Aggregate News Tone, Stock Returns, and Volatility

Dzieliński, Michał; Hasseltoft, Henrik

doi:10.2139/ssrn.2192532

Cited by 13 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Though the findings do not support that media content is a proxy for new information on intrinsic asset values or market volatility, the authors found that news sentiment impacts market yield (Tetlock, 2011). The studies in Dougal et al (2012), Dzielinski and Hasseltoft (2013) and Boudoukh et al (2013) confirm that textual information could briefly predict returns at the aggregate market level/individual stock level. However, Jegadeesh and Wu (2013) found that predictions at an individual stock level are limited.…”

Section: Theoretical Background and Literature Surveymentioning

confidence: 59%

Accounting for unadjusted news sentiment for asset pricing

Eachempati

Srivastava

2021

QRFM

View full text Add to dashboard Cite

Purpose A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment. Design/methodology/approach Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results. Findings Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6. Originality/value The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

show abstract

Section: Theoretical Background and Literature Surveymentioning

confidence: 59%

Accounting for unadjusted news sentiment for asset pricing

Eachempati

Srivastava

2021

QRFM

View full text Add to dashboard Cite

show abstract

“…While not surprising, it does provide confidence in the classification of negative and positive news articles. For equity returns, Dzielinski and Hasseltoft (2013) and Sinha (2016) find a similar relationship.…”

Section: Tablementioning

confidence: 67%

Dynamic jump intensities and news arrival in oil futures markets

Ensor

Ostdiek

et al. 2020

J Asset Manag

View full text Add to dashboard Cite

We introduce a new class of discrete-time models that explicitly recognize the impact of news arrival. The distribution of returns is governed by three factors: dynamics volatility and two Poisson compound processes, one for negative news and one for positive news. We show in a model-free environment that the arrival of negative and positive news has an asymmetric effect on oil futures returns and volatility. Using the first 12 futures contracts, our empirical results confirm that the effects of negative and positive news are described by different processes, a significant proportion of volatility is explained by news arrival and the impact of negative news is larger than that of positive news.

show abstract

“…Following the Loughran dictionary, we included the constraining sentiment, litigious sentiment, and uncertain sentiment in this study. We also aggregated the affective sentiment by using the number of positive words minus the number of negative words in our analysis (Dzielinski and Hasseltoft, 2013;Xun and Guo, 2017), labeled as optimism sentiment.…”

Section: Discussionmentioning

confidence: 99%

The role of the media in socially responsible investing

Lei

Zhang

2020

IJBM

View full text Add to dashboard Cite

PurposeThis study aims to understand how media content and media sentiment in corporate social responsibility (CSR) news coverage affect investment performance, as reflected in the S&P 500 Environmental and Socially Responsible Index from 2010 to 2016.Design/methodology/approachComputer-assisted content analysis and sentiment analysis are employed to analyze 818 CSR-related newspaper articles from mainstream newspapers. Autoregressive model is used to comprehend socially responsible investment (SRI) performance.FindingsThis study reveals the impact of media content and media sentiment of CSR-related news articles on SRI. The authors’ findings indicate that such topics as recognition of a company's CSR contributions in CSR-related news articles are positively associated with SRI performance, whereas topics such as tax avoidance and environmental protection show a negative relationship with SRI performance. In addition, this study contributes to the authors’ understanding of framing bias in investment by confirming a significant positive association between an uncertain or constraining media sentiment and SRI performance, as well as a negative relationship between a litigious sentiment and SRI performance.Originality/valueThere has been limited attention to examining the effect of media coverage of CSR on the financial market. Since SRI is one of the most useful financial indices for SRIs, it is meaningful to explore the relationship between media coverage of CSR and SRI. To fill the research gap, this study specifically examines how media coverage of CSR-related issues is associated with SRI performance.

show abstract

Aggregate News Tone, Stock Returns, and Volatility

Cited by 13 publications

References 43 publications

Accounting for unadjusted news sentiment for asset pricing

Accounting for unadjusted news sentiment for asset pricing

Dynamic jump intensities and news arrival in oil futures markets

The role of the media in socially responsible investing

Contact Info

Product

Resources

About