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One of the agency conflicts between investors and managers in fund management is reflected by risk‐taking behaviors led by their different goals. The investors may stop their investments in risky assets before the end of the investment horizon to minimize risk, while the managers may do so to entrench their reputation so as to pursue better opportunities in the labor market. This study aims to consider a one principal‐one agent model to investigate this agency conflict.

The paper derives optimal asset allocation strategies for both parties by extending the traditional dynamic mean‐variance model and considering possibilities of optimal early stopping. Doing so illustrates the principal‐agent conflict regarding risk‐taking behaviors and managerial investment myopia in fund management.

This paper not only paves the way for further studies along this line, but also presents results useful for practitioners in the money management industry.

According to the theoretical analysis and numerical simulations, the paper shows that potential early stop can make the agency conflict worsen, and it proposes a way to mitigate this agency problem.

As one of the exploratory studies in investigating agency conflict regarding risk‐taking behaviors in the literature, this study makes multiple contributions to the literature on fund management, asset allocation, portfolio optimization, and risk management.

Despite the existence of multiple asset replacement theories, the economic life replacement method remains a major practical technique for making rational machine replacement decisions. The purpose of this paper is to bridge this method with comprehensive data analytic tools and make it applicable it to modern business reality with abundant data on operating and replacement costs.

This study employs operations research, discrete and continuous optimization, applied mathematical modeling, data analytics, industrial economics and real options theory.

Constructed stochastic algorithms extend the deterministic economic life method and are compared to the contemporary theory of stochastic asset replacement based on real options and dynamic programming. It is proven that both techniques deliver similar results when the cost volatility is small. A major theoretic finding is that the cost uncertainty speeds up the replacement decision.

This research suggests that the proposed stochastic algorithms may become an important tool for managerial decisions about replacement of many similar machines with detailed data on operating and replacement costs.

Compared to the real options replacement theory, major advantages of the proposed algorithms are that they work equally well for any distribution of age-dependent stochastic operating cost. The algorithms are tested on a real industrial case about replacement of medical imaging devices. Numeric simulation supports obtained analytic outcomes.

The purpose of this study is to verify whether the trading strategy can beat the “buy-and-hold” strategy for the securitized real estate indices of six Asian economies: Hong Kong, China, Japan, Taiwan, Thailand and Malaysia.

This paper constructs a trading strategy from the Shiryaev-Zhou index and tests the strategy on the securitized real estate indices of six emerging Asian economies: Hong Kong, China, Japan, Taiwan, Thailand and Malaysia. The authors compare the resulting profits from using the trading strategy with the resulting profits from using the “buy-and-hold” strategy. The authors consider three cases: no transaction costs, 0.1 percent transaction costs, and 0.2 percent transaction costs.

The results show that the trading strategy the authors constructed generally outperforms the “buy-and-hold” strategy even in the presence of transaction costs. In particular, the authors have a new finding as follows: Thailand and Malaysia's securitized real estate indices fell drastically during the period of observation. However, applying the trading strategy to these two securitized real estate indices can still earn a profit.

The trading strategy is particularly useful in protecting investors from huge loss in adverse market conditions. The results can be applied to the field of finance/investment that investors can construct a trading strategy similar to the authors to earn more profits.

This study will consider cases where both buying and selling costs exist, so the scenario is more like stock transactions in real-life equity markets. Furthermore, in this paper, for each securitized real estate index, the authors plot a graph to show the holding and non-holding periods under the trading strategy. This would help the authors explain the resulting profit under the trading strategy. This kind of graphical analysis was neglected by Hui and Yam.

The purpose of this paper is to investigate how to determine optimal investing stopping time in a stochastic environment, such as with stochastic returns, stochastic interest rate and stochastic expected growth rate.

Transformation method was used for solving optimal stopping problem by providing a way to transform path‐dependent problem into a path‐independent one. Based on option pricing theory, optimal investing stopping time was thought of as an optimal executed timing problem of American‐style option.

First, the authors transform a path‐dependent stop timing problem to a path‐independent one with transformation under very general conditions, to directly use the existing conclusion of optimal stopping time literature. Second, when dynamics of capital growth is homogeneous, the authors changed the two dimensional optimal stop timing problem into a single dimension problem based on the assumption of zero exercise costs. Third, the authors investigated the comparative dynamics about asset selling boundary on asset value, state variable and return predictability. With constant discount rate and growth rate, the optimal selling timing depends on the simple comparison between capital cost and growth rate.

The paper's contributions to analysis method may be as follows. The authors demonstrate how to transform a path‐dependent stopping problem into a path‐independent one under general conditions. The transform method in this article can be applied to other path‐dependent optimal stopping problems. In particular, a Riccati ordinary differential equation for the transformation is set up. In most examples commonly met in finance, the equation can be solved explicitly.

