國立虎尾科技大學 |

Design, Development and Implementation of Decision Support Systems for Private Equity Investment.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Design, Development and Implementation of Decision Support Systems for Private Equity Investment./
作者:	Vroomen, Paul H.F.
面頁冊數:	1 online resource (185 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
標題:	Engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355131154

Design, Development and Implementation of Decision Support Systems for Private Equity Investment.
Vroomen, Paul H.F.

Design, Development and Implementation of Decision Support Systems for Private Equity Investment. - 1 online resource (185 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

The objective of this research is to design, develop and implement an intelligent decision support system (IDSS) for making rational private equity investment decisions. (Private equity investments are capital investments in enterprises that are not traded on public equity markets; they include Equity Buy-Out, Venture Capital, and the new Equity Crowd Funding (ECF) asset classes). The design and development of the IDSS requires the integration of investment science (valuation theory, portfolio theory, efficient market theory) and information science & technology (statistical learning, software engineering).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355131154Subjects--Topical Terms:

561152
Engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Design, Development and Implementation of Decision Support Systems for Private Equity Investment.
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245 1 0 $a Design, Development and Implementation of Decision Support Systems for Private Equity Investment.
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300 $a 1 online resource (185 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Adviser: Subhas Desa.
502 $a Thesis (Ph.D.) $c University of California, Santa Cruz $d 2017.
504 $a Includes bibliographical references
520 $a The objective of this research is to design, develop and implement an intelligent decision support system (IDSS) for making rational private equity investment decisions. (Private equity investments are capital investments in enterprises that are not traded on public equity markets; they include Equity Buy-Out, Venture Capital, and the new Equity Crowd Funding (ECF) asset classes). The design and development of the IDSS requires the integration of investment science (valuation theory, portfolio theory, efficient market theory) and information science & technology (statistical learning, software engineering).
520 $a The IDSS architecture is built around the two primary tasks in the due diligence process for evaluating private equity investments, namely, evaluating whether a prospective investment can generate returns commensurate with its risk, and then verifying that the enterprise has the appropriate attributes or success factors to achieve that rate of return. These two tasks are implemented as stages in the IDSS: the "Evaluate" stage (Stage 1), and the "Verify" stage (Stage 2). The motivating application for this IDSS is the emerging ECF asset class, which is applied in this research to demonstrate the financial engineering and statistical learning principles and processes developed.
520 $a A fundamental investment science result of this research is the derivation of the Capital Market Line (CML) for the private equity market, based on modern portfolio theory, efficient market theory, and historical financial performance metrics. The CML enables quantification of the target Internal Rate of Return (IRR) for individual assets in the market as a function of risk. Results show that the target IRR for ECF investments places significant constraints on the operating requirements of companies seeking ECF financing.
520 $a Achieving the target IRR requirement identified in Stage 1 is a necessary but not sufficient condition for making a positive investment decision. In the Verify stage statistical learning methods are applied to verify whether a prospective investment has the necessary attributes in terms of team competencies, product value proposition, and market dynamics to achieve the target IRR. This dissertation describes the processes developed and results achieved in this research for attribute selection and extraction, model selection, and model validation, which together form the core elements of Stage 2. The results of this part of the research demonstrate that the k-nearest-neighbors (kNN) algorithm yields the lowest loss function value of the set of 8 supervised, unsupervised and meta classifiers tested.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Engineering. $3 561152
650 4 $a Computer science. $3 573171
650 4 $a Finance. $3 559073
650 4 $a Information science. $3 561178
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a University of California, Santa Cruz. $b Technology and Information Management. $3 1179448
773 0 $t Dissertation Abstracts International $g 79-01B(E).
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