Stock Market Price Prediction Using LSTM RNN

2018 ◽  
pp. 493-503 ◽  
Author(s):  
Kriti Pawar ◽  
Raj Srujan Jalem ◽  
Vivek Tiwari
Keyword(s):  
Stock Market ◽  
Market Price ◽  
Price Prediction

scholarly journals Optimization of Artificial Neural Network for Stock Market Price Prediction Using an Enhanced Firefly Algorithm

2019 ◽  
Vol 13 (9) ◽  
pp. 532-543
Author(s):  
Ameen Ahmed Oloduowo ◽  
Fashoto Stephen Gbenga ◽  
Ogeh Clement ◽  
Balogun Abdullateef ◽  
Mashwama Petros
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Firefly Algorithm ◽  
Market Price ◽  
Price Prediction ◽  
Artificial Neural

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.

Sign in / Sign up

Export Citation Format

Share Document