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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 the study is applying and comparing models that predict optimal time for new product exit based on its demand pattern and survivability. This is to decide whether or not to continue investing in new product development (NPD).

The study investigates the optimal time for new product exit within the hi-tech sector by applying three models: the dynamic learning demand model (DLDM), the generalized Bass model (GBM) and the hazard model (HM). Further, for inter- and intra-model comparison, the authors conducted a simulation, considering Weiner and exponential price functions to enhance generalizability.

While higher price volatility signifies an unstable technology, greater investment into research and development (R&D) and marketing results in higher product adoption rates. Imitators have a more prominent role than innovators in determining the longevity of hi-tech products.

The study conducts a comparison of three different models considering time-varying parameters. There are four scenarios, considering variations in advertising intensity and content, word-of-mouth (WOM) effect, price volatility effect and sunk cost effect.

The purpose of this paper is to study whether it is a rational choice for a tax authority to impose an exit tax on capitalists.

The tax authority chooses a lump-sum exit tax to maximize a weighted objective of expected tax revenue and expected tax horizon. The tax revene consists of capital income taxes and exit taxes. Capitalists are motivated by sustainable capital accumulation and hence maximize the terminal capital stock.

The author finds that the objective function of the tax authority is strictly increasing in the exit tax, which holds for extensions with sales tax, labor income tax or proportional exit tax, and hence equilibrium exit tax is equal to an exogenous upper bound.

To the author’s knowledge, no existing literature investigates this issue theoretically, and hence the current paper represents the first attempt. The author hopes this theoretical analysis can trigger related empirical studies.

This paper aims to investigate how the passage of the Sarbanes Oxley Act (SOX) impacted the likelihood and timing of the decision of leveraged buyout (LBO) firms to exit via initial public offering (IPO) (reverse-LBO) and the mediating effect of reputed private equity (PE) firms.

The sample comprises firms that went private via LBO between 1990 and 2018. The authors use logistic and ordinary least square regression models to compare the effect of SOX on the re-listing decision and the time taken to re-list.

LBO firms were less likely to exit via public offering after SOX, and the time from LBO to IPO was significantly longer for exiting firms post-SOX. PE firm reputation partially reversed the reluctance to exit via IPO and shortened the time to exit.

The primary focus is RLBOs; the authors do not directly examine other methods of LBO exit. The findings have policy implications for unintended impacts of SOX. Despite the benefits of increasing transparency and protecting investors, SOX reduced the likelihood of going public and increased the time to IPO, potentially reducing product market competition.

RLBOs present a unique experimental setting as the authors can test the impact of SOX on both the likelihood and time to go public, whereas prior literature using first-time IPO samples are able to test only the likelihood. The authors also show that the reputation of the advising PE firm attenuates the reluctance and time taken for RLBOs to re-list. The authors are, thus, able to provide a new perspective on the impact of SOX on the going public decision.

The purpose of this paper is to propose a method to plan entry and exit times of a project delivering cash flows influenced by business cycles. In this setting, the profit yield by the project are driven by a geometric Brownian motion whose mean and variance switch between a finite number of regimes.

In the existing literacy, an activity is started or aborted once that the current potential profit jumps over a barrier. Due to operational delays, this investment rule can be inefficient in practice. For this reason, the approach developed in this work relies on the assumption that a manager chooses entry and exit times that maximize on average the expected discounted profits.

In this model, entry and exit times are then solutions of a simple non‐linear system of equations. The author also shows how the parameters ruling the switching regime cash flows associated to a project can be inferred from the stock market quotes of a company, active in the same sector of activities. To illustrate the tractability of the model, the author applies it to a project in the healthcare industry.

The model proposed in this paper is tractable for a wide set of investment/disinvestment problems.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

Corporate risk management is one of the critical concerns of managers when they make investment allocation decisions among multiple projects. The purpose of this paper is to address corporate investment issues illustrated by target‐beating in capital budgeting, and further discuss their applications in financial management, especially in venture capital finance.

