Complexity

Applications of Machine Learning Methods in Complex Economics and Financial Networks


Publishing date
01 Nov 2019
Status
Closed
Submission deadline
28 Jun 2019

1Fundação Getúlio Vargas, Brasília, Brazil

2Central Bank of Brazil, Brasília, Brazil

3University of São Paulo, Ribeirão Preto, Brazil

4Bilkent University, Ankara, Turkey

This issue is now closed for submissions.
More articles will be published in the near future.

Applications of Machine Learning Methods in Complex Economics and Financial Networks

This issue is now closed for submissions.
More articles will be published in the near future.

Description

The availability of large databases and significant improvements in computational power has been key determinants in the explosive increase of interest in machine learning. In this sense, machine-learning methods, such as neural networks and genetic algorithms, have been used as methodological tools to understand how complex adaptive systems behave and to integrate many streams of unstructured and structured data. Economics and finance, on the flipside, have experienced an increasing interest in microlevel analysis, but with the empirical methodologies restricted to mostly linear methods brought by traditional econometric methods.

This cross-discipline special issue aims at integrating conceptual methodologies of the machine-learning domain with empirical issues that are found in Economics and Finance. There is a large room for exploration at the intersection of these two areas. Machine learning goes beyond of regression methods and can be used as a variety of ways. Thus, it can give new insights to how economics and finance data are organized. The application of these methods may contribute to the debate on assessing, monitoring and forecasting economic and financial variables is quite relevant.

We welcome new insights, models, and applications in a wide variety of topics that bridge topics in machine learning to complex economics and finance network. The application and adaptation of unsupervised learning methods, such as data and community clustering, ranking, anomaly detection, and semisupervised and supervised learning techniques, such as classification and regression, applied to finance and economics, are of great interest. We are also looking for methods that ally high-frequency data, such as those arising from social network, with traditional machine learning and econometrics to forecast or describe economic and financial variables from new perspectives. There are many gaps in the literature and we hope to address some of them within this call for papers. We look for papers that contribute to the debate on the use of machine learning in complex economics and finance network.

There are many gaps in the literature and we hope to address some of them within this call for papers. We look for papers that contribute to the debate on the use of machine learning in economics and finance.

Potential topics include but are not limited to the following:

  • Machine learning and applications in complex economics and finance network
  • Deep learning and applications in complex economics and finance network
  • Complex financial stability issues discussed using machine learning methods
  • Systemic risk measurement using new complex models
  • Network prediction using new techniques
  • Interdependent networks and its implications
  • Discussing cross-system risk, default contagion, network topology, endogenous financial networks, network resilience, Bayesian dynamic financial networks using new models and insights
  • Complexity and financial regulation
  • Multiplex networks and link prediction—applications in economics and finance
  • Interbank connections, systemic relevance, and bank supervision using new chaotic models and insights
  • Econometrics of complex networks
  • Agent-based modelling for complex economics and finance network
  • Genetic algorithms in financial network
  • Cellular automata with machine learning in financial network
  • Neural networks in financial regulation
  • Machine learning based on evolutionary game theory in finance

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 5458941
  • - Research Article

Modeling Repayment Behavior of Consumer Loan in Portfolio across Business Cycle: A Triplet Markov Model Approach

Shou Chen | Xiangqian Jiang
  • Special Issue
  • - Volume 2019
  • - Article ID 4325125
  • - Research Article

Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies

Thiago Christiano Silva | Benjamin Miranda Tabak | Idamar Magalhães Ferreira
  • Special Issue
  • - Volume 2019
  • - Article ID 2360610
  • - Research Article

Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning

Manuel J. García Rodríguez | Vicente Rodríguez Montequín | ... | Joaquín M. Villanueva Balsera
  • Special Issue
  • - Volume 2019
  • - Article ID 7458961
  • - Research Article

Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network

Jying-Nan Wang | Jiangze Du | ... | Kin-Keung Lai
  • Special Issue
  • - Volume 2019
  • - Article ID 2834894
  • - Research Article

A Study of RMB Internationalization Path Based on Border Area Perspective

Po Sheng Ko | Cheng Chung Wu | ... | Zhongrong Xu
  • Special Issue
  • - Volume 2019
  • - Article ID 8460934
  • - Research Article

A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction

Tuong Le | Minh Thanh Vo | ... | Sung Wook Baik
  • Special Issue
  • - Volume 2019
  • - Article ID 8682124
  • - Research Article

A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction

Yajiao Tang | Junkai Ji | ... | Yuki Todo
  • Special Issue
  • - Volume 2019
  • - Article ID 9190273
  • - Research Article

Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning

Lixia Liu | Xueli Zhan
  • Special Issue
  • - Volume 2019
  • - Article ID 2946158
  • - Research Article

Application of BP Neural Network Model in Risk Evaluation of Railway Construction

Yang Changwei | Li Zonghao | ... | Zhu Liang
  • Special Issue
  • - Volume 2019
  • - Article ID 4132485
  • - Research Article

Stock Price Pattern Prediction Based on Complex Network and Machine Learning

Hongduo Cao | Tiantian Lin | ... | Hanyu Zhang
Complexity
 Journal metrics
Acceptance rate38%
Submission to final decision68 days
Acceptance to publication52 days
CiteScore2.690
Impact Factor2.591
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