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Predictive Factors

The models identify what we call the causally predictive factors: a small set of assets judged to be the most important driving forces behind your selected item's forecast. These are what we believe are pushing the search target(s).



Incyte Corp. Common Stock

Marriott International Class A Common Stock

SPDR S&P Dividend ETF

as of

Our Probabilistic Prediction

With the predictive features above, we run many models repeatedly and aggregate them to account for randomness introduced at the training start.

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Goodness of Probabilistic Prediction

When prediction is done well, the realised probability level should not be concentrated particularly around anywhere but uniformly between 0 and 1 (whitepaper). We draw a sample of the realised probability levels and check its empirical probability density. The dotted line for that of the uniform distribution.

Under the hypothesis the realised probability level follow the ideal uniform distribution, a p-value can be calculated for the chance (0 to 1) of obtaining this sample. A smaller p-value for a slimmer chance. Below uses significance level 0.001 as cut-off on the p-value. The user is free to use another e.g. 0.01, 0.05 for the test of goodness.

Invesco Nasdaq-100 ETF

🔴 p-value: 0.001 < 0.001

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🟢 p-value: 0.002 > 0.001

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SPDR Dow Jones Industrial Average ETF

🟢 p-value: 0.051 > 0.001

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Which direction do the bulk of the models think movement will shift towards? With each model casting a vote on the likely change, and following an averaging procedure of most recent net predictions, expected future positions are for the upcoming day

% Up % Down % Un­de­cided
Invesco Nasdaq-100 ETF 56% 6% 39%
SPDR S&P 500 ETF 90% 1% 9%
SPDR Dow Jones Industrial Average ETF 98% 1% 0%
Changes are with respect to the following time points:
Time Value
Invesco Nasdaq-100 ETF Fri 17 May 451.76
SPDR S&P 500 ETF Fri 17 May 529.45
SPDR Dow Jones Industrial Average ETF Fri 17 May 399.95