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An Algorithm for Matching Heterogeneous Financial Databases: A Case Study for COMPUSTAT/CRSP and I/B/E/S Databases

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  • Irene Rodriguez-Lujan
  • Ramon Huerta

Abstract

Rigorous and proper linking of financial databases is a necessary step to test trading strategies incorporating multimodal sources of information. This paper proposes a machine learning solution to match companies in heterogeneous financial databases. Our method, named Financial Attribute Selection Distance (FASD), has two stages, each of them corresponding to one of the two interrelated tasks commonly involved in heterogeneous database matching problems: schema matching and entity matching. FASD's schema matching procedure is based on the Kullback-Leibler divergence of string and numeric attributes. FASD's entity matching solution relies on learning a company distance flexible enough to deal with the numeric and string attribute links found by the schema matching algorithm, and it incorporates different string matching approaches such as edit-based and token-based metrics. The parameters of the distance are optimized using the F-score as cost function. FASD is able to match the joint Compustat/CRSP and Institutional Brokers' Estimate System (I/B/E/S) databases with an F-score over 0.94 using only a hundred of manually labeled company links.

Suggested Citation

  • Irene Rodriguez-Lujan & Ramon Huerta, 2016. "An Algorithm for Matching Heterogeneous Financial Databases: A Case Study for COMPUSTAT/CRSP and I/B/E/S Databases," Applied Economics and Finance, Redfame publishing, vol. 3(1), pages 161-172, February.
    • Handle: RePEc:rfa:aefjnl:v:3:y:2016:i:1:p:161-172
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    More about this item

    Keywords

    Compustat/CRSP; I/B/E/S; financial data; heterogeneous databases; company matching; schema matching; attribute matching; Kullback-Leibler divergence;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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