Thesis Abstract and Dissertation Abstracts
Industrial Sickness In India: An Empirical Analysis
Abstract Category: Other Categories
Course / Degree: Doctor of Philosophy
Institution / University: University of Calcutta, India
Published in: 2009
The basic issue of our research is to develop a predictive model, i.e., a model that can predict the probability of sickness that a firm is exposed to. This is necessary for getting a signal at the firm level so that the management can get prepared for turnaround management, if the probability of the firm being sick is found to be high. In our dissertation, we have performed this exercise in the context of Indian manufacturing industries. Quantitative analysis of industrial sickness based on financial data of a company has been done by various experts. The financial ratios used by them for developing empirical models are usually selected on the basis of some a priori considerations and do not appear to have any macro foundation. These ratios thus might not be robust for analysing firm level sickness. We have, therefore, taken a different approach in our research. We have started with macro ratios, i.e., the ratios that have been widely used by the economists for finding out the efficiency of a particular industry. The firm level data aggregated at industry level at various digits are published by Annual Survey of Industries (ASI).
We collected these data and organised them at two digit level for analysing industry level performance pertaining to various performance indicators with respect to the time series data. Such an analysis was done on the basis of a select set of macro ratios that might indicate the status of performance of a particular group of industry. The selection of macro ratios was done keeping an eye to the research need of identifying the financial ratios that would correspond to the selected macro ratios. We carried out various statistical analyses, namely, rank analysis, scatter plot, convergence and divergence analysis, cluster analysis and tested whether these select macro ratios can capture the heterogeneity or homogeneity in performance of the industry (NIC two digit level) groups. We found that the financial parameters selected by us can segregate the ‘good performing’ industry groups from ‘bad performing’ industry groups.
Following this, we identified such industry groups which appeared to perform well as also such group of industries which were harbouring most of the sick units. The focus of our research being on firm level analysis, we selected about one hundred firms, evenly drawn from both the groups, for which the balance sheet and profit and loss account data were available in the PROWESS database compiled by the Centre for Monitoring Indian Economy (CMIE). The time series data for ten successive years being thus collected from the PROWESS database, we performed the remaining part of our empirical exercise. The micro ratios corresponding to the macro ratios were utilised first for a discriminant analysis. The results were encouraging. The discriminant model developed by us exhibits high level of accuracy as a sick or healthy prediction procedure. The model correctly classifies 98 per cent of companies at one year prior to becoming sick.
The same level of accuracy was displayed at two year prior to becoming sick. Accuracy level at three years and four years prior to becoming sick was 92 per cent and 86 per cent respectively. We have, however, not restricted our analysis up to this stage. Our objective is to find out the probability of a company becoming healthy or sick in future on the basis of financial indicators that have been found to exhibit discriminating power to classify a company either as healthy or sick. Discriminant model does discriminate between the healthy group and the sick group of companies. The implication is that a post facto scenario can be verified by the model. Whether a particular company will belong to the predetermined group (healthy or sick) in future cannot be ascertained unless the ex post scenario is available. Since the classification by a score in a particular year does not, in any way, make a probability based statement on the future health of a particular company, such classification exercises would in effect provide no signal as regards its future financial health. In order to address such an issue, binary logistic regression was run to find out which of the core ratios could predict sickness of a company in a reasonably effective manner.
The predictive model developed by us appears to exhibit equally high level of accuracy. We observe that with probability of 0.72, 97 per cent of the selected companies are correctly classified into their respective predetermined group of our model. The model developed by us seem to have promise of easy applicability in practical decision making situations, especially because of its simplicity and user-friendliness. Both individual and corporate investor may use the models to check the worth of the stock they are planning to buy (or sell). The models would help the investors take either long or short position in the security market. The models also give promise of application in the business loan-evaluation sector. Banks and financial institutions may find our models useful for credit decision and risk analyses.
Above all, our predictive model seems to have wide application in turnaround management. Once a company would show fair probability of becoming sick in near future, management would be in a position to intervene immediately and take remedial measures for its turnaround. Similarly, lending institutions would be in a position to take decision on debt restructuring of a company. Lending institutions may avoid going in for debt restructuring for the company whose probability of getting sick is high.
Thesis Keywords/Search Tags:
predictive model, sickness, turnaround management
This Thesis Abstract may be cited as follows:
Reference: Edward Altman Model
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Dr. Dilip Kumar Datta (click to enlarge) |
Submission Details: Thesis Abstract submitted by Dilip Datta from India on 16-Sep-2011 10:39.
Abstract has been viewed 5158 times (since 7 Mar 2010).
Dilip Datta Contact Details: Email: sayantan.consultants@gmail.com Phone: +91-9831006577
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