Constructing a Dictionary for Financial Stability

Correa, Ricardo; Garud, Keshav; Londono-Yarce, Juan-Miguel; Mislang, Nathan

doi:10.17016/2573-2129.33

Cited by 11 publications

(29 citation statements)

References 7 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%

“…We use Correa et al (2017a) nancial dictionary, which was built using words from the Financial Stability Reports (FSRs) of 64 institutions published between 2000 and 2015. The dictionary is a renement of general dictionaries and nancial specic dictionaries proposed in the literature.…”

Section: Dictionary With Word Polaritiesmentioning

confidence: 99%

“…Although Correa et al (2017a) tailored their dictionary (from now on, CKJM dictionary) to assess sentiment in a nancial stability context, we cannot use it as it is in our sentiment analysis, for three reasons. First, the FSRs of Banco de México are not included in their sample, so the vocabulary in our data may dier from that in the dictionary.…”

Section: Dictionary With Word Polaritiesmentioning

confidence: 99%

“…CKJM dictionary is specically tailored for the context and structure of FSRs and Correa et al (2017a) highlight the importance of adapting a dictionary to the specic context where the text analysis will be performed. Although we focus our search on reliable sources and we expect well written tweets, we acknowledge that news reported on Twitter regarding the nancial sector may be dierent from what is reported in an FSR.…”

Section: Dictionary With Word Polaritiesmentioning

confidence: 99%

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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: Dictionary With Word Polaritiesmentioning

confidence: 99%

Section: Dictionary With Word Polaritiesmentioning

confidence: 99%

Section: Dictionary With Word Polaritiesmentioning

confidence: 99%

See 2 more Smart Citations

A Sentiment-based Risk Indicator for the Mexican Financial Sector

Rho¹,

Fernández²,

Palma³

2021

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show abstract

“…We complement the dictionary introduced byCorrea et al (2017), which can be used to analyze central bank communication, but is focused on the discussion of financial stability topics.…”

mentioning

confidence: 99%

How Much Information Do Monetary Policy Committees Disclose? Evidence from the FOMC's Minutes and Transcripts

Apel

Grimaldi²,

Hull

2019

SSRN Journal

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The purpose of central bank minutes is to give an account of monetary policy meeting discussions to outside observers, thereby enabling them to draw informed conclusions about future policy. However, minutes are by necessity a shortened and edited representation of a broader discussion. Consequently, they may omit information that is predictive of future policy decisions. To investigate this, we compare the information content of the FOMC's minutes and transcripts, focusing on three dimensions which are likely to be excluded from the minutes: 1) the committee's degree of hawkishness; 2) the chairperson's degree of hawkishness; and 3) the level of agreement between committee members. We measure committee and chairperson hawkishness with a novel dictionary that is constructed using the FOMC's minutes and transcripts. We measure agreement by performing deep transfer learning, a technique that involves training a deep learning model on one set of documents -U.S. congressional debates -and then making predictions on another: FOMC transcripts. Our findings suggest that transcripts are more informative than minutes and heightened committee agreement typically precedes policy rate increases.

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Making text count: Economic forecasting using newspaper text

Kalamara

Turrell²,

Redl

et al. 2022

J of Applied Econometrics

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Summary This paper examines several ways to extract timely economic signals from newspaper text and shows that such information can materially improve forecasts of macroeconomic variables including GDP, inflation and unemployment. Our text is drawn from three popular UK newspapers that collectively represent UK newspaper readership in terms of political perspective and editorial style. Exploiting newspaper text can improve economic forecasts both unconditionally and when conditioning on other relevant information, but the performance of the latter varies according to the method used. Incorporating text into forecasts by combining counts of terms with supervised machine learning delivers the highest forecast improvements relative to existing text‐based methods. These improvements are most pronounced during periods of economic stress when, arguably, forecasts matter most.

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Constructing a Dictionary for Financial Stability

Cited by 11 publications

References 7 publications

A Sentiment-based Risk Indicator for the Mexican Financial Sector

A Sentiment-based Risk Indicator for the Mexican Financial Sector

How Much Information Do Monetary Policy Committees Disclose? Evidence from the FOMC's Minutes and Transcripts

Making text count: Economic forecasting using newspaper text

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