Net Present Value Model Research Articles

Financial evaluation of value-creating biosimilar development candidates: a business case study of low-, medium- and high-sales biosimilars

ABSTRACT Background Selection of a biosimilar development candidate requires detailed and careful analysis to ensure value creation. Research design and methods We applied the net present value (NPV) model for financial evaluation of biosimilar development candidates for the global market. Biosimilar development candidates from three categories were identified based on the global sales of their respective original biological products (OBPs): >$1 and ≤$4 billion, >$4 and ≤$7 billion and >$7 and ≤$10 billion. The standard NPV and risk-adjusted NPV (rNPV) were calculated under the base-case and various scenarios related to development costs, sales, selling, general and administrative expense, cost of goods sold, and discount rates. Results A biosimilar to an OBP with sales of $1.6 billion has some financial risk. Biosimilars to OBPs with sales of $5.5 billion and $9.4 billion have very less financial risk under the base-case scenario of analysis. Sales of an OBP had impact on the financial valuation and attractiveness of a biosimilar. Sensitivity of rNPV to different parameters varied for three biosimilars. Discount rates, sales and development costs were important parameters affecting financial valuation of biosimilars. Conclusion Biosimilar development candidate selection requires a thorough financial evaluation considering product-, company- and market-specific aspects.

Dual-credit price variation and optimal electrification timing of traditional automakers: A dynamic programming approach

China’s Dual-Credit Policy (DCP) establishes a credit trading mechanism to promote automakers’ fuel efficiency and electrification investments. However, variations in credit prices are widely recognized as major constraints. This study develops a net present value (NPV) model and applies dynamic programming (DP) to investigate automakers’ optimal time for investing in electrification under the DCP. Stochastic price variations are captured by the drift and volatility parameters in geometric Brownian motion. The simulation results show that a 50% reduction in volatility leads to an average two-year advance in electrification, whereas an increase in drift produces only a small boost. However, it should be noted that limiting volatility through mandatory regulations would lead to a significant reduction in the efficiency of R&D subsidies. In addition, while the DCP pressures automakers toward electrification transitions, tightening them is not always effective, especially for those who do not intend to produce electric vehicles intensively after the transition. Our findings can help governments and automakers better understand the impact of credit price changes and make effective decisions.

Simulation of Spatial Service Range and Value of Carbon Sink Based on Intelligent Urban Ecosystem Management System and Net Present Value Models—An Example from the Qinling Mountains

Under the current background of carbon neutrality, the quantification of carbon neutrality in Qinling Mountains, the calculation of the spatial service scope, and the estimation of economic value are of great significance for the improvement of carbon neutrality spatial planning and the carbon trading market. The Intelligent Urban Ecosystem Management System (IUEMS) was used to calculate the carbon sequestration of the Qinling Mountains. The carbon emission of Qinling Mountains was spatialized through model fitting, using the night light data and energy consumption data of various cities. The static carbon content of the Qinling Mountains was obtained. Combined with the common gas diffusion coefficient, under normal temperature and pressure, the service range of the Qinling Mountains’ carbon neutralization to the surrounding area was calculated. The spatial distribution of carbon sequestration in 2030 and 2050 was simulated using Patch generating Land Use Simulation (PLUS) model and Net Present Value (NPV) model. Under two discount rates, the model could calculate the carbon value in 2012, 2016, 2020, 2030, and 2050, respectively. The results proved that the value of carbon in the Qinling Mountains is gradually increasing. Since 2012, the service range of carbon neutralization in the Qinling Mountains was 175–262.63 km, and the service range has been gradually expanded. The discount rate is inversely proportional to the carbon value.

