126 citations · 495 across the 20 of their papers we have counts for
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q-fin.CP2024
Evaluating utility in synthetic banking microdata applications
Hugo E. Caceres, Ben Moews
Financial regulators such as central banks collect vast amounts of data, but access to the resulting fine-grained banking microdata is severely restricted by banking secrecy laws.…
q-fin.CP2020★ 20 cited
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…
q-fin.CP2018★ 126 cited
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…