Predicting the Effects of News Sentiments on the Stock Market

Shah, Dev; Isah, Haruna; Zulkernine, Farhana

doi:10.1109/bigdata.2018.8621884

Cited by 90 publications

(34 citation statements)

References 12 publications

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“…Dev Shah et al [5] had studied the news and on the basis of that news they had do the sentiment analysis. In this paper they had find out the polarity for pharma sector.…”

Section: Literature Surveymentioning

confidence: 99%

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Stock Prediction by using NLP and Deep Learning Approach

Deshmukh¹

2021

TURCOMAT

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People have a tendency to analyze existing strategies and so planned new strategies for inventory prediction. We have used Sentiment evaluation and Technical evaluation through NLP and Deep mastering approach. In order to exploit benefits of sentiment analysis on enterprise associated inventory, we have proposed a model that will use the sentiment analysis on twits associated with special sectors that are Information Technology sector, Banking sector, Pharmaceutical sector, Automobile sector, Infrastructure sector which are extracted from twitter. These twits are extracted from twitter for calculating polarity. The rating of sentiment analysis is calculated here by using Natural Language Processing’s method. According to sector we've taken five groups. Top four performer businesses of every sector. Using polarity score we got finalized pinnacle ten groups with great sentiment rating. We then downloaded the CSV facts of historical share charge of top ten organizations that we've selected. Then downloaded CSV records are used to build a CNN version to predict in addition stock movement of these pinnacle ten companies.

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“…Dev Shah et al [5] had studied the news and on the basis of that news they had do the sentiment analysis. In this paper they had find out the polarity for pharma sector.…”

Section: Literature Surveymentioning

confidence: 99%

“…Business: In advancing field firms use it to build up their strategies, to know clients' sentiments towards product or entire, people answer their battles or item dispatches and why customers don't get some product [1] [5].…”

Section: Sentiment Intensity Analyzermentioning

confidence: 99%

Stock Prediction by using NLP and Deep Learning Approach

Deshmukh¹

2021

TURCOMAT

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“…News is relatively objective due to its reliance on facts, but social data can be based on facts or rumours created by other investors; thus social data is quite subjective (Jeong et al, 2018). In stock price prediction, in addition to historical prices, research have proven that financial news and social media can improve the accuracy of stock price prediction (Zhang et al, 2018;Shah et al, 2018). SA becomes an important process used to extract facts or moods from media and many SA techniques are used for the stock price prediction in existing research, summarised below.…”

Section: Sentiment Analysismentioning

confidence: 99%

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Obthong

Tantisantiwong

Jeamwatthanachai

et al. 2020

Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business

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Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. This paper reviews studies on machine learning techniques and algorithm employed to improve the accuracy of stock price prediction.

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“…The results exhibited significant performance of the stock market prediction model as well as preserved results interpretability. Shah et al analyzed the effects of news sentiments on the stock market [19]. They developed a dictionary-based sentiment analysis model and reported that news articles are powerful indicators for predicting short-term stock price movement.…”

Section: A Stock Prediction Advocates Related Workmentioning

confidence: 99%

Augmented Textual Features-Based Stock Market Prediction

2020

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Due to its dynamics, non-linearity and complexity nature, stock market is inherently difficult to predict. One of the attractive objectives is to predict stock market movement direction by using public sentiments analysis. However, there is an active debate about the usefulness of this approach and the strength of causality between stock market trends and sentiments. The opinions of researchers range from rejecting the relationship to confirming a clear causality between sentiments and trading in stock markets. Nevertheless, many advanced computational methods have adopted sentiment-based features, yet did not attain maturity and performance. In this paper, we are contributing constructively in this debate by empirically investigating the predictability of stock market movement direction using an enhanced method of sentiments analysis. Precisely, we experiment on stock prices history, sentiments polarity, subjectivity, N-grams, customized text-based features in addition to features lags that are used for a finer-grained analysis. Five research questions have been investigated towards answering issues associated with stock market movement prediction using sentiment analysis. We have collected and studied the stocks of ten influential companies belonging to different stock domains in NASDAQ. Our analysis approach is complemented by a sophisticated causality analysis, an algorithmic feature selection and a variety of machine learning techniques including regularized models stacking. A comparison of our approach with other sentiment-based stock market prediction approaches including Deep learning, establishes that our proposed model is performing adequately and predicting stock movements with a higher accuracy of 60%.

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Predicting the Effects of News Sentiments on the Stock Market

Cited by 90 publications

References 12 publications

Stock Prediction by using NLP and Deep Learning Approach

Stock Prediction by using NLP and Deep Learning Approach

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Augmented Textual Features-Based Stock Market Prediction

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