Uncertain Returns Research Articles

Dynamic and Cross-Platform Optimal Financial Transaction Taxation

This paper analyzes the welfare implications of a linear financial transactions tax in a multi-period portfolio selection model with heterogeneous agents. Under certain conditions over the redistribution of government revenues, with no Pigouvian motives, I find such a tax may induce first-order losses due to the distortion of idiosyncratic risk-sharing. A transaction tax induces both a contemporaneous inaction region and intertemporal shifting of transaction realizations. Given a segmented market over multiple trading platforms and the choice of at least two different tax instruments, I develop sufficient statistic formulas that differ from standard optimal commodity tax theory in that they account for untaxed capital accumulation, market structure rents, and uncertain returns.

Energy technology R&D portfolio management: Modeling uncertain returns and market diffusion

The allocation of research and development (R&D) funds across a portfolio of programs must simultaneously consider uncertainty from research outcomes and from market acceptance of the resulting technologies. We introduce a stochastic R&D portfolio management framework for addressing both sources of uncertainty and present numerical results for energy technology R&D strategy under uncertainties in climate policy and natural gas prices. Numerical experiments indicate that R&D may be more valuable in second-best planning environments where decision-makers use expected-value approaches, and recourse investments occur after R&D has reduced costs. We also find that deterministic R&D valuation approaches likely overestimate the expected value of R&D success but undervalue the optionality and hedging potential of technologies relative to sequential decision-making approaches under uncertainty. The results also highlight the role of R&D in second-best policy environments.

BIM adoption within Australian Small and Medium-sized Enterprises (SMEs): an innovation diffusion model

Despite the envisaged benefits of BIM adoption for SMEs, BIM in SMEs has remained an underrepresented area within the available academic literature. This study proposes and draws upon a framework grounded on innovation diffusion theory (IDT) to provide an illuminating insight into the current state of BIM and the main barriers to BIM adoption within Australian SMEs. Based on analyses of 135 questionnaires completed by SMEs through partial least squares structural equation modelling (PLS-SEM) and grounded on the proposed framework, the current state of BIM adoption and barriers to BIM adoption for SMEs are discussed. The findings show that currently around 42% of Australian SMEs use BIM in Level 1 and Level 2 with only around 5% have tried Level 3. It comes to light that lack of knowledge within SMEs and across the construction supply chain is not a major barrier for Australian SMEs. In essence, the main barriers stem from the risks associated with an uncertain return on investment (ROI) for BIM as perceived by key players in SMEs. The findings also show the validity of the framework proposed for explaining BIM adoption in Australian SMEs.

Who Goes First? Strategic Delay Under Information Asymmetry

This paper considers a timing game in which heterogeneously informed agents have the option to delay an investment strategically to learn about its uncertain return from the experience of others. I study the effects of information exchange through strategic delay on long-run beliefs and outcomes. Investment decisions are delayed when the information structure prohibits informational cascades. When there is only moderate inequality in the distribution of information, equilibrium beliefs converge in the long-run, and there is an insufficient aggregate investment relative to the efficient benchmark. When the distribution of information is more skewed, there can be a persistent wedge in posterior beliefs between well and poorly informed agents, because the poorly informed tend to drive out the well-informed.

Does Capital Tax Uncertainty Delay Irreversible Risky Investment?

Tax uncertainty is often claimed to be harmful for investments. Capital taxes, such as property and wealth taxes, are particularly exposed to tax uncertainty. Capital tax uncertainty emerges from expected tax reforms, the unclear outcome of future tax audits, and simplified estimates of capital tax bases in investment models. Uncertain returns on investment as well as stochastic taxation contribute to overall uncertainty and may significantly affect investment decisions. Hitherto, it is unknown how capital tax uncertainty affects investment timing. However, it is well known that both uncertainty and capital tax may be harmful for investment and decelerate investment activities. We are the first to study the investment timing effects of stochastic capital taxes in a real options setting with risky investment opportunities. Our results indicate that even risk neutral investors are sensitive with respect to capital tax risk and may react in a surprising manner to a newly introduced stochastic capital tax. As an apparently paradoxical investment effect, we find that increased capital tax uncertainty can accelerate risky investment if such uncertainty is sufficiently low compared to cash flow uncertainty. In contrast, high capital tax risk delays high-risk innovative investment projects. To reduce unintended consequences of uncertain tax policy, tax legislators and tax authorities should avoid high levels of capital tax uncertainty. Broadening the capital tax base or increasing the capital tax rate induces ambiguous timing effects. Furthermore, high-growth investments are likely to be postponed if they experience a capital tax cut. Since investment reactions upon tax reforms are well-known to affect income and wealth distribution, reliable estimations of the impact of taxes on economic decisions are necessary.

