Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial Market

Authors

DOI:

https://doi.org/10.9771/cp.v12i1.27260

Keywords:

Machine Learning, Financial Market, Investment, Trading, Prospection.

Abstract

This is a prospective study of the uses of machine learning technology applied to financial market trading and investment. Quick expansion on TI technology generated effective changes on daily financial routine. As futher changes may be expected, this prospection analyzed 257 patents from Espacenet database processed via Questel Orbit® software. It was found in 2016 a peak in 1st patent deposits that remains until the present. Most of applications was on outliers, feature selection and clustering. No holder was identified with financial core activity, pointing out the possibility of trade secret strategies. China and USA were the biggest depositors and the biggest market of these applications. The outstanding growth of the market and the identification of a horizon of technological maturation processes is a consequence of the recent development in process power, data readiness and cloud-based technology.

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Author Biographies

Vitor M. Quintella, Federal University of Bahia, Salvador, BA, Brazil

He holds a degree in Chemical Engineering from the Federal University of Bahia (2013) and a master's degree in Industrial Engineering from the Federal University of Bahia (2017). He is currently a University Professor and Consultant. He taught at the business school of the Federal University of Bahia (2016). Intern in the area of ​​Planning and Development of New Businesses at Braskem. He has experience in the area of ​​Production Engineering, with emphasis on Economic Engineering and in the area of ​​Corporate Finance, working mainly on the following topics: Corporate Risk, Futures Market, Project Evaluation, Risk Management, Petrochemical Commodity Industries.

Cristina M. Quintella, Federal University of Bahia, Salvador, BA, Brazil

Cristina Maria Assis Lopes Tavares da Mata Hermida Quintella graduated in 1983 with a bachelor's degree in Physics (Federal University of Rio de Janeiro), in 1985 a master's degree in Physical Chemistry (Institute of Chemistry of the Federal University of Rio de Janeiro), in 1993 an interdisciplinary PhD in Molecular Sciences (University of Sussex, UK) and has several qualifications in Intellectual Property and Technology Transfer (IP and TT) by WIPO and INPI. It's Prof. Head of the Federal University of Bahia, where he coordinates LabLaser/IQ/UFBA since 1994, and works mainly in: molecular dynamics and kinetics; spectroscopy; interfaces; biotechnology, oil production and transport; CO2; instrumentation. Her group won the Petrobras Technology Award 4 consecutive years (2003 to 2006) in three different themes and the Petrobras Inventor Award in 2008 and 2010. Britain, Japan and Russia. Several technologies that he developed are already technological innovation being used by society, for example by the companies QUIMIS, PETROBRAS, COSERN, and other newer technology-based companies operating, for example in EMBRAPII. He is currently a Productivity Scholarship Developer. Tech and CNPq Innovative Extension - Level 2. It is active in negotiation; technological prospecting; PI and TT, implemented and coordinated the Technological Innovation Center (NIT) at UFBA (2005 to 2014), was the first Innovation Coordinator at UFBA (2010 to 2014), has coordinated the NIT-NE Network since 2004, which comprises 52 institutions from the 9 NE states of Brazil and other states. Are you President of the National Forum Association of Innovation and Technology Transfer Managers? FORTEC (2014-18). He has supervised or advises more than 24 DR, 29 MS, 104 IC and 83 technological orientations (ITI and DTI), in addition to 11 post-doctoral supervisions. He has 21 years of experience in coordinating individual (CNPq), institutional (PIBIC/UFBA, Intellectual Property Nucleus UFBA-UFPb-UFS-CEFETBA) and inter-institutional projects (PADCT3, CYTED/LCDs, CTPetro/CNPq, CTPetro/FINEP, CTHidro /FINEP, CTInfra/FINEP, TIB/Verde and Amarelo/CNPq), participated in the elaboration and in the Management Committee of the multi-institutional PRODOC of Bahia, coordinated the institutional PIBIC.

Antônio Francisco A. Silva Junior, Federal University of Bahia, Salvador, BA, Brazil

Postdoctoral fellow as Visiting Researcher at New York University 2015-2016 (Finance and Risk Engineering Department). Doctor in Aeronautical and Mechanical Engineering, in 2006, by the Production Engineering department, of the Technological Institute of Aeronautics (ITA), with a thesis on foreign exchange interventions in financial crises. Graduated in Chemical Engineering in 1988, completed a master's degree in Engineering, in 1998, with a dissertation in the area of ​​Industrial Process Automation, and a master's degree in Administration, in 2000, with a dissertation in the area of ​​market risk management, from the Federal University of Bahia - UFBA He works at the Central Bank of Brazil in the area of ​​risk management, is an adjunct professor at the Faculty of Administration at UFBA, works as a participating professor in the Postgraduate Program in Industrial Engineering, at UFBA, and is a permanent professor at the Postgraduate Center in Administration NPGA-UFBA. His academic works are oriented towards risk management and investment decisions.

Cristiano de Oliveira Hora Fontes, Federal University of Bahia, Salvador, BA, Brazil

He holds a degree in Chemical Engineering from the Federal University of Bahia (1990), a Master's degree in Chemical Engineering from the Federal University of Bahia (1996) and a PhD in Chemical Engineering from the State University of Campinas (2001). He was a process engineer at Companhia Petroquímica de Camaçari (Pólo Petroquímica de Camaçari, 1990 to 1994), was a professor at the State University of Feira de Santana (UEFS-BA, 1998 to 2002) and is currently an associate professor at the Federal University of Bahia ( UFBA). He was general coordinator of the Graduate Program in Industrial Engineering at Escola Politécnica from 2008 to 2012 and vice-coordinator from 2012 to 2014. He develops, guides and publishes works in the areas of identification, control, optimization, techniques of artificial intelligence and pattern recognition applied to production systems. He holds a CNPq productivity scholarship (level 2) in the area of ​​Production Engineering, more specifically in the subarea of ​​Planning, Design and Control of Production Systems. He is a CNPq ad hoc consultant. He has taught in recent years Fundamentals of Heat Transfer for undergraduate courses in chemical and mechanical engineering and Fundamentals of Fuzzy Logic, clustering and pattern recognition for undergraduate and graduate students. In 2015, he completed a Post-Doctorate at the University of Waterloo (Ontario, Canada) on the subject of pattern recognition in uni and multivariate time series as a decision-making support tool. The work was developed in partnership with Professor Hector Budman and topics such as the application of pattern recognition in the detection and diagnosis of failures in production processes, under and over sampling in clustering problems involving time series and application of pattern recognition in process design and analysis.

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Published

2019-03-01

How to Cite

Quintella, V. M., Quintella, C. M., Silva Junior, A. F. A., & Fontes, C. de O. H. (2019). Exploratory Prospective Study of Methods of Machine Learning Patents Applied to the Financial Market. Cadernos De Prospecção, 12(1), 113. https://doi.org/10.9771/cp.v12i1.27260

Issue

Vol. 12 No. 1 (2019)

Section

Prospecções Tecnológicas de Assuntos Específicos

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Copyright (c) 2019 Cadernos de Prospecção

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