Sustainable transport has become a new paradigm offering efficient, equitable, and pro-environment transport services. Many intermodal freight systems, especially those for port-to-rail networks, consist of multiple routes starting from and ending at the same port in order to exploit economies of scale. It is of interest to railway operators, therefore, to improve the efficiency of the system by finding the optimal fleet size (the number of cars assigned to a route) and frequency for each route. This paper proposes a model which determines the optimal frequency of each route under the total fleet size constraint for the one-to-many distribution. Trains carry items from one port to their destinations on their predetermined routes. This paper focuses on situations in which items from one port are transported to many destinations via railroads. The tradeoffs between transportation and inventory costs determine optimal frequency under the total fleet size and capacity constraints. The optimal frequency and fleet size of each route are calculated and then updated at the end of each step of the model. The model that we have developed in this paper is validated by port-to-rail freight data from actual shipments in Korea. The results of the analysis show that the proposed model can provide a more reliable and realistic representation of the real one-to-many distribution than the other alternatives which are commonly used. This study not only forms the theoretical basis of an effective and rational freight operation, but it also contributes to the assessment of the existing and planned logistics systems.

Exit strategies are critical for external private equity holders, such as venture capitalists and business angels, to receive investment returns successfully. The paper models the exit decision as a fixed date with the option to exit early, and develop an approach to help private equity holders determine an optimal early exit region based on a target equity value and the time remaining.

The paper sets up a continuous time model to derive analytical solutions and apply simulations to numerical examples in this study.

By numerically analyzing the nature of the solution the paper illustrates that a higher return drift of the investee company, a lower return volatility of the investee company, and a higher target return of the private equity holder results a smaller early exit region.

This study helps determine the optimal time of stopping investments, and provides venture capitalists with a usable way to make exit decisions.

In this paper, we view an individual's annuitization decision as an American style call option whose underlying asset is financial wealth, which controls the distance to annuitization. We then derive a certain threshold of wealth over which the individual is optimal to annuitize all of her wealth. We particularly focus on the effects of liquidity constraints on the individual's optimal annuitization decision, concerning their effects on the optimal investment and consumption strategies. We show that the annuitization decision can be significantly affected by the extent to which individual borrowing is constrained. More specifically, the optimal decision is for the individual to annuitize earlier with the tighter liquidity constrains she is exposed to than initially planned. This is particularly relevant to today's pandemic situation especially with the growing concern about cutting credit limits.

In this paper, we develop various valuation models for closed-end mutual funds under different sets of stochastic processes for the underlying assets. Since we used different stochastic processes from previous literature, it was possible to derive more interesting implications regarding investment strategies, discount puzzles of the funds, and valuation models. In particular, by utilizing Brownian motions and optimal stopping time framework, we succeeded in developing more realistic valuation model, which indicates that we can understand more easily about decision makings regarding optimal timing of reorganization from the closed-end funds to open-ended funds, optimal timing of trading of closed-end funds to realize maximum profits, and optimal design of closed-end fund structure.

The purpose of this paper is to present a novel method to estimate the optimal software testing time which minimizes the relevant expected software cost via a refined neural network approach with the grouped data, where the multi-stage look ahead prediction is carried out with a simple three-layer perceptron neural network with multiple outputs.

To analyze the software fault count data which follows a Poisson process with unknown mean value function, the authors transform the underlying Poisson count data to the Gaussian data by means of one of three data transformation methods, and predict the cost-optimal software testing time via a neural network.

In numerical examples with two actual software fault count data, the authors compare the neural network approach with the common non-homogeneous Poisson process-based software reliability growth models. It is shown that the proposed method could provide a more accurate and more flexible decision making than the common stochastic modeling approach.

It is shown that the neural network approach can be used to predict the optimal software testing time more accurately.

This paper aims to focus on the problem of the optimal relocation policy for a firm that faces two types of uncertainty: one about the moments in which new (and more efficient) sites will become available; and the other regarding the degree of efficiency improvement inherent to each one of these new, yet to be known, potential location places.

The paper considers the relocation issue as an optimal stopping decision problem. It uses Poisson jump processes to model the increase in the efficiency process, where these jumps occur according to a homogeneous Poisson process, but the magnitude of these jumps can have special distributions. In particular it assumes that the magnitudes can be gamma‐distributed or truncated‐exponential distributed.

Particular characteristics concerning the expected optimal timing for relocation, the corresponding volatility and the value of the firm under the optimal relocation policy are derived. These results lead also to the conjecture that the optimal relocation policy is robust in terms of distributions of the degree of improvement of efficiency that are considered, as long as the expected values are the same.

The paper provides an innovative approach to relocation problems, using stochastic tools. Moreover, the use of the truncated exponential and the gamma distribution functions to model the Poisson jumps is particularly suitable, given the situation under study. To the authors' knowledge, this is the first time that this type of setting is used to tackle a real options problem.