The U-Shaped Oil Price: A Theory and Some Evidence

An empirical test for the theory of exhaustible resources requires an estimate of the extraction rent, that generally is not observable. This paper introduces a model of exploratory and developmental oil-well drilling, with geological constraints that cap the speed of pumping oil out of the ground. In contrast to the established view, as actuated by the Hotelling rule, that extraction rents rise more or less at the rate of discount, the extraction rent of this model rises or falls depending on whether or not new reserves can offset depletion effects in extraction. This extraction rent is empirically constructed for a state-level panel dataset on US oil exploitation and tests show that it rationalizes the data. In particular, estimating the model’s dynamic efficiency condition yields a point estimate for the discount rate of twenty percent, which is in line with reported industry rates. Moreover, it is observed that uncertain returns from drilling effort introduced precautionary behavior in the depletion of producing US oil reserves over the period 1955 to 2002.

Optimizing the Capital Rationing Decision with Uncertain Returns

ABSTRACTIn this article, we develop a new optimization model for capital rationing with uncertain project returns. Our model maximizes the probability of meeting a predefined target return by selecting a feasible set of projects subject to budget constraints in multiple time periods. We employ a mixed-integer nonlinear algorithm recently developed in the optimization field to solve the resulting nonconvex optimization problem to optimality. Our model and solution methods are tested and validated through a comprehensive computational experiment. Several managerial insights are obtained about the impact of available budget and target return on the optimal solutions. Notably, we have found that increasing target return may not necessarily result in an increase in optimal total expected return of the selected projects. Our model and solution method offer a unified and computationally tractable approach to precisely quantify the tradeoff between project returned and risk.

Optimizing the capital rationing decision with uncertain returns

ABSTRACTIn this article, we develop a new optimization model for capital rationing with uncertain project returns. Our model maximizes the probability of meeting a predefined target return by selecting a feasible set of projects subject to budget constraints in multiple time periods. We employ a mixed-integer nonlinear algorithm recently developed in the optimization field to solve the resulting nonconvex optimization problem to optimality. Our model and solution methods are tested and validated through a comprehensive computational experiment. Several managerial insights are obtained about the impact of available budget and target return on the optimal solutions. Notably, we have found that increasing target return may not necessarily result in an increase in optimal total expected return of the selected projects. Our model and solution method offer a unified and computationally tractable approach to precisely quantify the tradeoff between project returned and risk.

A robust optimisation model for hybrid remanufacturing and manufacturing systems under uncertain return quality and market demand

In remanufacturing research, most researchers predominantly emphasised on the recovery of whole product (core) rather than at the component level due to its complexity. In contrast, this paper addresses the challenges to focus on remanufacturing through component recovery, so as to solve production planning problems of hybrid remanufacturing and manufacturing systems. To deal with the uncertainties of quality and quantity of product returns, the processing time of remanufacturing, remanufacturing costs, as well as market demands, a robust optimisation model was developed in this research and a case study was used to evaluate its effectiveness and efficiency. To strengthen this research, a sensitivity analysis of the uncertain parameters and the original equipment manufacturer’s (OEM’s) pricing strategy was also conducted. The research finding shows that the market demand volatility leads to a significant increase in the under fulfilment and a reduction in OEM’s profit. On the other hand, recovery cost reduction, as endogenous cost saving, encourages the OEM to produce more remanufactured products with the increase in market demand. Furthermore, the OEM may risk profit loss if they raise the price of new products, and inversely, they could gain more if the price of remanufactured products is raised.

Impact of Bayesian Learning and Externalities on Strategic Investment

We investigate the interplay between learning effects and externalities in the problem of competitive investments with uncertain returns. We examine a game theoretic duopoly investment model in which (i) a firm can learn about the profitability of the investment by observing the performance of the first mover and (ii) externalities exist between the investments of two firms. We find a region of a war of attrition between the two firms in which the interplay between externalities and learning gives rise to counterintuitive effects on investment strategies and payoffs. In particular, we find that, contrary to the conventional war of attrition where an increase in benefits for the follower generally delays the first move, an increase in the rate of learning—which tends to benefit the follower—can hasten the first investment. This paper was accepted by James Smith, decision analysis.

Inventory control for returnable transport items in a closed-loop supply chain

An inventory control model for returnable transport items (RTI) where the manager selects the optimal length for inspection, repair, and purchase cycles is described. Repaired and newly obtained RTI are used in combination to satisfy current production requirements. Uncertain returns are incorporated into the model by determining a satisfactory safety stock level to buffer the inventory of used and repairable containers. The minimum cost solution is obtained when inspection and repair runs begin simultaneously. Cycle times are a function of the expected return rate and repairable percentage, while variability in these random assumptions affects the required safety stock.

