Stock price prediction through sentiment analysis of corporate disclosures using distributed representation

2018 ◽  
Vol 22 (6) ◽  
pp. 1395-1413 ◽  
Author(s):  
Misuk Kim ◽  
Eunjeong Lucy Park ◽  
Sungzoon Cho
Keyword(s):  
Sentiment Analysis ◽  
Stock Price ◽  
Distributed Representation ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Corporate Disclosures

scholarly journals Applying LSTM for Stock Price Prediction with Sentiment Analysis

2021 ◽  
Author(s):  
Alexandre Heiden ◽  
Rafael Stubs Parpinelli
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Market Volatility ◽  
Analysis Model ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial News ◽  
Social Media Platforms

Financial news has been proven to be valuable source of information for the evaluation of stock market volatility. Most of the attention has been given to social media platforms, while news from vehicles such as newspapers are not as widely explored. Newspapers provide, although in a smaller volume, more reliable information than social media platforms. In this context, this research aims to examine the influence of financial news within the stock price prediction problem, by using the VADER sentiment analysis model to process the news and feed the sentiments as a feature into a LSTM-based stock price prediction model, along with the historical data of the assets. Experiments indicate that the model has better results when the news’ sentiments are considered, and the model demonstrates potential to accurately predict stock prices up to around 60 days into the future.

scholarly journals Stock Market Prediction Using Twitter Sentiment Analysis

2021 ◽  
pp. 265-270
Author(s):  
Padmanayana ◽  
Varsha ◽  
Bhavya K
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Market Price ◽  
Future Price ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Media Platform ◽  
The Impact

Stock market prediction is an important topic in ?nancial engineering especially since new techniques and approaches on this matter are gaining value constantly. In this project, we investigate the impact of sentiment expressed through Twitter tweets on stock price prediction. Twitter is the social media platform which provides a free platform for each individual to express their thoughts publicly. Specifically, we fetch the live twitter tweets of the particular company using the API. All the stop words, special characters are extracted from the dataset. The filtered data is used for sentiment analysis using Naïve bayes classifier. Thus, the tweets are classified into positive, negative and neutral tweets. To predict the stock price, the stock dataset is fetched from yahoo finance API. The stock data along with the tweets data are given as input to the machine learning model to obtain the result. XGBoost classifier is used as a model to predict the stock market price. The obtained prediction value is compared with the actual stock market value. The effectiveness of the proposed project on stock price prediction is demonstrated through experiments on several companies like Apple, Amazon, Microsoft using live twitter data and daily stock data. The goal of the project is to use historical stock data in conjunction with sentiment analysis of news headlines and Twitter posts, to predict the future price of a stock of interest. The headlines were obtained by scraping the website, FinViz, while tweets were taken using Tweepy. Both were analyzed using the Vader Sentiment Analyzer.

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