RT Journal Article
T1 High-Frequency Trading in Bond Returns: A Comparison Across Alternative Methods and Fixed-Income Markets
A1 Alaminos Aguilera, David
A1 Salas-Compás, María Belén
A1 Fernández-Gámez, Manuel Ángel
K1 Beneficios
K1 Redes neuronales (Informática)
K1 Lógica difusa
AB A properly performing and efficient bond market is widely considered important for the smooth functioning of trading systems in general. An important feature of the bond market for investors is its liquidity. High-frequency trading employs sophisticated algorithms to explore numerous markets, such as fixed-income markets. In this trading, transactions are processed more quickly, and the volume of trades rises significantly, improving liquidity in the bond market. This paper presents a comparison of neural networks, fuzzy logic, and quantum methodologies for predicting bond price movements through a high-frequency strategy in advanced and emerging countries. Our results indicate that, of the selected methods, QGA, DRCNN and DLNN-GA can correctly interpret the expected bond future price direction and rate changes satisfactorily, while QFuzzy tend to perform worse in forecasting the future direction of bond prices. Our work has a large potential impact on the possible directions of the strategy of algorithmic trading for investors and stakeholders in fixed-income markets and all methodologies proposed in this study could be great options policy to explore other financial markets.
PB Springer Link
YR 2023
FD 2023
LK https://hdl.handle.net/10630/33607
UL https://hdl.handle.net/10630/33607
LA eng
NO Alaminos, D., Salas, M.B. & Fernández-Gámez, M.A. High-Frequency Trading in Bond Returns: A Comparison Across Alternative Methods and Fixed-Income Markets. Comput Econ (2023). https://doi.org/10.1007/s10614-023-10502-3
NO Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research was funded by the Universitat de Barcelona, under the grant UB-AE-AS017634.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026