Assessment of uncertain returns from investment in short rotation coppice using risk adjusted discount rates

The increasing demand for renewable energy resources increases interest in the use of short rotation coppice (SRC) as alternative land use activity. The high uncertainty attached to returns from SRC is one of the key adoption barriers to farmers. One possibility to account for the role in investment assessments is the use of project specific risk adjusted discount rates (RADR). In this article, we revisit the theoretical background of RADR and illustrate different assumptions using an example of poplar based SRC. Time-invariant RADR used in the current literature on SRC assessment are found to over-emphasize the role of risk for project assessment and usually give to little weights to returns in future periods, which are of particular relevance for long-term investments in SRC. Thus, the use of time invariant RADR is found to lead to biased recommendations towards the attractiveness of SRC and optimal policy support.

Getting More `Carbon Bang' for Your `Buck' in Acre State, Brazil

Acre State in Brazil is at the forefront of efforts to institutionalize jurisdictional-scale policies that aim to reduce emissions from deforestation and forest degradation (REDD ). Given limited REDD funds and uncertain returns from alternative land uses, this paper estimates the minimum incentive payment Acre’s government would have to pay forest landowners in each of its 22 municipalities to ensure forest conservation. Despite low profits but with relatively low conversion costs and stable returns over time, pasture generates the highest returns in 19 municipalities. Municipalities are ranked according to their relative policy costs, a ranking which is compared to the distribution of forest carbon stocks across Acre. Finally, the relative cost per ton of carbon is derived, which enables the identification of a group of 13 municipalities with the greatest potential for ‘carbon bang’ for a given ‘buck’.

Modeling Portfolio Optimization Problem by Probability-Credibility Equilibrium Risk Criterion

This paper studies the portfolio selection problem in hybrid uncertain decision systems. Firstly the return rates are characterized by random fuzzy variables. The objective is to maximize the total expected return rate. For a random fuzzy variable, this paper defines a new equilibrium risk value (ERV) with credibility level beta and probability level alpha. As a result, our portfolio problem is built as a new random fuzzy expected value (EV) model subject to ERV constraint, which is referred to as EV-ERV model. Under mild assumptions, the proposed EV-ERV model is a convex programming problem. Furthermore, when the possibility distributions are triangular, trapezoidal, and normal, the EV-ERV model can be transformed into its equivalent deterministic convex programming models, which can be solved by general purpose optimization software. To demonstrate the effectiveness of the proposed equilibrium optimization method, some numerical experiments are conducted. The computational results and comparison study demonstrate that the developed equilibrium optimization method is effective to model portfolio selection optimization problem with twofold uncertain return rates.

Does Capital Tax Uncertainty Delay Irreversible Risky Investment?

Tax uncertainty is often claimed to be harmful for investments. Capital taxes, such as property and wealth taxes, are particularly exposed to tax uncertainty. Capital tax un- certainty emerges from expected tax reforms, the unclear outcome of future tax audits, and simplified estimates of capital tax bases in investment models. Uncertain returns on investment as well as stochastic taxation contribute to overall uncertainty and may significantly affect investment decisions. Hitherto, it is unknown how capital tax uncertainty affects investment timing. However, it is well known that both uncertainty and capital tax may be harmful for investment and decelerate investment activities. We are the first to study the investment timing effects of stochastic capital taxes in a real options setting with risky investment opportunities. Our results indicate that even risk neutral investors are sensitive with respect to capital tax risk and may react in a surprising manner to a newly introduced stochastic capital tax. As an apparently paradoxical investment e¤ect, we find that increased capital tax uncertainty can accelerate risky investment if such uncertainty is such ciently low compared to cash flow uncertainty. In contrast, high capital tax risk delays high-risk innovative investment projects. To reduce unintended consequences of uncertain tax policy, tax legislators and tax authorities should avoid high levels of cap- ital tax uncertainty. Broadening the capital tax base or increasing the capital tax rate induces ambiguous timing effects. Furthermore, high-growth investments are likely to be postponed if they experience a capital tax cut. Since investment reactions upon tax reforms are well-known to affect income and wealth distribution, reliable estimations of the impact of taxes on economic decisions are necessary. (authors' abstract)

Delegated information acquisition with moral hazard

We analyze a principal–agent problem with moral hazard where a principal searches for an opportunity of uncertain return, and hires an agent to evaluate available options. The agent's effort affects the informativeness of a signal about an option's return. Based on the information provided by the agent, the principal decides whether to exercise the option at hand. We derive properties of the optimal contract in both static and dynamic versions of the problem. We show that there are intermediate values of the prior probability that the option is of high quality at which positive effort cannot be sustained. We also show that if the prior is below a threshold, then the agent is rewarded for delivering ‘bad news’ about the option's quality. We derive distortions (relative to the first best) on the implemented effort level and optimal stopping decision. For some parameter values, it is optimal for the principal to commit to an ex-post suboptimal stochastic decision to exercise an option.

