TY - UNPB
T1 - Cutting Through the Noise
T2 - A Deep Learning-Enhanced Particle Filter for High-Frequency Market Forecasting
AU - Konstantinou, Christos
AU - Ibikunle, Gbenga
AU - Rzayev, Khaladdin
AU - Cao, Yi
PY - 2024
Y1 - 2024
N2 - We introduce a deep learning-enhanced particle filter designed to reduce market microstructure noise in high-frequency stock prices. Unlike traditional approaches, our method does not rely on conventional assumptions about price dynamics. By tailoring noise filtration strategies to specific characteristics of financial instruments, we demonstrate significant variations in prediction performance across market environments: while ML significantly improves forecasting accuracy for stocks with higher market microstructure noise, it adversely affects predictive capabilities in low-noise settings by extracting signal rather than noise. These contrasting outcomes, statistically significant across noise quartiles, highlight the importance of selective application of noise reduction techniques in financial forecasting.
AB - We introduce a deep learning-enhanced particle filter designed to reduce market microstructure noise in high-frequency stock prices. Unlike traditional approaches, our method does not rely on conventional assumptions about price dynamics. By tailoring noise filtration strategies to specific characteristics of financial instruments, we demonstrate significant variations in prediction performance across market environments: while ML significantly improves forecasting accuracy for stocks with higher market microstructure noise, it adversely affects predictive capabilities in low-noise settings by extracting signal rather than noise. These contrasting outcomes, statistically significant across noise quartiles, highlight the importance of selective application of noise reduction techniques in financial forecasting.
M3 - Working paper
BT - Cutting Through the Noise
ER -