Survival drivers of post-incubated start-ups: The effect of academic governance

Incubators, spin-offs, industrial networks and consortiums are some of the examples to build-up university–industry links in fostering innovation. University incubators are well known for supporting the growth of start-ups by providing knowledge and research, as well as, sustaining entrepreneurship by the direct involvement of their faculty. In this regard, the aim of this paper is to examine the influence of faculty members on the financial performance of a sample of new technology based firms which have been previously incubated by different Italian University Incubators. Essentially, the results on the presence of academic governance in relation to the financial performance of the firm describe a certain dip, even when controlling for other variables such as the industry and the number of registered patents

Pengaruh variabel keuangan & non-keuangan terhadap financial distress

This study aims to provide empirical evidence of the ability to predict financial distress using financial variables (current ratio, cash flow operation, leverage, gross profit margin, and return on assets) and non-financial variables (going concern opinion, audit report lag, opinion shopping, the additional state capital, and subsidies). Model testing uses three steps. First, financial ratios (CR, CFO, LEV, GPM, ROA); second, non-financial ratios (GCO, ARL, SHOP, ASC, SUB); and third, all variables at once. This study uses panel data (2011-2020) with a sample size of 50 Indonesian SOEs. Data analysis uses ordinal logistic regression. The first test results show that CR and ROA positively affect financial distress, while LEV and GPM have a negative effect. The second test results show ARL has a negative effect, while SHOP and SUB have a positive effect. Meanwhile, the third test results show LEV, GPM, and ASC have a negative effect, while ROA and SUB have a positive effect. Based on the r-squared and correctly predicted values, the third model test results are better than the first and second models. Statistically, the ability to predict financial distress that combines financial and non-financial ratios is better than models that only use financial and non-financial ratios. Financial ratios are the most consistent predictor of financial distress in terms of significance

La influencia de la vulnerabilidad de los sectores en su supervivencia y probabilidad de insolvencia: el caso de las pequeñas y medianas entidades en España

This paper looks at a sample of small and medium entities in Spain and analyzes the effect of the vulnerability of sectors to insolvency on their survival and the probability that they will go bankrupt. We collected data from solvent and insolvent firms in Spain over the period 2012-2016, and grouped them according to the percentage of insolvencies by sector (highest, lowest, and a reference group). The results show that no differences in the endurance of the firms emerge among the groups, while some variables appear to be relevant when the logit analysis is applied. Survival depends on liquidity and size in all industries, but profitability and turnover are also essential for the group with the highest levels of insolvency. The probability of bankruptcy is mainly explained by turnover and short-term solvency. Size and turnover have negative effects on bankruptcy. Age is also a common factor, but with a different interpretation for each technique. The main contribution of this paper is the analysis of insolvency in the two dimensions of survival and probability according to the sectorial insolvency rate.Este artículo analiza la vulnerabilidad sectorial a la insolvencia basándose en el análisis de supervivencia y la probabilidad de la misma en una muestra de pequeñas y medianas empresas en España. Se han recogido datos de empresas solventes e insolventes para el período 2012-2016 y han sido agrupadas por el porcentaje de insolvencias por sectores (alto, bajo y un grupo de referencia). Los resultados muestran que no hay diferencias en la duración de las empresas entre los grupos y algunas variables parecen ser relevantes cuando se aplica el análisis logístico. La supervivencia depende de la liquidez y el tamaño en todos los sectores, pero la rentabilidad y la rotación son también esenciales para el grupo con más altos niveles de insolvencia. La probabilidad de quiebra se explica principalmente por la rotación y la solvencia a corto plazo. El tamaño y la rotación tiene efectos negativos sobre la quiebra. La edad del negocio es también un factor común, pero con diferentes interpretaciones para cada técnica. La principal contribución del artículo reside en el análisis de la insolvencia en dos dimensiones: supervivencia y probabilidad de acuerdo con la tasa de insolvencia sectorial

Corporate financial distress diagnosis model and application in credit rating for listing firms in China

