TY - JOUR
T1 - Stock price forecasting and news sentiment analysis model using artificial neural network
AU - Yadav, Somesh
AU - Suhag, Ritesh Singh
AU - Sriram, K. V.
N1 - Funding Information:
The authors are thankful to Mr. Nikhil Perule (undergraduate student of engineering, Manipal Institute of Technology) for his efforts and contribution towards improving this work.
Publisher Copyright:
Copyright © 2021 Inderscience Enterprises Ltd.
PY - 2021
Y1 - 2021
N2 - The stock market is highly volatile, and the prediction of stock prices has always been an area of interest to many statisticians and researchers. This study is an attempt to predict the prices of stock using artificial neural network (ANN). Three models have been built, one for the future prediction of stock prices based on previous trends, the second for prediction of next day closing price based on today's opening price, and the third one analyses the sentiment of news articles and gives scores based on the news impact. ANN is trained with the historical data using R-studio platform which is then used to predict the future values. Our experimental results for various stock prices showed that the model is effective using ANN.
AB - The stock market is highly volatile, and the prediction of stock prices has always been an area of interest to many statisticians and researchers. This study is an attempt to predict the prices of stock using artificial neural network (ANN). Three models have been built, one for the future prediction of stock prices based on previous trends, the second for prediction of next day closing price based on today's opening price, and the third one analyses the sentiment of news articles and gives scores based on the news impact. ANN is trained with the historical data using R-studio platform which is then used to predict the future values. Our experimental results for various stock prices showed that the model is effective using ANN.
UR - https://www.scopus.com/pages/publications/85109568602
UR - https://www.scopus.com/pages/publications/85109568602#tab=citedBy
U2 - 10.1504/IJBIDM.2021.115967
DO - 10.1504/IJBIDM.2021.115967
M3 - Article
AN - SCOPUS:85109568602
SN - 1743-8187
VL - 19
SP - 113
EP - 133
JO - International Journal of Business Intelligence and Data Mining
JF - International Journal of Business Intelligence and Data Mining
IS - 1
ER -