2019
DOI: 10.1007/s10588-019-09292-7
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Development of stock market trend prediction system using multiple regression

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Cited by 59 publications
(43 citation statements)
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“…Diagrammatic representation is shown in Fig. 1 that LR performs emotion classification using supervised machine learning approach [14,15,16,17].…”
Section: Classification Of Emotions From Text Using Logistic Regressionmentioning
confidence: 99%
“…Diagrammatic representation is shown in Fig. 1 that LR performs emotion classification using supervised machine learning approach [14,15,16,17].…”
Section: Classification Of Emotions From Text Using Logistic Regressionmentioning
confidence: 99%
“…The latest related work that can compare is (Zubair et al, 2019), the authors take multiple r-square for model accuracy measurement. Multiple r-square is also called the coefficient of determination, it shows the strength of predictor variables explaining the variation in stock return (Nagar, Anurag;Hahsler, 2012).…”
Section: Figure 13 Proposed Model Prediction Precision Comparison -Comentioning
confidence: 99%
“…(Ayo, 2014) leveraged analysis on the stock data from New York Stock Exchange (NYSE), while the weakness is they only performed analysis on closing price, which is a feature embedded with high noise. (Zubair et al, 2019) trained their proposed model on both individual stocks and index price, but as we have mentioned in the previous section, index price only consists of limited number of features and stock IDs, which will further affect the model training quality. For our proposed solution, we collected sufficient data from Chinese stock market, and applied FE + RFE algorithm on the original indices to get more effective features, the comprehensive evaluation result of 3558 stock IDs can reasonably explain the generalization and effectiveness of our proposed solution in Chinese stock market.…”
Section: Figure 13 Proposed Model Prediction Precision Comparison -Comentioning
confidence: 99%
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