Sector Analysis

The output for this page is generated by a combination of three analogs: PCTP (Price Change Tipping Point), RSI (Relative Strength Index) and MACD (Moving Average Convergence Divergence). All 3 use an optimized cross-over value.

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