Reference health
Enhancing Time Series Momentum Strategies Using Deep Neural Networks
27/27 checkable references clean · checked 2026-10-18
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
22 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 27 checked references that resolve
resolves10.1016/j.ijforecast.2018.06.001
The M4 Competition: Results, findings, conclusion and way forward
resolves10.1145/3278312
Proceedings of the 2018 International Conference on Machine Learning and Machine Intelligence
resolves10.2139/ssrn.3141294
Universal Features of Price Formation in Financial Markets: Perspectives From Deep Learning
resolves10.1371/journal.pone.0180944
A deep learning framework for financial time series using stacked autoencoders and long-short term memory
resolves10.2139/ssrn.3042173
Understanding the Momentum Risk Premium: An In-Depth Journey Through Trend-Following Strategies
resolves10.1111/j.1540-6261.1993.tb04702.x
Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency
resolves10.1111/0022-1082.00342
Profitability of Momentum Strategies: An Evaluation of Alternative Explanations
resolves10.3905/jfds.2019.1.1.159
Dynamic Replication and Hedging:
<i>A Reinforcement Learning Approach</i>
The 22 references without a DOI — listed, not checked
no DOI — not checkedref8
no DOI — not checkedTime-series extreme event forecasting with neural networks at uber
no DOI — not checkedDisease-atlas: Navigating disease trajectories using deep learning
no DOI — not checkedref13
no DOI — not checkedTensorFlow: Large-scale machine learning on heterogeneous systems
no DOI — not checkedAutomatic differentiation in PyTorch
no DOI — not checkedref18
no DOI — not checkedref19
no DOI — not checkedDeep state space models for time series forecasting
no DOI — not checkedA disentangled recognition and nonlinear dynamics model for unsupervised learning
no DOI — not checkedGenerative adversarial nets
no DOI — not checkedThresholded ConvNet ensembles: Neural networks for technical forecasting
no DOI — not checkedMomentum strategies offer a positive point of skew
no DOI — not checkedref34
no DOI — not checkedref39
no DOI — not checkedWaveNet: A generative model for raw audio
no DOI — not checkedref45
no DOI — not checkedAdam: A method for stochastic optimization
no DOI — not checkedDropout: A simple way to prevent neural networks from overfitting
no DOI — not checkedA theoretically grounded application of dropout in recurrent neural networks
no DOI — not checkedref49
no DOI — not checkedref50
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