Assessing News Contagion in Finance

Cerchiello, Paola; Nicola, Giancarlo

doi:10.3390/econometrics6010005

Cited by 24 publications

(12 citation statements)

References 31 publications

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“…More data can offer other types of information not available in the data at hand. Moreover the application of other text analysis technique like topic modeling (Cerchiello and Nicola, 2018) rather than the creation of manual dictionaries to make the process more automated and therefore decrease the time spent in preprocessing, can improve even more the quality of the analysis.…”

Section: Discussionmentioning

confidence: 99%

On the Improvement of Default Forecast Through Textual Analysis

Cerchiello

Scaramozzino

2020

Front. Artif. Intell.

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Textual analysis is a widely used methodology in several research areas. In this paper we apply textual analysis to augment the conventional set of account defaults drivers with new text based variables. Through the employment of ad hoc dictionaries and distance measures we are able to classify each account transaction into qualitative macro-categories. The aim is to classify bank account users into different client profiles and verify whether they can act as effective predictors of default through supervised classification models.

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Section: Discussionmentioning

confidence: 99%

On the Improvement of Default Forecast Through Textual Analysis

Cerchiello

Scaramozzino

2020

Front. Artif. Intell.

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“…However, the second option is rarely feasible because in order to fit a good classifier, a huge amount of pre-classified examples is needed and this represents a particularly complicated task when dealing with short and extremely non conventional text like micro-blogging chats (Cerchiello and Nicola, 2018). Insofar, we decided to focus on a dictionary based approach, adapting appropriate lists of positive and negative words relevant to ICOs topics in English language.…”

Section: Methodsmentioning

confidence: 99%

Initial Coin Offerings: Risk or Opportunity?

Toma

Cerchiello

2020

Front. Artif. Intell.

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Initial coin offerings (ICOs) are one of the several by-products in the world of the cryptocurrencies. Start-ups and existing businesses are turning to alternative sources of capital as opposed to classical channels like banks or venture capitalists. They can offer the inner value of their business by selling "tokens," i.e., units of the chosen cryptocurrency, like a regular firm would do by means of an IPO. The investors, of course, hope for an increase in the value of the token in the short term, provided a solid and valid business idea typically described by the ICO issuers in a white paper. However, fraudulent activities perpetrated by unscrupulous actors are frequent and it would be crucial to highlight in advance clear signs of illegal money raising. In this paper, we employ statistical approaches to detect what characteristics of ICOs are significantly related to fraudulent behavior. We leverage a number of different variables like: entrepreneurial skills, Telegram chats, and relative sentiment for each ICO, type of business, issuing country, team characteristics. Through logistic regression, multinomial logistic regression, and text analysis, we are able to shed light on the riskiest ICOs.

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“…Social media is rapidly replacing non-digital media as a convenient and fast medium for communicating and sharing information, allowing users to interactively express their one-sided views on the housing market. The role of social media in sentiment detection has received increasing attention from researchers (Cerchiello & Nicola, 2018). Social media data have the characteristics of full samples, high frequency, and immediacy, and a sentiment index based on social media data is highly representative and forward-looking (Da et al, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, existing housing-related research on market sentiment is seriously lacking, most existing studies have relied on indirect indicators such as relatively lagging macroeconomic data, and the use of social media data, which can indicate the public's immediate attitudes toward the latest information, is still uncommon. The impact of this data source on home-buyer sentiment is evident in the modern information society (Cerchiello & Nicola, 2018). Sinyak et al (2021) found that social media of the real estate market can be considered a valuable and innovative source of market sentiment and can provide real estate researchers and valuers with reliable leading market indicators.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Effect of Market Sentiment on Housing Price: Evidence From Social Media Data in China

Wang

Liu

2022

International Journal of Strategic Property Management

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Market sentiment has become more easily spread between cities through social media. This study investigates the spatial effect of market sentiment on housing price in a social media environment. In order to extract home-buyer sentiment from social media, we use text sentiment analysis techniques and build a novel housing market sentiment index. A spatial econometric model of housing price volatility is subsequently constructed and the housing market sentiment index is included as an independent variable in the model. Using panel data from 30 large and medium-sized cities in China for 20 quarters from 2016 to 2020, the spatial effect of market sentiment on housing price is empirically analyzed by calculating direct and indirect effects. The results show that market sentiment had a significant positive effect on housing prices in the local and neighboring cities over the research period. However, the impact of market sentiment on housing price was heterogeneous in terms of geographical region; the direct effect was stronger in the eastern region than in the central and western regions, and the indirect effect was significant only in the eastern region. These findings can provide references for government to formulate housing market regulation policies and measures based on market sentiment.

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Assessing News Contagion in Finance

Cited by 24 publications

References 31 publications

On the Improvement of Default Forecast Through Textual Analysis

On the Improvement of Default Forecast Through Textual Analysis

Initial Coin Offerings: Risk or Opportunity?

Spatial Effect of Market Sentiment on Housing Price: Evidence From Social Media Data in China

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