Research
In this page, I have selected some special fields that I have contributed in these last years. Since I am selecting some of them, I am clearly not considering some part of my research here. If the information you are looking for is not here, eventually you may find this information in my Brazilian Lattes Curriculum, where you can also find my complete list of publications. It is also worth mentioning here that my research is partially supported by the Brazilian agency CNPQ. I divided my research into the following lines: (1) Prediction Policy and Machine Learning problems; (2) Finance and Backing; (3) Complex Networks; (4) Dynamics Systems, Control Theory and Optimization; (5) Agent-Based Models; (6) Complex Phenomena in Financial Time Series.
Prediction Policy and Machine Learning Problems
We have used several techniques of machine learning, data science and econometrics to explore interesting prediction policy problems.
Some selected publications
Piccioni, C.A.; Bastos, S.B.; Cajueiro, D.O. Stock Price Reaction to Environmental, Social, and Governance News: Evidence from Brazil and Financial Materiality. Sustainability 2024, 16, 2839. https://doi.org/10.3390/su16072839
D. P. Brown, D. O. Cajueiro, A. Eckert and D. Silveira. Information and Transparency: Using Machine Learning to Detect Communication Between Firms. Stanford Computational Antitrust. Paper.
Silveira, D.; Fiuza, E. P. S.; Moraes, L. B.; Cajueiro, D. O. Who Are You? Cartel Detection Using Unlabeled Data. Forthcoming in International Journal of Industrial Organization, 2023.
De Figueredo F. C. ; Meuller B ; D. O. Cajueiro . A natural language measure of ideology in the Brazilian Senate. Revista Brasileira de Ciência Política, p. 1-32, 2022.
Silveira, Douglas; Vasconcelos, S. ; Resende, M. ; Cajueiro, D. O. Won't Get Fooled Again: A supervised machine learning approach for screening gasoline cartels. Energy Economics, v. 105, p. 105711-105711, 2021.
Albuquerque P. C.; Cajueiro, D. O.; Rossi, M. D. C. . Machine learning models for forecasting power electricity consumption using a high dimensional dataset. Expert Systems with Applications, 2021.
Finance and Banking
We have tried to answer important questions of finance and banking using economic theory point of view, empirical and computational tools.
Some selected publications
Carvalho, A.V.C., Silveira, D., Ely, R.A. et al. A logarithmic market scoring rule agent-based model to evaluate prediction markets. Journal of Evolutionary Economics (2023).
Adão, L. F. S.; Silveira, D. ; ELy, R. A. ; Cajueiro, D. O. The Impacts of Interest Rates on Banks' Loan Portfolio Risk-taking. Journal of Economic Dynamics and Control, v. 144, p. 104521 2022.
Barroso, R.V.; Lima, J. I. A. V. ; Lucchetti, A. H. and Cajueiro, D. O. Interbank network and regulation policies: an analysis through agent-based simulations with adaptive learning. Journal of Network Theory In Finance v. 2, p.53-86, 2016. O Framework para desenvolvimento colaborativo está disponível no github.
Mídia: Simulated banking system shows pros and cons of Basel III
Mídia: Simulated Banking System Shows Pros and Cons of Basel III
3. Fazio, D. M., Tabak, B. M., and Cajueiro, D. O. Inflation Targeting: Is IT to blame for Banking System Instability. Journal of Banking & Finance, v. 59, 76-97, 2015.
4. Tabak, B. M., Fazio, D. M. and Cajueiro, D. O. Systemically important banks and financial stability: The case of Latin America. Journal of Banking & Finance, v. 37, p. 3855-3866, 2013.
5. Tabak, B. M., Fazio, D. M. and Cajueiro, D. O. The relationship between banking market competition and risk-taking: Do size and capitalization matter? Journal of Banking & Finance, v. v. 36, p. 3366-3381, 2012.
6. Tabak, B. M., Fazio, D. M. and Cajueiro, D. O. The Effects of loan portfolio concentration on Brazilian banks' return and risk. Journal of Banking & Finance, v. 35, p. 3065-3076, 2011.
7. Cajueiro, D. O. and Yoneyama, T. Optimal Portfolio and Consumption in a Switching Diffusion Market. Brazilian review of econometrics, v. 24, n. 2, p. 227-248, 2004.
Complex networks
We have also developed some interesting applications of complex networks.
Some selected publications
1. D. O. Cajueiro; Bastos, S. B.; Pereira, C. C.; Andrade, R. F. S. A model of indirect contagion based on a news similarity network. Journal of Complex Networks. , 2021.
2. D. O. Cajueiro, F. N. Mundim, J. I. F. Martins, P. A. M. Sakowski, and R. F. S. Andrade. Markov chain approach to model intertemporal choices and coverages in air transport markets. Physical Review E 100, 062303, 2019.
