News Sentiment to Market Impact and its Feedback Effect

Mo, Sheung Yin Kevin; Liu, Anqi; Yang, Steve Y.

doi:10.2139/ssrn.2567433

Cited by 7 publications

(9 citation statements)

References 23 publications

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“…As Zullow's ground-breaking research indicates, a large number of studies in economics and related fields show that there is a more general relationship between economic conditions and public sentiment. More recent exemplars of this have used digital resources to investigate corpus-level or massive sets of data, such as those obtained from blogs and social media posts (e.g., Gruhl et al, 2005;Ranco et al, 2015), books (e.g., Bentley et al, 2014), and public news articles (e.g., Chan, 2003;Shapiro et al, 2020;Mo et al, 2016). For instance, Bollen et al (2011) examined whether public mood states could predict changes in the stock market by analysing the overall valence and a range of moods expressed within a large corpus of social media posts, showing that changes in the combined calm and happiness mood dimensions were predictive of changes in the Dow Jones Industrial Average values with a three-day lead (Bollen et al, 2011).…”

Section: Lyrical Themes and Socioeconomic Conditionsmentioning

confidence: 99%

Popular music lyrics and the COVID-19 pandemic

Putter

Krause

North

2021

Psychology of Music

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A limited amount of previous research suggests that deteriorating socioeconomic conditions may be associated with greater popularity of music lyrics featuring negative emotional content and references to relationships. The present research considered this in charting popular music before and during the first 6 months of the COVID-19 pandemic. A dataset based on the song lyrics of the top-5 charting weekly songs in the United Kingdom and the United States from January 1999 to August 2020 was computer-analyzed for interpersonal variables, such as satisfaction and human interest, and positive and negative emotional valence. Results indicated lower satisfaction and human interest in lyrics in the United States and United Kingdom in the first 6 months of the COVID-19 pandemic compared to the lyrics in charting songs in 2015–2019. The US charting songs in 2020 also saw higher leveling and negative emotional content, and, when considering monthly data from 1999 to 2020, there was a positive association between economic misery and the number of negatively valenced words. The findings broaden our understanding of the relationship between significant global events and trends in popular music.

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Section: Lyrical Themes and Socioeconomic Conditionsmentioning

confidence: 99%

Popular music lyrics and the COVID-19 pandemic

Putter

Krause

North

2021

Psychology of Music

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“…Using a theoretical framework, Arnold and Brunner (2015) showed that positive feedback trading causes price overreaction and the impacts of feedback trading would be dampened if news is incorporated into price in time. Mo et al (2016) analyzed more than 12 million news articles and documented the presence of a significant feedback effect between news sentiment and market returns across the major indices in the US financial market.…”

Section: Investor Sentiment and Market Return Literaturementioning

confidence: 99%

“…Shleifer (2000) argued that investor sentiment is the cause of numerous market anomalies such as noise trader risk, limits of arbitrage, and closed-end fund puzzle. This claim has been echoed by a number of empirical studies showing predictive power of investor sentiment to financial market prices (Antweiler and Frank 2004, Tetlock 2007, Tetlock et al 2008, Mo et al 2016. On the other hand, other studies showed market price movements affect investor sentiment such as Antoniou et al (2005), Salm and Schuppli (2010), and Arnold and Brunner (2015).…”

Section: Introductionmentioning

confidence: 99%

Applications of a multivariate Hawkes process to joint modeling of sentiment and market return events

Yang

Liu

Chen

et al. 2017

Quantitative Finance

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To investigate the complex interactions between market events and investor sentiment, we employ a multivariate Hawkes process to evaluate dynamic effects among four types of distinct events: positive returns, negative returns, positive sentiment and negative sentiment. Using both intraday S&P 500 return data and Thomson Reuters News sentiment data from 2008 to 2014, we find: a) self-excitation is strong for all four types of events at 15 minutes time scale; b) there is a significant mutual-excitation between positive returns and positive sentiment, and negative returns and negative sentiment; c) decay of return events is almost twice as fast as sentiment events, which means market prices move faster than investor sentiment changes; d) positive sentiment shocks tend to generate negative price jumps; and e) the crossexcitation between positive and negative sentiments is stronger than their self-excitation. These findings provide further understanding of investor sentiment and its intricate interactions with market returns.

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“…Their work analyzes sentiment at the topic level and not at the sentence level as in [15,25]. Other recent works in the literature that perform sentiment analysis include [16,17,19,28,32]. These works utilize sentiment dictionaries and extract sentiments from text and predict stock price movements or market returns.…”

Section: Related Literaturementioning

confidence: 99%

Sentiment analysis of financial news articles using performance indicators

Krishnamoorthy

2017

Knowl Inf Syst

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Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely rely on domain specific dictionaries. However, dictionary based methods often fail to accurately predict the polarity of financial texts. This paper aims to improve the state-of-the-art and introduces a novel sentiment analysis approach that employs the concept of financial and non-financial performance indicators. It presents an association rule mining based hierarchical sentiment classifier model to predict the polarity of financial texts as positive, neutral or negative. The performance of the proposed model is evaluated on a benchmark financial dataset. The model is also compared against other state-of-the-art dictionary and machine learning based approaches and the results are found to be quite promising. The novel use of performance indicators for financial sentiment analysis offers interesting and useful insights. Sentiment analysis and opinion mining [31,37] has received significant attention in the literature due to its wide applicability in business, management and social science disciplines. It has been effectively applied in domains such as movies [31,37], product reviews [5], travel reviews [7,37] and finance [2,11,23,24,34,35]. This is an accepted version of the article published in Knowledge and Information Systems Journal. The final authenticated version is available online at: https://doi.

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News Sentiment to Market Impact and its Feedback Effect

Cited by 7 publications

References 23 publications

Popular music lyrics and the COVID-19 pandemic

Popular music lyrics and the COVID-19 pandemic

Applications of a multivariate Hawkes process to joint modeling of sentiment and market return events

Sentiment analysis of financial news articles using performance indicators

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