Sentiment in Central Banks' Financial Stability Reports

Correa, Ricardo; Garud, Keshav; Londoño, Juan M.; Mislang, Nathan

doi:10.2139/ssrn.3091943

Cited by 9 publications

(32 citation statements)

References 32 publications

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“…Bruno et al (2018b) build a dictionary to analyze sentiment in Italian texts, while Bruno et al (2018a) apply the same dictionary to tweets about selected Italian banks extracting sentiment indicators and relate them to some banks' nancial variables, nding a positive correlation between them and the sentiment for some of the banks in their sample. Correa et al (2017;2017a) also apply sentiment analysis to the central bank's Financial Stability Reports. In particular, they analyze the relationship between the nancial cycle and the sentiment conveyed in these ocial publications.…”

Section: Big Data Analysis In Central Banksmentioning

confidence: 99%

“…Our paper builds on the work by Correa et al (2017), and it explores alternative techniques that may be suitable for sentiment analysis in social media. We apply the model of neural networks and transfer learning developed by Howard and Ruder (2018) and the Baseline for Multilingual Sentiment Analysis (b4msa) model proposed by Tellez et al (2017).…”

Section: Big Data Analysis In Central Banksmentioning

confidence: 99%

“…Our rst approach replicates the Correa et al (2017) methodology based on a previously built nancial dictionary with word polarities. This methodology works through word counts.…”

Section: Sentiment Classiersmentioning

confidence: 99%

“…The use of a set dictionary does not allow an evolution of the language or the incorporation of new topics in the discussion. This model is better suited to analyze documents with a specic structure, that does not change in the short run, such as the policy reports of Central banks (Correa et al, 2017;Moreno Bernal and González Pedraz, 2020), or the minutes of institutional meetings (Hansen et al, 2018), although there are also examples of dictionary approaches used to analyze twitter messages (Bruno et al, 2018b).…”

Section: Sentiment Classiersmentioning

confidence: 99%

“…Once the tweets are classied, the Twitter sentiment index can be built. We base our methodology on Correa et al (2017).…”

Section: Twitter Sentiment Indexmentioning

confidence: 99%

See 4 more Smart Citations

A Sentiment-based Risk Indicator for the Mexican Financial Sector

Rho¹,

Fernández²,

Palma³

2021

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We apply text analysis to Twitter messages in Spanish to build a sentiment- based risk index for the financial sector in Mexico. We classify a sample of tweets for the period 2006-2019 to identify messages in response to positive or negative shocks to the Mexican financial sector. We use a voting classifier to aggregate three different classifiers: one based on word polarities from a pre-defined dictionary; one based on a support vector machine; and one based on neural networks. Next, we compare our Twitter sentiment index with existing indicators of financial stress. We find that this novel index captures the impact of sources of financial stress not explicitly encompassed in quantitative risk measures. Finally, we show that a shock in our Twitter sentiment index correlates positively with an increase in financial market risk, stock market volatility, sovereign risk, and foreign exchange rate volatility

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Section: Big Data Analysis In Central Banksmentioning

confidence: 99%

Section: Big Data Analysis In Central Banksmentioning

confidence: 99%

“…Our rst approach replicates the Correa et al (2017) methodology based on a previously built nancial dictionary with word polarities. This methodology works through word counts.…”

Section: Sentiment Classiersmentioning

confidence: 99%

Section: Sentiment Classiersmentioning

confidence: 99%

“…Once the tweets are classied, the Twitter sentiment index can be built. We base our methodology on Correa et al (2017).…”

Section: Twitter Sentiment Indexmentioning

confidence: 99%

See 3 more Smart Citations

A Sentiment-based Risk Indicator for the Mexican Financial Sector

Rho¹,

Fernández²,

Palma³

2021

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Spread the Word: International spillovers from central bank communication

Armelius

Bertsch

Hull

et al. 2020

Journal of International Money and Finance

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We use computational linguistic methods and a novel dataset to measure the sentiment component of central bank communications in 23 countries over the 2002-2016 period. We first construct a Granger causality network to identify how sentiment is transmitted across central banks. The network structure suggests that comovement in sentiment is not reducible to comovement in output across countries. We also show that some central banks in the network, such as the Federal Reserve and the Bundesbank, tend to cause sentiment shifts in other central banks; whereas other central banks, such as the European Central Bank and the Bank of Japan, tend to be shifted by other central banks. Finally, we use a structural VAR to demonstrate that sentiment shocks generate crosscountry spillovers in sentiment, policy rates, and real variables.

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Financial stability and the Fed: Evidence from congressional hearings

2021

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This article retraces how financial stability considerations interacted with US monetary policy before and during the Great Recession. Using text-mining techniques, this article innovates by constructing indicators for financial stability sentiment expressed during testimonies of five Federal Reserve Chairs.Including these text-based measures adds explanatory power to Taylor-rule models. Negative financial stability sentiment coincided with a more accommodative monetary policy stance than implied by standard Taylor-rule factors, even during the decades before the Great Recession. These findings are consistent with a preference for monetary policy reacting to financial instability rather than acting pre-emptively to a perceived build-up of risks.

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Sentiment in Central Banks' Financial Stability Reports

Cited by 9 publications

References 32 publications

A Sentiment-based Risk Indicator for the Mexican Financial Sector

A Sentiment-based Risk Indicator for the Mexican Financial Sector

Spread the Word: International spillovers from central bank communication

Financial stability and the Fed: Evidence from congressional hearings

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