RESEARCH ON THE MACRO NET FINANCIAL ASSETS VALUE EFFECT OF MONETARY POLICY

This paper focuses on the impact of Chinese and US monetary policy on the net financial assets value of macro balance sheet from both theoretical and empirical aspects and reveals the sectoral solvency risk conduction path based on the balance sheet channel. In addition, the paper is focused on the effects of the interest rate as a target tool for monetary policy on the macro net financial assets. In the theoretical analysis, the net present value model of the economy is constructed, and a general equilibrium model representing the relationship between the real interest rate and net asset value of five sectors is derived (government, financial, resident, enterprise and central bank sector). This model explains the basic principle how interest rates affect net financial assets values. The dataset includes the central bank, commercial banks and shadow banks, and the stock and equity liabilities of the debtor are taken as the net asset of financial institutions during the period 2000–2016. The empirical results show that an increase in the real deposit interest rate improves the net financial assets value of the four sectors, and an increase in the real loan interest rate reduces the net financial assets value of the four sectors, while the effect of the real loan interest rate is greater than the real deposit interest rate. The effect ranking of interest rates on the four sectors is financial, enterprise, government, and resident sector. Overall, loose monetary policies can reduce macro-financial risks through the balance sheet channel, while the negative effects of long-term low-interest policies should be prevented; the macro-policies should hedge sectoral risks triggered by the exit of the easing policy via the macro balance sheet channel.

Predicting the surfactant-polymer flooding performance in chemical enhanced oil recovery: Cascade neural network and gradient boosting decision tree

Surfactant-polymer flooding is one of the most important enhanced oil recovery (EOR) techniques, which refers to the injection of surfactant slugs and polymer drives. Two crucial decision-making parameters in EOR operations are net present value (NPV) and oil recovery factor (RF). Herein, various intelligent models, based on multilayer perceptron (MLP), cascade neural network (CNN), radial basis function (RBF), neural networks as well as support vector regression (SVR), and decision tree (DT) algorithms are proposed toward estimating these two parameters with respect to polymer drive size, surfactant slug size, the salinity of polymer drive, Kv/Kh ratio, surfactant concentration, and polymer concentration in polymer drive and surfactant slug. The results exhibited the outperformance of the CNN model trained with the Levenberg Marquardt algorithm in forecasting the RF and NPV with average absolute errors of 0.66% and 1.95%, respectively. Moreover, the results of the sensitivity analysis reflected that the most effective inputs on the predicted value of RF were surfactant concentration and surfactant slug size, while surfactant concentration and polymer concentration in surfactant slug could considerably affect the NPV model’s output. Lastly, the outlier detection analysis revealed that the employed data is valid and only two points were detected as outliers.

New Method of Sensitivity Computation Based on Markov Models with Its Application for Risk Management

Sensitivity analysis is at the core of risk management for financial engineering; to calculate the sensitivity with respect to parameters in models with probability expectation, the most traditional approach applies the finite difference method, whereafter integration by parts formula was developed based on the Brownian environment and applied in sensitivity analysis for better computational efficiency than that of finite difference. Establishing a similar version of integration by parts formula for the Markovian environment is the main focus and contribution of this paper. It is also shown by numerical simulation that our proposed methodology and approach outperform the traditional finite difference method for sensitivity computation. For empirical studies of sensitivity analysis on an NPV (net present value) model, we show the approaches of modeling, especially for parameter estimation of Markov chains given data of company loan states. Applying our newly established integration by parts formula, numerical simulation estimates the variations caused by the capital return rate and multiplier of overdue loan. Furthermore, managemental implications of these results are discussed for the effectiveness of modeling and the investment risk control.

Estimating The Appropriate Size Of Global Pull Incentives For Antibacterial Medicines.

Antibacterial medicines should be foundational for modern medicine-a key part of the infrastructure of contemporary practice. Recently, however, antibacterials have struggled commercially. Even with "push" incentives (grants paid before regulatory approval), antibacterials have failed on the market because revenues are tied to volume sold. There are policy initiatives under way in the United States and United Kingdom that explore paying for exceptional antibacterials with "pull" incentives (paid after regulatory approval) by delinking the payments from volume via other payment formats such as market entry rewards and subscriptions. This article discusses these initiatives but also proposes an expected net present value model for calculating the global incentives required to create a functional antibacterial market, exploring options such as antibacterial subscriptions, market entry rewards, push incentives, higher prices, and drug development through charitable efforts. The model estimates that current push incentives should be continued, but governments must also enact pull incentives that will add several billion dollars to the global revenue stream of a highly innovative antibacterial, reduced by any grants received supporting clinical development of that product. The amounts in the proposed Pioneering Antibiotic Subscriptions to End Upsurging Resistance (PASTEUR) Act of 2021 and a UK pilot program are well within the bounds of an effective antibacterial pull incentive.

