¶ … Discriminate Analysis and Other Models to Predict Bankruptcy
Type of Data Used
Selection of Keywords
Authenticity and Validity
Methods Used for the Prediction of Bankruptcy
Discriminant analysis
Logit analysis
Neural networks
Distance to Default
There has been a constant increase in the attempts that are made to predict bankruptcy because of the deteriorating consequences that are associated with this phenomenon. These consequences include the following: The negative social and economic consequences for the investors and creditors who are associated with the bankrupted organization. The social and economic consequences that the competitors and government, who are associated with the affected organization, would confront. This research will explore various methods that are used for the prediction of bankruptcy. It will highlight the functioning of these models. In addition to that, this paper will also list down the advantages and disadvantages of all of the discussed models as well.
Using Discriminate Analysis and Other Models to Predict Bankruptcy
Introduction
There has been a constant increase in the attempts that are made to predict bankruptcy because of the deteriorating consequences that are associated with this phenomenon. (Santos & Cortez et al., 2006)These consequences include the following:
The negative social and economic consequences for the investors and creditors who are associated with the bankrupted organization. (Santos & Cortez et al., 2006)
The social and economic consequences that the competitors and government, who are associated with the affected organization, would confront. (Santos & Cortez et al., 2006)
The two basic kinds of models that are generally adopted for the prediction of bankruptcy are discussed in the following section:
Accounting ratios-based models: These models consist of the statistical techniques, which include discriminant analysis and logistic regression models. (Santos & Cortez et al., 2006)
Market-based models. In this category the KMV model of Moody was adapted. (Santos & Cortez et al., 2006)
This research will explore various methods that are used for the prediction of bankruptcy. It will highlight the functioning of these models. In addition to that, this paper will also list down the advantages and disadvantages of all of the discussed models as well.
Research Approach
This methods that have been used for the collection, analysis and interpretation of the data, in the course of this research, are highlighted by the following section. In addition, this section also discusses the strategy that has been deployed for the presentation of this data under this research.
Research Method
For the purpose of this research, the research has utilized a mixed research methodology. Such a methodology consists of a combination of the characteristics of both the qualitative as well as the quantitative research methodology. (Saunders and Lewis et al., 2003)
Mixed Research Methodology
The assumptions used by this type of research methodology are Post positivist and constructivist assumptions in nature. In addition to that, this type of methodology also uses pragmatic assumptions. (Saunders and Lewis et al., 2003)
This type of research methodology generally employs a mixed strategy of inquiry. The experimental, quasi-experimental, narrative and ethnographic designs are included in the strategy of inquiry of this research. (Saunders and Lewis et al., 2003)
The mixed research methodology deploys a balanced combination of both emerging as well as predetermined methods of research. A combination of statistical data, close ended questions as well as open ended questions is deployed by such research methodology for the purpose of collection and evaluation of data. (Saunders and Lewis et al., 2003)
In addition to that, a number of other sources, including performance, attitude, observation and census data, image analysis, audio visual data, text analysis and field observation, are also deployed by the above mentioned research methodology for the collection of data and information for the purpose of the research under consideration. (Saunders and Lewis et al., 2003)
Type of Data Used
The two primary types of data can be named as secondary data and primary data. Primary data can be defined as the new and fresh data that is first handedly collected by the researcher himself. This data is generally collected by the researcher for the purpose of the research that he or she aims to conduct. A number of varying tools are used by the researcher to collect this type of data. These tools include questionnaires, interviews, field observations and experiments etcetera. (Saunders and Lewis et al., 2003)
Secondary data, on the other hand, can be defined as the type of data which exists previously in relation to the topic under consideration. Such data is collected by various individual researchers,...
Our semester plans gives you unlimited, unrestricted access to our entire library of resources —writing tools, guides, example essays, tutorials, class notes, and more.
Get Started Now