Optimal Investment Decision of Security Investment Fund Based on the Experiment Design

2008 ◽  
Author(s):  
Wei Cheng ◽  
Jin-yu Wang ◽  
Jing-ting Ma
Keyword(s):  
Investment Decision ◽  
Optimal Investment ◽  
Experiment Design ◽  
Investment Fund ◽  
Security Investment ◽  
Security Investment Fund

scholarly journals Two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve

2018 ◽  
Vol 11 (4) ◽  
pp. 794 ◽  
Author(s):  
Mega Aria Pratama ◽  
Cucuk Nur Rosyidi ◽  
Eko Pujiyanto
Keyword(s):  
Quality Improvement ◽  
Optimization Model ◽  
System Modeling ◽  
Investment Decision ◽  
Optimal Investment ◽  
Investment Fund ◽  
Stage Model ◽  
Component Allocation ◽  
Quality Investment ◽  
Two Stages

Purpose: The aim of this research is to develop a two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve. The first stage model is developed to determine the optimal selection of process/suppliers and the component allocation to those corresponding process/suppliers. The second stage model deals with quality improvement efforts to determine the optimal investment to maximize Return on Investment (ROI) by taking into consideration the learning and forgetting curve.Design/methodology/approach: The research used system modeling approach by mathematically modeling the system consists of a manufacturer with multi suppliers where the manufacturer tries to determine the best combination of their own processes and suppliers to minimize certain costs and provides funding for quality improvement efforts for their own processes and suppliers sides.Findings: This research provides better decisions in make or buy analysis and to improve the components by quality investment considering learning and forgetting curve.Research limitations/implications: This research has limitations concerning investment fund that assumed to be provided by the manufacturer which in the real system the fund may be provided by the suppliers. In this model we also does not differentiate two types of learning, namely autonomous and induced learning.Practical implications: This model can be used by a manufacturer to gain deeper insight in making decisions concerning process/suppliers selection along with component allocation and how to improve the component by investment allocation to maximize ROI.  Originality/value: This paper combines two models, which in previous research the models are discussed separately. The inclusions of learning and forgetting also gives a new perspective in quality investment decision.

scholarly journals European Option Based R&D Investment Decision Making under Uncertainties

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Qing Miao ◽  
Boyang Cao ◽  
Minghui Jiang
Keyword(s):  
Decision Making ◽  
Research And Development ◽  
Joint Venture ◽  
Investment Decision ◽  
Optimal Investment ◽  
European Option ◽  
Project Investment ◽  
Research Joint Venture ◽  
Investment Decision Making ◽  
D Investment

This paper establishes the payoff models of the European option for research and development (R&D) projects with two enterprises in a research joint venture (RJV). The models are used to assess the timing and payoffs of the R&D project investment under quantified uncertainties. After the option game, the two enterprises can make optimal investment decision for the R&D project investment in the RJV.

scholarly journals The Term Structure of Variance Swap Rates and Optimal Variance Swap Investments

2010 ◽  
Vol 45 (5) ◽  
pp. 1279-1310 ◽  
Author(s):  
Daniel Egloff ◽  
Markus Leippold ◽  
Liuren Wu
Keyword(s):  
Term Structure ◽  
Investment Decision ◽  
Market Price ◽  
Optimal Investment ◽  
Stock Index ◽  
Structure Variation ◽  
Variance Swap ◽  
Variance Risk ◽  
Negative Estimate ◽  
Swap Rates

AbstractThis paper performs specification analysis on the term structure of variance swap rates on the S&P 500 index and studies the optimal investment decision on the variance swaps and the stock index. The analysis identifies 2 stochastic variance risk factors, which govern the short and long end of the variance swap term structure variation, respectively. The highly negative estimate for the market price of variance risk makes it optimal for an investor to take short positions in a short-term variance swap contract, long positions in a long-term variance swap contract, and short positions in the stock index.

The Dynamic Programming Model for Investment Decision of Enterprise

2014 ◽  
Vol 519-520 ◽  
pp. 1468-1471
Author(s):  
Jun Quan Gong ◽  
Xiao Hong Qin
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Capital Investment ◽  
Programming Model ◽  
Investment Decision ◽  
Fuzzy Optimization ◽  
Optimal Investment ◽  
Financial Resources ◽  
Risks And Benefits ◽  
Dynamic Programming Model

Enterprise often face to limit financial resources but also have to consider how to invest effectively on a number of projects in the various factors of the risks and benefits in different periods. In order to assure the optimal investment results of capital investment, this paper has established dynamic programming model which is multi-dimensional and multi-objective and fuzzy optimization, dynamic programming and genetic algorithm is combination to solve investment decision of enterprise. At last, this paper through an example to verify the validity of dynamic programming model.

Technological Innovation, Product Life Cycle and Market Power: A Real Options Approach

2015 ◽  
Vol 14 (01) ◽  
pp. 93-113 ◽  
Author(s):  
Johnson T. S. Cheng ◽  
I-Ming Jiang ◽  
Yu-Hong Liu
Keyword(s):  
Life Cycle ◽  
Real Options ◽  
Market Power ◽  
Technological Innovation ◽  
Product Life Cycle ◽  
Investment Decision ◽  
Investment Decisions ◽  
Optimal Investment ◽  
Product Life ◽  
Real Options Approach

This paper employs a real options approach to analyze optimal investment decisions. When investment projects have the characteristics of irreversibility, uncertainty and the option to wait or exit, the traditional net present value (NPV) method would underestimate the value of investment, since it neglects the values of timing and operational flexibility. The distinctive feature of this paper is that the effects of product life cycle (PLC) as well as market power are incorporated into the model. In addition, and different to the approach in Liao et al. [Optimal investment decision and product life cycle: A real options approach, Sun Yat-Sen Management Review 11(3) (2003) 1–36], we introduce the concept of technological innovation into the model. It is shown that the optimal waiting time for the investment is longer than both those in the American call options model of McDonald and Siegel [The value of waiting to invest, Quarterly Journal of Economics 101(4) (1986) 707–727], which does not incorporate dividend yield, and Liao et al. [Optimal investment decision and product life cycle: A real options approach, Sun Yat-Sen Management Review 11(3) (2003) 1–36], but is shorter than that in Dixit and Pindyck's [Investment under Uncertainty (Princeton University Press, Princeton, NJ, 1994)] model, which incorporates dividend yield. Finally, a comparative static is used to analyze the determinants of optimal investment decisions. Our results indicate that the investment-ratio threshold will be higher, and thus the optimal entry time for an investment will be delayed, when (1) the PLC is longer, (2) the uncertainty is greater, (3) the discounting rate is higher, (4) market power is larger, (5) jump size intensity is stronger and (6) the payoff out ratio (R&D/revenue) is larger.

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