Inter-well interference and well spacing optimization for shale gas reservoirs

Design and optimization of well spacing is a key indicator for evaluating the development effect of shale gas reservoirs. On the basis of theoretical understanding, and after the verification by analogy, numerical simulation, and economic evaluation, a complete workflow from inter-well interference simulation and dynamic data diagnosis to multi-well production simulation and well spacing optimization was formed. First, a pressure detection boundary propagation model is established to simulate the response degree of inter-well interference under different connected conditions. Second, inter-well interference is identified and diagnosed depending on the inter-well interference response behaviors and the interpretation of performance data from gas wells. Third, taking the geological interpretation and dynamic analysis results as basic parameters, a multi-well numerical model for volume fracturing in gas reservoirs is established to simulate the production performance of gas field, and then well spacing is optimized in combination with the net present value model. The application in the Ning 201 well block in the Changning National Shale Gas Demonstration Area has shown that a smaller well spacing can allow a premature inter-well interference and also the enhancement of recovery in the entire block. Given the current fracturing scale and parameter system, the well spacing of 300–400 m can be optimized to 260–320 m, that is, the number of wells per unit area increases by 20%–30%. As a result, the recovery percent of reserves in the block increase by about 10%. The net present value of the block rises, but the corresponding optimal well spacing does not change, with the production period.

Markov chain optimization of repair and replacement decisions of medical equipment

• The study determines the optimal repair or replacement decision based on the product lifecycle data. • A Markov Decision Process (MDP) framework is proposed for optimizing repair decisions. • A dataset of medical device repair and maintenance is used to show the application of the model. • Product lifecycle data is essential for lowering repair and replacement costs. The cost of repair and maintenance of medical devices can be fairly burdensome to the healthcare industry. Healthcare providers often consider plans such as increasing in-house repairs, using multiple repair service providers, or timely replacement of devices to overcome this issue. This study aims to develop a data-driven Markov Decision Process (MDP) framework based on Discrete-Time Markov Chain (DTMC) model to optimize medical equipment repair and replacement decisions. Effective decision-making on whether to repair or replace is crucial to managing the product lifecycle cost and the costs to healthcare facilities. The study determines the optimal repair or replacement decision based on the product lifecycle data and current product failure status. It utilizes a net present value model to maximize expected value over an infinite time horizon. The study uses a dataset of 24,516 repair and maintenance records of 5,171 individual medical devices of a particular type to extract parameters needed for the optimization model. The dataset provides a rich baseline for analyzing different failures and event modes during product lifespan such as battery-related issues, random failure, preventive maintenance, and physical damage. It further quantifies the chance of moving from one product status to another. The model outcomes are discussed for a particular case. The findings reveal the most frequent reasons for failures and the most economically viable repair and replacement decision for each end-of-use device based on their current condition. Several sensitivity analyses are conducted to clarify the impact of operating revenue and warranty time on the repair or replacement.

Worth the Hype? Tall Vines Raise Profits for Some – Tradeoffs for All

<h3>Summary</h3> <h3>Goals:</h3> The sudden popularity of tall vines among grapegrowers took some nurseries by surprise. Is this popularity rooted in economic reality? This analysis quantifies the claimed benefits of tall vines relative to regular vines from the grower9s perspective using a net present value (NPV) model. We leverage these potted green vine value estimates to discuss the forces that explain the recent emergence of tall vines in the California grape industry and their prospects for the future. <h3>Key Findings:</h3> The NPV of planting tall vines instead of regular vines varies widely by grape variety and region in California because of regional differences in input costs and grape prices. Under reasonable assumptions, the estimated profit differential of tall vines over regular vines ranges from $1.64/vine to $7.31/vine. A break-even analysis indicates that tall vines are more profitable if the yield drag (i.e., reduced future yield due to pushing young vines into early production) is less than 35%, while regular vines are more profitable if the yield drag is greater than 55%. <h3>Impact and Significance:</h3> The expected future profits generated by a grapevine at the time of planting accrue slowly over its productive lifespan, which can complicate growers’ investment decisions. Our NPV model captures key elements of a grower9s decision to plant tall vines versus regular vines. While the true value of planting tall vines may vary by grower depending on their production conditions and market circumstances, the model, results, and discussion of this paper can help growers to evaluate more objectively the potential benefits of tall vines. This paper also informs stakeholders more generally about the potential effect of vine height differentiation in the broader industry.

