Valuation Model Research Articles

Transdiagnostic Anxiety-Related Increases in Information Sampling are Associated With Altered Valuation.

Excessive information sampling in psychiatric patients characterized by high trait anxiety has been inconsistently linked with alterations in inferential and valuation processes. Methodological limitations could account in part for these inconsistencies. To address this, computational models of inference and valuation were applied to data collected from a transdiagnostic sample of adults with and without an anxiety or compulsive disorder using a version of the beads task with enhanced experimental controls. Participants diagnosed with an anxiety or compulsive disorder (n = 35) and healthy controls (n = 23) completed the beads task with three majority-to-minority ratios of blue versus green beads (60:40, 75:25, 90:10). First, a Bayesian belief-updating model was fit to quantify the iterative process by which new information (bead color) and prior beliefs were integrated to influence current beliefs about jar identity. Next, a parameterized partially observable Markov decision process model was used to parse the contribution of value-based decisions to sampling behavior and included a relative subjective cost parameter, Csub , for each bead-ratio condition. Higher trait anxiety was associated with more draws-to-decision, most robustly in the 90:10 bead-ratio condition. Only relative subjective cost of sampling decisions, and not inferential differences in weighting of new or old information, satisfactorily accounted for this relation. Specifically, lower Csub(0.9) was associated with more trait anxiety and more draws-to-decision. In a condition with high objective evidence strength, transdiagnostic trait-anxiety-related increases in information sampling were explained by a cost-benefit analysis where relatively higher subjective cost was assigned to an incorrect guess, highlighting valuation as a potential treatment target for future research.

Research on the Application of Mathematics in Finance

Abstract. Mathematics is a fundamental science and a problem-solving method that plays an important role in revealing the essential laws of the development of certain things. It has rigorous logic, high abstractness, and wide applicability. This brings great difficulties to enterprises, especially high school mathematics because it involves a wide range of knowledge points and complex calculations. Among them, the formation of financial mathematics has greatly promoted the development of China's financial industry. Moreover, in solving financial problems, relevant knowledge and theories of mathematics can be used to analyze and explore financial laws, and the application effect is significant, for example, using deterministic mathematical method and nondeterministic mathematical method to solving question about financial investment and returns; building securities investment portfolio model or asset valuation model to calculating and expressing some economic models. Based on this, this article discusses the application of mathematical knowledge to several financial problems, analyzes the current problems, and looks forward to the future of financial mathematics.

Race, profit, and algorithms: Neighborhood-level analysis of iBuyers’ profit margin

ABSTRACT iBuyers are firms that use automated valuation models (AVMs), streamline home buying processes, and provide all-cash offers to purchase homes. Although the previous literature has explored the roles and limitations of iBuyers in the housing market, empirical research on the racial implications of these algorithmic home buying processes remains understudied. Using a spatial lag model, this study shows the spatial clustering of iBuyer profit margins, that iBuyers gain more profits when they resell to individuals than institutions, and that some iBuyers have a statistically significant correlation between their profit margins and the proportion of marginalized racial groups within a census tract, while controlling for individual housing characteristics, neighborhood housing quality and demand, and neighborhood amenities and socioeconomic factors. These findings suggest that the more adeptly iBuyers can forecast housing values, the greater the potential to maximize profits from homeowners in communities of color. Consequently, this research contributes to the understanding of how technological mechanisms operate within a purportedly race-neutral framework and advocates for the development and deployment of algorithmic systems guided by the principles of antisubordination, rather than relying solely on notions of “fairness” and anticlassification.

Informing healthy urban policy making through quantitative health impact appraisal – a co-designed approach

Abstract Quantitative health impact appraisal for land use development is currently challenging. We show how a co-designed process between urban decision makers and academics can help to improve ways of deriving evidence for creating healthier cities. The Health Appraisal of Urban Systems (HAUS) economic valuation model was developed with local partners to inform a strategic framework for a UK urban regeneration site. HAUS is a comparative risk assessment model which allows the user to understand mechanisms for change through applying an impact-pathway approach to quantify changes in health. It is informed by systematic reviews of epidemiology and societal costs of 85 individual health end-points. Scenarios combined a range of characteristics including air and noise pollution, green space, crime, walkability, food environment and transport. Health outcomes include morbidity and mortality impacts. Results show the value of a holistic approach to comparing health risks in designing urban living. By monetising outcomes, practitioners were able to understand the relative magnitude of risk within a specific area and compare alternate urban development scenarios. Compared to an unmanaged development, the Strategic Framework can mitigate health risks by improving safety and quality of the public realm and add health benefits by encouraging activity and active commuting. Flood risks are managed, and green infrastructure improved and extended. These benefits extend to communities outside the site boundaries. Over 25 years, the PV benefits are £60 - 80 million in averted health costs. Strategic planning for urban areas can unlock the potential of the public realm to tackle health inequalities and mitigate serious risks to public health. Timely access to quantitative information on health can help inform understanding of unhealthy environments, strengthen arguments for good design, reinforce capacity of planning teams and so improve the quality of urban developments. Speakers/Panelists Odile Mekel NRW Centre for Health, Bochum, Germany

