Present Value Model Research Articles

Knowledge Transfer and Innovation: Universities as Catalysts for Sustainable Decision Making in Industry

The urgency of addressing the climate crisis has heightened the need to make sustainable decisions. Universities and research institutions are uniquely able to help develop and disseminate useful information for industry, specifically small and medium enterprises. This case study examines the collaboration between the Guelph Turfgrass Institute (GTI) and the turfgrass industry that culminated in the creation of a “Turf Net Present Value Model”. This model enables turfgrass installers, managers, and local municipalities to determine the structure of their turfgrass systems, natural or artificial, and estimate the net present value (NPV) over an extended period. A conceptual model for effective industry-university collaboration is then used to evaluate the knowledge transfer between academia and industry, showcasing a knowledge-transfer framework for improved decision-making and sustainability in sports facilities.

Fused Filament Fabrication and Injection Moulding of Plastic Packaging: An Environmental and Financial Comparative Assessment

The drive towards smart and sustainable manufacturing is leading companies to opt for environmentally conscious technologies. This study assesses the environmental and financial feasibility of using additive manufacturing, in this case, fused filament fabrication (FFF), instead of injection moulding (IM) to mass-produce cosmetic plastic packaging. Using a life cycle assessment (LCA), the environmental impacts of the raw material production and manufacturing processes were assessed for both technologies. The results showed that using FFF creates a five times greater environmental impact, with printing energy consumption generating 80% of the impact. Using costing models, the cost per product produced using IM and FFF was evaluated, and the models showed that the raw material costs comprise the highest share in both cases. A net present value (NPV) model over twelve years indicated that the FFF NPV was seventeen times higher than that of IM. When testing for quality, the packages produced using IM were superior overall. This study concludes that FFF is more expensive and environmentally impactful when compared to IM.

The Extension and Improvement of the Forest Land Net Present Value Model and Its Application in the Asset Evaluation of Cunninghamia lanceolata Forest Land

In order to solve the problem in that the classical forest land expectation value method ignores the actual forest stock volume when assessing the income at the end of the current production cycle in the forest, and fill the research gap in this area, the technical system of the forest land asset evaluation was enriched. The forest land returns were divided into two parts, i.e., the segmented forest land return price from the growth of the actual forest stand to the end of the growth cycle (Bu1), and the segmented forest land return price for an infinite number of growth cycles after the growth of the actual forest stand to the end of the growth cycle (Bu2). Through structure, the forest land gain price expansion model was obtained, and the stand quality including the average diameter at breast height, average height, stock volume, and outturn of stand as dummy variables were used to construct the growth harvest model related to asset evaluation. Taking Cunninghamia lanceolata forest land as an example, the traditional asset evaluation methods were comparatively analyzed. The residual standard deviation (RSD) of the growth model was less than 10%, the total relative error (TRE) and mean system error (MSE) were within ±10%, the mean prediction errors (MPE) were less than 5%, and the mean percentage standard errors (MPSE) were less than 10%, respectively. Combining the forest land net present value expansion model with the traditional evaluation method, the evaluation value of the forest land assets was subsequently calculated, and accordingly, the forest land asset evaluation prime stand factors were predicated. It was found that the valuation results of the forest land net present value expansion model were consistent with the actual situation. The forest land net present value expansion model can therefore be used for asset evaluation of tree forest land (including natural uneven-aged forest land), bamboo forest land, shrub forest land, and economic forest land, and provide new technical support for forest land asset evaluation.

Stock return predictability and cyclical movements in valuation ratios

According to present-value models, financial valuation ratios should predict future stock returns or cash flows; however, when tested empirically, these ratios show little power. This paper develops insights into stock return predictability and reconciles the contradictory findings about the information provided by financial ratios. We decompose a financial ratio into a slow-moving component that reflects the time-varying local mean, and a cyclical component that reflects the transitory deviations of the ratio from its local mean. The cyclical components deliver substantially improved in- and out-of-sample forecast gains of stock returns and cash flows relative to the original financial ratios and the historical average benchmark. Conversely, the slow-moving components fail to predict returns, and therefore they are found to disguise the predictive information contained in the financial ratios for stock returns and cash flows.

Offset Well Design Optimization Using a Surrogate Model and Metaheuristic Algorithms: A Bakken Case Study

Fracture-driven interaction FDI (colloquially called “Frac-hit”) is the interference of fractures between two or more wells. This interference can have a significant impact on well production, depending on the unconventional play of interest (which can be positive or negative). In this work, the surrogate model was used along with metaheuristic optimization algorithms to optimize the completion design for a case study in the Bakken. A numerical model was built in a physics-based simulator that combines hydraulic fracturing, geomechanics, and reservoir numerical modeling as a continuous simulation. The stress was estimated using the anisotropic extended Eaton method. The fractures were calibrated using Microseismic Depletion Delineation (MDD) and microseismic events. The reservoir model was calibrated to 10 years of production data and bottom hole pressure by adjusting relative permeability curves. The stress changes due to depletion were calibrated using recorded pressure data from MDD and FDI. Once the model was calibrated, sensitivity analysis was run on the injected volumes, the number of clusters, the spacing between clusters, and the spacing between wells using Sobol and Latin Hypercube sampling. The results were used to build a surrogate model using an artificial neural network. The coefficient of correlation was in the order of 0.96 for both training and testing. The surrogate model was used to construct a net present value model for the whole system, which was then optimized using the Grey Wolf algorithm and the Particle Swarm Optimization algorithm, and the optimum design was reported. The optimum design is a combination of wider well spacing (1320 ft), tighter cluster spacing (22 ft), high injection volume (1950 STB/cluster), and a low cluster number per stage (seven clusters). This study suggests an optimum design for a horizontal well in the Bakken drilled next to a well that has been producing for ten years. The design can be deployed in new wells that are drilled next to depleted wells to optimize the system’s oil production.

Information Aggregation Bias and Samuelson's Dictum

AbstractUnder the assumption of incomplete information, idiosyncratic shocks may not dissipate in the aggregate. An econometrician who incorrectly imposes complete information and applies the law of large numbers may be susceptible to information aggregation bias. Tests of aggregate economic theory will be misspecified even though tests of the same theory at the microlevel deliver the correct inference. A testable implication of information aggregation bias is “Samuelson's Dictum” or the idea that stock prices can simultaneously display “microefficiency” and “macroinefficiency;” an idea accredited to Paul Samuelson. Using firm‐level data from the Center for Research in Security Prices, we present empirical evidence consistent with Samuelson's dictum. Specifically, we conduct two standard tests of the linear present value model of stock prices: a regression of future dividend changes on the dividend‐price ratio and a test for excess volatility. We show that the dividend price ratio forecasts the future growth in dividends much more accurately at the firm level as predicted by the present value model, and that excess volatility can be rejected for most firms. When the same firms are aggregated into equal‐weighted or cap‐weighted portfolios, the estimated coefficients typically deviate from the present value model and “excess” volatility is observed; this is especially true for aggregates (e.g., S&P 500) that are used in most asset pricing studies. To investigate the source of our empirical findings, we propose a theory of aggregation bias based on incomplete information and segmented markets. Traders specializing in individual stocks conflate idiosyncratic and aggregate shocks to dividends. To an econometrician using aggregate data, these assumptions generate a rejection of the present value model even though individual traders are efficiently using their available information.

Optimization of the Lateral Length of Shale-Gas Horizontal Wells Based on Geology–Engineering–Economy Integration

Horizontal wells with extended lateral lengths and large-scale hydraulic fracturing are a key technology for shale gas development. Lateral length is the key factor in determining the production and economic benefits of horizontal wells. Therefore, based on geology–engineering–economy integration, a method for optimizing the lateral length of shale-gas horizontal wells is established. Through fracture-shape prediction, productivity simulation and input–output analysis, the net present-value model of the technical–economic evaluation of the economic lateral length is established. A comprehensive evaluation of lateral lengths in Changning Block is then conducted. The results show that, under the current geological, engineering, and economic conditions in Changning Block, a horizontal well with a lateral length between 175 m and 3508 m is economically viable, and the optimal economic lateral length is 2000 m. The porosity and thickness of the reservoir matrix, the production time, the drilling investment, and the price of the natural gas wellhead in the first year have a great impact on the economic lateral length. On one hand, we can increase the drilling rate by increasing the technical research and development efforts. On the other hand, we can improve the construction management level to reduce investment and reasonably increase the price subsidy to optimize the lateral length of shale-gas horizontal wells.

