The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper investigates the modeling of risk due to market and funding liquidity by capturing the joint dynamics of three time series: the treasury-Eurodollar spread, the VIX, and a metric derived from the S&P 500 spread. We propose a two-regime mean-reverting model for explaining the behaviour of three time series, which mirror liquidity levels for financial markets. An expectation-maximisation...
The purpose of this paper is to propose and validate the combined model for bankruptcy prediction for the Malaysian firms. This combined model is adopted from previous studies by combining Ohlson logit model, Springate-Canadian model and macroeconomic factors. The proposed combined model is developed by using the financial and macroeconomic constructs. The result indicates that logistic regression...
Based on a lot of related literatures, the authors suggest a Financial Distress Prediction System incorporated the Expected Default Frequency (EDF) into Logit regression model. The empirical findings suggest that the EDF calculated by KMV model is significantly associated with the probability of default in both 3rd and 4th quarters prior to the financial crisis of sample firms. Thus, an incorporation...
This paper studies debt holders' belief updating and equity owners' financing decisions under asymmetric information during financial distress. This is done within a continuous-time framework, where the relevant state variable is assumed to follow an arithmetic Brownian motion (ABM). ABM can take negative values and has very realistic feature compared with geometric Brownian motion (GBM). Using Chapter...
In the predicting financial distress, we know that irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper use rough sets as a preprocessor of SVR to select a subset of input variables and employ the particle swarm optimization algorithm (PSOA) to optimize...
This paper studies how to establish models for predicting financial distress in China's listed companies. We firstly select 26 companies with financial distress and 54 matching companies' panel data as samples, then use panel data model to conduct an empirical study. The research indicates that: (1) The predictability precision is 91.25%, 92.5%, 91.25% and 87.5% for T-1, T-2, T-3 and T-4, respectively,...
Financial distress and bankruptcy of companies may cause the resources to be wasted and the investment opportunities to be faded. Bankruptcy prediction by providing necessary warnings can make the companies aware of this problem so they can take appropriate measures with these warnings. The aim of this study is model development for financial distress prediction of listed companies in Tehran stocks...
Recent outbreak of corporate financial crises worldwide has brought attention to the need for a new international financial architecture which rests on crisis prediction and crisis management. Financial data have been widely used by researchers to predict financial crisis, but few studies exploit the use of non-financial indicators in corporate governance to construct financial crisis prediction model...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.