QUANT v1

QUANT v1

ABSTRACT: QUANT v1 (v1 = version 01[.01.07]) is a tool for the stock investor. It gives an indication how reliable and predictable a particular stock market is. This version is for the NYSE and NASDAQ of Wall Street, because the QUANT Index is calculated exclusively from recent Dow Jones Industrial Average stock quote data. QUANT [...]

ABSTRACT:
QUANT v1 (v1 = version 01[.01.07]) is a tool for the stock investor. It gives an indication how reliable and predictable a particular stock market is. This version is for the NYSE and NASDAQ of Wall Street, because the QUANT Index is calculated exclusively from recent Dow Jones Industrial Average stock quote data. QUANT has now an event horizon of 90 trading days setting. This means that QUANT gives an indication how good the NYSE and NASDAQ markets are for short time investments, in other words it advices on (buying / selling) portfolio management within these 90 trading days. The $69.99 price (1x) for indefinite time is partly based on the estimated Return-Of-Investment (~1000x) (ROI = 1 : ~1000) by using this app. This ROI is easily achieved by the average individual investor QUANT users. The relative high price ($69.99)is also based on the fact that this app is the spin-off from no less than 10 years of computer scientific, mathematical and statistical research!

NB: Because the QUANT Index is very sensitive for time shifts, qua the 90% Confidence Level significance test of the graph data points and entropy values over the different 90 trading day blocks, graphs of only one or more trading days difference might not completely look the same. However the general trend in the QUANT Index always remains preserved. Moreover at the tested (present 2013-02-08) 23 trading days back in time the forecast in the graph (90% true BLACK Line; 90% false GREY LINE) was always below (and outside) the graph. Then a BLACK or GREY line is plotted parallel at the bottom X-Axis, otherwise it would be somewhere in the graph. Hypothetically it could also be above (and outside) then these lines are plotted at the top of the graph. Calibration is now done dynamically.

THE QUANT INDEX:
Stock quote data –you can read here any kind of “real world” number distribution– follow the Pareto power law. [1] And the Benford formula by the way. [2] However Pareto (entropy) is the topic of this paper. When a power law applies one can plot relative frequencies against the values on log-log paper and always find a straight line. Actually you are then plotting the Pareto’s power law formula (1.):

y = Pr(X > x) = (xm/x)^k (1.) x >= xm

log(y) = log(xm^k)–klog(x) (2.) straight line!

This power law has many properties, as you can see from the given reference [1], including entropy (3.):

QUANT = Pareto entropy = Hf = log(xm/k) + (1/k) + 1 = – [log(k/xm)-(1/k)-1] (3.)

Because this entropy formula is crucial for my research, I verified it analytically. [3]
How to curve fit data with the power law, according to the bin method, is described already and applied in a similar way as for my “Black Swan Robust” app. [4]


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Windows RT

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