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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Finanční analýza společnosti Metrostav a.s. / The financial analysis of the company Metrostav a.s.

Malíková, Pavlína January 2009 (has links)
The thesis aim is to examine and evaluate the Metrostav a.s financial health during the years 2005 and 2009 even in the context of economic crisis. The thesis is divided into two main parts. The first one, theoretical - methodological part, describes the various methods of financial analysis, which are gradually being applied in the practical part. The content of the practical part is a brief description of the company and the construction sector, followed by the very core of financial analysis. At the end there are summarized learned knowledge of applied methods and interpreted results of financial analysis.
2

The application of PROMETHEE multi-criteria decision aid in financial decision making: case of distress prediction models evaluation

Mousavi, Mohammad M., Lin, J. 2020 May 1922 (has links)
No / Conflicting rankings corresponding to alternative performance criteria and measures are mostly reported in the mono-criterion evaluation of competing distress prediction models (DPMs). To overcome this issue, this study extends the application of the expert system to corporate credit risk and distress prediction through proposing a Multi-criteria Decision Aid (MCDA), namely PROMETHEE II, which provides a multi-criteria evaluation of competing DPMs. In addition, using data on Chinese firms listed on Shanghai and Shenzhen stock exchanges, we perform an exhaustive comparative analysis of the most popular DPMs; namely, statistical, artificial intelligence and machine learning models under both mono-criterion and multi-criteria frameworks. Further, we address two prevailing research questions; namely, "which DPM performs better in predicting distress?" and "will training models with corporate governance indicators (CGIs) enhance the performance of models?”; and discuss our findings. Our multi-criteria ranking suggests that non-parametric DPMs outperform parametric ones, where random forest and bagging CART are among the best machine learning DPMs. Further, models fed with CGIs as features outperform those fed without CGIs.
3

A dynamic performance evaluation of distress prediction models

Mousavi, Mohammad M., Ouenniche, J., Tone, K. 27 October 2022 (has links)
Yes / So far, the dominant comparative studies of competing distress prediction models (DPMs) have been restricted to the use of static evaluation frameworks and as such overlooked their performance over time. This study fills this gap by proposing a Malmquist Data Envelopment Analysis (DEA)-based multi-period performance evaluation framework for assessing competing static and dynamic statistical DPMs and using it to address a variety of research questions. Our findings suggest that (1) dynamic models developed under duration-dependent frameworks outperform both dynamic models developed under duration-independent frameworks and static models; (2) models fed with financial accounting (FA), market variables (MV), and macroeconomic information (MI) features outperform those fed with either MVMI or FA, regardless of the frameworks under which they are developed; (3) shorter training horizons seem to enhance the aggregate performance of both static and dynamic models.
4

Development Of Distress Prediction Models For Small Scale Enterprises Using Organisational/Managerial & Financial Ratio Variables

Gowda, Manje 03 1900 (has links) (PDF)
No description available.

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