3. Izawa, M. Oliveira, F. A. Cajueiro, D. O. Mello, B. A. Pendular behavior of public transport networks. Physical Review E, v. 96, p. 1-9, 2017. Editor Synopsis of the PRE paper
4. Mello, B. A, Batistuta, L. H., Boueri, R. Cajueiro, D. O. Measuring the flow of information among cities using the diffusion power. Physics Letters A 374, 126-130, 2009.
5. Borges, E. P. Cajueiro, D. O. Andrade, R. F. S. Mapping dynamical systems onto complex networks. European Physical Journal B, v. 58, p. 469-474, 2007.
Dynamic Systems, Control Theory and Optimization
I try to deal with the decision making problem using dynamic systems modeling, economic theory approach, optimization principles and control theory.
Some selected publications
1. BASTOS, SAULO B. ; Cajueiro, Daniel O. Modeling and forecasting the early evolution of the Covid-19 pandemic in Brazil. Scientific Reports, v. 10, p. 19457, 2020.
Media:
Valor Econômico: Benefícios Econômicos do Isolamento Social
2. MORATO, MARCELO M. ; BASTOS, SAULO B. ; Cajueiro, Daniel O. ; NORMEY-RICO, JULIO E. . An optimal predictive control strategy for COVID-19 (SARS-CoV-2) social distancing policies in Brazil. Annual Reviews in Control, v. 50, p. 417-431, 2020.
Arxiv: arxiv.org/pdf/2005.10797
3. Cajueiro, D. O. and Andrade, R. F. S. Controlling self-organized criticality in sandpile models. Physical Review. E, v. 81, 015102R, 2010.
4. Cajueiro, D. O. Optimal navigation in complex networks. Physical Review E, v. 79, p. 046103, 2009.
5. Cajueiro, D. O. and Andrade, R. F. S. Learning paths in complex networks. Europhysics Letters, v. 87, p. 58004, 2009.
6. Cajueiro, D. O. and Maldonado W. L. Role of optimization in the human dynamics of task execution. Physical Review E, v. 77, p. 035101R, 2008.
7. Cajueiro, D. O. Agent preferences and the topology of networks. Physical Review E, v. 72, n. 4, p. 047104, 2005.
8. Cajueiro, D. O. and Hemerly, E. M. Comments on 'Adaptive control and identification using one neural network for a class of plants with uncertainties. IEEE Transactions on Systems, Man and Cybernetics. Part A. Systems and Humans, v. 32, p. 279-280, 2002.
See also the Wikipedia page about Self-organized criticality control: http://en.wikipedia.org/wiki/Self-organized_criticality_control
Agent-based models
We have been trying to understand social, economical and financial systems using agent-based models.
Some selected publications
Oestereich, A. L., Pires, M. A., Crokidakis, N., & Cajueiro, D. O. Optimal rewiring in adaptive networks in multi-coupled vaccination, epidemic and opinion dynamics. Chaos, Solitons & Fractals, 176, 114125, 2023.
A. L. Oestereich, N. Crokidakis and D. O. Cajueiro Impact of memory and bias in kinetic exchange opinion models on random networks. Physica A, v. 607, p. 128199, 2022. Arxiv: arxiv.org/abs/2204.04295 .
Lustosa, B. C. and Cajueiro, D. O. Constrained information minority game: How was the night at El Farol?. Physica. A, v. 389, p. 1230-1238, 2010.
Mello, B. A. Cajueiro, D. O. Minority games, diversity, cooperativity and the concept of intelligence. Physica. A, v. 387, p. 557-566, 2008.
Complex Phenomena in Financial Time Series
We have been trying to understand financial times series using the Hurst exponent. [You may find many of the computer program codes that were used evaluate the Hurst's exponent here: How to evaluate the Hurst exponent?
1. Cajueiro, D. O. Tabak, B. M. Fluctuation dynamics in US interest rates and the role of monetary policy. Finance Research Letters, Volume 7, Issue 3, September 2010, Pages 163-169
2. Cajueiro, D. O. Tabak, B. M. Long-range dependence and market structure. Chaos, Solitons & Fractals, Volume 31, Issue 4, February 2007, Pages 995-1000.
3. Cajueiro, D. O. Tabak, B. M. Possible causes of long-range dependence in the Brazilian stock market. Physica A: Statistical Mechanics and its Applications, Volume 345, Issues 3–4, 15 January 2005, Pages 635-645
4. Cajueiro, D. O. and Tabak, B. M. The rescaled variance statistic and the determination of the Hurst exponent. Mathematics and Computers in Simulation 70, 172 -179, 2005.
5. Cajueiro, D.O Tabak, B. M. The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient. Physica A: Statistical Mechanics and its Applications 336 (3), 521-537, 2004.