Applications of news analytics in finance: A review

Mitra, Leela; Mitra, Gautam

doi:10.1002/9781118467411.ch1

Cited by 20 publications

(11 citation statements)

References 46 publications

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“…Tetlock, Saar‐Tsechansky, and Macskassay () show that the number of negative words forecasts negative earnings, and news stories that focus on fundamentals are more informative than other articles. For a comprehensive treatment of news analytics, see Mitra and Mitra ().…”

Section: Text Analyticsmentioning

confidence: 99%

The future of fintech

Das

2019

Financial Management

133

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This article describes the growing field of financial technology (fintech) and the different financial paradigms and technologies that support it. Fintech is primarily a disintermediation force where disruptive technologies are the drivers. This framework discusses 10 primary areas in fintech comprising a taxonomy, which categorizes research in the field and also proposes a pedagogical structure. Pitfalls of fintech are also analyzed. Overall, the great strides made in computing technology, mathematics, statistics, psychology, econometrics, linguistics, cryptography, big data, and computer interfaces have combined to create an explosion of fintechs.

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Section: Text Analyticsmentioning

confidence: 99%

The future of fintech

Das

2019

Financial Management

133

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“…(ii) H2: We also hypothesize similar clustering effects for news sentiment events (sentiment self-excitation): positive to positive sentiment and negative to negative sentiment. This is because sentiment is a reflection of social belief and it is not unusual to believe investors hold 'me-too' kind of mentality to respond to news shocks, especially towards to the release of extreme news articles (Mitra and Mitra 2011). (iii) H3: Mutual-excitation between positive return and negative return can be predicted and we think they are often associated with bubbles and crashes, particularly, when market corrects quickly from price shocks (Daniel et al 1998).…”

Section: Introductionmentioning

confidence: 98%

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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“…See the Handbook of News Analytics in Finance(Mitra & Mitra, 2011) for further details on TRNA data set…”

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confidence: 99%