Deciding the financial health of dot-coms using rough sets
Introduction
The number of companies that primarily conducted their business using the Web (dot-coms) grew tremendously in the 1990s. According to Hendershott [13], “dot-coms sell products through a Web-based store (online retailers and auction sites) and/or generate revenue by selling market opportunities to merchants who want access to the dot-com's users”. Many startup companies used the medium to open new businesses or provide new channels for existing businesses. Companies like Amazon.com changed the retailing business. The growth also led to the formation of online subsidiaries of existing companies, who utilized the medium to expand their businesses. In addition, several online companies grew to facilitate communication and transact business using the technology. The dot-coms rapidly raised enormous amounts of venture capital.
Their apparent success was, however, short lived. Their stock prices started to tumble from March 2000 and their market value declined rapidly. According to Mathieson [18], there were 121 closures of dot-coms worldwide in the last quarter of 2000 and webmergers.com reported that 564 dot-com ventures failed between January 2000 and June 2001 (59% of them were B2C firms). The financial pundits had already conjectured that the demise of dot-coms was inevitable. From 1999, it was clear that the amount of venture capital that had funded the growth of dot-coms was almost exhausted and there were no new sources [16].
It has been suggested that dot-coms’ inability to improve revenues and earnings, failure to post profits, attempt to capture a major market share quickly, and a tendency to operate in limited geographical areas were among the main causes of failure [30]. Others identified an emphasis on providing free services, lack of solid business models, limited vision, improper channel management, heavy emphasis on meaningless advertising, and a wish to expand quickly were also major factors that hurt dot-coms [26]. The authors of [37] explored the managerial, organizational, and environmental characteristics responsible for failure of five prominent dot-coms. Some researchers also indicated that the discrepancy between actual performance and future expectations was exemplified by their high price-to-earnings ratios in early 2000 [4]. Our research attempted to find out whether financial ratios could have predicted the viability of dot-coms. The analysis of the relationship between financial ratios as independent variables and financial health as dependent variable was studied using the method of rough sets. We also identified financial ratios that were highly predictive of the financial future of the companies and determined business rules linking the financial ratios in order to identify whether a dot-com was financially healthy.
It is important to identify companies that market technologies that are viable, have a solid business model, and can sustain funding and growth. This is likely to be more important because of the expectation of a second dot-com boom; e.g., a survey by Actinic Software of small and medium retailers reported a 60% increase in Web based sales in November and December 2004. They remarked “Each year adds to the feeling that the original dot-com boom hype wasn’t so much wrong as too early” [14]. Also, though the $20.9 billion invested in 2876 deals in 2004 is only 20% of the venture capital spending in 2000 it was the first increase in 3 years, suggesting that the dot-com phenomenon is not over [32]. Fortune magazine also reported that “The not-so-surprising result is that the Internet industry isn’t just back, it's better than it was before” [15].
Section snippets
The method of rough sets
Rough sets theory deals with uncertainty and vagueness in the classification of objects in a set. It is founded on the belief that every object is associated with some information and objects that are associated with the same information are similar and belonged to the same class. Although somewhat similar to statistical probability theory and other soft approaches, such as fuzzy sets, the rough sets approach is significantly different. Fuzzy sets are useful for handling imprecision when
Literature review
The prediction of financial health of a company is similar to the problem of predicting bankruptcy, which is a well-researched area where several techniques have been used. (Some notable examples include the use of multiple discriminant analysis by Altman [2], multi-criteria decision aid methodology by Dimitras et al. [6], support vector machines by Fan and Palaniswami [7], neural networks by Fletcher and Goss [8], recursive partitioning algorithm by Frydman et al. [9], mathematical programming
Numerical experimentation
Data from financial statements of 240 dot-coms were collected using the WRDS (Wharton Research Data Services) database. The companies identified as dot-coms either had the suffix .com in their name or conducted business primarily using the Web. Half of these companies were identified as unhealthy if their stock prices were less than 10 cents (output = 0) and the remaining ones were classified as financially healthy (output = 1). Some well-known examples of financially healthy dot-coms were
Conclusion
From the experiments conducted it can be concluded that the rough sets method correctly classified financially healthy and unhealthy dot-coms. Three variables RE/TA, S/MC, and S/TA appeared to be the three major predictors as they occurred most frequently in the generated reducts. A disadvantage of the rough sets method, however, was that it often resulted in the generation of many rules associated with each class. By increasing and decreasing the number of rules and checking their effect on
Acknowledgements
The author wants to thank the anonymous reviewers of this paper for their constructive comments which greatly improved the overall quality and readability of this paper. This research is supported by a grant received by the author from the Research Grants Council of Hong Kong under the Competitive Earmarked Research Grants scheme (Project code HKU 7131/04E). The author also thanks Mr. James Pang for his help in conducting the numerical experiments reported in this paper.
Indranil Bose is a associate professor of Information Systems at School of Business, the University of Hong Kong. His degrees include B.Tech. from the Indian Institute of Technology, M.S. from University of Iowa, M.S. and Ph.D. from Purdue University. He has research interests in telecommunications, data mining, electronic commerce, and supply chain management. His teaching interests are in telecommunications, database management, data mining, and decision science. His publications have
References (42)
- et al.
The integrated methodology of rough set theory and artificial neural network for business failure prediction
Expert Systems with Applications
(2000) - et al.
Business data mining—a machine learning perspective
Information & Management
(2001) - et al.
Business failure prediction using rough sets
European Journal of Operational Research
(1999) - et al.
A survey of business failures with an emphasis on prediction methods and industrial applications
European Journal of Operational Research
(1996) - et al.
Forecasting with neural networks: an application using bankruptcy data
Information & Management
(1993) - et al.
A hybrid intelligent system for predicting bank holding structures
European Journal of Operational Research
(1998) New value: wealth creation (and destruction) during the internet boom
Journal of Corporate Finance
(2004)- et al.
The language of quarterly reports as an indicator of change in the company's financial status
Information & Management
(2005) - et al.
Genetic programming and rough sets: a hybrid approach to bankruptcy classification
European Journal of Operational Research
(2002) - et al.
Probabilistic, fuzzy and rough concepts in social choice
European Journal of Operational Research
(1996)
Applying rough sets to market timing decisions
Decision Support Systems
Prediction of company acquisition in Greece by means of the rough set approach
European Journal of Operational Research
Economic and financial prediction using rough sets model
European Journal of Operational Research
Sorting through the dot bomb rubble: how did the high-profile e-tailers fail?
International Journal of Information Management
Mining stock price using fuzzy rough set system
Expert Systems with Applications
A bibliography of neural network business applications research: 1994–1998
Computers and Operations Research
Neural network applications in finance: a review and analysis of literature
Information & Management
Financial ratios, discriminant analysis and the prediction of corporate bankruptcy
The Journal of Finance
Selecting bankruptcy predictors using a support vector machine approach
Introducing recursive partitioning for financial classification: the case of financial distress
The Journal of Finance
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Indranil Bose is a associate professor of Information Systems at School of Business, the University of Hong Kong. His degrees include B.Tech. from the Indian Institute of Technology, M.S. from University of Iowa, M.S. and Ph.D. from Purdue University. He has research interests in telecommunications, data mining, electronic commerce, and supply chain management. His teaching interests are in telecommunications, database management, data mining, and decision science. His publications have appeared in Communications of AIS, Communications of the ACM, Computers and Operations Research, Decision Support Systems and Electronic Commerce, Ergonomics, European Journal of Operational Research, Information and Management, and Operations Research Letters.