The Impact of the Sharing Economy on Household Bankruptcy

Powered by digital technologies, many peer-to-peer platforms, or what is called the sharing economy, have emerged in the past decade. Although the impact of the sharing economy has received considerable attention over the past few years, extant research has not fully documented the impact of the sharing economy on consumers, workers, industry, or society as a whole. In this study, we exploit the geographical and temporal variation in Uber’s entry to examine its impact on the personal bankruptcy rate as well as on other consumer credit default rates. We empirically document the changes in personal bankruptcy filings after Uber’s entry, and show that personal bankruptcy filings under Chapter 7 experience a drop of 0.047 per 1,000 people after Uber enters a county, which translates to a 3.26% reduction in quarterly bankruptcy filings. Uber’s entry also leads to a reduction in Chapter 13 personal bankruptcy filings, but to a smaller degree (0.018 cases per 1,000 people per quarter). We check the validity of our estimates using business bankruptcy filings, which we find are uncorrelated with Uber’s entry.

Faktor-Faktor Yang Mempengaruhi Tingkat Kebangkrutan Pada Perbankan Syariah Periode 2013-2019

This research was conducted using banking financial statements for seven years, namely the period 2013 - 2019 from banks engaged in the financial sector. The analytical method used is financial ratio analysis By using financial ratio analysis, it can be seen that the LDR, NPL, CAR and NIM affect the level of bankruptcy in banks this can occur due to internal factors contained in a bank. Then the conclusion is that the LDR, NPL, CAR and NIM affect the bank's bankruptcy level. It can be seen in the financial statements of the results of the LDR, NPL, CAR and NIM produced by banks increasing and decreasing during the research period, this has caused the bankruptcy rate of banks to also increase and decrease. The banking industry tries to maintain its Z-score by maintaining the value of the LDR, NPL, CAR and NIM variables on the financial statements, although the factors produced are not too large and even below average, the factors have increased. The number of factors in the financial statements that are affected by the level of financial ratios and income that has decreased due to banks that have not been able to emphasize their income. Banks conduct policies well by maintaining health and profit growth in terms of financial statements. Keywords: LDR, NPL, CAR, NIM and Bank's Bankruptcy Rate

Financial Distress and Forewarning Bankruptcy: An Empirical Analysis of Textile Sector in Pakistan

This study aims to measure the financial distress and forewarn bankruptcy in Textile Sector of Pakistan using popular statistical measures i.e., Z-Score, O-Score, Probit and D-Score. First, applicable financial ratios (profitability, liquidity, leverage, market ratios) and scores (Z-Score, O-Score, Probit and D-Score) of all 77 textile companies were calculated then estimated scores were compared with cut-off point of respective model. Based on findings, models are categorized in two groups: (a) Group-I (Z-Score and O-Score), (b) Group-II (Probit and D-Score). Results indicate that some of the textile firms are about to face financial distress in near future, which could ultimately lead those firms to bankruptcy. The findings of Group-I indicate that about 43% - 44% companies in the textile sector are in the phase of financial distress; whereas the results of Group-II reveal that about 8% - 16% companies are in financial distress phase. Thus, we could draw two conclusions: (1) the two models (Z-Score and O-Score) in Group-I were found to be robust for assessing financial distress and (2) the two models (Probit and D-Score) in Group-II were found to be less rigorous in forecasting financial distress. The previous studies attempted to compare the prediction accuracy of various models by examining the data of both financially distress firms and financially stable firms. But this study is aimed to foretell bankruptcy using comprehensive models (Z-Score, O-Score, Probit and D-Score), to compare the consistency of results across all four models of the study and to categorize financially stable and financially distress companies under each model. The findings of the study are expected to be beneficial at coutry level, firm level and indiviual level such as government and regulatory bodies of Pakistan can intervene to avert bankruptcy rate, management can devise appropriate strategies to reduce financial distress. Moreover. investors can safeguard their investment by making right decissions based on the findings.

The Prediction of Bankruptcy With Altman Z-Score and Springate S-Score Models In Sharia Banks Period 2012-2017

The Background of this research is to know and to analyze the prediction of company bankruptcy of BUMN Syariah Banks in BEI periods 2012-2017 which focuses object are three companies which have all criteria in analysis by method Z-score Altman and S-Score Springate. As we know, the goal of company is not being bankrupt so that the company needed a method to predict the bankruptcy as soon as possible. This reserch uses Z-Score Altman and S-Score Springate, this methods used to analize financial statements. Goals of this research is to know the potential of bankruptcy, rate of bankruptcy, and insolvensy ranking of BUMN Syariah Banks in BEI periods 2012-2017. Three companies taken as object are Bank Mandiri Syariah, Bank BNI Syariah and Bank BRI Syariah, analyze by using financial statements by the year 2012 to 2070 to find values of variables and then calculated the value of each variable into Altman’s Formula and Springate’s Formula to produce the score.

Analysis of the stability factors of Ukrainian banks during the 2014–2017 systemic crisis using the Kohonen self-organizing neural networks

The article proposes an approach to analyzing reliability factors of commercial banks during the 2014–2017 systemic crisis in the Ukrainian banking system, using the Kohonen self-organizing neural networks and maps. As a result of an experimental study, data were obtained on financial factors affecting the stability of a commercial bank in a crisis period. It has been concluded that during the banking crisis in Ukraine in 2014–2017, the resource base of a bank was the main factor of this bank stability. The most preferred sources of resources were funds from other banks (bankruptcy rate of 5.7%) and legal entities (bankruptcy rate of 8%), and the least stable were funds from individuals (bankruptcy rate of 28.5%). The relationship between financial stability and the amount of capital and the structure of bank loans is less pronounced. However, one can say that banks that focused on lending to individuals experienced a worse crisis than banks whose main borrowers were legal entities. The tools considered in the article (the Kohonen self-organizing neural networks and maps) allow for efficiently segmenting data samples according to various criteria, including bank solvency. The “hazardous” zones with a high bankruptcy rate (up to 49.2%) and the “safe” zone with a low rate of bankruptcy (6.3%) were highlighted on the map constructed. These results are of practical value and can be used in analyzing and selecting counterparties in the banking system during a downturn.