A decision-making approach considering technology progress for investment in oil sands projects: An empirical analysis in Canada

This study presents a new decision-making approach for international oil firms to evaluate oil sands projects in Canada and examine the effects of technical progress on investment valuations. The results reveal that the linear net present value model in previous studies dramatically underestimates economic profits and the breakeven price estimated by the proposed approach (L-shaped NPV model) will be lower than US$50 per barrel for oil sands projects. This work also finds that some oil enterprises with advanced technology could reduce their oil sands exploration costs to approximately $32.73 per barrel. Furthermore, this study indicates that the profits from extraction cost reduction can cover the losses caused by some risk factors, such as exchange rates, inflation, and natural gas prices. The main contribution of this paper is to offer a flexible investment evaluation model that can assist oil companies in evaluating their own oil sands projects with different firm-specific technical progress characteristics.

Use of panel time-series data with cross-section dependence in evaluating farmland valuation: a cautionary note

ABSTRACT The note highlights how misspecification of cross-section dependence structure in panel time-series data can lead to erroneous conclusions on farmland valuation. Combining the sample information from time-series and cross-section dimensions by using panel time-series data can improve inference on the net present value hypothesis for farmland. However, cross-section dependence must be addressed to take advantage of the additional information from this type of data. We consider three classes of panel unit root models that account for cross-section dependence through (1) common factor extraction, (2) block bootstrapping and (3) spatial dependence to explore whether farmland values can be explained by their economic fundamentals, given that the appropriate cross-section specification is implemented in testing. Results show that only spatial dependence approach accurately characterizes cross-section dependence in the Iowa panel time-series data, highlighting the importance of model selection when using data with cross-section dependence. Once the econometric model is specified with the underlying spatial cross-section dependence structure, the market valuation of Iowa farmland is mainly determined by fundamentals as predicted by the net present value model.

How to value a real estate company? Alexander &amp; Baldwin, Inc. (ALEX)

PurposeThis study aims to provide students an opportunity to analyze the financial performance of a publicly listed real estate company and estimate its instinct value by applying appropriate financial models and approaches.Theoretical basisThree major valuation models/approaches generated by financial theory and practice to estimate the intrinsic value of a security: discounting cash-flows valuation (DCF and NPV) – valuation through adjusted net asset and liquidation value (NAV) – relative valuation through price and value multiples (valuation multiple analysis and precedent transactions analysis). Wholly owned subsidiaries versus and joint venture ones.Research methodologyAnalyze financial information of all segments in a multiple-business firm, and apply suitable financial models and approaches among net asset value model (NAV), discounted cash flow (DCF) or net present value (NPV) model, valuation multiple analysis and precedent transactions analysis to estimate the intrinsic value of the whole firm.Case overview/synopsisThis decision-based case allows students to explore the business valuation process for a public listed real estate company, Alexander &amp; Baldwin, Inc. (NYSE: ALEX). Based on financial statements analysis and forward-looking financial expectation on ALEX, this case elevates students' understanding and practice of valuating this multiple-business firms by applying appropriate financial models and approaches among NAV, DCF or NPV, valuation multiple analysis and precedent transactions analysis and enable students to make their investment decisions of buying, holding or selling the company’s stocks.Complexity academic levelThis case is most appropriate for graduate courses such as corporate finance, investments, personal finance, real estate finance and financial markets and institutes.

