Using social media mining technology to assist in price prediction of stock market

Wang, Yaojun; Wang, Yaoqing

doi:10.1109/icbda.2016.7509794

Cited by 28 publications

(21 citation statements)

References 8 publications

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“…To achieve higher prediction accuracy some of other moods like foreign mood state and political mood states are need to be considered. This paper proposes an effective and accurate stock market prediction technique by combining the social media mining technology with the stock prices [16]. This analyse is similar to our approach of sentiment analysis.…”

Section: Related Workmentioning

confidence: 94%

“…In the experimental study the prediction of stock market was carried out for the company Arab Bank In the previous work 20 items small -cap stocks were randomly selected from Chinese stock market and the social media data fetched from Sina Weibo, Tong Hua Network and Dong Fang Cai Fu networkShun and aims to predict the stock price accurately [16].…”

Section: Resultsmentioning

confidence: 99%

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Sentiment Analysis of Twitter and RSS News Feeds and Its Impact on Stock Market Prediction

Bharathi¹,

Geetha²,

Sathiynarayanan³

2017

IJIES

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In the recent, past accurate forecasting of stock market has become a challenging task. The proposed approach helps to achieve high precision in stock market prediction by combining the sensex points, with Really Simple Syndication (RSS) news feeds and Tweets. The algorithm focuses on the correlation between the stock market values, sentiments of tweets and RSS feeds for a particular period of time. In this algorithm, a trained model is used for stock market prediction rates. Experimental study focuses on the stock market sensex prices, RSS news feeds and tweets which are collected for the company ARBK from Amman Stock Exchange (ASE). This paper follows two types of hypothesises. Null Hypothesis H0: Stock level indicators predict the trend of stock rates at an allowable rate of minimum 80% above. Alternate Hypothesis Ha: Stock level indicators along with the sentiment analysis of RSS news feeds and tweets as stock enhances the accuracy of prediction. Our experimental study has proved the correlation between Stock level indicators and RSS news feeds and tweets and has shown significant improvement of 20% in prediction accuracy. .

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Section: Related Workmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Sentiment Analysis of Twitter and RSS News Feeds and Its Impact on Stock Market Prediction

Bharathi¹,

Geetha²,

Sathiynarayanan³

2017

IJIES

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“…Yaojun Wang et al [2] used social media sites to gather data for their research. In this research paper, their focus was on the share price movements in the market.…”

Section: Literature Surveymentioning

confidence: 99%

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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“…Risk lies at the core of security investments. Low levels of risk are associated with low potential returns and vice versa (Wang and Wang, 2016a). The balance between the desire for the lowest possible risk and the highest possible return is the tradeoff of risk and return (Liu and Ren, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Due to the complexity of the stock market, traditional statistical models for time series forecasting is restricted (Cai et al, 2012). Fluctuation in time series leads to a lot of noises due to the nonlinear, non-stationary, selective and dynamic nature of the approach (Wang and Wang, 2016b). Investors make decisions based on available data by considering the data which have impacts on the financial instruments (Shynkevich et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Integrated Multiple Linear Regression-One Rule Classification Model for the Prediction of Stock Price Trend

Han¹,

Nordin²

2017

Journal of Computer Science

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Abstract:One of the main problems of predicting stock price with regression approach is overfitting a model. An overfit model becomes tailored to fit the random noise in the dataset rather than reflecting the overall population. For this it is necessary to construct an integrated regression-classification model to approximate the true model for the entire population in the dataset. The proposed model integrates Multiple Linear Regression algorithm and One Rule (OneR) classification algorithm. Initially the prediction was treated with regression approach where the outputs were in numerical values. After that a classification model was used to interpret the regression outputs and then classified the outcomes into Profit and Loss class labels. The test results were compared to those obtained with standard classification algorithms which included OneR, Zero Rule (ZeroR), Decision Tree and REP Tree. The results showed that the regression-classification model were significantly more successful than the standard classification algorithms.

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Using social media mining technology to assist in price prediction of stock market

Cited by 28 publications

References 8 publications

Sentiment Analysis of Twitter and RSS News Feeds and Its Impact on Stock Market Prediction

Sentiment Analysis of Twitter and RSS News Feeds and Its Impact on Stock Market Prediction

Stock Prediction by using NLP and Deep Learning Approach

Integrated Multiple Linear Regression-One Rule Classification Model for the Prediction of Stock Price Trend

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