What Data Series Matter? Explaining key trends and factors generated by Artificial Intelligence

21 Pages Posted: 20 Sep 2021 Last revised: 4 Nov 2021

See all articles by Irene Aldridge

Irene Aldridge

AbleMarkets.com; Cornell University; BigDataFinance.org; ABLE Alpha Trading, LTD

Date Written: September 17, 2021

Abstract

We show a simple way to let the data speak for themselves. Specifically, we show how a large mixed bag of data, potentially embedded with missing data points and collinearities, and therefore unsuitable for traditional econometric analysis, can be useful in building fast and meaningful big data and artificial intelligence analyses and predictions. What’s more, our technique helps the results of the analyses to be easily interpreted by researchers. We use these techniques to build a surprisingly profitable E-mini crude oil futures trading strategy with monthly reallocations, delivering annualized returns of 100%+ with Sharpe ratio exceeding 2.2.

Keywords: asset pricing, artificial intelligence, pca, svd, factors, econometrics

JEL Classification: G17

Suggested Citation

Aldridge, Irene, What Data Series Matter? Explaining key trends and factors generated by Artificial Intelligence (September 17, 2021). Available at SSRN: https://ssrn.com/abstract=3925856 or http://dx.doi.org/10.2139/ssrn.3925856

Irene Aldridge (Contact Author)

AbleMarkets.com ( email )

New York, NY 10128
United States

HOME PAGE: http://www.AbleMarkets.com

Cornell University ( email )

Ithaca, NY 14853
United States

BigDataFinance.org ( email )

United States

ABLE Alpha Trading, LTD ( email )

New York, NY 10004
United States

HOME PAGE: http://www.ablealpha.com

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