Machine Learning and Traditional Econometric Models: A Systematic Mapping Study

This research has been supported by the project "INTELFIN: Artificial Intelligence for investment and value creation in SMEs through competitive analysis and business environment", Reference: RTC-2017-6536-7, funded by the Ministry of Science, Innovation and Universities (ChallengesCollaboration 2017), the State Agency for Research (AEI) and the European Regional Development Fund (ERDF).Machine Learning (ML) is a disruptive concept that has given rise to and generated interest in different applications in many fields of study. The purpose of Machine Learning is to solve real-life problems by automatically learning and improving from experience without being explicitly programmed for a specific problem, but for a generic type of problem. This article approaches the different applications of ML in a series of econometric methods. Objective: The objective of this research is to identify the latest applications and do a comparative study of the performance of econometric and ML models. The study aimed to find empirical evidence for the performance of ML algorithms being superior to traditional econometric models. The Methodology of systematic mapping of literature has been followed to carry out this research, according to the guidelines established by [39], and [58] that facilitate the identification of studies published about this subject. Results: The results show, that in most cases ML outperforms econometric models, while in other cases the best performance has been achieved by combining traditional methods and ML applications. Conclusion: inclusion and exclusions criteria have been applied and 52 articles closely related articles have been reviewed. The conclusion drawn from this research is that it is a field that is growing, which is something that is well known nowadays and that there is no certainty as to the performance of ML being always superior to that of econometric models.project "INTELFIN: Artificial Intelligence for investment and value creation in SMEs through competitive analysis and business environment" - Ministry of Science, Innovation and Universities (ChallengesCollaboration 2017) RTC-2017-6536-7State Agency for Research (AEI)European Commissio

A Linguistic Recommender System For University Digital Libraries To Help Users In Their Research Resources Accesses

The Web is one of the most important information media and it is influencing in the development of other media, as for example, newspapers, journals, books, libraries, etc. Moreover, in recent days people want to communicate and collaborate. So, libraries must develop services for connecting people together in information environments. Then, the library staff needs automatic techniques to facilitate that a great number of users can access to a great number of resources. Recommender systems are tools whose objective is to evaluate and filter the great amount of information available on the Web. We present a model of a fuzzy linguistic recommender system to help University Digital Library users in their research resources accesses. This system recommends researchers specialized and complementary resources in order to discover collaboration possibilities to form multi-disciplinaryy groups. In this way, this system increases social collaboration possibilities in a university framework and contributes to improve the services provided by a University Digital Library

Análisis de la web semántica: estado actual y requisitos futuros

The vast and growing number of web resources show that lexical-statistical aids by themselves cannot resolve the problem of information retrieval. The "semantic web" project tries to solve these problems (and to do much more) by having the computers comprehend the content of the resources. This paper examines the current status of the required 'la-yers' put forward by Berners-Lee for the development of the Semantic web: namely, the uniform identification layer, the representation layer, the logical layer, and the security layer

EFECTOS ECONÓMICO DE LA CRISIS FINANCIERA INTERNACIONAL 2007 EN PANAMÁ.

We present the transmission channels, in Latina America and the Caribbean, of the international financial crisis, generated in the United States, followed by approach of its effects and the macroeconomic impact on engines development of the Republic of Panama, basic for economic and financial analysis of the country.Se consideran para América Latina y El Caribe los canales de transmisión de la Crisis Financiera Internacional generada en los EUA, se sintetizan sus efectos y las políticas económicas adoptadas así como también: los acuerdos globales de los macro organismos internacionales. Efectos y medidas regionales que se focalizan en el impacto económico que genera la misma en los motores del desarrollo de la República de Panamá, información fundamental para los análisis económicos y financieros del país

Consensus in a fuzzy environment: a bibliometric study

In today’s organizations, group decision making has become a part of everyday organizational life. It involves multiple individuals interacting to reach a decision. An important question here is the level of agreement or consensus achieved among the individuals before making the decision. Traditionally, consensus has been meant to be a full and unanimous agreement. However, it is often not reachable in practice. A more reasonable approach is the use of softer consensus measures, which assess the consensus in a more flexible way, reflecting the large spectrum of possible partial agreements and guiding the discussion process until widespread agreement is achieved. As soft consensus measures are more human-consistent in the sense that they better reflect a real human perception of the essence of consensus, consensus models based on these kind of measures have been widely proposed. The aim of this contribution is to present a bibliometric study performed on the consensus approaches that have been proposed in a fuzzy environment. It gives an overview about the research products gathered in this research field. To do so, several points have been studied, among others: countries, journals, top contributing authors, most cited keywords, papers and authors. This allows us to show a quick shot of the state of the art in this research area

Smoke and fire dynamics in atria and large enclosures: An overview

The proliferation of atria within modern large buildings is relatively recent. An atrium can be defined as a large open space connecting two or more storeys. Atria are important architectonical features since the 60's and can be found, among others, in shopping centres, office buildings and high-rise buildings, airports, stations and sports centres. However, the atrium represents an innovative, complex and non conventional architectonical element that can lead to fire environments diverging significantly from those in conventional compartments used in the development of current codes and standards. They are a source of discussion in the fire safety community because smoke can easily spread from one floor to another making the traditional methodologies for compartmentation of little or null effect. The design of smoke management in atria has been based on prescriptive codes since the 70's. It was not until the mid 80's that the phenomena started to be the objective of both experimental and numerical studies. It is because of the subsequent improved understanding on fire dynamics and smoke management together with the increased computing power available nowadays, that there is a progressive movement from prescriptive-based to performance-based codes which is also chaning the way atria are designed. The aim of the present work is to provide a broad overview of the current state-of-the-art of fires in atria. A brief introduction to the characteristics and types of atrium structures is presented first. Then, a description of the fire dynamics as well as the main safety problems that arise in case of fire are considered. A historical discussion of the research, including experimental and numerical studies, is presented, and current design methodologies and fire safety strategies are discussed. The final part of the overview addresses the international tendencies towards the introduction of performance-based fire protection codes. This overview highlights the need for further experimental studies and validations of numerical simulations for a wider range of fire conditions. © 2010 by Nova Science Publishers, Inc. All rights reserved

A bibliometric analysis of the first twenty years of soft computing

© 2018, Springer International Publishing AG. Soft Computing was launched in 1997. Today, the journal is becoming twenty years old. Motivated by this anniversary, this article develops a bibliometric analysis of the journal in order to identify the leading trends of the journal in terms of publications and citations. The work considers several issues including the leading authors, institutions and countries. The study also uses a software to develop a graphical analysis. The results show a significant increase of the journal during the last years that has consolidated the journal as a leading one in the field

Machine learning methods for systemic risk analysis in financial sectors.

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.This research has been partially supported by grants from the National Natural Science Foundation of China (#U1811462, #71874023, #71771037, #71725001, and #71433001)