With the enforcement of the removal system for distressed firms and the new Bankruptcy Law in China's securities market in June 2007, the development of the bankruptcy process for firms in China is expected to create a huge impact. Therefore, identification of potential corporate distress and offering early warnings to investors, analysts, and regulators has become important. There are very distinct differences, in accounting procedures and quality of financial documents, between firms in China and those in the western world. Therefore, it may not be practical to directly apply those models or methodologies developed elsewhere to support identification of such potential distressed situations. Moreover, localized models are commonly superior to ones imported from other environments. Based on the Z-score, we have developed a model called Z_China score to support identification of potential distress firms in China. Our four-variable model is similar to the Z-score four-variable version, Emerging Market Scoring Model, developed in 1995. We found that our model was robust with a high accuracy. Our model has forecasting range of up to three years with 80 percent accuracy for those firms categorized as special treatment (ST); ST indicates that they are problematic firms. Applications of our model to determine a Chinese firm's Credit Rating Equivalent are also demonstrated. © 2010 Higher Education Press and Springer-Verlag Berlin Heidelberg

Дискриминантная модель банкротства предприятий Сахалинской области

Проводится анализ деятельности предприятий базовых отраслей Сахалинской области. На основе данных бухгалтерской отчетности оцениваются показатели финансовой деятельности и строится дискриминантная модель оценки финансовой устойчивости предприятий.The analysis of the activities of enterprises of the basic branches of the Sakhalin region is carried out. Financial performance indicators are evaluated on the basis of accounting data and a discriminant model for assessing the financial stability of enterprises is built

An empirical diagnosis of multiple state financial distress in the Chinese equity market : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

The Chinese equity market where firms do not die is saddled by an increasing number of zombie financially distressed firms resulting from a relatively low delisting rate, a weak Enterprise Bankruptcy Law process and a profit-based delisting system that is undermined by earnings management. As a result, it is difficult for investors and creditors to assess or predict the financial distress state of firms to reduce loss. This research uses panel data of 1,415 Chinese non-financial listed firms on the Shanghai and Shenzhen Stock Exchanges for the period 2009 to 2018. Using a two-stage multinomial logit model, this research models financial distress as a multiple state process of four financial distress states (NFDIS, FWEAK, FWEAK, FDIST) which is an improvement on the conventional binary state approach. The empirical findings show a nonlinear relationship between financial ratios and corporate governance factors and financial distress. Specifically, a change in a firm’s financial leverage and cash flow from finance has a significant positive effect on the probability of the firm in a financially distressed state: FDECL, FWEAK or the FDIST state. Inversely, a change in a firm’s asset management efficiency, profitability, liquidity, cash flow from operations, dividend payment, market valuation, board structure or ownership structure has a significant negative effect on the probability of the firm in a financially distressed state: FDECL, FWEAK or the FDIST state. Notably, the further a firm’s financial health deteriorates, the lesser its probability of recovery. Further to the two-year consecutive loss criteria, this research found that firms in the early FDECL state (ST firms) do experience distress symptoms of poor asset management efficiency, high cash flow from finance, low dividend pay-out, poor board structure and low percentage of institutional ownership. Firms in the FWEAK state also experience, in addition to the same symptoms as firms in the FDECL state, poor liquidity, cash flow from operations and poor market valuation. Firms in the terminal FDIST state experience the same symptoms as firms in the FDECL and FWEAK states in addition to high financial leverage. Although firms across the four financial distress states may experience similar distress symptoms, the magnitude of these symptoms at each distress state is significantly different as they are incremental. The empirical findings imply that early financial distress may not be detected relying solely on accrual or market information as the case of the ST delisting criteria. This is because accrual-based ratios and market-based ratios are less effective in diagnosing the symptoms experienced by firms in the early FDECL state than they are in identifying the symptoms in the late FWEAK state or terminal FDIST state. Cash flow-based ratios and corporate governance factors improve the predictive and explanatory power of accrual and market-based ratios assessing aspects of financial distress not assessed by accrual-based ratios or market-based ratios