Kemampuan Model Z- Score dan Model Springate Dalam Memprediksi Financial Distress BPR Multi Artha Sejahtera

The purpose of this study was to test the ability of the model or analytical tool used to predict the bankruptcy of the company, namely the Altman's Z-afternoon model and the Springate model of BPR Multi Artha Sejahtera whose license has been revoked by the Financial Services Authority (OJK) through Commissioner Decree Number 16 / KDK.03 / 2016 with company considerations deteriorating. The data used is secondary data, namely the 2012-2015 BPR Multi Artha Sejahtera financial report data obtained from Bank Indonesia reports. The data is then analyzed using the Altman Z-score (Z-Score) and Springate (S-Score) formulas to detect whether or not there are indications of bankruptcy before BPR Multi Artha Sejahtera is actually declared bankrupt. The results of this study concluded that overall, both Z-core and S-Score were able to predict the bankruptcy rate of BPR Multi Artha Sejahtera during 2011 - 2015. In the case of BPR Multi Artha Sejahtera bankruptcy the use of S-Score to predict bankruptcy is more appropriate in predicting bankruptcy.

Risk shocks with time-varying higher moments

Within the context of a financial accelerator model, we model time-varying uncertainty (i.e. risk shocks) through the use of a mixture normal model with time variation in the weights applied to the underlying distributions characterizing entrepreneur productivity. Specifically, we model capital producers (i.e. the entrepreneurs) as either low-risk (a relatively small second moment of productivity) or high-risk (a relatively large second moment of productivity) and the fraction of both types is time-varying. We show that this modeling feature implies that the aggregate distribution of productivity shocks is non-normal and has time varying kurtosis and skewness; both of these features have important effects on equilibrium characteristics. In particular, after estimating the steady-state share and the change in the fraction of risky entrepreneurs, we show that a small change in the fraction of risky types can result in a large quantitative effect of a risk shock relative to standard models for both financial and real variables. Moreover, the bankruptcy rate and the risk premium in the economy are very sensitive to a change in the composition of entrepreneurs.

Bankruptcy Forecasting and Economic Sustainability Profile of the Market Leading Islamic Banking Countries

This study used bankruptcy forecasting as a proxy for measuring economic sustainability profile of the Islamic banks in the market leading Islamic banking countries. The countries are Malaysia, Saudi Arabia, Iran, UAE and Kuwait. A sample of 29 Islamic banks with a post-crisis period data from 2009-2013 was collected for empirical testing. Results indicated that Saudi Arabian Islamic banks recorded the most minimal bankruptcy rate of 29 percent, followed by UAE with 31 percent, Kuwait with 48 percent, Malaysia with 55 percent and Iran with 68 percent respectively. The results further indicated that profitability, liquidity, insolvency, and productivity ratios have a significant positive impact on bankruptcy profile of the selected Islamic banks. This study lends credence to multiple stakeholders for taking appropriate measures regarding the deteriorating economic sustainability of the Islamic banks in the market leading Islamic banking countries. It also urges to develop a separate Shariah-based sustainability measurement framework for the Islamic banks.

ANALISIS LAPORAN KEUANGAN SEBAGAI ALAT PREDIKSI KEBANGKRUTAN PADA PERUSAHAAN PROPERTI YANG TERDAFTAR DI BURSA EFEK INDONESIA

This study aims to determine the level of corporate bankruptcy by the method of Altman Z-Score on Property Company listed on the Indonesia Stock Exchange. The variable used in this research is Total Value Z-Score as independent variable and Corporate Bankruptcy Rate as dependent variable. Where the ZScore Value is measured by the ratio found by Altman consisting of 5 (five) ratios ie Working Capital to Total Assets Ratio (X1), Retained Earnings to Total Assets Ratio (X2), Earnings Before Interest and Taxes to Total Assets Ratio (X3), Market Value Eguity to Book Value of Total Debt Ratio (X4), Sales to Total Assets Ratio (X5). The results of this study indicate that Altman's method can Model Altman Z-score can predict the state of the property company in Indonesia Stock Exchange. From 2 companies taken as sample one of the companies indicated bankruptcy indicated in good health, it is proven from Z-score value of company that has more than 2.99 that is 0,761 in 2014. 0,148 in the year 2015 and value of Z equal to 0,5501 in 2016. While other companie indicated in good health by the company Z-score value of less than 1.80 is 3,234 in 2014, 3,232 in 2015, and 3,84 in 2016. Keywords: Z-score Altman, Bankruptcy Rate

Credit Relationships and Business Bankruptcy during the Great Depression

Credit relationships are sticky. Stickiness makes relationships beneficial to borrowers in times of their own distress but makes them potentially problematic when lenders themselves face hardship. To examine the role of credit relationships during a financial crisis, we exploit a natural experiment in Mississippi during the Great Depression that generated plausibly exogenous differences in financial distress for banks. Using new data drawn from the publications of the credit rating agency Dun & Bradstreet and from original bankruptcy filings, we show that financial distress increased business exit but did not increase the bankruptcy rate. Financial distress caused both banks and trade creditors to recalibrate their collections strategies, which is revealed by changes in the geographical distribution of the creditors of bankrupt businesses. (JEL G21, G24, G33, N12, N22, N82)