Forecasting daily stock market return using dimensionality reduction

By Xiao Zhong, David Enke. Expert systems with applications, 2017

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Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations (2017, methodology): “In financial time series data, the raw prices of the markets, the statistical functions calculated from the raw prices, and the technical indicators giving information about the market tendency, volatility and momentum are used as features [19].”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, methodology): “• The ﬁrst baseline algorithm is the one reported in (Zhong & Enke, 2017).”
An innovative neural network approach for stock market prediction (2018, methodology): “The accuracy of the Shanghai A-share composite index by LSTM is 57%, the same with literature [13] on the S&P 500.”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…page Metrics Article List Return Niaki & Hoseinzade (2013); Ding et al. (2015); Bao et al. (2017); Chen et al. (2017); Lee & Soo (2017); Singh & Srivastava (2017); Zhong & Enke (2017); Fischer & Krauss (2018); Hu et al. (2018b); Matsubara et al. (2018); Oncharoen & Vateekul (2018); Sezer &…”