Making covariance-based portfolio risk models sensitive to the rate at which markets reflect new information

diBartolomeo, Dan; Warrick, Sandy

doi:10.1016/b978-075066006-8.50013-6

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…Recently, the role of market news sentiment, in particular machine-driven sentiment signals, and their implication for financial market processes, has captured the attention of both investment practitioners and academics. There is a growing body of research that argues that news items from different sources influence investor sentiment, and hence asset prices, asset price volatility and risk (Tetlock, 2007;Tetlock et al, 2008;Da et al, 2011;Barber and Odean, 2008;diBartolomeo and Warrick, 2005a;Mitra et al, 2008;Dzielinski et al, 2011). Bhattacharya et al (2009) explore news items that came out between 1996 and 2000 on 458 Internet initial public offerings (IPOs) and a matching sample of 458 non-Internet IPOs (a total of 171,488 news items).…”

Section: Introductionmentioning

confidence: 99%

Daily market news sentiment and stock prices

2019

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Section: Introductionmentioning

confidence: 99%

Daily market news sentiment and stock prices

2019

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“…Impacts of investors' sentiment to financial market have been well documented in a number of behavioral finance studies [3,33,12,25,9]. However, since investors' sentiment is an abstract concept, researchers have put a lot of effort in finding adequate and reliable proxies to represent its underlying mechanism.…”

Section: Introductionmentioning

confidence: 99%

Stock portfolio selection using learning-to-rank algorithms with news sentiment

2017

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In this study, we apply learning-to-rank algorithms to design trading strategies using relative performance of a group of stocks based on investors' sentiment toward these stocks. We show that learning-to-rank algorithms are effective in producing reliable rankings of the best and the worst performing stocks based on investors' sentiment. More specifically, we use the sentiment shock and trend indicators introduced in the previous studies, and we design stock selection rules of holding long positions of the top 25% stocks and short positions of the bottom 25% stocks according to rankings produced by learning-to-rank algorithms. We then apply two learning-to-rank algorithms, ListNet and RankNet, in stock selection processes and test long-only and long-short portfolio selection strategies using 10 years of market and news sentiment data. Through backtesting of these strategies from 2006 to 2014, we demonstrate that our portfolio strategies produce risk-adjusted returns superior to the S&P500 index return, the hedge fund industry average performance-HFRIEMN, and some sentiment-based approaches without learning-to-rank algorithm during the same period.

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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 andOdean (2008), diBartolomeo andWarrick (2005), Mitra et al (2009), andDzielinski et al (2011)). Cahan et al (2009), andHafez andXie (2012), used RavenPack data to examine diversification benefits.…”

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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Making covariance-based portfolio risk models sensitive to the rate at which markets reflect new information

Cited by 14 publications

References 19 publications

Daily market news sentiment and stock prices

Daily market news sentiment and stock prices

Stock portfolio selection using learning-to-rank algorithms with news sentiment

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

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