• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.135-140
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• 2004

It considers advertising as an instrument to increase the stock of goodwill or reputation. summarising the effects of past and current advertising expenditures carried out by a firm. on the current demand for her goods. A relevant result emerging from the Dorfman-Steiner (1954) condition. establishing that advertising investment is proportional to sales. This paper investigates optimal advertising level between the good firm and the bad firm. So. We knew that between the good firm and the bad firm are establishing the Dorfman-Steiner condition.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.2
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• pp.83-95
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• 2014

In this paper, we develop a portfolio selection model that can be used to invest in markets with margin requirements such as the foreign exchange market. An investment algorithm to implement the proposed portfolio selection model based on objective historical data is also presented. We further conduct empirical analysis on the performance of a hypothetical investment in the foreign exchange market, using the proposed portfolio selection model and investment algorithm. Using 7 currency pairs that recorded the highest trading volume in the foreign exchange market during the most recent 10 years, we compare the performance of 1) the Dollar Index, 2) a 1/N Portfolio which equally allocates capital to all N assets considered for investment, and 3) a hypothetical investment portfolio selected and managed according to the portfolio selection model and investment algorithm proposed in this paper. Performance is compared in terms of accumulated returns and Sharpe ratios for the 10-year period from January 2003 to December 2012. The results show that the hypothetical investment portfolio outperforms both benchmarks, with superior performance especially during the period following financial crisis. Overall, this paper suggests that a mathematical approach for selecting and managing an optimal investment portfolio based on objective data can achieve outstanding performance in the foreign exchange market.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.957-962
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• 2008

We propose a model for the line expansion problem in the AMOLED (Active Matrix Organic Light Emitting Diodes) industry, which now faces market uncertainty: for example, changing customer needs, technological development path, etc. We focus on the optimal investment time and size of the AMOLED production lines. In particular, employed here is the ROV (Real Options Valuation) model to show how to capture the value of line expansion and to determine the optimal investment time. The ROV framework provides a systematic procedure to quantify an expected outcome of a flexible decision which is not possible in the frame of the traditional NPV (Net Present Value) approach. Furthermore, we also use Monte Carlo simulation to measure the uncertainty associated with the line expansion decision; Monte Carlo simulation estimates the volatility of a decision alternative. Lastly, we present a scenario planning to be conducted for what-if analysis of the ROV model.

• Korean Management Science Review
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• v.27 no.2
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• pp.21-29
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• 2010

We consider an economic approach for spectrum operations in mobile networks. We present the investment function of base stations according to the number of FAs assuming the linearity of cell splitting. We show that there is an economic amount of spectrum which corresponds to the optimal number of FAs that minimizes the investment. We analyze the impact of the cost structure and the traffic distribution in base stations on the economic amount of spectrum. This paper is applicable to an economic spectrum operation for mobile operators. In addition, the national regulatory authority can use the economic amount of spectrum as the minimal amount for spectrum allocation.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.797-799
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• 1997

This paper presents an operation and planning model of integrated energy systems which consist of small scale cogeneration systems, thermal accumulator, ice storage and electrical energy storage systems. In the proposed planning model, an optimization of total cost which contains investment, operation, thermal shortage and salvage costs has carried out with the maximum principle based on the lifetime of each system component and unit price per capacity. From this model, optimal investment capacity per annum can be determined during the studied periods using the marginal costs according to the operation characteristics of each system component.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.2
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• pp.49-66
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• 2014

This paper studies optimal consumption and investment behaviors of an individual when risky asset returns and her income are affected by the business cycle. The investor considers the incomplete information risk of unobservable macroeconomic conditions and updates her belief of expected risky asset returns through Bayesian learning. We find that the optimal investment strategy, certainty equivalent wealth, and portfolio hedging demand significantly depend on the belief about the macroeconomic conditions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.63-71
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• 2009

The traditional portfolio optimization problem is to find an investment plan for securities with reasonable trade-off between the rate of return and the risk. The seminal work in this field is the mean-variance model by Markowitz, which is a quadratic programming problem. Since it is now computationally practical to solve the model, a number of alternative models to overcome this complexity have been proposed. In this paper, among the alternatives, we focus on the Mean Absolute Deviation (MAD) model. More specifically, we developed an algorithm to obtain an optimal portfolio from the MAD model. We showed mathematically that the algorithm can solve the problem to optimality. We tested it using the real data from the Korean Stock Market. The results coincide with our expectation that the method can solve a variety of problems in a reasonable computational time.

• Journal of the Korean Mathematical Society
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• v.59 no.2
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• pp.311-335
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• 2022

This paper treats Merton's classical portfolio optimization problem for a market participant who invests in safe assets and risky assets to maximize the expected utility. When the state process is a d-dimensional Markov diffusion, this problem is transformed into a problem of solving a Hamilton-Jacobi-Bellman (HJB) equation. The main purpose of this paper is to solve this HJB equation by a deep learning algorithm: the deep Galerkin method, first suggested by J. Sirignano and K. Spiliopoulos. We then apply the algorithm to get the solution to the HJB equation and compare with the result from the finite difference method.

• Proceedings of the KIEE Conference
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• 1986.07a
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• pp.318-321
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• 1986

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.506-516
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• 2005

This paper deals with an investment scheduling problem of maximizing net present value of dividend with reinvestment allowed, where each investment has certain capital requirement and generates deterministic profit. Such deterministic profit is calculated at completion of each investment and then allocated into two parts, including dividend and reinvestment, at each predetermined reinvestment time point. The objective is to make optimal scheduling of investments over a fixed planning horizon which maximizes total sum of the net present values of dividends subject to investment precedence relations and capital limit but with reinvestment allowed. In the analysis, the scheduling problem is transformed to a kind of parallel machine scheduling problem and formulated as an integer programming which is proven to be NP-complete. Thereupon, a depth-first branch-and-bound algorithm is derived. To test the effectiveness and efficiency of the derived algorithm, computational experiments are performed with some numerical instances. The experimental results show that the algorithm solves the problem relatively faster than the commercial software package (CPLEX 8.1), and optimally solves the instances with up to 30 investments within a reasonable time limit.