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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.

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.

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.

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.

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.

By using higher moments, this paper extends the quadratic local risk-minimizing approach in a general discrete incomplete financial market. The local optimization subproblems are convex or nonconvex, depending on the moment variants used in the modeling. Inspired by Lai et al. (2006), the authors propose a new multiobjective approach for the combination of moments that is transformed into a multigoal programming problem.

The authors evaluate financial derivatives with American features using local risk-minimizing strategies. The financial structure is in line with Schweizer (1988): the market is discrete, self-financing is not guaranteed, but deviations are controlled and reduced by minimizing the second moment. As for the quadratic approach, the algorithm proceeds backwardly.

In the context of evaluating American option, a transposition of this multigoal programming leads not only to nonconvex optimization subproblems but also to the undesirable fact that local zero deviations from self-financing are penalized. The analysis shows that issuers should consider some higher moments when evaluating contingent claims because they help reshape the distribution of global cumulative deviations from self-financing.

A detailed numerical analysis that compares all the moments or some combinations of them is performed.

The quadratic approach is extended by exploring other higher moments, positive combinations of moments and variants to enforce asymmetry. This study also investigates the impact of two types of exercise decisions and multiple assets.

The purpose of this paper is to address the issues whether or not minimal repair is effective for a repairable item with a known life distribution and when the minimal repair process should be stopped.

The life restoration degree (LRD) following minimal repair is defined and related to the shape parameter of the distribution so that a choice between minimal and perfect repairs can be made based on the shape parameter. Three replacement policies with minimal repair are considered and the corresponding decision rules are derived to determine when the minimal repair process should be stopped.

Main findings are: first, the LRD of minimal repair is inversely or approximately inversely proportional to the shape parameter, second, the effectiveness of minimal repair increases as the cost ratio of perfect and minimal repairs increases and the shape parameter decreases, and third, the unconditional mean residual life equal the mean time between the first and second failures.

The results can be easily used for maintenance strategy selection and maintenance decision optimization of repairable items.

This study aims to generalize the following result of McDonald and Siegel (1986) on optimal investment: it is optimal for an investor to invest when project cash flows exceed a certain threshold. This study presents other results that refine or extend this one by integrating timing flexibility and changes in cash flows with time-varying transition probabilities for regime switching. This study emphasizes that optimal thresholds are either overvalued or undervalued in the absence of time-varying transition probabilities. Accordingly, the stochastic nature of transition probabilities has important implications to the search for optimal timing of investment.

In a global environment where terrorist organisations based in a poor country target a rich nation, this paper aims to study the properties of a dynamically incentive compatible contract designed by the target nation that involves joint counter-terror tasks with costly participation by each country. The counter-terror operations are however subject to ex post moral hazard, so that to incentivise counter-terror, the rich country supplies developmental aid. Development aid also helps avoid unrest arising from counter-terror activities in the target nation. However, aid itself can be diverted to non-developmental projects, generating a novel interlinked moral hazard problem spanning both tasks and rewards.

The authors use a dynamic model where the aid giving countries and aid receiving countries behave strategically. Then they solve for the sub game perfect Nash equilibrium of this game.

The authors characterise the optimal contract, showing that the dynamic structure of counter-terror resembles the shock-and-awe discussed by military strategists. The authors then prove that it is not necessarily the case that a more hawkish (resp. altruistic) donor is less pro-development (resp. softer on terror). In addition, the authors show that it may be easier to contract for higher counter-terror inputs when the recipient is more sympathetic to terrorists. The authors also discuss other problems faced by developing nations where this model can be readily adopted and the results can endorse appealing policy implications.

The authors characterise the optimal contract, showing that the dynamic structure of counter-terror resembles the shock-and-awe discussed by military strategists. It is proved that it is not necessarily the case that a more hawkish (resp. altruistic) donor is less pro-development (resp. softer on terror). In addition, the authors show that it may be easier to contract for higher counter-terror inputs when the recipient is more sympathetic to terrorists. Other problems faced by developing nations are also discussed where this model can be readily adopted, and the results can endorse appealing policy implications. These results have important policy implications, in particular in today’s world.

Almost everything around us is the output of software-driven machines or working with software. Software firms are working hard to meet the user’s requirements. But developing a fault-free software is not possible. Also due to market competition, firms do not want to delay their software release. But early release software comes with the problem of user reporting more failures during operations due to more number of faults lying in it. To overcome the above situation, software firms these days are releasing software with an adequate amount of testing instead of delaying the release to develop reliable software and releasing software patches post release to make the software more reliable. The paper aims to discuss these issues.

The authors have developed a generalized framework by assuming that testing continues beyond software release to determine the time to release and stop testing of software. As the testing team is always not skilled, hence, the rate of detection correction of faults during testing may change over time. Also, they may commit an error during software development, hence increasing the number of faults. Therefore, the authors have to consider these two factors as well in our proposed model. Further, the authors have done sensitivity analysis based on the cost-modeling parameters to check and analyze their impact on the software testing and release policy.

From the proposed model, the authors found that it is better to release early and continue testing in the post-release phase. By using this model, firms can get the benefits of early release, and at the same time, users get the benefit of post-release software reliability assurance.

The authors are proposing a generalized model for software scheduling.