Economic stimulus through bank regulation: Government responses to the COVID-19 crisis

Polyzos, Stathis; Samitas, Aristeidis; Kampouris, Elias

doi:10.1016/j.intfin.2021.101444

Cited by 28 publications

(18 citation statements)

References 111 publications

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“…In the cryptocurrency field, machine learning-based approaches, such as SVM and RF, are used for trading strategies (Sebastiao and Godinho 2021 ). RF and long short-term memory, which is a deep learning method, are combined to analyze the effect of COVID-19 on bank regulations (Polyzos et al 2021 ). We explained various machine learning regression methods used in this study in the following.…”

Section: Methodsmentioning

confidence: 99%

Predicting cash holdings using supervised machine learning algorithms

Özlem

Tan

2022

Financ Innov

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This study predicts the cash holdings policy of Turkish firms, given the 20 selected features with machine learning algorithm methods. 211 listed firms in the Borsa Istanbul are analyzed over the period between 2006 and 2019. Multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), decision trees (DT), extreme gradient boosting algorithm (XGBoost) and multi-layer neural networks (MLNN) are used for prediction. Results reveal that MLR, KNN, and SVR provide high root mean square error (RMSE) and low R2 values. Meanwhile, more complex algorithms, such as DT and especially XGBoost, derive higher accuracy with a 0.73 R2 value. Therefore, using advanced machine learning algorithms, we may predict cash holdings considerably.

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Section: Methodsmentioning

confidence: 99%

Predicting cash holdings using supervised machine learning algorithms

Özlem

Tan

2022

Financ Innov

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“…This means that regulation leads to a lower level of resources available for economic activities. In this context, Polyzos et al (2021) have suggested relaxing bank regulations. However, Montgomery (2005) has found that the regulatory capital ratio does not have any effect on bank portfolios.…”

Section: Source(s): Gmpi Imf Databasementioning

confidence: 99%

“…This means that regulation leads to a lower level of resources available for economic activities. In this context, Polyzos et al. (2021) have suggested relaxing bank regulations.…”

Section: Background Literaturementioning

confidence: 99%

“…After facing the COVID-19 pandemic, banks must be prepared for very difficult scenarios ahead (Elnahass et al, 2021) and these findings will provide help. Further, an important gap has been filled by this study because effects of the coronavirus pandemic on the banking industry have not been analyzed thoroughly (Polyzos et al, 2021). As far as the authors know, such a model has not been analyzed in the context of Balkan states by any past study.…”

Section: Introductionmentioning

confidence: 99%

“…, 2021) and these findings will provide help. Further, an important gap has been filled by this study because effects of the coronavirus pandemic on the banking industry have not been analyzed thoroughly (Polyzos et al. , 2021).…”

Section: Introductionmentioning

confidence: 99%

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Does the market discipline banks? Evidence from Balkan states

Afzal

Firdousi

2022

JRF

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PurposeThis research is designed to investigate the presence of market discipline in the banking sector, across Balkan states in Europe. Specifically, the effects of CAMEL variables on the cost of funds and deposit-switching have been assessed.Design/methodology/approachThe CAMEL method of bank evaluation has been applied as well as two measures for market discipline (costs of funds and deposit-switching behaviour). Data have been obtained for 10 Balkan states for the 2006–2019 period. For data analysis, ordinary least squares (OLS) and fixed effects models have been utilized. The generalized method of moments (GMM) method has been deployed as well as a dynamic panel model.FindingsEvidence of market discipline has been found, in the form of a higher cost of funds in the context of capital adequacy (but not for other CAMEL variables). Evidence of market discipline in the form of deposit-switching, however, has not been found. In addition, it has been discovered that bank size and gross domestic product (GDP) growth lower the costs of funds for banks.Originality/valueIn the wake of the pandemic, banks need to prepare themselves for very difficult situations and relevant studies can provide help. Therefore, this research has contributed to the developing literature on this topic. In addition, the findings have important practical implications. Results show that banks should maintain adequate levels of capital if they want to control their costs of funds. Results also show that market discipline, in the form of higher costs of funds, can be imposed on banks to discourage excessive risk-taking. Findings highlight the value of appropriate policies and strong supervision of the financial industry. Findings also underline the importance of offering financial incentives to banks. For example, if banks know they will be able to avoid higher costs of funds by controlling their risk levels, they will avoid unrestrained risk-taking.

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The resource reallocation effect of monetary policy

Zhang

Shi

2022

Int. J Fin Econ

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This paper studies the heterogeneous responses of firms to monetary policy shocks and the possible adverse effect of expansionary monetary policy on resource reallocation. Using Chinese firm‐level data, we find that when expansionary monetary shock hits the economy, less productive firms are more responsive than productive firms in terms of resource accumulation. We identify two mechanisms for this effect. First, less productive firms accumulate net worth more slowly. They have higher leverage ratio and are more sensitive to monetary policy changes (the intensive margin). Second, monetary expansion relaxes borrowing constraints, which enables unproductive firms to survive. The congestion effect prevents resources from allocating to more productive firms. Our finding is robust even if we use private firm subsamples or avoid the massive fiscal stimulus in China around 2009. We provide a simple model of firm dynamics to capture these effects.

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Economic stimulus through bank regulation: Government responses to the COVID-19 crisis

Cited by 28 publications

References 111 publications

Predicting cash holdings using supervised machine learning algorithms

Predicting cash holdings using supervised machine learning algorithms

Does the market discipline banks? Evidence from Balkan states

The resource reallocation effect of monetary policy

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