Efficacy of News Sentiment for Stock Market Prediction

Kalra, Sneh; Prasad, Jay Shankar

doi:10.1109/comitcon.2019.8862265

Cited by 29 publications

(7 citation statements)

References 9 publications

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“…Sneh Kalra [10] states that the applied model investigates the impact on the stock market of assessing various sorts of stock related story with numerical historical information. An endeavor is needed to develop a viable framework for forecasting future market movements.…”

Section: Related Workmentioning

confidence: 99%

Survey on Stock Market Prediction using Deep Learning

V.M

2024

IJSREM

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The share market is a very unpredictable entity that is extremely sensitive to different sorts of criteria. Substantial research has been done in the field of financial markets projection to discover the many characteristics and elements connected with price volatility. The routine movements of the financial markets is exceedingly turbulent and dynamic. It is impacted by a large number of underlying elements and intricate interactions, resulting in a substantial percentage of non linearity. According to certain beliefs on the behavior of the financial markets, stock exchange progression is entirely unpredictable. Therefore, to achieve effective prediction of the stock markets through the use of machine learning approaches a number of related works have been analyzed. This analysis has allowed for the development of a useful and highly effective methodology that will be discussed in the upcoming editions of this research article. Key Words: K Nearest Neighbors, Long Short Term Memory, Stock Market Prediction.

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Section: Related Workmentioning

confidence: 99%

Survey on Stock Market Prediction using Deep Learning

V.M

2024

IJSREM

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“…Stock market is a risky investment, the author proposed the sentiment model based on investor's sentiment correlated to three criteria, (i). Positive correlation between stock market trends and Gubabased sentiment, (ii) Arrival of bear market analyzed by a long lasting Guba-based sentiment, (iii) Huge in post volumes and agreement, would be more proficiency bullishness, and (iv) News sentimental model analysis [19][20][21].…”

Section: Literature Reviewmentioning

confidence: 99%

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Mohan¹,

Raja²,

Velmurugan³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss. The proposed model uses a real time dataset of fifteen Stocks as input into the system and based on the data, predicts or forecast future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting is done for the next quarter. Our model uses 3 main concepts for forecasting results. The first one is for stocks that show periodic change throughout the season, the 'Holt-Winters Triple Exponential Smoothing'. 3 basic things taken into conclusion by this algorithm are Base Level, Trend Level and Seasoning Factor. The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm. The second concept is 'Recurrent Neural Network'. The specific model of recurrent neural network that is being used is Long-Short Term Memory and it's the same as the Normal Neural Network, the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop. The third concept is Recommendation System which filters and predict the rating based on the different factors.

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“…Because of the exponential growth of different social media platforms nowadays, news sentiment analysis plays important for predicting stock prices [17][18]. With the development of technology & high availability of the internet, the use of news sentiments becomes necessary to forecast financial trends & markets more precisely [5][6].…”

Section: Literature Surveymentioning

confidence: 99%

News Based Sentiment Analysis Using Tanh Grey Wolf Optimizer (Tgwo) for Stock Price Prediction

Gupta,

Jadon

2023

IJEAST

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Research on stock market forecasting has always been important to the financial sector. Shares price forecasting plays a significant role in raising investors' interest in an organization, which has a beneficial effect on the growth of shareholders in its stock. A substantial reward might have been available if the stock price had been correctly predicted. Due to advancements in science, technology, and the market economy, more elements today affect a company's price trend compared to the past. The traditional analytical technique cannot explain the fluctuations in stock price caused by important information hidden from view. Predicting these stock markets using confidential information can be achieved by several deep-learning time series models based on RNN and its derivatives, such as LSTM and GRU. However, their performance still needs to be improved. The use of optimization techniques like GWO, has improved the accuracy of these models, and to increase the reliability of the prediction, news sentiment has been used. The study comprised three phases. In the first phase, this study proposed a Tanh Grey Wolf Optimizer algorithm to increase the efficiency & accuracy of the model. Tanh is used to reduce the infinite search space to -1 and 1. Sigmoid and tanh are similar functions, but tanh has a more extensive range and is more symmetrical around the origin, resulting in results that are not biased. This property allows the potential for Tanh to have a superior gradient, which leads to more accuracy compared to the present basic GWO and binary GWO, which use sigmoid. In the second phase, News sentiment analysis used word embeddings to increase the predictions' reliability. In the last phase, these predictions are ensemble with news sentiment scores and classify the score into five classes, i.e., Strong Sell, Sell, Hold, Strong Buy, and Buy.

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Efficacy of News Sentiment for Stock Market Prediction

Cited by 29 publications

References 9 publications

Survey on Stock Market Prediction using Deep Learning

Survey on Stock Market Prediction using Deep Learning

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

News Based Sentiment Analysis Using Tanh Grey Wolf Optimizer (Tgwo) for Stock Price Prediction

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