Equity portfolio risk estimation using market information and sentiment

Mitra, Leela; Mitra, Gautam; diBartolomeo, Dan

doi:10.1080/14697680903448361

Cited by 39 publications

(3 citation statements)

References 7 publications

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“…Various papers have explored the influence of news items on asset prices and volatility. Examples of this are given by: Tetlock et al ( 2008 ), Da et al ( 2011 ), Barber and Odean ( 2008 ), diBartolomeo and Warrick ( 2005 ), Mitra et al ( 2009 ), and Dzielinski et al. ( 2011 )).…”

Section: Introductionmentioning

confidence: 99%

Trump’s COVID-19 tweets and Dr. Fauci’s emails

Allen

McAleer

2022

Scientometrics

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The paper features an analysis of former President Trump’s early tweets on COVID-19 in the context of Dr. Fauci’s recently revealed email trove. The tweets are analysed using various data mining techniques, including sentiment analysis. These techniques facilitate exploration of content and sentiments within the texts, and their potential implications for the national and international reaction to COVID-19. The data set or corpus includes 159 tweets on COVID-19 that are sourced from the Trump Twitter Archive, running from 24 January 2020 to 2 April 2020. In addition we use Zipf and Mandelbrot’s power law to calibrate the extent to which they differ from normal language patterns. A context for the emails is provided by the recently revealed email trove of Dr. Fauci, obtained by Buzzfeed on 1 June 2021 obtained under the Freedom of Information Act.

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Section: Introductionmentioning

confidence: 99%

Trump’s COVID-19 tweets and Dr. Fauci’s emails

Allen

McAleer

2022

Scientometrics

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“…Other research on this topic argues that news items from different sources influence investor sentiment, which feeds into asset prices, asset price volatility and risk (see, among others, Tetlock [11] Tetlock, Macskassy and Saar-Tsechansky [12] (2008), Da, Engleberg and Gao, [13], Barber and Odean [14], diBartolomeo and Warrick [15], Mitra, Mitra and diBartolomeo [16], and Dzielinski, Rieger and Talpsepp [17]. The diversification benefits of the information impounded in news sentiment scores provided by RavenPack were demonstrated by Cahan, Jussa and Luo [18], and Hafez and Xie [19], who examined the benefits in the context of popular asset pricing models.…”

Section: Introductionmentioning

confidence: 99%

Fake News and Propaganda: Trump’s Democratic America and Hitler’s National Socialist (Nazi) Germany

Allen

McAleer

2019

Sustainability

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This paper features an analysis of President Trump’s two State of the Union addresses, which are analysed by means of various data mining techniques, including sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. We also apply Zipf and Mandelbrot’s power law to assess the degree to which they differ from common language patterns. To provide a contrast and some parallel context, analyses are also undertaken of President Obama’s last State of the Union address and Hitler’s 1933 Berlin Proclamation. The structure of these four political addresses is remarkably similar. The three US Presidential speeches are more positive emotionally than is Hitler’s relatively shorter address, which is characterised by a prevalence of negative emotions. Hitler’s speech deviates the most from common speech, but all three appear to target their audiences by use of non-complex speech. However, it should be said that the economic circumstances in contemporary America and Germany in the 1930s are vastly different.

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“…The dynamics of asset prices, in particular their volatility, is clearly affected by news events. These events are classified and quantified, and news sentiment can be utilised to enhance volatility prediction (see, e.g., (Mitra et al 2009)). Sentiment Analysis is further used to improve trading decisions in equity markets.…”

Section: Introductionmentioning

confidence: 99%

Macroeconomic News Sentiment: Enhanced Risk Assessment for Sovereign Bonds

Erlwein-Sayer¹

2018

Risks

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We enhance the modelling and risk assessment of sovereign bond spreads by taking into account quantitative information gained from macro-economic news sentiment. We investigate sovereign bonds spreads of five European countries and improve the prediction of spread changes by incorporating news sentiment from relevant entities and macro-economic topics. In particular, we create daily news sentiment series from sentiment scores as well as positive and negative news volume and investigate their effects on yield spreads and spread volatility. We conduct a correlation and rolling correlation analysis between sovereign bond spreads and accumulated sentiment series and analyse changing correlation patterns over time. Market regimes are detected through correlation series and the impact of news sentiment on sovereign bonds in different market circumstances is investigated. We find best-suited external variables for forecasts in an ARIMAX model set-up. Error measures for forecasts of spread changes and volatility proxies are improved when sentiment is considered. These findings are then utilised to monitor sovereign bonds from European countries and detect changing risks through time.

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Equity portfolio risk estimation using market information and sentiment

Cited by 39 publications

References 7 publications

Trump’s COVID-19 tweets and Dr. Fauci’s emails

Trump’s COVID-19 tweets and Dr. Fauci’s emails

Fake News and Propaganda: Trump’s Democratic America and Hitler’s National Socialist (Nazi) Germany

Macroeconomic News Sentiment: Enhanced Risk Assessment for Sovereign Bonds

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