126 citations · 146 across the 6 of their papers we have counts for
7 papers
Data-Driven Measures of High-Frequency Trading
Gbenga Ibikunle, Ben Moews, Dmitriy Muravyev +1
Public data do not identify high-frequency trading (HFT), and standard proxies do not separate liquidity-supplying from liquidity-demanding strategies. We overcome this measurement…
DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networks
Aijie Shu, Wenbin Wu, Gbenga Ibikunle +1
Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies. Thus, a shock to one token may result in…
Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading
Adamantios Ntakaris, Gbenga Ibikunle
High-frequency trading (HFT) has transformed modern financial markets, making reliable short-term price forecasting models essential. In this study, we present a novel approach to…
Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks
Adamantios Ntakaris, Gbenga Ibikunle
This study presents an autonomous experimental machine learning protocol for high-frequency trading (HFT) stock price forecasting that involves a dual competitive feature importanc…
Data-driven measures of high-frequency trading
G. Ibikunle, B. Moews, D. Muravyev +1
Public data do not identify high-frequency trading (HFT), and standard proxies do not separate liquidity-supplying from liquidity-demanding strategies. We overcome this measurement…
Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning
Ben Moews, Gbenga Ibikunle
Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep lea…
Lagged correlation-based deep learning for directional trend change prediction in financial time series
Ben Moews, J. Michael Herrmann, Gbenga Ibikunle
Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriousl…