Project Payoff Research Articles

Strategic Expert Committees and the Markets That Assess Them: A Laboratory Experiment

Committees of experts are widely used to make decisions. We experimentally investigate the relationship between decision making in committees and the assessment of the ability of committee members by evaluators, comparing observed behavior with theoretical predictions. Treatments vary in whether members care only about a state-dependent project payoff or also about assessments and whether evaluators can base their assessments only on the decision the committee makes or also on cheap-talk statements made by committee members on their confidence in the committee decision. Evidence for the equilibrium predictions is mixed; for example, contrary to theory, committees with a concern for their assessment do not distort their decisions more than committees without, whereas in line with theory, evaluators give higher assessments to committees that take the risky decision rather than the riskless. We analyze whether evaluators rationally base their assessments on observed behavior of committees using an orthogonality test. In treatments with cheap-talk statements, assessments are quite rational; in treatments without, they are too low on average. We investigate whether committees best reply to expected project payoffs and, in treatments in which members’ payoffs also depend on assessments, to predicted assessments conditional on observed committee behavior. In all treatments, committees respond to the possibilities to raise expected payoffs but do not use them as much as predicted by theory. We conclude by showing that the amount of information about committee members’ abilities that ends up in assessments is considerably larger when evaluators observe committees’ decisions and statements rather than only decisions. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: We gratefully acknowledge financial support from the Dutch Research Council (NWO) under the grant 400-09-338; and Erasmus University Rotterdam [Grant CSTO 2014–54]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03007 .

Accelerability vs. Scalability: R&D Investment Under Financial Constraints and Competition

I develop a continuous-time model to examine how the interaction between competition and financial constraints affects firms’ research and development (R&D) strategies. The model integrates two key characteristics of R&D investment: accelerability (i.e., higher R&D intensity leads to faster discovery) and scalability (i.e., higher R&D intensity leads to higher project payoff). I find that firms react strategically to their rivals’ financial constraints when making investment decisions in a duopoly R&D race. In particular, firms respond positively to the R&D intensity of an unconstrained rival, while they respond in a hump-shaped fashion to the R&D intensity of a constrained rival. As a result, a constrained firm can pre-empt an unconstrained competitor in market equilibrium. Accelerability is necessary for such pre-emption to occur, and scalability generally reduces its likelihood. Comparison with a monopoly benchmark shows that the economic mechanism differs from over-investment induced by financial constraints alone. The model also generates new implications regarding how project characteristics and cash flow risks impact R&D decisions. This paper was accepted by Gustavo Manso, finance. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4503 .

The Estimation Method for Project Payoff - In Case of Probabilistic Activity Durations and Existence of Drop-out Option -

본 연구는 프로젝트의 수익 추정 방식에 대한 연구이다. 본 연구에서 활동의 기간은 확률적이고 프로젝트 중도 포기 옵션이 존재한다. 수익 추정을 위해 두 가지 추정 방법을 제안하였다. 첫 번째는 활동 기간의 기댓값을 사용하는 방법이고 두 번째는 활동 기간의 확률분포를 사용하여 수익의 분포를 생성하는 방법이다. 모의실험을 통해 두 방법의 유효성을 검증하였고 두 방법의 장단점을 파악하였다. 본 연구의 성과는 확률적 프로젝트 일정문제에 공통적으로 적용될 수 있다.

Management forecast errors and corporate investment efficiency

This paper examines the relation between management forecast errors and corporate investment efficiency. If managers’ forecasting biases systematically affect both external earnings forecasts and internal investment project payoff forecasts, errors in management earnings forecasts will be linked to corporate investment decisions. Consistent with this conjecture, I find that signed management forecast errors are associated with abnormal investment; specifically, more optimistic forecasts are associated with more over-investment, while more pessimistic forecasts are associated with more under-investment. Furthermore, I find that the link between forecast errors and abnormal investment is stronger when analyst feedback reinforces managers’ forecast errors and when managers display persistent forecast errors. Additional tests that consider managers’ disclosure incentives reveal that the link between forecast errors and abnormal investment is stronger when forecast errors are less likely driven by managers’ intentional distortions.

Information Aggregation via Contracting

Much has been studied about the role of the market in information aggregation. This paper argues that a well-designed contract in general, and some simple affine contract under standard conditions in specific, could play a similar role. Our results revisit a simple question: When a group of investors with dispersed private information simultaneously invest in a risky project, how should they divide the project payoff? The results also shed new light on institution-market relationships and provide potential guidance for several emerging FinTech applications.

Investment Analysis under Equity Default Swaps with Asymmetric Information

We assume an entrepreneur (borrower) must borrow money from a lender (bank) to start a project in a single-period model. The debt is secured by an insurer who takes the project and pays the lender all the outstanding principal and interest in case of default. The borrower grants the insurer a fraction of the money borrowed, or of the project's payoff, or of a call option underlying on the project payoff. The corresponding three parties' agreement is called the fee-for-guarantee swap (FGS), equity-for-guarantee swap (EGS), and option-for-guarantee swap (OGS) respectively. We assume the project payoff follows a lognormal distribution. The variance of its logarithm is common knowledge but the mean is only known to the borrower and follows a two-point distribution to the insurer, i.e. there exists asymmetric information. We show that asymmetric information benefits low-profitability borrowers or insurers at the expense of high-profitability ones. The benefit and expense under OGS are the most while those under FGS are the least. In a pooling equilibrium, a high-profitability borrower must transfer a fraction of its profit to the low-profitability one. There is no social welfare loss only if the net investment value of the low-profitability project is positive. In a separating equilibrium, the high-profitability borrower might transfer a fraction of its profit to the insurer and surprisingly, the net investment value of a high-profitability borrower increases with investment cost.

Dynamic order acceptance and capacity planning in a stochastic multi-project environment with a bottleneck resource

We study the integration of order acceptance and capacity planning in multi-project environments with dynamically arriving projects. We model this planning problem as a continuous-time Markov decision process to determine long-term optimal decisions. We examine whether macro-process planning should be performed before or after order acceptance. We characterise the structure of optimal policies, and explore the dependence on a number of parameters such as project payoff, project cost and order arrival time. We also look into the effects of set-up costs and the use of non-regular capacity.

The use of fuzzy real option valuation method to rank Giga Investment Projects on Iran's natural gas reserves

Giga investments are exposed to numerous syste­matic and unsystematic (or specific) risks that significantly influence their feasibility and value. In addition to the conventional issues and complexities in evaluating investment projects, factors such as long construction time, high amount of capital required and irreversibility of investment, increase uncertainty of cash flows in these types of projects. As a consequence, decision makers are usually not satisfied with the results of the most popular va­luation methodology – discounted cash flow analysis – in valuation of Giga investments. The most promising methodology of valuing flexibility in such investment projects is the real options valuation which quantifies the value of embedded flexibilities through option pricing techniques. In this paper, fuzzy real options valuation techniques are used to rank Giga project proposals faced in Iran's gas industry. To this end, different scenarios of project payoff and future uncertainties are quantified, using fuzzy numbers based on findings from earlier real option valuation methods.

Profit-Sharing: A Contracting Solution to Harness Wisdom of the Crowd

When a group of investors with dispersed private information jointly invest in a risky project, how should they divide the project payoff? The typical approach with common stocks rewards investors in proportion to their initial investment, but is it really optimal? Answers to these questions have implications for many business activities in our information age, including the efficacy of the emerging practice of security-based crowdfunding in harnessing the wisdom of the crowd. This paper shows that simple profit-sharing contracts with decentralized decision making could first best coordinate individuals' investment choices, and hence optimally utilize the group's collective wisdom. This result connects the traditional diversification insight underpinning portfolio theory to investment under private information.

Population-based metaheuristics for R&D project scheduling problems under activity failure risk

In this paper, we study scheduling of R&D projects in which activities may to be failed due to the technological risks. We consider two introduced problems in the literature referred to as R&D Project Scheduling Problem (RDPSP) and Alternative Technologies Project Scheduling Problem (ATPSP). In the both problems, the goal is the maximization of the expected net present value of activities where activities are precedence related and each of them is accompanied with a cost, a duration and a probability of technical success. In RDPSP, a project payoff is obtained if all activities are succeeded while in ATPSP, if one of activities is implemented successfully, the project payoff is attained. We developed a solution representation for each of these problems and developed two population-based metaheuristics including scatter search algorithm and genetic algorithm as solution approaches. Computational experiments indicate scatter search outperforms genetic algorithm and also available exact solution algorithms.

Asymmetric information, foreign entry and multi-period credit competition in banking industry

In a credit market, a low-cost foreign entrant is likely less informed than a domestic bank with information endowments. We present a steady-state model in an infinite-period framework, and analyze how the informational asymmetry affects the lending equilibrium and the foreign banks’ entry modes. Firstly, we show that the domestic bank's initial informational advantage from its relatively large market share at the beginning of competition is not enough to hinder the entry of a low-cost foreign bank. In addition, the foreign bank is more likely to be the winner in the steady state of an infinite-period game than that in a static game. Secondly, the comparative static analysis show that the likelihood of foreign bank becoming the winning party increases with borrower quality, project payoff and the growth rate of potential new borrowers. Thirdly, when the foreign bank wins game in the steady state, its market share is decreasing in project payoff and the growth rate of new borrowers, while changes non-monotonically with borrower quality. Finally, we extend our model to characterize the domestic bank's sustainable advantage in being relatively familiar with new market, and provide valuable explanations for the observed entry modes of foreign banks in China.

An exact method for scheduling of the alternative technologies in R&D projects

A fundamental challenge associated with research or new product development projects is identifying that innovative activity that will deliver success. In such projects, it is typically the case that innovative breakthroughs can be achieved by any of several possible alternative technologies, some of which may fail due to the technological risks involved. In some cases, the project payoff is obtained as soon as any single technology is completed successfully. We refer to such a project as alternative-technologies project and in this paper we consider the alternative-technologies project scheduling problem. We examine how to schedule alternative R&D activities in order to maximize the expected net present value, when each technology has a cost and a probability of failure. Although a branch-and-bound algorithm has been presented for this problem in the literature, we reformulate the problem and develop a new and improved branch-and-bound algorithm. We show using computational results that the new algorithm is much more efficient and outperforms the previous one.

Screening and Financial Contracting in the Face of Outside Competition

This paper considers an investor who, at a cost, can acquire a signal about an entrepreneurial project's payoff. The problem for this investor is that uninformed investors can compete to provide funding and the informed investor's contract offer conveys information to the entrepreneur about the project's likely payoffs, affecting the attractiveness of the entrepreneur's uninformed funding alternatives. I determine how the investor's choices of signal quality and contracting terms are affected by project primitives. I prove that equilibrium expected payoffs in this signaling game are unique and that equilibrium strategies solve simple optimal contracting problems, showing that informed investors can use equity, and that uninformed investors compete with simple debt. Finally, I show that introducing the possibility of commitment by the informed investor alters the structure of the game, but not equilibrium payoffs.

Value of a Protective Abandon Put Option for Investors of Small and Large Firms with Different Capital Structures

This paper investigates the value of protective abandon put option from the equity holder’s perspective of a large and a small company. Motivation for this topic arises from the perception that certain market imperfections related to corporate and personal taxes tend to favor a large firm and its investors if there is a risk of a loss or even bankruptcy. Using a flow-to-equity model this paper investigates how these market imperfections affect the project value with and without a protective put option with different capital structures. Because the put option causes asymmetry to the project payoff, semivariance is used as a risk measure instead of the commonly used variance. The results of the analysis indicate that a protective put option is more valuable to small company investors because it narrows the downside risk and thus enables better capital structure and mitigates certain imperfections of taxation. However, as long as there is a risk of loss, large firms and their investors have a tax advantage and thus they value a similar project as being more valuable.

Contracting on real option payoffs

We consider the design of contracts that pay managers on the basis of a project's payoff. We show that a contract that induces appropriate timing of project investment by a privately informed risk neutral manager will not offer proper incentives to forgo perk consumption at the time of investment. Under the firm's optimal contract within the class of payments that do not depend on time of investment, the manager waits too long to invest. The optimal contract can be implemented with a call option on the project payoff.

Project Valuation Under Incomplete Capital Markets: The Case of Non-Diversified Entrepreneurs

Traditional project valuation usually overlooks the important risk analysis process because it relies in the complete market assumption. In a complete market it will be possible to either find twin securities or elaborate a dynamic investment portfolio to replicate the project risk and payoff in every state of nature at any moment in the future. Furthermore, since one assumes well-diversified investors what matters is the project value as if it were traded in the capital market. Unfortunately, the assumption of complete markets hardly holds in reality, especially in emerging markets full of illiquid securities and where financial mechanisms such as short sales or buying on margin are prohibited or non-existent in practice. In this work one assumes incomplete markets and designs a risk analysis procedure that can be applied whenever there is no tradable benchmark in two situations: when investors hold a well-diversified investment portfolio and when investors are non-diversified entrepreneurs. The former case is important because during the last decade there has been an increase of foreign direct investment in emerging economies, so some global diversified entrepreneurs have put their money in these markets. The latter case is especially important for emerging markets where a high proportion of total enterprises are family business or are owned by a single non-diversified entrepreneur. In either case it is possible to use a clear cut investment rule, such as the Net Present Value, but there is no a single market value for the project. In the case of global diversified investors it is just one possible value and for non-diversified entrepreneurs what matters is the project value given the project total risk that they face.

