Implementing deep neural networks for financial market prediction on the Intel Xeon Phi

2015

59 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, ML Systems & Hardware, Economics & Operations Research

Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, neutral): “[199] used Intel Xeon Phi to speedup the price movement direction prediction problem using DFFN.”
Long short‐term memory networks for CSI300 volatility prediction with Baidu search volume (2018, neutral): “In the past years, initial evidence has been established that machine learning techniques are capable of identifying (non-linear) structures in financial market data [10, 13, 2].”
Threshold-based portfolio: the role of the threshold and its applications (2017, neutral): “Researchers have successfully modeled financial time series by focusing primarily on prediction accuracy or automatic trading rules [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16].”
Classification-based financial markets prediction using deep neural networks (2016, neutral): “…γ ← γ/ 2 17: end for 18: Return ﬁnal weights w ( l ) i,j and the mini-batch with in each epoch set is deﬁned as As mentioned earlier, the mini-batching formulation of the algorithm facilitates eﬃcient parallel implementation, the details and timings of which are described by Dixon et al. (2015).”
Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (2017, unknown): “Dixon et al. (2015) run a similar strategy in a high-frequency setting with ﬁve-minute binned return data.”
Deep learning with long short-term memory networks for financial market predictions (2017, unknown): “Introduction Prediction tasks on financial time series are notoriously difficult, primarily driven by the high degree of noise and the generally accepted, semi-strong form of market efficiency (Fama, 1970). Yet, there is a plethora of well-known capital market anomalies that are in stark contrast wi”