2023
Denanti, S. P.; Yunita, I.; Widarmanti, T.; Machado, J. M. Ferreira
The Correlation of Headline News Sentiment and Stock Return during Dividend Period Proceedings Article
Em: Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 9798350328028, (cited By 0; Conference of 2023 International Conference on Digital Business and Technology Management, ICONDBTM 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:195011).
Resumo | Links | BibTeX | Etiquetas: Asymmetric information; Company performance; Dividend period; FinBERT; Headline news; Informed decision; Investment decisions; Sentiment analysis; Stock price fluctuation; Stock returns, Costs; Financial markets; Investments, Sentiment analysis
@inproceedings{Denanti2023,
title = {The Correlation of Headline News Sentiment and Stock Return during Dividend Period},
author = {S. P. Denanti and I. Yunita and T. Widarmanti and J. M. Ferreira Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180008658&doi=10.1109%2fICONDBTM59210.2023.10327342&partnerID=40&md5=cf21fdc9bbb11f8c4a8c6e089f91285e},
doi = {10.1109/ICONDBTM59210.2023.10327342},
isbn = {9798350328028},
year = {2023},
date = {2023-01-01},
journal = {2023 International Conference on Digital Business and Technology Management, ICONDBTM 2023},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Stock price fluctuations require investors to gather more information to make informed decisions for optimal returns. Dividend announcements are the basis for investors to make investment decisions, as they contain asymmetric information about company performance. However, during the dividend period stock prices often fluctuate, which can make it difficult for investors to make decisions. Therefore, market participants can use sentiment analysis to assess company performance and assist in making investment decisions. The purpose of this study is to analyse headline sentiment during the dividend period, and how it relates to the stock returns of companies included in the LQ45 Index from 2018 to 2022. In conducting the sentiment analysis, the FinBERT model was used to classify dividend news headlines into positive, negative, and neutral sentiment. Then, a Spearman rank correlation test is conducted with the closing price of the stock to see the relationship. The results show that the sentiment formed by news headlines is dominated by neutral sentiment (46%), followed by positive sentiment (28%) and negative sentiment (26%). The study, conducted over the 2018-2022 dividend period, shows a positive relationship between news headlines and stock returns. The analysis shows that the sentiment conveyed in news headlines has a statistically significant positive correlation with changes in company stock returns. These findings suggest that the sentiment expressed in news headlines can serve as a valuable indicator for predicting and understanding fluctuations in stock returns during dividend periods. © 2023 IEEE.},
note = {cited By 0; Conference of 2023 International Conference on Digital Business and Technology Management, ICONDBTM 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:195011},
keywords = {Asymmetric information; Company performance; Dividend period; FinBERT; Headline news; Informed decision; Investment decisions; Sentiment analysis; Stock price fluctuation; Stock returns, Costs; Financial markets; Investments, Sentiment analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Stock price fluctuations require investors to gather more information to make informed decisions for optimal returns. Dividend announcements are the basis for investors to make investment decisions, as they contain asymmetric information about company performance. However, during the dividend period stock prices often fluctuate, which can make it difficult for investors to make decisions. Therefore, market participants can use sentiment analysis to assess company performance and assist in making investment decisions. The purpose of this study is to analyse headline sentiment during the dividend period, and how it relates to the stock returns of companies included in the LQ45 Index from 2018 to 2022. In conducting the sentiment analysis, the FinBERT model was used to classify dividend news headlines into positive, negative, and neutral sentiment. Then, a Spearman rank correlation test is conducted with the closing price of the stock to see the relationship. The results show that the sentiment formed by news headlines is dominated by neutral sentiment (46%), followed by positive sentiment (28%) and negative sentiment (26%). The study, conducted over the 2018-2022 dividend period, shows a positive relationship between news headlines and stock returns. The analysis shows that the sentiment conveyed in news headlines has a statistically significant positive correlation with changes in company stock returns. These findings suggest that the sentiment expressed in news headlines can serve as a valuable indicator for predicting and understanding fluctuations in stock returns during dividend periods. © 2023 IEEE.
2021
Saragih, P. S.; Witarsyah, D.; Hamami, F.; MacHado, J. M.