Using cost-benefit analysis to understand adoption of winter cover cropping in California's specialty crop systems

Winter cover crops could contribute to more sustainable agricultural production and increase resiliency to climate change; however, their adoption remains low in California. This paper seeks to understand barriers to winter cover crop adoption by monetizing their long-term economic and agronomic impacts on farm profitability in two of California's specialty crop systems: processing tomatoes and almonds. Our modeling effort provides a present, discounted valuation of the long-term use of winter cover crops through a cost-benefit analysis. A net present value model estimates the cumulative economic value of this practice. We then explore how the long-term trade-offs associated with winter cover crops can affect an operation's profits under a spectrum of hypothetical changes in California's agricultural landscape. Our analysis sheds light on the barriers to adoption by reporting benefit-cost ratios that indicate profitability across several scenarios; however, benefits and costs accrue differently over time and with long planning horizons. At the same time, a small portion of gained benefits are external to the grower. Findings from this study reveal that winter cover crops in California can be profitable in the long-term, but the extent of profit depends on the cropping system, extent of irrigation savings due to improved soil function, access to financial subsidies and climate change. Winter cover crops can return positive net benefits to growers who have flexible contractual obligations, can wait for the long-term return on investment and manage cover crops as closely as cash crops. This analysis contributes to the study of conservation agriculture practices by explaining possible reasons for low adoption through an economic valuation of the implications of soil management choices and policy counterfactuals.

On the effect of geographical, topographic and climatic conditions on feed-in tariff optimization for solar photovoltaic electricity generation: A case study in Iran

One of the important factors of sustainable development is renewable energies penetration in the energy systems. The present study evaluates the optimum feed-in tariff of photovoltaic electricity production based on the available downward solar radiation potential of each province of Iran while this potential calculated considering geographical, topographic and climatic conditions. For downward solar radiation modeling a set of mathematical, geometric and spatial models were used. Also, the net present value model was applied for evaluates the optimum feed-in tariff of photovoltaic electricity production. The results showed that downward solar radiation potential varies from 380 to 578 W m−2 across the Iran country. Furthermore, the optimal photovoltaic electricity generated feed-in tariff varies from 0.0835 to 0.1272 United States dollar for a different region in the country. Based on government approved feed-in tariff and the current study the provinces of Ardebil and Kohkiluyeh and Boyer-Ahmad are the riskiest and the securest regions for investors in case of photovoltaic project, respectively.

Estimation of the Value of Ecosystem Carbon Sequestration Services under Different Scenarios in the Central China (the Qinling-Daba Mountain Area)

The Qinling-Daba Mountain area is a transitional zone between north and south China and not much is known about its carbon storage, particularly its pool of soil organic carbon (SOC). Given this shortcoming, more reliable information regarding its SOC is needed. In light of this, we quantified above and below-ground carbon sinks using both the Carnegie-Ames-Stanford approach (CASA) model and an improved carbon cycle process model. We also assessed the net present value (NPV) for carbon budgets under different carbon price and discount rate scenarios using the NPV model. Our results indicated that the net primary productivity (NPP) was lower in places with low density forests that were situated at high elevation. The spatial distribution of carbon storage depended on NPP production and litter decompositon, which reflected specific vegetation as well as temperature and moisture gradients. The lowest amounts of carbon storage were in the center of the Qinling Mountains and also partly in the Daba area, which is a location associated with sparse grassland. Contrastingly, the broad-leaved forested area showed the highest amount of carbon storage. NPV was positively correlated with discount rate and carbon prices, thus resulting in the highest values in the forests and grassland. The net present value of total soil carbon sequestration in the six scenarios in 2015 was 3.555 b yuan, 3.621 b yuan, 5.421 b yuan, 5.579 b yuan, 7.530 b yuan, 7.929 b yuan; The net present value of total soil carbon sequestration in 6 scenarios in 2017 is 2.816 b yuan, 2.845 b yuan, 4.361 b yuan, 4.468 b yuan, 6.144 b yuan, 6.338 b yuan (billion = 109; b; RMB is the legal currency of the China, and its unit is yuan, 1 euro = 7.7949 yuan, and 1 pound = 9.2590 yuan). Levying a carbon tax would be a notable option for decision makers as they develop carbon emission reduction policies. Given this, incorporating discount rates and carbon pricing would allow for more realistic value estimations of soil organic carbon. This approach would also provide a theoretical basis and underscore the practical significance for the government to set a reasonable carbon price.

