Board of Directors' Profile: A Case for Deep Learning as a Valid Methodology to Finance Research.

César Vaca · Fernando Tejerina Gaite · Benjamín Sahelices

3 citations0/0 files storedInternational Journal of Interactive Multimedia and Artificial Intelligence

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Computational and Text Analysis Methods Topic Modeling AI and HR Technologies

Abstract

This paper presents a Deep Learning (DL) model for natural language processing of unstructured CVs to generate a six-dimensional profile of the professional experience of the Spanish companies' board of directors. We show the complete process starting with open data extraction and cleaning, the generation of a labeled dataset for supervised learning, the development, training and validation of a DL model capable of accurately analyzing the dataset, and, finally, a data analysis work based on the automated generation of the professional profiles of more than 6,000 directors of Spanish listed companies between 2003 and 2020. An RNN-LSTM neural network has been trained in three phases starting from a random initial state, (1) learning of basic structures of the Spanish language, (2) fine tuning for scientific texts in the field of economics and finance, and (3) regression modeling to generate a six-dimensional profile based on a generalization of sentiment classification systems. The complete training has been carried out with very low computational requirements, having a total duration of 120 hours of processing in a low-end GPU. The results obtained in the validation of the DL model show great accuracy, obtaining a value for the standard deviation of the mean error between 0.015 and 0.033. As a result, we have been able to outline with a high degree of reliability the profile of the listed Spanish companies' board of directors. We found that the predominant profile is that of directors with experience in executive or consultancy positions, followed by the financial profile. The results achieved show the potential of DL in social science research, particularly in Finance.

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References

2000Boards of Directors as an Endogenously Determined Institution: A Survey of the Economic Literature 2020Measuring Corporate Culture Using Machine Learning 2008Financial expertise of directors 2013Political Connections and the Cost of Bank Loans 2017Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach*2011Legal Expertise on Corporate Audit Committees and Financial Reporting Quality 2020A Text-Based Analysis of Corporate Innovation 2014Professors in the Boardroom and Their Impact on Corporate Governance and Firm Performance

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2024Adoption of artificial intelligence and machine learning in banking systems: a qualitative survey of board of directors

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