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Data 2017, 2(2), 20; doi:10.3390/data2020020

Open Source Fundamental Industry Classification

1,2,†,* and 3
1
Quantigic® Solutions LLC, 1127 High Ridge Road, #135, Stamford, CT 06905, USA
2
Business School & School of Physics, Free University of Tbilisi, 240 David Agmashenebeli Alley, Tbilisi 0159, Georgia
3
Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
Disclaimer: This address is used by the corresponding author for no purpose other than to indicate his professional affiliation as is customary in publications. In particular, the contents of this paper are not intended as an investment, legal, tax or any other such advice, and in no way represent views of Quantigic® Solutions LLC, the website www.quantigic.com or any of their other affiliates.
*
Author to whom correspondence should be addressed.
Received: 1 May 2017 / Revised: 13 June 2017 / Accepted: 14 June 2017 / Published: 17 June 2017
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Abstract

Abstract: We provide complete source code for building a fundamental industry classification based on publicly available and freely downloadable data. We compare various fundamental industry classifications by running a horserace of short-horizon trading signals (alphas) utilizing open source heterotic risk models (https://ssrn.com/abstract=2600798) built using such industry classifications. Our source code includes various stand-alone and portable modules, e.g., for downloading/parsing web data, etc. View Full-Text
Keywords: industry classification; fundamental; open source; source code; stocks; hierarchy; GICS; BICS; ICB; NAICS; SIC; TRBC; quantitative trading; trading signal; alpha; risk model; mean-reversion; optimization; short-horizon; backtest; simulation; download industry classification; fundamental; open source; source code; stocks; hierarchy; GICS; BICS; ICB; NAICS; SIC; TRBC; quantitative trading; trading signal; alpha; risk model; mean-reversion; optimization; short-horizon; backtest; simulation; download
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kakushadze, Z.; Yu, W. Open Source Fundamental Industry Classification. Data 2017, 2, 20.

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