Using Market News Sentiment Analysis for Stock Market Prediction
(1) Background: Since the current crises that has inevitably impacted the financial market, market prediction has become more crucial than ever. The question of how risk managers can more accurately predict the evolution of their portfolio, while taking into consideration systemic risks brought on b...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/22/4255 |
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| author | Marian Pompiliu Cristescu Raluca Andreea Nerisanu Dumitru Alexandru Mara Simona-Vasilica Oprea |
| author_facet | Marian Pompiliu Cristescu Raluca Andreea Nerisanu Dumitru Alexandru Mara Simona-Vasilica Oprea |
| author_sort | Marian Pompiliu Cristescu |
| collection | DOAJ |
| description | (1) Background: Since the current crises that has inevitably impacted the financial market, market prediction has become more crucial than ever. The question of how risk managers can more accurately predict the evolution of their portfolio, while taking into consideration systemic risks brought on by a systemic crisis, is raised by the low rate of success of portfolio risk-management models. Sentiment analysis on natural language sentences can increase the accuracy of market prediction because financial markets are influenced by investor sentiments. Many investors also base their decisions on information taken from newspapers or on their instincts. (2) Methods: In this paper, we aim to highlight how sentiment analysis can improve the accuracy of regression models when predicting the evolution of the opening prices of some selected stocks. We aim to accomplish this by comparing the results and accuracy of two cases of market prediction using regression models with and without market news sentiment analysis. (3) Results: It is shown that the nonlinear autoregression model improves its goodness of fit when sentiment analysis is used as an exogenous factor. Furthermore, the results show that the polynomial autoregressions fit better than the linear ones. (4) Conclusions: Using the sentiment score for market modelling, significant improvements in the performance of linear autoregressions are showcased. |
| first_indexed | 2024-03-09T18:11:09Z |
| format | Article |
| id | doaj.art-b289000ac68844f2948c700f5715dadc |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T18:11:09Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-b289000ac68844f2948c700f5715dadc2023-11-24T09:08:31ZengMDPI AGMathematics2227-73902022-11-011022425510.3390/math10224255Using Market News Sentiment Analysis for Stock Market PredictionMarian Pompiliu Cristescu0Raluca Andreea Nerisanu1Dumitru Alexandru Mara2Simona-Vasilica Oprea3Faculty of Economic Sciences, Lucian Blaga University of Sibiu, 550324 Sibiu, RomaniaFaculty of Economic Sciences, Lucian Blaga University of Sibiu, 550324 Sibiu, RomaniaFaculty of Economic Sciences, Lucian Blaga University of Sibiu, 550324 Sibiu, RomaniaDepartment of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010374 Bucharest, Romania(1) Background: Since the current crises that has inevitably impacted the financial market, market prediction has become more crucial than ever. The question of how risk managers can more accurately predict the evolution of their portfolio, while taking into consideration systemic risks brought on by a systemic crisis, is raised by the low rate of success of portfolio risk-management models. Sentiment analysis on natural language sentences can increase the accuracy of market prediction because financial markets are influenced by investor sentiments. Many investors also base their decisions on information taken from newspapers or on their instincts. (2) Methods: In this paper, we aim to highlight how sentiment analysis can improve the accuracy of regression models when predicting the evolution of the opening prices of some selected stocks. We aim to accomplish this by comparing the results and accuracy of two cases of market prediction using regression models with and without market news sentiment analysis. (3) Results: It is shown that the nonlinear autoregression model improves its goodness of fit when sentiment analysis is used as an exogenous factor. Furthermore, the results show that the polynomial autoregressions fit better than the linear ones. (4) Conclusions: Using the sentiment score for market modelling, significant improvements in the performance of linear autoregressions are showcased.https://www.mdpi.com/2227-7390/10/22/4255sentiment analysismarket predictionBeautifulSoupARXVADER |
| spellingShingle | Marian Pompiliu Cristescu Raluca Andreea Nerisanu Dumitru Alexandru Mara Simona-Vasilica Oprea Using Market News Sentiment Analysis for Stock Market Prediction Mathematics sentiment analysis market prediction BeautifulSoup ARX VADER |
| title | Using Market News Sentiment Analysis for Stock Market Prediction |
| title_full | Using Market News Sentiment Analysis for Stock Market Prediction |
| title_fullStr | Using Market News Sentiment Analysis for Stock Market Prediction |
| title_full_unstemmed | Using Market News Sentiment Analysis for Stock Market Prediction |
| title_short | Using Market News Sentiment Analysis for Stock Market Prediction |
| title_sort | using market news sentiment analysis for stock market prediction |
| topic | sentiment analysis market prediction BeautifulSoup ARX VADER |
| url | https://www.mdpi.com/2227-7390/10/22/4255 |
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