Evaluating multiple classifiers for stock price direction prediction

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An up-to-date comparison of state-of-the-art classification algorithms (2017, background): “New classiﬁers may have been compared to a reference set of classiﬁers by domain experts with ﬁne-tuned settings, but the evaluation is limited to their speciﬁc domains, e.g. Ngai, Xiu, and Chau (2009) and Ballings, den Poel, Hespeels, and Gryp (2015) , and a small number of available data sets.”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “Article Prediction Model Baselines Bao et al. (2017) WT+SAEs+LSTM WT+LSTM, LSTM, RNN Lee & Soo (2017) RNN+CNN LSTM Qin et al.”
Thresholded ConvNet ensembles: neural networks for technical forecasting (2018, background): “In their study of European nancial markets, Ballings et al (2015) evaluated the classi cation accuracy of ensemble methods against single classi ers. eir empirical work highlighted the e ectiveness of random forests in classifying stock price movements and motivates their inclusion in our list of…”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, methodology): “…(GA), (Hu et al., 2015b; Brown et al., 2013; Hu et al., 2015a; Atsalakis & Valavanis, 2009) are among other tools and techniques that have been applied for feature extraction from raw ﬁnancial data and/or making predictions based on a set of variables (Ou & Wang, 2009; Ballings et al., 2015).”