Sentiment Analysis of Social Media Twitter with Case of Large Scale Social Restriction in Jakarta using Support Vector Machine Algorithm Proceedings Article
Em: Institute of Electrical and Electronics Engineers Inc., 2021, ISBN: 9781665437097, (cited By 0; Conference of 2021 International Conference Advancement in Data Science, E-learning and Information Systems, ICADEIS 2021 ; Conference Date: 13 October 2021 Through 14 October 2021; Conference Code:177133).
Resumo | Links | BibTeX | Etiquetas: Bag of words; Classification process; COVID-19; F1 scores; Jakarta; Large-scales; LSSR; Sentiment analysis; Social media; Support vector machines algorithms, Extraction; Social networking (online); Support vector machines, Sentiment analysis
@inproceedings{Saragih2021,
title = {Sentiment Analysis of Social Media Twitter with Case of Large Scale Social Restriction in Jakarta using Support Vector Machine Algorithm},
author = {P. S. Saragih and D. Witarsyah and F. Hamami and J. M. MacHado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126541289&doi=10.1109%2fICADEIS52521.2021.9701961&partnerID=40&md5=4f324699bafcb89732b5245f423ef393},
doi = {10.1109/ICADEIS52521.2021.9701961},
isbn = {9781665437097},
year = {2021},
date = {2021-01-01},
journal = {2021 International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2021},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {When the Large-Scale Social Restrictions (LSSR or PSBB in Indonesian) policy was implemented it the policy was not entirely obeyed by the community which then reaped various opinions and responses on various social media, especially on Twitter. This study aims to conduct a sentiment analysis to find out the cause or phenomena that occur based on the opinions or views of Twitter. The Tweet data about the implementation of LSSR both part 1 and part 2 in Jakarta were obtained as many as 1080 opinions using the crawling method then the data is manually labelled with two labels, which are positive and negative after labelled the data is cleaned after and the data is processed by being weighted using the Bag of Words and TF-IDF extraction feature. The classification process is carried out with four splitting data scenarios, with 60:40, 70:30, 80:20, 90:10 then classified using the Support Vector Machines algorithm. The final result of this study shows that the classification accuracy results using the Support Vector Machine algorithm with 90:10 data splitting ratio using the TFIDF extraction feature is superior with an accuracy value of 85.185% and F1-Score 72.413%, which is better when compared to the Bag of words extraction feature which produces an accuracy value of 83.333% and F1-Score 66.666%. As for this study, Twitter users tend to give opinions with negative sentiments, which contain complaints and discomfort regarding the implementation of the LSSR policies, both the first LSSR and the second LSSR. Finally, the results of this research are also expected to be input for the government when making better policies in the future. © 2021 IEEE.},
note = {cited By 0; Conference of 2021 International Conference Advancement in Data Science, E-learning and Information Systems, ICADEIS 2021 ; Conference Date: 13 October 2021 Through 14 October 2021; Conference Code:177133},
keywords = {Bag of words; Classification process; COVID-19; F1 scores; Jakarta; Large-scales; LSSR; Sentiment analysis; Social media; Support vector machines algorithms, Extraction; Social networking (online); Support vector machines, Sentiment analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
When the Large-Scale Social Restrictions (LSSR or PSBB in Indonesian) policy was implemented it the policy was not entirely obeyed by the community which then reaped various opinions and responses on various social media, especially on Twitter. This study aims to conduct a sentiment analysis to find out the cause or phenomena that occur based on the opinions or views of Twitter. The Tweet data about the implementation of LSSR both part 1 and part 2 in Jakarta were obtained as many as 1080 opinions using the crawling method then the data is manually labelled with two labels, which are positive and negative after labelled the data is cleaned after and the data is processed by being weighted using the Bag of Words and TF-IDF extraction feature. The classification process is carried out with four splitting data scenarios, with 60:40, 70:30, 80:20, 90:10 then classified using the Support Vector Machines algorithm. The final result of this study shows that the classification accuracy results using the Support Vector Machine algorithm with 90:10 data splitting ratio using the TFIDF extraction feature is superior with an accuracy value of 85.185% and F1-Score 72.413%, which is better when compared to the Bag of words extraction feature which produces an accuracy value of 83.333% and F1-Score 66.666%. As for this study, Twitter users tend to give opinions with negative sentiments, which contain complaints and discomfort regarding the implementation of the LSSR policies, both the first LSSR and the second LSSR. Finally, the results of this research are also expected to be input for the government when making better policies in the future. © 2021 IEEE.