Measuring bank efficiency: DEA application

The paper aims to improve the methodology of measuring efficiency of Latvian banks. Efficiency scores were calculated with application of non-parametric frontier technique Data Envelopment Analysis (DEA). Input-oriented DEA model under Variable Returns to Scale (VRS) assumption was used. Potential model variables were selected based on the intermediation and profitability approach. Fourteen alternative models with different inputs-outputs combinations were developed for the research purposes. To substantiate the variables selection for DEA model the received data was processed, using such methods, as correlation analysis, linear regression analysis, analysis of mean values, and two-samples Kolmogorov-Smirnov test. The research results assisted the authors in providing general recommendations about the variables selection for DEA application in the Latvian banking sector. The present research contributes to the existing analytical data on bank performance in Latvia. The empirical findings provide a background for further studies, in particular, the efficiency of Latvian banks could be analysed in the extended time period

Dataset from the US Peer-to-peer Lending Platform with Macroeconomic Variables

We aggregate the United States (US) state-level data with LendingClub's loan book covering the period from 2008–2019. LendingClub is a FinTech lending company that provides loans through technology-driven platform. It was one of the pioneering and leading US peer-to-peer (P2P) lending platforms. Our dataset consists of over two million observations (N=2,703,430) with diverse loan, borrowers and state specific features. We provide the description of variables, descriptive statistics, and STATA code with the full dataset. The US possesses significant cross-state variation in terms of economic and demographic characteristics while having risk-sharing policies in the federal level to protect states' creditworthiness. This unique feature of our combined database creates an ideal opportunity to explore the P2P lending market within the context of macroeconomic variables. As the dataset covers 12-year period for all US states, it enables further cross-sectional and longitudinal analyses of FinTech lending market

Liquidity Risk in FinTech Lending

Liquidity Risk in FinTech LendingTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Dataset from the US Peer-to-peer Lending Platform with Macroeconomic Variables

We aggregate the United States (US) state-level data with LendingClub's loan book covering the period from 2008–2019. LendingClub is a FinTech lending company that provides loans through technology-driven platform. It was one of the pioneering and leading US peer-to-peer (P2P) lending platforms. Our dataset consists of over two million observations (N=2,703,430) with diverse loan, borrowers and state specific features. We provide the description of variables, descriptive statistics, and STATA code with the full dataset. The US possesses significant cross-state variation in terms of economic and demographic characteristics while having risk-sharing policies in the federal level to protect states' creditworthiness. This unique feature of our combined database creates an ideal opportunity to explore the P2P lending market within the context of macroeconomic variables. As the dataset covers 12-year period for all US states, it enables further cross-sectional and longitudinal analyses of FinTech lending market.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Liquidity Risk in FinTech Lending

Liquidity Risk in FinTech LendingTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Dea Application In Banking: Relationship Between Efficiency Scores And Bank Size

The paper aims to investigate the relationship between banks’ size and efficiency scores. The study applies Data Envelopment Analysis (DEA) as a tool for measuring bank relative efficiency. Sample includes European banks and the data for the analysis was extracted form BankScope data base. Intermediation approach to banking has taken as a conceptual basis for the choice of variables. Input-oriented DEA model under the assumption of variable returns to scale (VRS) was applied. Besides, the relationship between bank efficiency scores and traditional performance ratios was examined. The paper contributes to the existing literature, filling the gap in regard to bank efficiency measuring in new member states of the European Union