The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression Y Peng, PHM Albuquerque, JMC de Sá, AJA Padula, MR Montenegro Expert Systems with Applications 97, 177-192, 2018 | 300* | 2018 |
An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data Y Peng, MH Nagata Chaos, Solitons & Fractals 139, 110055, 2020 | 135 | 2020 |
Na era das máquinas, o emprego é de quem? Estimação da probabilidade de automação de ocupações no Brasil PH Albuquerque, CAPB Saavedra, RL de Morais, PF Alves, Y Peng Texto para Discussão, 2019 | 76 | 2019 |
Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata MK Severino, Y Peng Machine Learning with Applications 5, 100074, 2021 | 73 | 2021 |
Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators Y Peng, PHM Albuquerque, H Kimura, CAPB Saavedra Machine Learning with Applications 5, 100060, 2021 | 65 | 2021 |
Between nonlinearities, complexity, and noises: an application on portfolio selection using kernel principal component analysis Y Peng, PHM Albuquerque, IF do Nascimento, JVF Machado Entropy 21 (4), 376, 2019 | 30 | 2019 |
Non-linear interactions and exchange rate prediction: Empirical evidence using support vector regression P Yaohao, PHM Albuquerque Applied Mathematical Finance 26 (1), 69-100, 2019 | 30 | 2019 |
Efficiency of the Brazilian credit unions: A joint evaluation of economic and social goals E Sousa de Abreu, H Kimura, LM Araújo Neto, Y Peng Latin American Business Review 19 (2), 107-129, 2018 | 19 | 2018 |
Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting PHM Albuquerque, Y Peng, JPF Silva Journal of Forecasting 41 (8), 1701-1724, 2022 | 17 | 2022 |
The robot from Ipanema goes working: estimating the probability of jobs automation in Brazil PHM Albuquerque, CAPB Saavedra, RL de Morais, Y Peng Latin American Business Review 20 (3), 227-248, 2019 | 16 | 2019 |
Os três tipos de aprendizado de máquina H Honda, M Facure, P Yaohao Brasília: lamfo, 2017 | 14 | 2017 |
O desafio do pareamento de grandes bases de dados: mapeamento de métodos de record linkage probabilístico e diagnóstico de sua viabilidade empírica P Yaohao, LF Mation Instituto de Pesquisa Econômica Aplicada (Ipea), 2018 | 12 | 2018 |
Mapping regional business opportunities using geomarketing and machine learning MFF Oliveira, PHM Albuquerque, PY Hao, PA Henrique Gestão & Produção 27, e4158, 2020 | 8 | 2020 |
Statistical analysis of the Chinese COVID-19 data with Benford’s law and clustering Y Peng, MH Nagata Laboratório de Aprendizado de Máquina em Finanças e Organizações (LAMFO), 2020 | 7 | 2020 |
Portfolio selection with support vector regression: multiple kernels comparison PAMB Henrique, PHM Albuquerque, SSDF Marcelino, Y Peng International Journal of Business Intelligence and Data Mining 18 (4), 395-410, 2021 | 5 | 2021 |
Probability of informed trading: a Bayesian approach L Bosque, P Albuquerque, Y Peng, C Da-Silva, E Nakano International Journal of Applied Decision Sciences 13 (2), 183-214, 2020 | 5 | 2020 |
Machine learning methods for financial forecasting and trading profitability: Evidence during the Russia–Ukraine war Y Peng, JG de Moraes Souza Revista de Gestão 31 (2), 152-165, 2024 | 2 | 2024 |
Are Fintechs worth investing? Case study for startup using real option theory JCF Souza, JGDM Souza, PY Hao International Journal of Development Research 11 (04), 45999-46007, 2021 | 2 | 2021 |
Stochastic volatility modelling in portfolio selection via sequential Monte Carlo simulation IFD Nascimento, PHM Albuquerque, Y Peng International Journal of Portfolio Analysis and Management 2 (3), 249-267, 2021 | 2 | 2021 |
Survey on-demand: a versatile scientific article automated inquiry method using text mining applied to asset liability management IFD Nascimento, PHDM Albuquerque, Y Peng International Journal of Business Intelligence and Data Mining 18 (3), 261-290, 2021 | 2 | 2021 |