Using Financial News Sentiment for Stock Price Direction Prediction

Fazlija, Bledar; Harder, Pedro

doi:10.3390/math10132156

Cited by 25 publications

(13 citation statements)

References 22 publications

(33 reference statements)

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“…Sinha et al (2022) investigate FinBERT’s eligibility for aggregate stock market prediction by studying the influence of sentiment on 900 Indian companies. Fazlija and Harder (2022) show that FinBERT can be used to predict the S&P 500 movement. The latter study is especially relevant since we investigate the influence of COVID-19 sentiment on the S&P 500.…”

Section: Covid-19 News Sentiment Constructionmentioning

confidence: 99%

Machine learning sentiment analysis, COVID-19 news and stock market reactions

Costola

Nofer

Hinz

et al. 2023

Research in International Business and Finance

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Section: Covid-19 News Sentiment Constructionmentioning

confidence: 99%

Machine learning sentiment analysis, COVID-19 news and stock market reactions

Costola

Nofer

Hinz

et al. 2023

Research in International Business and Finance

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“…In recent years, the interest in predicting stock market prices rose so has the number of published papers on that subject (Fazlija and Harder, 2022). One stream of research is based on traditional time series methodologies.…”

Section: Review Of Literaturementioning

confidence: 99%

Predicting stock market using natural language processing

Puh

Babac

2023

AJB

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PurposePredicting the stock market's prices has always been an interesting topic since its closely related to making money. Recently, the advances in natural language processing (NLP) have opened new perspectives for solving this task. The purpose of this paper is to show a state-of-the-art natural language approach to using language in predicting the stock market.Design/methodology/approachIn this paper, the conventional statistical models for time-series prediction are implemented as a benchmark. Then, for methodological comparison, various state-of-the-art natural language models ranging from the baseline convolutional and recurrent neural network models to the most advanced transformer-based models are developed, implemented and tested.FindingsExperimental results show that there is a correlation between the textual information in the news headlines and stock price prediction. The model based on the GRU (gated recurrent unit) cell with one linear layer, which takes pairs of the historical prices and the sentiment score calculated using transformer-based models, achieved the best result.Originality/valueThis study provides an insight into how to use NLP to improve stock price prediction and shows that there is a correlation between news headlines and stock price prediction.

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“…The highest accuracy results were scored by using CNN and LSTM algorithms. B. Fazlija et al, 2022 [20] discovered the financial sentiment knowledge from the news article. Then apply the predicted sentiment scores to estimate the stock market price direction by using the BERT algorithm.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

Ahmad¹,

Umar²

2023

IJCAI

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Recently, extensive research in the field of financial sentiment analysis has been conducted. Sentiment analysis (SA) of any text data denotes the feelings and attitudes of the individual on particular topics or products. It applies statistical approaches with artificial intelligence (AI) algorithms to extract substantial knowledge from a huge amount of data. This study extracts the Sentiment polarity (negative, positive, and neutral) from financial textual data using machine learning and deep learning algorithms. The constructed machine learning model used Multinomial Naïve Bayes (MNB) and Logistic regression (LR) classifiers. On the other hand, three deep learning algorithms have been utilized which are Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results of the MNB and LR obtained a good and very good rate of accuracy respectively. Likewise, the results of RNN, LSTM, and GRU obtained an excellent rate of accuracy. It can be concluded from the outcomes that the used preprocessing stages made a positive impact on the accuracy rate. Povzetek: Raziskava analizira finančna besedila z uporabo klasičnega strojnega učenja in globokega učenja z namenom iskanja sentimenta (negativno, pozitivno, nevtralno).

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Using Financial News Sentiment for Stock Price Direction Prediction

Cited by 25 publications

References 22 publications

Machine learning sentiment analysis, COVID-19 news and stock market reactions

Machine learning sentiment analysis, COVID-19 news and stock market reactions

Predicting stock market using natural language processing

Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

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