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Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning
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 learning offering a way to gain insights into correlations in markets as complex systems.
Lagged correlation-based deep learning for directional trend change prediction in financial time series
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 notoriously difficult to predict example of such systems.
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