Venture capital, entrepreneurship, and long-run performance prediction: An application of data mining
| dc.contributor.advisor | Thompson, James R. | |
| dc.contributor.advisor | Williams, Edward E. | |
| dc.creator | Miller, John Michael | |
| dc.date.accessioned | 2009-06-04T07:02:48Z | |
| dc.date.available | 2009-06-04T07:02:48Z | |
| dc.date.issued | 2003 | |
| dc.description.abstract | The critical nature of the venture capital-entrepreneur relationship is emphasized by the 46.4% exponential growth rate of venture capital investments throughout the 1990s. It is that time in the venture capital cycle between the time the first stage funding is made and the venture capitalist exits that the greatest opportunity exists for the venture capitalist to influence the outcome of his limited partners' investment. Theories have been offered to explain the effectiveness of the venture capitalist through agency, procedural justice, information, environment, and power theories. The first stage of this study investigates the predictive ability of the entrepreneur's attitudes toward his venture capital partner for long-term performance using entrepreneur attitudes in the light of these theories. The focus of the second and third stages of this analysis is on the ability of internal auditing and environmental factors characterizing the firm at the time of its IPO as predictors of long-term investor wealth appreciation. Data mining involves conducting all three steps in the development of a mathematical model of any phenomenon: structure generation, parameter estimation, and model confirmation, on the same set of data. In this development of a prediction scheme of firm performance we focus on model generation and preliminary model parameter estimation. The data for these analyses were obtained from a 1990 survey of top management of 145 venture capital funded enterprises, plus SEC filings on 563 Initial Public Offerings (IPOs) issued in 1997, stock market prices, and public accounting data. Both sets of data are treated according to an operational measurement theory rather than the traditional representational mode. As a result: (1) entrepreneur appreciation for strategic information, and new idea support, from his venture capitalist, are found to be predictive of subsequent business performance as successful IPO or merger/acquisition harvests; (2) routine application of non-parametric methods to wealth appreciation data for the time 1997--2001 casts doubt on the characterization of that time as a "boom," while confirming the anomaly of IPO underperformance; and (3) accounting data available at the time of IPO may be able to predict subsequent stock market performance three years out. | |
| dc.format.extent | 151 p. | en_US |
| dc.format.mimetype | application/pdf | |
| dc.identifier.callno | THESIS STAT. 2003 MILLER | |
| dc.identifier.citation | Miller, John Michael. "Venture capital, entrepreneurship, and long-run performance prediction: An application of data mining." (2003) Diss., Rice University. <a href="https://hdl.handle.net/1911/18594">https://hdl.handle.net/1911/18594</a>. | |
| dc.identifier.uri | https://hdl.handle.net/1911/18594 | |
| dc.language.iso | eng | |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | |
| dc.subject | Management | |
| dc.subject | Statistics | |
| dc.title | Venture capital, entrepreneurship, and long-run performance prediction: An application of data mining | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Statistics | |
| thesis.degree.discipline | Engineering | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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