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A simulation methodology is applied to the loan loss reserve process of an agricultural lender. Weaknesses of the point‐estimate approach to estimating loan loss reserves are addressed with a “bottom‐up” model. Modeling includes consideration of the producer’s and the lender’s diversification efforts. Implementation of this model will provide the lender a better understanding of the institution’s portfolio risk, as well as the credit risk associated with each loan. This study compares the lender’s loan loss estimates to a distribution of losses with associated probabilities. The comparative results could provide the lender a basis for setting probability levels for determining the regulatory required level of loan loss reserve.

This paper aims to examine the post-merger changes in the credit risk profile of merging bank holding companies and tests whether there is an increase in credit risk after a merger due to changes in the mix of loans in the portfolio.

The authors use the expected variability of the credit risk of a loan portfolio based on the mix of loan types in the portfolio and the variability of the industry credit losses of each type following the standard Markowitz procedure for finding the standard deviation of an investment portfolio. The authors then test to see whether there has been a significant change in the expected variability (the credit risk profile) after a merger.

The authors find that there are significant differences in both the level and variability of loan charge-offs and non-performing loans (NPL) among the various loan categories. The authors also find significant changes in the mix of loan categories in the loan portfolio after a merger. In addition, the authors find that the expected variability in both the charge-off rate and the NPL rate rises significantly after a merger.

This is the first of two papers looking at post-merger changes in credit risk based simply on the changes in the mix of loan types; it does not consider the actual post-merger credit performance of the specific mergers. That will be addressed in a subsequent paper.

Financial analysts evaluating banking merger announcements may wish to include the impact of the likely shifts in loan mix and credit risk shown in this paper as they project the likely impact of the merger.

This paper addresses an aspect of bank mergers that has not been addressed in the literature, the impact of mergers on credit risk. The results are likely to be useful to investors, financial analysts and regulators.

The purpose of this paper is to test whether diversification of credit portfolios across economic sectors leads to improved profitability and reduced credit risks for Ghanaian banks that have been characterized by high non-performing loans in recent times (IMF, 2011).

Static and dynamic estimations, namely Prais-Winsten, fixed and random effect estimators, feasible generalized least squares as well as the system generalized methods of moments are employed on the annual data of 30 Ghanaian banks that operated between 2007 and 2014 to determine the effect of loan portfolio diversification on bank performance.

The study shows that loan portfolio diversification does not improve banks’ profitability nor does it reduce banks’ credit risks.

The study focuses on a single banking system in Africa largely as a result of data limitation.

The study emphasizes the need for banks to perform a careful assessment of the effects of their lending policies geared toward increased sectoral diversification on their monitoring efficiency and effectiveness. A further investment in loan screening and monitoring is necessary to minimize credit risks.

This study is the first to present empirical evidence on the effects of loan portfolio diversification on bank performance in an emerging banking market in Africa.

The purpose of this paper is to determine the impact of loan concentration on the returns of Indonesian banks and examines whether bank ownership types affect the relationship between concentration and returns.

This research uses heuristic measures of concentration: The Hirschman–Herfindahl index and Deviation from Aggregated Averages are applied to Indonesian banks across all sectors. The data covers the pre and post global financial crises periods from 2003-2011 for 109 commercial banks in Indonesia. Panel feasible generalised least squares analysis was applied.

The findings show that loan concentration increases bank returns. The positive effect of concentration on returns tends to be more significant for domestic-owned banks. In addition, the interaction effect shows that the positive effect of concentration on returns is less for foreign-owned banks.

The Indonesian central bank changes to the reporting format of sectoral loan allocation by banks since 2012 in terms of the Indonesian Banking Statistics Details of Enhancement matrix requires separate data analysis for 2012 onwards. The findings of this paper could be enhanced by more detailed data like interest rate expenses and bank level sectoral non-performing loans data.

The findings suggest that a focus strategy provides better returns. Moreover, bank ownership types is an important factor to consider when setting a bank lending policy.

This paper is among the few studies where different measures of loan concentration in combination with measures of return are applied in Indonesia as an emerging Asian country. The research also provides evidence of the impact of concentration on the interest earnings of the loan portfolios of banks in addition to return on assets and return on equity that are generally applied as measures of return in previous research.

Keeping in view the robust growth of Islamic banking around the globe, this study aims to comparatively analyze the association between liquidity creation and liquidity risk for Islamic banks (IBANs) and conventional banks (CBANs) in Pakistan and Malaysia over a period of 2004–2021. The moderating role of bank loan concentration on the aforementioned relationship is also studied.

