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The application of technical trading rules developed from spot market prices on futures market prices using CAPM
Er, Hakan; Hushmat, Adnan - In: Eurasian business review 7 (2017) 3, pp. 313-353
Futures markets have seen a phenomenal success since their inception both in developed and developing countries during the last four decades. This success is attributable to the tremendous leverage the futures provide to market participants. This study contributes to the literature by analyzing...
Persistent link: https://www.econbiz.de/10011813470
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Modeling the yield curve of BRICS countries: Parametric vs. machine learning techniques
Castello, Oleksandr; Resta, Marina - In: Risks 10 (2022) 2, pp. 1-18
curve of the BRICS countries (Brazil, Russia, India, China, South Africa). To such aim, we applied the Dynamic De Rezende …-Ferreira five-factor model with time-varying decay parameters and a Feed-Forward Neural Network to the bond market data of the BRICS … comprehensive investigation of the bond market in the BRICS countries examined both by time and maturity; working on five countries …
Persistent link: https://www.econbiz.de/10013200927
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Forecasting the term structure in emerging markets using extensions of the dynamic Nelson-Siegel model
Anisimov, Maksim - 2020
Persistent link: https://www.econbiz.de/10012288379
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Application of machine learning in quantitative investment strategies on global stock markets
Grudniewicz, Jan; Ślepaczuk, Robert - 2021
Persistent link: https://www.econbiz.de/10012816704
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Inflation prediction in emerging economies : machine learning and FX reserves integration for enhanced forecasting
Mirza, Nawazish; Rizvi, Kumail Abbas; Naqvi, Bushra; … - In: International review of financial analysis 94 (2024), pp. 1-11
Persistent link: https://www.econbiz.de/10014543932
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Machine learning and the cross-section of emerging market stock returns
Hanauer, Matthias Xaver; Kalsbach, Tobias - In: Emerging markets review 55 (2023), pp. 1-38
Persistent link: https://www.econbiz.de/10014481087
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Application of machine learning in algorithmic investment strategies on global stock markets
Grudniewicz, Jan; Ślepaczuk, Robert - In: Research in international business and finance 66 (2023), pp. 1-24
Persistent link: https://www.econbiz.de/10014463386
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A machine learning approach to GDP nowcasting : an emerging market experience
Ghosh, Saurabh; Ranjan, Abhishek - In: Bulletin of monetary economics and banking 26 (2023), pp. 33-54
Persistent link: https://www.econbiz.de/10014266367
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Forecasting the term structure of crude oil futures prices with neural networks
Baruník, Jozef; Malinská, Barbora - 2015
The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed...
Persistent link: https://www.econbiz.de/10011378719
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A coherent framework for predicting emerging market credit spreads with support vector regression
Anderson, Gary S.; Audzeyeva, Alena - 2019
variables motivated by economic theory, we apply our framework to identify relatively small sets of SVR models with su perior … limited to the country-specific credit-spread-curve factors, lending some support to the rational expectation theory of the …
Persistent link: https://www.econbiz.de/10012182398