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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).

SPDR S&P Dividend ETF

U.S. / Australia Foreign Exchange Rate AUDUSD

Liberty Global plc Class A Ordinary Shares

O'Reilly Automotive, Inc. Common Stock

Marriott International Class A Common Stock

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.

2024-05-20T12:36:28.270503 image/svg+xml Matplotlib v3.7.3,
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.


šŸŸ¢ p-value: 0.007 > 0.001

2024-05-20T12:36:28.518583 image/svg+xml Matplotlib v3.7.3,

Which direction do the bulk of the models think movement will shift towards? With each model casting a vote on the likely change with respect to a value of 529.45 on the Fri 17 May, and following an averaging procedure of the most recent net predictions, expected future positions are

% Up % Down % Un­de­cided
coming day 90% 1% 9%
the day in 1 months 91% 1% 8%
the day in 2 months 94% 1% 5%
the day in 3 months 97% 1% 2%