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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