Is stock BBS content correlated with the stock market? &amp;#x2014; A Japanese case

Maruyama, Ken; Suwa, Hirohiko; Umehara, Eiichi; Ohta, Yuichi

doi:10.1109/icsmc.2009.5346768

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…Two methods for stock market prediction using text classification NLP techniques for the Chinese language context are worth noticing. The first one has been presented in the paper "Impacts of Internet Stock News on Stock Markets Based on Neural Networks" [10] by Xun Liang. The paper explores the relationship between stock news on the internet and the stock markets in the Chinese context using neural networks.…”

Section: Svr Classificationmentioning

confidence: 99%

“…This paper contributes to stock market prediction similar to [9] but with some improvements. The paper has been done in the Chinese context and therefore deals with Chinese natural language processing similar to [10]. In the proposed system in this paper, procedure of interpreting the web news for stock market prediction has been divided into two definite phases -1.…”

Section: Svr Classificationmentioning

confidence: 99%

“…The authors claim that results of this experiment enhanced the results in [9] by over 7 percent at 69%. But the disadvantage of this method is that the structure of the neural network is more than twice as large as the network in [10]. A lexicon based sentiment analysis has been proposed by Sahar Sohangir et al in their paper "Financial Sentiment Lexicon Analysis" [12] presents a new method for sentiment analysis for stock market prediction.…”

Section: Application Of Neural Network Nlp Techniques Tomentioning

confidence: 99%

“…But the disadvantage of this method is that the structure of the neural network is more than twice as large as the network in [10]. A lexicon based sentiment analysis has been proposed by Sahar Sohangir et al in their paper "Financial Sentiment Lexicon Analysis" [12] presents a new method for sentiment analysis for stock market prediction. Here data from Stocktwits (a website that aggregates market analyses from Twitter and then further condenses them into a curated and focused stream of data) is used as a dataset for the sentiment analysis.…”

Section: Application Of Neural Network Nlp Techniques Tomentioning

confidence: 99%

See 3 more Smart Citations

Predicting Indian Stock Prices through Sentiment Score and Data Mining Techniques

Govind*,

Prasanth N,

kumar

et al. 2019

IJRTE

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Much is being said about the use of artificial intelligence to assist in the financial markets but there is a surprising lack of actual data supporting one trading algorithm over another. While there are numerous research that explore the possibilities of machine learning and deep learning techniques for stock market prediction, due to the lack of inter-domain expertise (people who are experts in both data science and finance), most of these projects use relatively elementary methods and fail to account for many underlying constraints. This paper aims to create a stock market investment aide which predicts the price action of stock market instruments using neural networks that learn time series patterns in the historical price data, correlates the prediction with the sentiment score of the market for that day and thereby give the investor/trader a buy or sell signal. The model developed has been successful in predicting price action of the NIFTY50 index with an accuracy of 89 percent. The prediction aided with the correlation from the sentiment analysis can give the investor an added confidence while making investment decisions on the stock market.

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Section: Svr Classificationmentioning

confidence: 99%

Section: Svr Classificationmentioning

confidence: 99%