The estimation of house bubbles during the time of pandemic: a study of Australia

ABSTRACT While the economy slid into a recession since the onset of the COVID-19 pandemic, the house prices in many markets have reached historical highs. This research aims to examine how much the recent large increases in the prices can be explained by bubbles and whether the overvaluation is due to momentum or intrinsic bubbles. A present value model is constructed to estimate real estate bubbles with one dynamic and three assumed net rental yields. The empirical focus is on Sydney, Melbourne and Brisbane, the three largest residential property markets in Australia, that have experienced a rapid growth in house prices since the outbreak of coronavirus. The finding demonstrates the evidence of bubbles in all three markets during the study period but the degree of overvaluation varies. We also discover that momentum bubbles exist in these markets but the overvaluation is mainly explained by intrinsic bubbles.

Comparison of Net Present Value Model and Internal Rate of Return Model in Investment Decisions

The continued operation of a company cannot be separated from the right economic decisions. When making investment decisions, NPV and IRR models are often used due to their excellence. However, the two models sometimes yield different results when comparing and selecting investment projects; After researching the relevant literature, in this article, we give a detailed introduction to the NPV and IRR models, and illustrate the economic implications of the two models. However, in practical applications, the NPV and IRR models do not always produce the same outcomes, and in certain cases, contradictions can arise. This paper analyzes the reasons for the contradiction between the two through case studies, and analyzes the applicability of the two models in order to better apply them. The study found that the IRR model produces inaccurate results because of its own insensitivity to situations when it comes to comparing projects of different sizes, different return patterns or comparing projects of investing and financing. But the NPV model, due to the simple calculation of the indicators, is consistent with the financial management objectives of the enterprise, the investment assumptions are relatively reasonable, and there is no problem of multiple solutions or no solutions. When using the IRR indicator and the NPV indicator, when conflicting results are obtained through the use of the NPV indicator and the IRR indicator, the NPV indicator should be chosen to assess the viability of investment projects.

Revisiting the Long-Run Dynamic Linkage between Dividends and Share Price with Advanced Panel Econometrics Techniques

The log-linearized present value model (PVM) has been widely used in corporate finance to understand the long-run relationship between share price and dividends using panel data. However, the application of recently established panel econometric approaches that account for slope heterogeneity and cross-section dependency in the recent literature regarding the long-run link between share price and dividends in an Indian setting is limited. This paper re-examines the log-linearized PVM in an Indian setting using newly developed panel unit root, cointegration, and long-run dynamic estimation approaches. This study employed a panel dataset of 60 Bombay Stock Exchange (BSE)-listed Indian firms paying regular dividends for 28 years (1990–2017). The study found unit root, cointegration, and a long-run relationship between dividend and share price series for Indian firms during a 28-year sample period. By demonstrating the presence of a long-run link between share price and dividends, this paper contributes to the literature on the PVM, which is crucial in comprehending market rationality and share price behavior in India. This paper also discusses issues related to panel data, such as cross-section dependency and slope heterogeneity, as well as panel econometric approaches that can be applied in the appropriate settings.

Developing a Regime-Switching Present Value Model: Switching Fundamentals and Bubbles

We develop a present-value model where the fundamental and non-fundamental components switch between distinct regimes. The non-fundamental component is specified as a periodically collapsing bubble of Balke and Wohar [Balke, N. S., & Wohar, M. E. (2009). Market fundamentals versus rational bubbles in stock prices: A Bayesian perspective. Journal of Applied Econometrics, 24(1), 35–75. https://doi.org/10.1002/jae.1025]. The fundamental component is constructed as in van Binsbergen and Koijen [van Binsbergen, J. H., & Koijen, R. S. J. (2010). Predictive regressions: A present-value approach. The Journal of Finance, 65(4), 1439–1471. https://doi.org/10.1111/j.1540-6261.2010.01575.x], by treating the expectations of market fundamentals as latent variables. Unlike existing methods, e.g. [Zhu, X. (2015). Tug-of-war: Time-varying predictability of stock returns and dividend growth. Review of Finance, 19(6), 2317–2358. https://doi.org/10.1093/rof/rfu047; Choi, K. H., Kim, C., & Park, C. (2017). Regime shifts in price-dividend ratios and expected stock returns: A present-value approach. Journal of Money, Credit and Banking, 49(2–3), 417–441. https://doi.org/10.1111/jmcb.12384; Chan, J. C., & Santi, C. (2021). Speculative bubbles in present-value models: A Bayesian Markov-switching state space approach. Journal of Economic Dynamics and Control, 127, 1–26], ours requires no unnecessary approximations, accommodates flexible forms of regime-switching, and the resulting present-value formula is internally consistent.