Towards a Better Uncertainty Quantification in Automated Valuation Models

AbstractThis study introduces a novel framework for quantifying prediction uncertainty in automated valuation models (AVMs), crucial tools in modern real estate finance. While non-linear AVMs excel in predictive performance, their limited methods for assessing prediction uncertainty reduces reliability and practical utility. We address this gap by proposing an approach for quantifying the uncertainty associated with predicted house prices and by introducing a model-specific AVM uncertainty estimate (AVMU) for AVM comparisons. Using a dataset of 51,747 historical apartment transactions in Oslo, Norway, we train three AVMs (XGBoost, random forest, support vector machine) to predict sales prices. Thereafter, we develop three base uncertainty estimators (direct loss estimation, bootstrap ensemble, quantile regression) and three meta estimators (average regressor, voting regressor, stacked generalization) for uncertainty quantification. Conformal calibration aligns the outputted uncertainty estimates from the six estimators with standard deviations of corresponding prediction errors. Having strong positive correlations with observed absolute prediction errors, the calibrated uncertainty estimators are shown to effectively capture prediction uncertainty. While the direct loss estimation excels among base estimators, the voting regressor and stacked generalization meta estimators consistently outperform it. Furthermore, by using the AVMU estimate from the stacked generalization meta estimator we can successfully identify the best-performing AVM for three separate apartment portfolios without knowing true sales prices. This alignment of the mean estimated prediction uncertainty with observed deviations underlines the utility of pre-factual AVMU estimates for model comparisons. In conclusion, our framework helps bridge prediction accuracy and uncertainty for AVMs, enhancing their reliability and supporting informed decision making for stakeholders.

Market concentration of the Brazilian hospital medical supplementary health system

BackgroundThe Brazilian supplementary health market has undergone transformations in recent years due to constant mergers and acquisitions of by large corporations, contributing to the increase in market concentration, especially in the poorest and least developed regions of the country. Thus, given the care it provides and its economic relevance, understanding the fundamentals of these movements, the likely consequences and trends for the health market are relevant, important, and strategic.ObjectiveTo understand the general and specific context of Brazilian supplementary health, its scenarios, and trends, with emphasis on the analysis of market concentration and recent mergers and acquisitions.MethodologyThe research is applied, descriptive and exploratory and uses secondary data from various sources, submitted to quantitative data analysis methods. The data are organized into three groups: historical and regulatory documents; industry data; and market.ResultsThe results show the growing concentration of the market promoted by large publicly traded corporations, the growing relevance of tech startups on the healthcare landscape, the predominant use of the relative valuation model, with implicit multiples for asset pricing and the prevalence of corporate health plans.ConclusionThe growing concentration of the system projects a market with fewer options and less competitiveness, since the growth of large operators is evident, in addition to the relevant increase in the number of complaints from users of the system, which signals the growing gap between the expectations of users and the levels of quality care offered. The study also highlights the predominance of corporate health plans, revealing the direct relationship between access to supplementary health services and employability rates. The analysis of M&A operations, in addition to the increase in market concentration, reveals the prevalence of the use of the relative valuation model and implicit multiples for the pricing of traded assets. This denotes the future expectation of wealth generation, at least equivalent to the historical series of the sector, on the part of investors, whose frustration may signal the decreasing attractiveness of resources and M&A operations in the sector in the coming years.

Health benefits of US light-duty vehicle electrification: Roles of fleet dynamics, clean electricity, and policy timing

We present a dynamic perspective to quantify the air quality-related health impacts of the electrification of light-duty vehicles in the United States between 2022 and 2050. Using a fleet turnover model and future electricity generation mix scenarios, we compare ambitious vehicle electrification to fleet renewal relying on newer internal combustion engine vehicles, without electric vehicles. The model includes vehicle-level pollutant emission factors and a reduced complexity air quality and valuation model and covers direct (tailpipe, brake wear, and tire wear) and indirect (production of electricity and liquid fuels) emissions of NOx, SO2, PM2.5, NH3, and VOCs, with a breakdown at the county level to identify geographical disparities in the distribution of health impacts. Short-term health benefits are mostly generated by reductions in NOx emissions from newer gasoline vehicles, while fleet electrification generates further benefits in the long term. The electricity mix plays a crucial role in the success of electrification policies. With continued grid decarbonization, electrification would reduce harmful air quality-related health impacts cumulatively by 84 to 188 billion USD over the study period, compared with fleet renewal without electric vehicles. In contrast, artificially freezing the 2022 grid would make electrification responsible for 32 to 71 billion USD additional health disbenefits compared with fleet renewal. Finally, we show that while fleet electrification achieves most of its benefits over fleet renewal in the long term, delaying the implementation of such policies would sacrifice meaningful cumulative benefits.

