Long Short-Term Memory (LSTM) Algorithm Based Prediction of Stock Market Exchange

2021 ◽  
Author(s):  
KARUNAKAR POTHUGANTI
Keyword(s):  
Stock Market ◽  
Short Term Memory ◽  
Market Exchange ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Trading the stock market using Google search volumes: a long short-term memory approach

2019 ◽  
Vol 3 (1) ◽  
pp. 3 ◽  
Author(s):  
Joseph St. Pierre ◽  
Mateusz Klimkiewicz ◽  
Adonay Resom ◽  
Nikolaos Kalampalikis
Keyword(s):  
Stock Market ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Google Search

scholarly journals Stock Prediction Using Sentiment Analysis and LSTM

2021 ◽  
Vol 9 (9) ◽  
pp. 1752-1756
Author(s):  
Ms. Anjima K. S
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Programming Language ◽  
Stock Prices ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Python Programming Language ◽  
Long Short Term Memory ◽  
Python Programming

Abstract: The stock market is a difficult area to anticipate since it is influenced by a variety of variables at the same time. The stock exchange is where equities are exchanged, transferred, and circulated. This research proposes a hybrid algorithm that predicts a stock's next day closing prices using sentiment analysis and Long Short Term Memory. The LSTM model seems to be quite popular in time-series forecasting, which is why it was selected for this project. Our proposed methodology makes use of the temporal association between public opinion and stock prices. Part-of-speech tagging is used to do sentiment analysis, and Long Short Term Memory is utilized to predict the stock's next day closing price. When these two factors are combined, we get a good picture of the stock's future. In this project, two main datasets have been used: HCLTECH company stock data and the news related to each stock of the HCL company for each day. The project is implemented by using the python programming language. The python programming language has been used to execute the project. This also incorporates machine learning along with public feedback. Sentiment analysis enables us to evaluate a diversity of political and economic factors, which have a significant impact on the stock market. Keywords: LSTM, sentiment analysis, RNN, Back propagation neural network.

scholarly journals Lbl - Lstm : Log Bilinear And Long Short Term Memory Based Efficient Stock Forecasting Model Considering External Fluctuating Factor

2020 ◽  
Vol 9 (4) ◽  
pp. 2057-2063 ◽  
Keyword(s):  
Stock Market ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Forecasting Model ◽  
Short Term ◽  
Stock Market Prediction ◽  
Term Memory ◽  
Stock Market Performance ◽  
Long Short Term Memory ◽  
Lending Rates

Stock market prediction problem is considered to be NP-hard problem because of highly volatile nature of stock market. In this paper, effort has been made to design efficient stock forecasting model using log Bilinear and long short term memory (LBL-LSTM) considering external fluctuating factor such as varying Bank's lending rates. The external factor bank's lending rates affects stock market performance ,as it plays vital role for the purchase of stocks in case of financial crisis faced by various business enterprises. Proposed LBL-LSTM based model shows performance improvement over existing machine learning algorithms used for stock market prediction.

Sign in / Sign up

Export Citation Format

Share Document