Optimizing agricultural land-use portfolios with scarce data—A non-stochastic model

The theory of portfolio selection has often been applied to help improve economic decisions about the environment. Applying this theory requires information on the covariance of uncertain returns between all combinations of the economic options and also assumes that returns are normally distributed. As it is usually difficult to fulfill all data requirements and assumptions, this paper proposes a variant of robust portfolio optimization as an alternative that needs less pre-information. The approach considers future uncertainties in a non-stochastic fashion through possible deviations from the nominal return of land-use alternatives. Maximizing the economic return of the land-use portfolio is conditional on meeting an inclusive set of constraints. These demand that a pre-defined return threshold is achieved by the robust solution for each uncertainty scenario considered. Based on data for eight agricultural crops common in the Ecuadorian lowlands, a comparison with portfolios generated by classical stochastic mean-variance optimization shows greater land-use diversification (through increased Shannon indices), but only moderate expected economic loss of non-stochastic robust land-use portfolios. We conclude that non-stochastic derivation of land-use portfolios is a good alternative to the classical stochastic model, in situations where information on economic input parameters is scarce.

A Multi-stages Decision Approach for Managerial Flexibility of Energy R&D Project under Fuzzy Environment

In recent years, many countries and firms seek the new and renewable energy to cope with the impending global environmental crisis, such as depletion of fossil-based energy, climate change to control emissions of greenhouse gases. This paper aims to take the perspective of the firm, which undertakes the energy R\&D project to maximize profits implying minimization of total cost as well. Incorporating technical and market risks into energy R\&D project is crucial, in that the managers often face the rapidly changing environment full of uncertainties. The firms should incorporate managerial flexibility into energy R\&D project decision not only reducing uncertain risks, but also increasing potential market payoff. This research considers a multi-stages decision model in which real-option-based analysis is applied for energy R\&D project under fuzzy environment. Specifically, the market payoff is obtained when the new and renewable energy product is commercialized to market, while energy R\&D investment costs are exhausted gradually. Furthermore, the uncertain development performance and market information are described as fuzzy variables by credibility theory. Instead of the traditional real option pricing methods, the dynamic programming methodology that captures the uncertain product development performance and final market return is developed to more effectively characterize the managerial flexibility. This method can reflect the multi-stages nature of R\&D programme, while helping decision-makers take the optimal investment decision and capture future market opportunities of energy products.

Modeling fuel treatment impacts on fire suppression cost savings: A review

High up-front costs and uncertain return on investment make it difficult for land managers to economically justify large-scale fuel treatments, which remove trees and other vegetation to improve conditions for fire control, reduce the likelihood of ignition, or reduce potential damage from wildland fire if it occurs. In the short-term, revenue from harvested forest products can offset treatment costs and broaden opportunities for treatment implementation. Increasingly, financial analysis of fuel treatments is also incorporating long-term savings through reduced fire suppression costs, which can be difficult to quantify. This paper reviews the findings and lessons from recent modeling work evaluating the potential relationship between fuel treatments and avoided fire suppression costs. Across studies, treatments are generally predicted to reduce future fire suppression costs, although the magnitude of savings is unlikely to fully offset fuel treatment costs. This funding gap highlights the importance of forest product revenues in facilitating landscape-scale treatment. Factors influencing the effects of fuel treatment investments on fire suppression costs include the causal pathway linking treatment inputs to suppression cost outcomes; the spatiotemporal uncertainty of wildfire-treatment interactions; and the scale of fuel treatment programs.

Benefit and risk analysis of consignment contracts

Consignment contracts are widely implemented in virtual market. We consider a supplier produces product and sells it to market through a retailer by consignment contract. In a unified model assumption, considering random demand and customers’ uncertain return behavior, we focus on two popular consignment contracts: vendor managed consignment inventory contract and consignment contract with revenue sharing. Comparing analytical results between the two contracts, we find that (1) the retailer and whole supply chain will benefit more from the consignment contract with revenue sharing, (2) the supplier will prefer to vendor managed consignment inventory contract, (3) both the retailer and supply chain will share a higher risk under the consignment contract with revenue sharing, the supplier’s shares of risk and profit highly depend on his share of the total expected cost, (4) all the profit and risk functions are decrease in uncertainty of customer return. We believe that these managerial insights can advance the understanding about use of the two consignment contracts in supply chain management with both benefit and risk considerations.