Open Source Fundamental Industry Classification

Data 2(2) (2017) 20

68 Pages Posted: 19 Apr 2017 Last revised: 26 Dec 2017

See all articles by Zura Kakushadze

Zura Kakushadze

Quantigic Solutions LLC; Free University of Tbilisi

Willie Yu

Duke-NUS Medical School - Centre for Computational Biology

Date Written: June 13, 2017

Abstract

We provide complete source code for building a fundamental industry classification based on publically 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 (http://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.

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

JEL Classification: G00

Suggested Citation

Kakushadze, Zura and Yu, Willie, Open Source Fundamental Industry Classification (June 13, 2017). Data 2(2) (2017) 20. Available at SSRN: https://ssrn.com/abstract=2954300 or http://dx.doi.org/10.2139/ssrn.2954300

Zura Kakushadze (Contact Author)

Quantigic Solutions LLC ( email )

1127 High Ridge Road #135
Stamford, CT 06905
United States
6462210440 (Phone)
6467923264 (Fax)

HOME PAGE: http://www.linkedin.com/in/zurakakushadze

Free University of Tbilisi ( email )

Business School and School of Physics
240, David Agmashenebeli Alley
Tbilisi, 0159
Georgia

Willie Yu

Duke-NUS Medical School - Centre for Computational Biology ( email )

8 College Road
Singapore, 169857
Singapore

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