Thermal Fragmentation as a Possible, Viable, Alternative Mining Method in Narrow Vein Mining?

In currently used mining methods, blasting techniques often causes dilution of valuable ore and results in costly processing requirements. In the context of narrow vein mining of thin and highly concentrated orebodies there is a need of a mining method that can reduce dilution in order to remain economically viable. This research project explored the viability of a new mining technology, thermal fragmentation, in narrow vein mining. Thermal fragmentation technology uses a flame jet to produce extreme heat that spalls the surrounding rock to a strategically located drill hole, as an alternative to traditional blasting. This paper creates a net present value (NPV) model of a mining method using thermal fragmentation, as well as for an existing method used for narrow vein mining; comparisons and evaluations were made regarding the feasibility of the new technology. It was found that while overall costs for thermal fragmentation were relatively high, reductions in wages, haulage and processing costs, as well as increased productivity and ore recovery, meant that the new method would improve the financial performance of any operation. These results identify that there is an opportunity to introduce the thermal fragmentation technology to narrow vein mines within Australia, in order to lower costs and increase profit.

Based-estimated volatility Real Option evaluation method in drilling exploration and development

Oil and gas field development are characterized by high risks and uncertainties, thus making relevant risk evaluation is an essential decision to reduce development losses. On the basis of comprehensive analysis of traditional evaluation methods, combined with a gas field development example, the Real Option method is used to break through the “static” evaluation of the Net Present Value of traditional assessment, and the uncertainty factor is taken as an active impact factor, which the bigger to may excavate greater result. The benefits are calculated by numerical simulation using Monte Carlo Simulation method to establish a Real Option model based on volatility estimation. The evaluation results show that the Monte Carlo Simulation uses the correlation between the indicators to establish a Net Present Value model, which can comprehensively consider a variety of factors, and can also obtain the corresponding cumulative probability. The results are more objective and more realistic, and more reasonable evaluation.

Evaluation of Decision-Making for the Optimal Value of Sustainable Enterprise Development under Global 100 Index Thinking

This study seeks the best economic returns of a company’s sustainable business process, employs the Triple Bottom Line Model using the Global 100 Index as the decision variable, and follows the Geometric Brownian Motion, so as to determine the optimal timing for the input of environmental and social costs. The results of the sensitivity analysis show that when the average growth rate of the Global 100 Index is low, the optimal timing for the company’s input of environmental costs and social costs can be obtained. Analysis of the numerical example shows that, based on the financial value of the economic factor, companies should invest in environmental costs as soon as possible. This study replaces the conventional net present value model with the options evaluation model, uses the Global 100 Index as the threshold for decision-making evaluation to provide a more complete decision-making evaluation reference for enterprises, and makes up for the gap in recent research regarding investment time and decision variables. The study results introduce potential strategic value evaluations into the evaluation model of long-term uncertain sustainable operation value, which is more appropriate for the evaluation of the real sustainable operation value. It also provides implementation strategies for decision-makers to mitigate risks under uncertain environments and is the major difference and value of the Real Options Approach (ROA) to supplement Net Present Value (NPV) principles. The results of this study provide a reference for the sustainable development decision-making of corporate sustainability and feasibility and offer an important link in the value chain of food industry operations and management.

Stochastic risk analysis in Monte Carlo simulation: a case study

This paper proposes a model to aid in the decision making to evaluate in Monte Carlo (MC) simulation, some risk analysis measures as the Net Present Value (NPV) through the well known RISK software. A case study of launching a new product in the Ramdy company is performed to examine quantitative stochastic risks in a decision making process. Real data allows to know the input distributions, then, MC Simulations were performed by generating input Latin Hypercube Samples. Success was measured using NPV output samples. Additional analysis suggested that the growth rate of the second year variable was sensitive to our NPV model.