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Table 1 A summary of related studies

From: A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

Reference Technique No. of data source Input data source Sock data Reported Results
[1] Co-evolving tensor-based learning 2 W & SM China A-share and HK stock 55–63%
[7] extended coupled hidden Markov 2 W & HSD China A-share 52–63%
[44] NN, LR, SVM, KNN, RF, AB & KF 2 HSD & MD NS NS
[45]   2 W & HSD S&P 500 SPY index p-value better than 0.05
[24] CNN and RNN 2 HSD & W   
[8] Multi-Source Multiple Instance Learning 3 HSD, SM & W China A-share 62.1%
[43] Delta Naive Bayes (DNB) 3 W, SM & GS Argentina, Peru & Mexico p-value (0.583–0.702)
[9] ANN 3 W, SM and GS Ghana Accuracy (49.4 – 77.12)%
  1. HSD historical stock data, W = Web news, SM = social media, MD = macroeconomic data, NN = Neural Networks, LR = Logistic Regression, KNN = K-Nearest Neighbor, RF = Random Forest, AB = AdaBoost, KF = Kernel Factor, NS = not stated, GS = Google search volumes