News-Based Sentiment Indicators

Huang, Chengyu; Simpson, Sean; Ulybina, Daria; Roitman, Agustin

doi:10.2139/ssrn.3523146

Cited by 3 publications

(2 citation statements)

References 0 publications

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“…Furthermore, looking at the actual daily nowcasts in Figure 9, we observe that text is particularly helpful in predicting the start of the crisis in 2008 rather than the subsequent recovery. This provides supporting evidence that this alternative type of information is able to provide early warning signals of future economic disruptions (Nyman et al, 2018;Huang et al, 2019).…”

Section: Great Recession and Its Aftermathmentioning

confidence: 56%

Nowcasting Euro Area GDP with News Sentiment: A Tale of Two Crises

2021

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Section: Great Recession and Its Aftermathmentioning

confidence: 56%

Nowcasting Euro Area GDP with News Sentiment: A Tale of Two Crises

2021

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“…We begin our analysis by first constructing sentiment indices for the property market in Hong Kong using newspaper information 2 . Using textual information to compile sentiment index is a rapidly growing area (see e.g., Huang et al, 2019; Shapiro et al, 2020; Soo, 2018). For our purpose, we find it satisfactory to use a simple Boolean approach, similar to that of Soo (2018) and Gao and Zhao (2018).…”

Section: Methodsmentioning

confidence: 99%

A robust textual analysis of the dynamics of Hong Kong property market

Wong¹,

Kwong²,

Luk³

et al. 2022

Pacific Economic Review

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Market sentiments influence the dynamics of Hong Kong’s macro‐critical property market, but the unobservable nature of market sentiments makes it difficult to systemically assess this sentiment channel. Using text mining techniques, this paper sets up a news‐based property market sentiment index and a Google Trends‐based buyer incentive index for Hong Kong and studies the sentiment channel of transmission in the Hong Kong property market. The news‐based property market sentiment index can reflect the change in sentiments in past key events, with the sentiments in the primary market tending to lead that of the secondary market during the low housing supply period. For the Google Buyer Incentive Index, we find that it has value‐added in forecasting (or nowcasting) the official property price index. In mapping out the sentiment channel using a structural vector‐autoregressive model, we find that an improvement in market sentiments could stimulate buyers’ incentives, which then together would affect property prices and transaction volumes.

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