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This paper aims to present a financial valuation framework based on the real options theory to evaluate investments in toll road projects delivered under the two‐phase development plan.

The approach is based on applying the real options theory to evaluate investments in toll road projects. In particular, the risk‐neutral valuation method is used for pricing flexibility embedded in the two‐phase development plan. Risk‐neutral binomial lattice is used to model traffic uncertainty and to find the optimal time for the toll road expansion. Probabilistic life cycle cost and revenue analysis is conducted to characterize the investor's financial risk profile and determine the flexibility value of the expansion option.

The flexible, two‐phase development plan can improve the investor's financial risk profile in the toll road project through limiting the downside risk of overinvestment (i.e. decreasing the probability of investment loss) and increasing the expected investment value in a highway project.

Private and public sectors can benefit from this valuation framework and use tax dollars and users' fees effectively through avoiding overinvestment in toll road projects.

The framework consists of several integrated features, which distinguish it from existing investment valuation models. The risk‐neutral valuation method for pricing flexibility embedded in the two‐phase development plan is applied. This real options framework is capable of characterizing traffic boundary, at which it is optimal for the investor to expand the toll road. Further, this framework provides the likelihood distribution of when the investor may expand the toll road.

Building developments are often capital intensive, have a long payback period and many associated risks and uncertainties. This makes investments in building projects to be a big challenge. This study aims to develop a computerized simulation-based binomial model (CSBBM) for building investment appraisal with a view to improving the economic sustainability of proposed building projects.

Mathematical equations and algorithms were developed based on the binomial method (BM) of real options analysis and then implemented on a computer system. A hybrid algorithm that integrates Monte Carlo simulation (MCS) and BM was also developed. A real-life project was used to test the model. Sensitivity analysis was also conducted to explore the influence of input variables on development option value (DOV).

The test result shows that the model developed provides a better estimate of the value of an investment when compared with traditional net present value technique, which underestimate the value. Moreover, inflation rate (i) and rental value (Ri) are the most sensitive variables for DOV. An increase in i and Ri by just 5% causes a corresponding increase in DOV by 202% and 132%, respectively. While the least sensitive variable is the discount rate (r), as an increase in r by 5% causes a corresponding decrease in DOV by just 9%. The CSBBM is capable of determining the optimal time of development of buildings with an accuracy of 80.77%.

The hybrid model produces higher DOV than that of only the BM because MCS considers randomness in uncontrollable variables. Thus, building investment decision-makers should always use MCS to complement the BM in an investment analysis.

There is limited evidence on the use of this kind of hybrid model for determining DOV in practice.

The purpose of this article is to propose a detailed methodology to estimate, model and incorporate the non-constant volatility onto a numerical tree scheme, to evaluate a real option, using a quadrinomial multiplicative recombination.

This article uses the multiplicative quadrinomial tree numerical method with non-constant volatility, based on stochastic differential equations of the GARCH-diffusion type to value real options when the volatility is stochastic.

Findings showed that in the proposed method with volatility tends to zero, the multiplicative binomial traditional method is a particular case, and results are comparable between these methodologies, as well as to the exact solution offered by the Black–Scholes model.

The originality of this paper lies in try to model the implicit (conditional) market volatility to assess, based on that, a real option using a quadrinomial tree, including into this valuation the stochastic volatility of the underlying asset. The main contribution is the formal derivation of a risk-neutral valuation as well as the market risk premium associated with volatility, verifying this condition via numerical test on simulated and real data, showing that our proposal is consistent with Black and Scholes formula and multiplicative binomial trees method.

Describes statistical methods applied to sensory discrimination tests. Illustrates binomial and chi‐square statistical analysis and discusses similarity testing, power and replication in discrimination testing.

Investment decisions by agribusiness firms are costly and subject to high volatility and uncertainty. In many cases, the project value is not only determined by its cash‐flows stream and financial side effects but also by the presence of substantial future uncertainty such as project implementation delay and growth opportunities. The purpose of this paper is to evaluate an agribusiness project taking into account these two options and to illustrate the how risks that evolve over time can affect sequential investment decisions in the oleic oil industry in Tunisia.

The methodology used to capture the investment project value and analyze the impact of lags between the initial investment decision and its implementation on project value is based on a decision tree method and binomial lattice method (which adds growth option). The project valuation is based, first on actual data at the time of the initial decision and second the authors use the full information to report on the true value of the investment opportunity as real time evolved.

