Avoiding financial instability: essays on bank performance and riskiness
Notice bibliographique
Résumé
Financial stability is the ability of capital markets to perform their essential function, which is to channel funds to entities that have productive investments.Different issues regarding bank performance and riskiness cause friction in the financial intermediation process, shattering financial stability and increasing the chance of a financial crisis.As the maintenance of a sound financial system is important for economic development, we developed three essays covering gaps in the literature on bank riskiness and performance evaluation, whose correct understanding is important for a sound financial system.Firstly, we focus on bank opacity and evaluate whether macroeconomic variables can improve the forecast of the financial performance of banks by using accruals-based measures of banking performance (net-interest income, non-interest income, and loan loss provision) and the novel cash flow-based measures that act as a proxy of financial intermediation (credit and liability cash flow).The results from out-of-sample forecasts indicate that the macro variables can be used to forecast financial performance only when the cash flow-based measures are used to measure banking performance, reinforcing the importance of cash flow, which has been neglected by the banking literature for bank evaluation.The second essay analyses what is at stake with the banking system as banks are on the brink of losing non-interest income due to an increase in competition from fintechs.We show the relevance of non-interest income for banking profitability and if there is a compensatory effect to financial intermediation earnings in relation to bank profitability, which smooths earnings in economic downside, helping, thus, financial stability.Our findings suggest that non-interest income positively impacts bank profitability, decreases bank riskiness, and presents a compensatory effect to financial intermediation earnings in relation to bank profitability.Lastly, we find that non-interest income is more relevant to profitability than financial intermediation earnings for large banks.For the small banks, financial intermediation earnings are more relevant, which shows that larger banks shall be, at first, the most affected by the potential loss of non-interest income.The third essay evaluates whether banks act in a forward-looking way by increasing expected loss provision when there is contemporaneous loan growth.As accounting regulations around the world changed in later years to account for foreseeable credit risk; thus, it is crucial to assess whether the increase in bank riskiness with new loans is softened by a concomitant increase in expected loss provision.The results indicate that contemporaneous loan growth increases bank riskiness, but banks increase expected loss provisions respectively, which shows they act prudently regarding provisioning, benefiting, thus, financial stability.In addition, it was found that when loan growth occurs during higher financial uncertainty times, banks allocate more expected loss provisions to account for an increase in credit risk.Lastly, as the Brazilian banking industry is heterogeneous, we find that small banks set higher expected loss provisions than larger banks for a given increase in the loan portfolio.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,005 | 0,008 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».