Measuring Portfolio Valuation

2017 ◽  
Author(s):  
David P. Bernstein
Keyword(s):  
Portfolio Valuation

Optimal Design Decisions in Product Portfolio Valuation

2002 ◽  
Author(s):  
Panayotis Georgiopoulos ◽  
Ryan Fellini ◽  
Michael Sasena ◽  
Panos Y. Papalambros
Keyword(s):  
Product Development ◽  
Optimal Designs ◽  
Product Development Process ◽  
Product Portfolio ◽  
Design Decisions ◽  
Business Decisions ◽  
Business Context ◽  
Valuation Process ◽  
Product Valuation ◽  
Portfolio Valuation

Product portfolio valuation is a core business milestone in a firm’s product development process: Determine what will be the final value to the firm derived from allocating assets into an appropriate product mix. Optimal engineering design typically deals with determining the best product based on technological (and, occasionally, cost) requirements. Linking technological with business decisions allows the firm to follow a product valuation process that directly considers not only what assets to invest but also what are the appropriate physical properties of these assets. Thus, optimal designs are determined within a business context that maximizes the firm’s value. The article demonstrates how this integration can be accomplished analytically using a simple example in automotive product development.

New Entropy Based Distance for Training Set Selection in Debt Portfolio Valuation

2012 ◽  
Vol 7 (2) ◽  
pp. 60-69
Author(s):  
Tomasz Kajdanowicz ◽  
Slawomir Plamowski ◽  
Przemyslaw Kazienko
Keyword(s):  
Machine Learning ◽  
Distance Measure ◽  
Prediction Performance ◽  
Training Set ◽  
Learning Tasks ◽  
Real Domain ◽  
Valuation Process ◽  
Proper Training ◽  
Training Set Selection ◽  
Portfolio Valuation

Choosing a proper training set for machine learning tasks is of great importance in complex domain problems. In the paper a new distance measure for training set selection is presented and thoroughly discussed. The distance between two datasets is computed using variance of entropy in groups obtained after clustering. The approach is validated using real domain datasets from debt portfolio valuation process. Eventually, prediction performance is examined.

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