The effect of news and public mood on stock movements

Li, Qing; Wang, Zhihui; Li, Ping; Liu, Ling; Gong, Qixu; Chen, Yuanzhu

doi:10.1016/j.ins.2014.03.096

Cited by 216 publications

(145 citation statements)

References 52 publications

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“…Varying across different time lags, the study confirms that news sentiment has a significant negative relation with the market returns at lag-5 while market returns also have a more short-term impact toward news sentiment at lag-1. This result is encouraging as the presence of feedback effect in news sentiment is a novel finding compared to existing literature which has often emphasized the single direction of the news sentiment impact (Li et al 2014a;Li et al 2014b;Smales 2014b). It also sheds light on the formation of news sentiment and how it interacts with the market movement.…”

Section: Feedback Discussionsupporting

confidence: 55%

“…Through computerized news handling techniques, news articles are converted to a quantitative measure for sentiment representation. In the field of behavioral finance, news sentiment has often been used as a proxy for gauging investor's sentiment and reflecting the aggregate confidence of the society toward financial markets (Li et al 2014a). Intuitively, news sentiment becomes a key factor in driving financial market movement, and as a result, a large surge of sentiment can enhance the predictability of the market's direction.…”

Section: Introductionmentioning

confidence: 99%

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News sentiment to market impact and its feedback effect

Liu

Yang

2016

Environ Syst Decis

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Although market feedback on investor sentiment effect has been conceptually identified in the existing finance literature and investment strategies have been designed to explore this effect, there lacks systematic analysis in a quantified manner on such effect. Digitization of news articles and the advancement of computational intelligence applications have led to a growing influence of news sentiment over financial markets in recent years. News sentiment has often been used as a proxy for gauging investor sentiment and reflecting the aggregate confidence of the society toward future market. Previous studies have primarily focused on elucidating the unidirectional impact of news sentiment on market returns and not vice versa. In this study, we analyze more than 12 millions of news articles and document the presence of a significant feedback effect between news sentiment and market returns across the major indices in the US financial market. More specifically, we find that news sentiment exhibits a lag-5 effect on market returns and conversely market returns elicit consistent lag-1 effects on news sentiment. This aligns well with our intuition that news sentiment drives trading activity and investment decisions. In turn, heightened investment activity further stimulates involuntary responses, which manifest in the form of more news coverage and publications. The evidence presented highlights the strong correlation between news sentiment and market returns and demonstrates the benefits of advancing knowledge in data-driven modeling and its interaction with market movements.

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Section: Feedback Discussionsupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

News sentiment to market impact and its feedback effect

Liu

Yang

2016

Environ Syst Decis

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“…This result is consistent with Wu et al (), who reported that prediction at the individual stock level will have preferable results than prediction at the industry level because features affect the predictive power of stock forum sentiment. In addition, Li et al () found that the impact of media on firms varies according to firm characteristics and article content, which confirms our conclusion too.…”

Section: Discussionmentioning

confidence: 99%

Tehran stock exchange prediction using sentiment analysis of online textual opinions

Ghahfarrokhi

Shamsfard

2020

Intell Sys Acc Fin Mgmt

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Summary We investigate the impact of social media data in predicting the Tehran Stock Exchange variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from http://Sahamyab.com/stocktwits for about 3 months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon‐based and learning‐based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi‐regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in the Tehran Stock Exchange is different depending on their attributes. Moreover, we indicate that only comments volume could be useful for predicting the closing price, and both the volume and the sentiment of the comments could be useful for predicting the daily return. We demonstrate that users' trust coefficients have different behaviours toward the three stocks.

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“…Sentence 3 also indicates increase of a financial entity (share capital). Hence, the sentence is likely to be considered as positive by systems that use a combination of LM dictionary and financial entities (or noun phrases/named entities) [17,25].…”

Section: Dnb Nord Of Norway Is the Most Likely Nordic Buyer For Citadmentioning

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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The effect of news and public mood on stock movements

Cited by 216 publications

References 52 publications

News sentiment to market impact and its feedback effect

News sentiment to market impact and its feedback effect

Tehran stock exchange prediction using sentiment analysis of online textual opinions

Sentiment analysis of financial news articles using performance indicators

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