지역별 주택의 시장근본가격(market fundamental price)추정에 관한 실증연구

In this study, the market fundamental price of house price was estimated from August 2001 to April 2022 in based on the present value model and compared with the actual house sale price.
 As a result, First, the pattern of the difference between the actual housing sale price and the market fundamental price of each model was similar in other regions except Incheon, and although it was not exactly the same, it was divided into four periods. Second, The market fundamental price of houses estimated according to the three models showed different volatility by period and region. Third, the estimate of the third model showed less volatility compared to the estimates of other models and closely followed the actual apartment sale price. Fourth, Factors the interest rate and rent growth rate which are factors constituting the market fundamental price had different effects depending on the period.

Assessing the Financial Value of Decentralized Clinical Trials

BackgroundDeployment of remote and virtual clinical trial methods and technologies, referred to collectively as decentralized clinical trials (DCTs), represents a profound shift in clinical trial practice. To our knowledge, a comprehensive assessment of the financial net benefits of DCTs has not been conducted.MethodsWe developed an expected net present value (eNPV) model of the cash flows for new drug development and commercialization to assess the financial impact of DCTs. The measure of DCT value is the increment in eNPV that occurs, on average, when DCT methods are employed in comparison to when they are not. The model is populated with parameter values taken from published studies, Tufts CSDD benchmark data, and Medable Inc. data on DCT projects. We also calculated the return on investment (ROI) in DCTs as the ratio of the increment in eNPV to the DCT implementation cost.ResultsWe found substantial value from employing DCT methods in phase II and phase III trials. If we assume that DCT methods are applied to both phase II and phase III trials the increase in value is $20 million per drug that enters phase II, with a seven-fold ROI.ConclusionsDCTs can provide substantial extra value to sponsors developing new drugs, with high returns to investment in these technologies. Future research on this topic should focus on expanding the data to larger datasets and on additional aspects of clinical trial operations not currently measured.

Investment strategies under stochastic electricity prices and implications for charging infrastructure network coverage: A case of photovoltaic pavements

For enhancing the charging infrastructure network coverage in populated areas, photovoltaic pavement (PVP) has been considered an innovative option that caters to distributed power generation and electric vehicles (EVs) charging in motion. However, the promotion of PVP has not yet served its intended purpose. This paper develops a gross net present value (GNPV) model to investigate the optimal timing to invest in PVP under stochastic electricity prices and time-dependent investment costs. Based on the photovoltaic highway project in Shandong, China, our numerical results reveal that the volatility of electricity prices, construction investment costs, and solar panels’ energy conversion efficiency significantly restricts the diffusion of PVP, resulting in the current dilemma of PVP adoption. However, the rapid decline in investment costs and the rapid increase in the number of EVs are not always effective in accelerating the PVP investment process, especially in some high electricity price regions, such as the southern areas of China. The findings of this paper will provide theoretical support and decision-making recommendations for the management evaluation of PVP projects, the optimization of charging facility networks, and the formulation of EV incentive measures.

An experimental analysis of German farmers’ decisions to buy or rent farmland

Farmland is an essential agricultural production factor that farmers can choose to either buy or rent. In this paper, we apply a discrete choice experiment to analyse German farmers’ individual buying and rental decisions for farmland. Our results reveal that farmers have a higher willingness to buy than to rent farmland. Covariates such as farmers’ risk attitude affect the decisions in the discrete choice experiment while no effect was observable for individual expectations about future farmland prices. Direct payments considerably raise farmers’ willingness to buy and rent farmland. Farmers’ decisions deviate substantially from normative predictions from the present value model.

Horizon Bias and the Term Structure of Equity Returns

AbstractWe label the degree to which individuals are more optimistic at long horizons relative to short horizons as the horizon bias. We examine whether time-series variation in the horizon bias can explain the time-series variation in the equity term structure. We use analyst earnings forecasts to measure the degree of the horizon bias in the stock market. Consistent with the intuition from a stylized present value model, we find that periods of above-average horizon bias are associated with negative term premiums, whereas periods of below-average horizon bias are associated with positive term premiums.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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.