Sentiment Analysis of Financial News: Mechanics and Statistics

Arratia, Argimiro; Ávalos, Gustavo; Cabaña, Alejandra; Duarte-López, Ariel; Renedo-Mirambell, Martí

doi:10.1007/978-3-030-66891-4_9

Cited by 5 publications

(4 citation statements)

References 48 publications

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“…The primary goal of this paper is to provide users of sentiment data with recommendations on the factors to take into account when creating sentiment indicators. The focus is on sentiment indicators that are taken from financial news and used in financial forecasting (8) . Natural Language Processing research has focused a lot of attention on the task of Sentiment Analysis, especially in the financial sector.…”

Section: Fig 1 the Arrangement Of Sentiment Analysis In Natural Langu...mentioning

confidence: 99%

Discovering Untapped Potential in Finance Markets Using NLP-Driven Sentiment Analysis

Shahapur,

Koralli,

Chippalakatti

et al. 2024

IJST

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Objectives: The main objective in Sentiment Analysis, and financial data is to decode sentiments through the use of Natural Language Processing (NLP) and Machine Learning (ML). This provides insights into market trends that are essential for risk management and well-informed financial decisions. By removing emotion from financial writing, gives stakeholders practical advice for wise investments in dynamic markets. The main objective is to increase the accuracy of Multinomial Naïve Bayes (Multinomial NB). In this work, we used purely ML models to get the accuracy more than the previous works, which got 74%, and in this work, we can see the accuracy increased to approximately 82%. Methods: This specific Research Work employs Machine Learning approaches to extract sentiment polarity (positive, negative, and neutral) from financial text data. The used ML models are- Multinomial Naïve Bayes (Multinomial NB), Logistic Regression (LR), KNeighbors, Decision Tree, and Random Forest algorithms are used for Sentiment Analysis. Findings: Studies conducted in the financial sector have shown that sentiment and informational substance in stock news have a big impact on market factors such as trading volume, volatility, stock prices, and corporate earnings. This study involves approximately 6000 newspaper articles to extract the polarity from financial text data. The incorporation of Multinomial NB of Natural Language Processing techniques results in a marginal improvement in the sentiment classification model performance among all the models used, where the previous works got the lowest accuracy of 74%. Novelty: This research introduces advanced ML models for extracting financial sentiment from text, including Random Forest, LR, KNeighbors, Decision Tree, and Multinomial NB previously unexplored in similar studies. Notably, the Multinomial NB model accuracy rose significantly from 65% to 82%. The study also presents sentiment segregation (positive, negative, neutral) visualized in a donut chart. Keywords: Sentiment Analysis, Machine Learning, Natural Language Processing, Financial data, Stock market prediction

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Section: Fig 1 the Arrangement Of Sentiment Analysis In Natural Langu...mentioning

confidence: 99%

Discovering Untapped Potential in Finance Markets Using NLP-Driven Sentiment Analysis

Shahapur,

Koralli,

Chippalakatti

et al. 2024

IJST

View full text Add to dashboard Cite

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“…( 2018 ) and Arratia et al. ( 2021 ) suggest that the Quanteda R package is one of the most efficient software packages to process sentiment in financial texts. This approach is also commonly used in the economics and finance literature (e.g., Dybowski and Kempa 2020 , Rybinski 2020 , Ferrara et al.…”

Section: Notesmentioning

confidence: 99%

“…2 To calculate the number of negative words and positive words, we utilise the Quanteda R package. Benoit et al (2018) and Arratia et al (2021) suggest that the Quanteda R package is one of the most efficient software packages to process sentiment in financial texts. This approach is also commonly used in the economics and finance literature (e.g., Dybowski and Kempa 2020, Rybinski 2020, Ferrara et al 2021 The Quanteda R package identifies the sentiment by using the Lexicoder Sentiment Dictionary (Young and Soroka 2012).…”

Section: Notesmentioning

confidence: 99%

COVID‐19 Disclosures and Market Uncertainty: Evidence from 10‐Q Filings

Hao

Pham

2022

Australian Accounting Review

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We examine whether the quarterly filing COVID‐19 disclosures reduce uncertainty for investors and analysts. We find a negative relationship between COVID‐19 disclosure and return volatility, suggesting COVID‐19 disclosure reduces investor uncertainty. This reduction effect concentrates mainly during the short window following 10‐Q releases and phases out over time. We then detect that industry‐wide COVID‐19 disclosure dispersion is positively associated with return volatility, suggesting high variation of industry‐wide COVID‐19 disclosures reduces information comparability across firms, resulting in increased investor uncertainty. Moreover, we find that COVID‐19 disclosures are positively associated with analysts’ downward earnings forecast revisions and negatively associated with analyst forecast dispersion after 10‐Q releases, suggesting the disclosures reduce information risk even for sophisticated market participants. Further analyses show that COVID‐19 disclosures are negatively associated with future financial and operational performances (i.e., sales, operating cash flow, operating income and ROA). Lastly, we find that the low readability of COVID‐19 disclosure attenuates the negative relation between COVID‐19 disclosure and market volatility. Collectively, our findings suggest that 10‐Q COVID‐19 disclosures contain value‐relevant information that temporarily assists market participants in evaluating the changes in firms’ values in the time of a crisis.

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“…Because of this, media coverage of earnings influences cash flow mispricing (Drake, Guest, & Twedt, 2014; Drake, Roulstone, & Thornock, 2012), trading volume (Bonsall, Green, & Muller, 2020; Bushee, Core, Guay, & Hamm, 2010; Engelberg & Parsons, 2011), stock volatility (Griffin, Hirschey, & Kelly, 2011), postearnings announcement drift (Ben-Rephael, Da, & Israelsen, 2017; Frederickson & Zolotoy, 2016), and trading activity of short sellers (Rees & Twedt, 2021). Practitioners are even beginning to build sentiment-based trading models to predict the stock performance of firms based on media coverage of earnings (Arratia, Avalos, Cabaña, Duarte-López, & Renedo-Mirambell, 2021).…”

mentioning

confidence: 99%

Media Coverage of Earnings Announcements: How Newsworthiness Shapes Media Volume and Tone

Oliver

Campbell²,

Graffin³

et al. 2022

Journal of Management

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Media coverage of earnings is consequential for firms. As such, firms work hard to ensure their performance beats analyst estimates to avoid negative coverage. However, the relationship between performance and coverage might not be as straightforward as firms assume because media coverage is a socially constructed process that reflects journalists’ social and cognitive biases while producing newsworthy content. With this in mind, we unpack the concept of newsworthiness and develop theory regarding how the media targets, in the earnings context, deviance that is socially significant for stakeholders or attaches a deviance frame to news of social significance. In doing so, we examine how the media’s pursuit of newsworthiness shapes the relationship between critical characteristics of earnings announcements—including the firm’s earnings performance, its press releases surrounding earnings, its prior reputation, and its prior media visibility—and media volume and tone. The results of our empirical tests are broadly consistent with our theorizing. Our theory and findings contribute to research on earnings, media coverage, and social evaluations.

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Sentiment Analysis of Financial News: Mechanics and Statistics

Cited by 5 publications

References 48 publications

Discovering Untapped Potential in Finance Markets Using NLP-Driven Sentiment Analysis

Discovering Untapped Potential in Finance Markets Using NLP-Driven Sentiment Analysis

COVID‐19 Disclosures and Market Uncertainty: Evidence from 10‐Q Filings

Media Coverage of Earnings Announcements: How Newsworthiness Shapes Media Volume and Tone

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