The COVID-19 shock and consumer credit: Evidence from credit card data

Horváth, Ákos; Kay, B. P.; Wix, Carlo

doi:10.1016/j.jbankfin.2023.106854

Cited by 13 publications

(2 citation statements)

References 49 publications

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“…In response to the negative public perception regarding the use of credit cards, stakeholders should conduct an evaluation of the performance and development of credit cards with the aim of introducing products that provide benefits and are friendly to all segments of society. Empirical findings indicate that the use of credit cards has led to increased consumer spending behavior and exacerbated credit card debt levels during the Covid-19 pandemic (Horvath et al, 2023). One of the recurring issues posing a threat to the sustainability of credit cards is the potential for financial fraud and credit card forgery (Rai & Dwivedi, 2020).…”

Section: Discussionmentioning

confidence: 99%

Twitter Sentiment Analysis on Credit Card

Syahdatul Maulida,

Aam Slamet Rusydiana

2022

MSR

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This research aims to evaluate the public's response to the global development of credit cards using primary data from Twitter tweets between January 1, 2019, and March 28, 2023. The research method employed is a qualitative approach using Python Library software called VADER (Valence Aware Dictionary and Sentiment Reasoner) to classify sentiments in these tweets. The research results indicate that the majority of the public has a neutral sentiment, accounting for approximately 97.6% towards credit cards, while positive sentiment reaches 1.7%, and negative sentiment is approximately 0.7%. Some keywords frequently appearing in the tweets include credit card, card needed, ASTRA Coins, card debt, and mobile users. It is expected that this research on public sentiment can assist relevant stakeholders in taking appropriate steps to enhance credit card usage and increase awareness and support for it. We also analyze the shariah perspective on credit card.

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Section: Discussionmentioning

confidence: 99%

Twitter Sentiment Analysis on Credit Card

Syahdatul Maulida,

Aam Slamet Rusydiana

2022

MSR

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“…Gönül & Öztekin (2021) explain that declining economic performance will also make the credit decline depressed. The economic downturn makes companies reduce their production and from the public side hold back their consumption, which ultimately leads to a decrease in lending (Horvath et al, 2021).…”

Section: Credit Demandmentioning

confidence: 99%

Credit Crunch and Monetary Policy During Covid-19 Pandemic

HS,

Sugiharso Safuan

2023

JDE

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Under crisis conditions, the significant decline in bank credit growth is associated with the credit crunch phenomenon. The ability of the banking system to provide credit in the economy is limited compared to the demand for credit. During the Covid-19 pandemic, credit growth in Indonesia reached its lowest point when compared to the pre-Covid-19 period. However, the causative factor is still ambiguous. Using a credit market disequilibrium model estimated with Maximum Likelihood, this study tested whether the decline in credit during the Covid-19 pandemic was a credit crunch phenomenon. The results of this study show that the parameter of the probability of credit decline during the Covid-19 pandemic is an insignificant credit crunch phenomenon. This means that the estimated demand for credit is less than the excess supply. Thus, the implications for the role of monetary policy by lowering interest rates have been hampered due to the decline in economic activity during the Covid-19 pandemic.

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Credit card loss forecasting: Some lessons from COVID

Sengupta,

Wheeler

2024

Journal of Forecasting

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Models developed by banks to forecast losses in their credit card portfolios have generally performed poorly during the COVID‐19 pandemic, particularly in 2020, when large forecast errors were observed at many banks. In this study, we attempt to understand the source of this error and explore ways to improve model fit. We use account‐level monthly performance data from the largest credit card banks in the U.S. between 2008 and 2018 to build models that mimic the typical model design employed by large banks to forecast credit card losses. We then fit these on data from 2019 to 2021. We find that COVID‐period model errors can be reduced significantly through two simple modifications: (1) including measures of the macroeconomic environment beyond indicators of the labor market, which served as the primary macro drivers used in many pre‐pandemic models and (2) adjusting macro drivers to capture persistent/sustained changes, as opposed to temporary volatility in these variables. These model improvements, we find, can be achieved without a significant reduction in model performance for the pre‐COVID period, including the Great Recession. Moreover, in broadening the set of macro influences and capturing sustained changes, we believe models can be made more robust to future downturns, which may bear little resemblance to past recessions.

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The COVID-19 shock and consumer credit: Evidence from credit card data

Cited by 13 publications

References 49 publications

Twitter Sentiment Analysis on Credit Card

Twitter Sentiment Analysis on Credit Card

Credit Crunch and Monetary Policy During Covid-19 Pandemic

Credit card loss forecasting: Some lessons from COVID

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