Selectionism and Learning in Projects with Complexity and Unforeseeable Uncertainty

Companies innovating in dynamic environments face the combined challenge of unforeseeable uncertainty (the inability to recognize the relevant influence variables and their functional relationships; thus, events and actions cannot be planned ahead of time) and high complexity (large number of variables and interactions; this leads to difficulty in assessing optimal actions beforehand).There are two fundamental strategies to manage innovation with unforeseeable uncertainty and complexity: trial and error learning and selectionism. Trial and error learning involves a flexible (unplanned) adjustment of the considered actions and targets to new information about the relevant environment as it emerges. Selectionism involves pursuing several approaches independently of one another and picking the best one ex post.Neither strategy nor project management literatures have compared the relative advantages of the two approaches in the presence of unforeseeable uncertainty and complexity. We build a model of a complex project with unforeseeable uncertainty, simulating problem solving as a local search on a rugged landscape. We compare the project payoff performance under trial and error learning and selectionism, based on a priori identifiable project characteristics: whether unforeseeable uncertainty is present, how high the complexity is, and how much trial and error learning and parallel trials cost. We find that if unforeseeable uncertainty is present and the team cannot run trials in a realistic user environment (indicating the project's true market performance), trial and error learning is preferred over selectionism. Moreover, the presence of unforeseeable uncertainty can reverse an established result from computational optimization: Without unforeseeable uncertainty, the optimal number of parallel trials increases in complexity. But with unforeseeable uncertainty, the optimal number of trials might decrease because the unforeseeable factors make the trials less and less informative as complexity grows.

Selectionism and Learning in Complex and Ambiguous Projects

Project management literature has increasingly recognized that established project management methods work well for projects with moderate complexity and uncertainty, but have limitations in projects with ambiguity (the inability to recognize the relevant influence variables and their functional relationships; thus, events and actions cannot be planned ahead of time) and high complexity (large number of variables and interactions; this leads to a difficulty of assessing optimal actions beforehand). There are two fundamental strategies to manage projects with ambiguity and complexity: trial & error learning and selectionism. Trial & error learning involves a flexible adjustment of the project approach to new information about the relevant environment, as it emerges. Selectionism involves pursuing several approaches independently of one another and picking the best one ex post. Previous work has combined the two approaches under uncertainty but not under the combined influence of project ambiguity and complexity. We build a model of a complex project with ambiguity, simulating problem-solving as a local search on a rugged landscape. We compare the project payoff performance under trial & error learning and selectionism, based on a priori identifiable project characteristics: whether ambiguity is present, how high the complexity is, and how much trial & error learning and parallel trials cost. We find that if ambiguity is present and the team cannot run trials in a realistic user environment (reflecting the project's true market performance), trial & error learning becomes more attractive relative to selectionism as the project's complexity increases. Moreover, the presence of ambiguity may reverse an established result from computational optimization: without ambiguity, the optimal number of parallel trials increases in complexity. But with ambiguity, the optimal number of trials may decrease because the ambiguous factors make the trials less and less informative as complexity grows.

On Uncertainty, Ambiguity, and Complexity in Project Management

This article develops a model of a project as a payoff function that depends on the state of the world and the choice of a sequence of actions. A causal mapping, which may be incompletely known by the project team, represents the impact of possible actions on the states of the world. An underlying probability space represents available information about the state of the world. Interactions among actions and states of the world determine the complexity of the payoff function. Activities are endogenous, in that they are the result of a policy that maximizes the expected project payoff.A key concept is the adequacy of the available information about states of the world and action effects. We express uncertainty, ambiguity, and complexity in terms of information adequacy. We identify three fundamental project management strategies: instructionism, learning, and selectionism. We show that classic project management methods emphasize adequate information and instructionism, and demonstrate how modern methods fit into the three fundamental strategies. The appropriate strategy is contingent on the type of uncertainty present and the complexity of the project payoff function. Our model establishes a rigorous language that allows the project manager to judge the adequacy of the available project information at the outset, choose an appropriate combination of strategies, and set a supporting project infrastructure—that is, systems for planning, coordination and incentives, and monitoring.

Time to build options in construction processes

Time to build is a very important factor in a real estate development venture. Delay in completion of a project not only affects the financing costs and the rental revenue but also it may, on a more strategic note, determine the success or failure of a project. A time to build option model consisting of a stochastic rate of completion and a stochastic net project payoff is applied to the sequential construction process of a large scale construction project. The results of the sensitivity analysis show that the optimal payoff value, that triggers the exercise of the option to invest at a maximum rate, increases positively with the increases in cash flow volatility, input cost uncertainty, excess asset return per unit risk and maximum rate of investment. However, it has a negative relationship with the rental yield. In a case study involving a commercial project, the premium for hedging the payoff risks by pre-leasing a project was estimated at 11.29%, whereas the additional cost incurred for shielding a project against input cost risks in a design and build contract was estimated at 7.80%, where each is given as a percentage of the total construction costs.