Value‐at‐risk, a typical down‐side risk measure which is considered more appropriate for economic agents, is applied to the analysis. Probability theory and optimal control methodologies are used to derive analytical solutions.

By maximizing the probability of beating a pre‐determined target, an analytical optimal corporate investment allocation strategy is presented, and the corresponding probability and expected earliest time of success derived.

Various types of utility functions of economic agents and other dynamic downside risk measures can be considered in future research along this line.

This paper paves the road for applications of continuous‐time downside risk in making corporate investment decisions, especially in the field of new venture finance.

As one of the early studies investigating optimal investment decisions in continuous‐time downside risk‐based capital budgeting system, this project sheds light on corporate risk management, and provides risk‐averse decision makers with an effective tool.

The purpose of the study is to test how land owners respond to the appreciation of land values in the presence of speculation. This paper introduces the concept of “land hoarding,” which is land owners' response to higher land prices by selling more land up to a point beyond which accelerated land price appreciation would lead to land hoarding. Specifically, this paper examines the effect of land value appreciation higher than the opportunity cost of selling the land (measured by treasury‐bill (T‐bill) rate) on land sale and land hoarding.

A theoretical framework is developed to understand the demand for agricultural land retention with and without speculation, the former informing land hoarding behavior. A linear regression model was introduced and estimated using ordinary least square (OLS) method. A panel data model and analysis is also introduced, and following appropriate model selection tests, a fixed effect panel data estimation method is implemented. Data from 48 states, spanning from 1950 to 2004, are utilized.

An inverse relationship is found between the rate of land value appreciation and the demand for land by farmers, suggesting that the standard direct relationship between appreciation and land supplied to development holds. However, the additional finding of an inverse relationship between the rate of land value appreciation in excess of the risk‐free rate of return and agricultural land development confirms the existence of an identifiable speculative demand component that involves land hoarding.

To the extent to which the findings are broadly applicable, one policy implication is that enhanced land retention can be achieved through market mechanisms. For example, the notion that reduced T‐bill rates can actually result in market triggered land preservation is an interesting policy related finding. Equally interesting is the notion that policies that can trigger increases in the rate of appreciation of farmland may also potentially result in the agricultural hoarding of land. Obviously, enhanced profitability in agriculture due to programs targeting viability, commodity price support, reduction of regulation or market expansion programs can potentially affect land retention.

This paper introduces the “land hoarding hypothesis.” High rates of land appreciation can be expected to signal that holding the land may yield better returns than selling it, suggesting that if rates of land appreciation become significantly high enough, farmers may begin to hoard land, not sell it, to maximize long‐term returns. This concept can be valuable to market‐based agricultural land retention programs at the urban fringe. By linking speculative behavior, land demand and existence of a hoarding behavior under some conditions, this paper adds value and originality to the literature.

The purpose of this study is to develop optimal evacuation plan to provide valuable theoretical and practical insight in the fire evacuation work of similar structures, by proposing a systematic simulation-based guided-evacuation agent-based model (GAM) and a three-stage mathematical evacuation model to investigate how to simulate, assess and improve the performance efficiency of the evacuation plan.

The authors first present the self-evacuation and guided-evacuation models to determine the optimal evacuation plan in ship chamber. Three key performance indicators are put forward to quantitatively assess the evacuation performance within the two fire scenarios. The evacuation model in tower is built to obtain the dividing points of the three different fire evacuation plans.

The study shows that the optimal evacuation plan determined by the GAM considering social relationships effectively relieves the congestion or collision of evacuees and improves the evacuation uniformity. The optimal evacuation plan not only solves the crush caused by congestion or collision of evacuees but also can greatly shorten the evacuation time for passenger ship fire.

This study establishes the GAM considering the interactive evacuee characteristics and the proportion of evacuees guided by the crew members to make the optimal evacuation plan more time-efficient. The self-evacuation process is simulated to assess the performance of the guided-evacuation strategies, which are used to verify the effectiveness and feasibility of the optimal evacuation plan in this research.