Prediction of stock trend has been an intriguing topic and is extensively studied by researchers from diversified fields. Machine learning, a well-established algorithm, has been also studied for its potentials in pre...Prediction of stock trend has been an intriguing topic and is extensively studied by researchers from diversified fields. Machine learning, a well-established algorithm, has been also studied for its potentials in prediction of financial markets. In this paper, seven different techniques of data mining are applied to predict stock price movement of Shanghai Composite Index. The approaches include Support vector machine, Logistic regression, Naive Bayesian, K-nearest neighbor classification, Decision tree, Random forest and Adaboost. Extracting the corresponding comments between April 2017 and May 2018, it shows that: 1) sentiment derived from Eastmoney, a social media platform for the financial community in China, further enhances model performances, 2) for positive and negative sentiments classifications, all classifiers reach at least 75% accuracy and the linear SVC models prove to perform best, 3) according to the strong correlation between the price fluctuation and the bullish index, the approximate overall trend of the closing price can be acquired.展开更多
文摘Prediction of stock trend has been an intriguing topic and is extensively studied by researchers from diversified fields. Machine learning, a well-established algorithm, has been also studied for its potentials in prediction of financial markets. In this paper, seven different techniques of data mining are applied to predict stock price movement of Shanghai Composite Index. The approaches include Support vector machine, Logistic regression, Naive Bayesian, K-nearest neighbor classification, Decision tree, Random forest and Adaboost. Extracting the corresponding comments between April 2017 and May 2018, it shows that: 1) sentiment derived from Eastmoney, a social media platform for the financial community in China, further enhances model performances, 2) for positive and negative sentiments classifications, all classifiers reach at least 75% accuracy and the linear SVC models prove to perform best, 3) according to the strong correlation between the price fluctuation and the bullish index, the approximate overall trend of the closing price can be acquired.