Findings show that time to build is a very important factor in valuing an agribusiness especially when efficiency is strongly governed by climatic conditions and international market uncertainty. Our real options approach shows that production delays can deteriorate the follow‐on project value by as much as 53 percent. The implicit growth option falls to only 27 percent of the total project value while it was about 58 percent according to the standard forecast. The delay in project implementation not only affects the firm project financing costs and the loss of revenue, but also it contributes to modify the initial marketing strategy.

The paper is a first application of real option approach to the oleic oil industry. The methodology used in the paper can be adapted by practitioners and investors to adequately value oleic projects.

Real option valuation is capable of accounting for uncertainties in residential development projects but still lacks practical adoption due to limited evidence to support application of the theory in practice. The purpose of this paper is to use option valuation to value staging option embedded in residential projects and compare with results from DCF to determine which of the two methods delivers superior results.

The fuzzy payoff method (FPOM), a real options model that uses scenario planning approach to generate a range of figures, from which a single-numerical value is computed for decision-making.

The results showed that the use of a range of figures was able to represent uncertainties to a higher degree of accuracy than the static DCF. As a result, the FPOM was able to capture about 3 per cent of the value of the project that was missed by the DCF. The staging option offers an opportunity to abandon unprofitable phases of a project, thereby limiting downside losses. Thus, real option models are practically applicable to cases in property sector.

Residential property developers must consider flexibility in financial feasibility evaluation of development because of the embedded value in uncertain property projects. It is important to account for optionality in financial evaluation of property projects for value maximisation.

The FPOM has been used for the first time to evaluate a horizontal phasing of a residential development project.

The purpose of this paper is to present a methodology to evaluate the capital investments in infrastructure projects managed by private operators considering uncertainties in the operation and maintenance of the infrastructure components.

The methodology described in this paper is based on two major sources of information: deterioration curves of the infrastructure systems obtained from Markov chain models and the value of flexibility obtained from a real options analysis.

Using this methodology, it is possible to determine whether there is value if project managers adopt flexible strategies in determining capital investments. These strategies refer to the opportunities of postponing, deferring or canceling capital investments required to maintain the operation of the infrastructure systems.

The model utilizes Monte Carlo simulation and real options analysis to overcome the complexities associated with the solution of the differential equations that represent the variability of the main factors in the project cash flow.

The methodology presented in this paper can be used by public officials, private investors, and asset managers to determine the value of flexibility associated with the strategies required to maintain the operation of infrastructure assets.

This study aims to investigate the impact of country-level corruption and firms’ anti-bribery policies on analyst coverage. Analyst coverage has been identified as a powerful tool to detect fraud and should equally act as a possible tool to reduce corruption.

This study used a negative binomial count regression method on a longitudinal data set of a sample of S&P Global 1200 companies for the years 2010-2015. To control for potential endogeneity bias and improve the reliability of the estimation, both country-level corruption and firms’ anti-bribery policies variables were instrumented.

After controlling potential endogeneity bias, the results show that the adoption of anti-bribery policies at firm level attracts more analysts to follow a firm. The results for corruption at country level show that analyst coverage increases in less corrupted countries indicating that the costs of corruption exceed its potential benefits. When the variables corruption at country level and anti-bribery policies are interacted, the relationship is positive and highly significant.

Given the potential important role played by anti-corruption measures, firms are encouraged to adopt them to reduce the incidence of corruption and to increase analyst coverage, which will reinforce the benign effect of monitoring.

Although the literature on corruption at the country level is rich, it is geared towards the determinants of corruption in contrast to its consequences, and fewer studies have focused on the impact of corruption at firm level because of data limitations. This paper addresses this gap and contributes to the literature on the consequences of corruption at firm level.

This paper aims to propose an advanced tracking control of the uncertain nonlinear dynamic system using a novel hybrid fuzzy linear quadratic regulator (LQR)-proportional-integral-derivative (PID) sliding mode control (SMC) optimized by differential evolution (DE) algorithm.

First, a swing-up and balancing control is presented for an experimental uncertain nonlinear Pendubot system perturbed with friction. The DE-based optimal SMC scheme is used to optimally swing up the Pendubot system to the top equilibrium position. Then the novel hybrid fuzzy-based on LQR fusion function and PID controller optimized by DE algorithm is innovatively applied for balancing and control the position of the first link of the Pendubot in the down-right position with tracking sinusoidal signal reference.

Experimental results demonstrate the robustness and effectiveness of the proposed approach in balancing control for an uncertain nonlinear Pendubot system perturbed with internal friction.

This manuscript is an original research paper and has never been submitted to any other journal.

The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.

Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values.

The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction.

The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed.

This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.