Abstract The main purpose of this study is to investigate the best predictor firm performance among different proxies. A sample 287 Czech firms was taken from automobile, construction, and manufacturing sectors. Panel data acquired Albertina database for time period 2016 2020. Three proxies performance, return assets (RoA), equity (RoE), capital employed (RoCE) were used as dependent variables....

One of the challenging issues for investors and professionals is appropriate models to evaluate financial situation of the firms. In this regard, many models have been extracted by researchers using different financial ratios to resolve these issues. However, choosing a model based on the conditions and users’ needs is complex. The main objective of this study is to identify the effect of conti...

Financial distress is of crucial importance in financial management especially in the case of competitive environment. Failure is not an impulsive outcome and it grows constantly in stages. A spontaneous protective effort could be accommodated if the company is anticipated to be proceeding in the direction of potential bankruptcy and this can help alleviate the financial distress to all investo...

In this paper, we compare some traditional statistical methods for predicting financial distress to some more ‘‘unconventional’’ methods, such as decision tree classification, neural networks, and evolutionary computation techniques, using data collected from 200 Taiwan Stock Exchange Corporation (TSEC) listed companies. Empirical experiments were conducted using a total of 42 ratios including ...

Traditionally, statistical techniques such as multivariate discriminant analysis and logistic regression analysis have been applied for predicting financial distresses by analyzing financial ratios. In addition to statistical methods, recent studies suggest that backpropagation neural networks (BPNs) and support vector machines (SVMs) can be alternative approaches for classification tasks. Henc...