Regression estimation methods such as fixed effect, random effect and generalized least square are deployed for obtaining results. Liquidity creation Burger Bouwman measure (cat fat and noncat fat) and Basel-III liquidity risk measure (liquidity coverage ratio) are also used.

The results give us insight that liquidity creation is positively and significantly related to liquidity risk in both IBANs and CBANs of Pakistan and Malaysia. This relationship has been moderated negatively (reversed) and significantly by credit concentration showing the importance of risk management and loan portfolio concentration.

It is analyzed that during the process of liquidity creation, IBANs in Pakistan faced more liquidity risk for both on and off-balance sheet transactions in the presence of moderation of loan concentration than IBANs in Malaysia necessitating strategic policy-making for important aspects of liquidity risk management and loan concentration while creating liquidity.

Such studies comparing IBANs and CBANs comparison keeping in view liquidity creation, liquidity risk and loan concentration are either limited or nonexistent.

In 2009, effective the second-quarter, the financial accounting standards board mandated that all banks need to disclose the fair value of loans in their 10-Q filings in addition to their 10-K filings. This paper aims to investigate whether these disclosures reduced the level of information asymmetry about the riskiness of bank loan portfolios during the financial crisis.

The paper examines the impact of these disclosures on the bid-ask spread of a panel of 246 publicly traded bank holding companies. The spread serves as a proxy for information asymmetry and the ratio of the fair value of a bank’s loan portfolio to its book value is a proxy for the credit and liquidity risk associated with the same. The reaction to the first-quarter filing serves as a control to assess the reaction at the time of the second-quarter filing.

There is a significant negative association between bid-ask spread and the ratio indicating that the fair value information was useful in reducing information asymmetry during the financial crisis. A pattern was observed in the information dissemination related to the fair value of loans that is consistent with the literature that documents a delayed investor reaction to complex financial information.

Investors may use the fair value information to better assess the risk profile of a BHC’s loan portfolio. Also, loan fair values provide managers with data to better implement stress test models and determine optimal capital buffers.

Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.

This paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.

The research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.

Unlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

Marketplace lending has substantially changed since the first peer-to-peer lending platforms emerged in 2006. The industry is now an alternative to bank lending, predicted to total $70 billion for consumer and business loans worldwide by 2030. Marketplace lending is often deemed less safe than bank loans, mainly due to these portfolios' high degree of hidden information. These include needing more information on borrowers and potential correlations between them, which might lead to higher risk than is apparent at first glance. Deterministic processes cannot capture tail risk appropriately, so platforms and lenders should employ stochastic processes. This chapter introduces a Monte Carlo simulation and machine learning (ML) process to evaluate and monitor portfolios. For marketplace lending to become a viable and sustainable alternative to bank lending platforms, they must better evaluate, monitor, and manage tail risk in marketplace loans and develop tools to monitor and manage financial risk losses.

The IRB capital requirements of Basel II define the minimum level of capital that the bank has to retain to cover the current risks of its portfolio. The major risk that many banks are facing is credit risk and Basel II provides an approach to calculate its capital requirement. It is well known that Pillar I Basel II approach for credit risk capital requirements does not include concentration risk. The paper aims to propose a model modifying Basel II methodology (IRB) to include name concentration risk.

The model is developed on data based on a portfolio of Greek companies that are financed by Greek commercial banks. Based on the initial portfolio, new portfolios were simulated having a range of different credit risk parameters. Subsequently, the credit VaR of various portfolios was regressed against the credit risk indicators such as Basel II capital requirements, modified Herfindahl Index and a non-linear model was developed. This model modifies the Pillar I IRB capital requirements model of Basel II to include name concentration risk.

As the Pillar I IRB capital requirements model of Basel II does not include concentration risk, the credit VaR calculations performed in the present work appeared to have gaps with the Basel II capital requirements. These gaps were more apparent when there was high concentration risk in the credit portfolios. The new model bridges this gap providing with a correction coefficient.

The credit VaR of a loan portfolio could be calculated from the bank easily, without the use of additional complicated algorithms and systems.

The model is constructed in such a way as to provide an approximation of credit VaR satisfactory for business loan portfolios whose risk parameters lie within the range of those in a realistic bank credit portfolio and without the application of Monte Carlo simulations.