A novel approach to commercial property valuation: Successive term indexing and its competitive implications

The development of innovative valuation models in the commercial real estate sector is crucial for enhancing competitiveness, as they provide stakeholders with the accuracy and adaptability needed to navigate and capitalize in an increasingly complex market landscape. This paper aims to improve initial valuations provided by real estate agents by updating their timeliness while also enhancing their accuracy. Leveraging statistical techniques based on hedonic regression, it introduces a novel mechanism, successive term indexing, enabling the reassessment of widely varying commercial properties with initial assessments that are at least one term old, where a term typically encompasses one year. The model's novelty lies in its approach to indexing individual property characteristics across two successive terms, using a k-means algorithm to categorize all numerical variables and stepwise selection. The former enables differing index values for varying sizes, while the latter allows dynamic evaluation of the significance of regressors in time. Applied to data from a banking institution, the model showcases strong predictive accuracy with unique reassessment ratios ranging from -3.8% to 5.2% for 2022. With its nuanced analysis of market dynamics and indexing capabilities, the mechanism intersects the elements of PPIs and AVMs, presenting a significant methodological advancement and a practical, simple-to-use tool for valuation.

Navigating the Nexus: A Comprehensive Analysis of the Evolving Interplay Between Environmental, Social and Governance (ESG) and Sustainability in Contemporary Business

This research, based on an in-depth review of 67 papers, explores the intersection of environmental, social and governance (ESG) and sustainability practices in modern business. The study underlines the distinction between these concepts and their evolving significance in the global business landscape. With a methodology anchored in the PRISM framework, the systematic literature review reveals key insights. Notably, it identifies the global adoption of ESG and sustainability strategies, driven by stakeholder demands for transparency. The systematic literature review methodology ensures a comprehensive analysis, providing actionable insights for businesses navigating responsible and resilient strategies. The study also points to future research avenues, emphasising areas such as social origins and consumer behaviour, alternative ESG ratings, ESG failures and challenges, ESG integration into valuation models, sustainable investment strategies in specific sectors and the impact of ESG during crises like COVID-19. The research contributes to a comprehensive understanding of the relationship between ESG and sustainability. The findings offer practical implications for organisations aligning with ethical practices, meeting stakeholder expectations and securing a sustainable future.

Comprehensive Review on Valuation Method for Bill of Quantities in Prefabricated Shear Wall Structures Using BIM

Prefabricated shear wall structure is widely used, it can not only meet the structural performance needs of buildings, but also greatly improve the construction efficiency of buildings. The bill of quantities valuation model has important theoretical and practical significance for optimizing the cost control of prefabricated shear wall structure and improving the efficiency of project management. From the perspective of combining theory and practice, this paper explores the bill of quantity valuation method of assembled shear wall structure under BIM environment, integrates BIM technology and SQL Sever database technology, and extracts the attribute information of model components through the secondary development of Revit with the data platform provided by BIM. The construction of bill of quantities is realized according to the specification of bill of quantities, and the association between bill of quantities and model components is established to realize the visual traceability query of bill of quantities, which provides a BIM technology-based construction engineering measurement and valuation technology and method, hoping to provide a new perspective and theoretical basis for related fields.

A Novel Urine DNA Predictor for Noninvasive Early Diagnosis and Monitoring Minimal Residual Disease of Upper Tract Urothelial Carcinoma.

For early detection and postoperative monitoring of upper tract urothelial carcinoma (UTUC), the traditional detection method was limited to its invasiveness and insufficient sensitivity. We aim to use urine tumour DNA (utDNA) for detecting minimal residual disease (MRD), early diagnosis and perioperative monitoring in UTUC. We previously established a utDNA multidimensional bioinformatic valuation model, named utLIFE, using low-coverage whole-genome sequencing and targeted deep sequencing. This prospective cohort enrolled 93 patients diagnosed with UTUC without metastasis. We collected morning urine samples on the day of surgery and the discharge day after the operation for utLIFE testing. In addition, we also enrolled 80 healthy controls to further validate the specificity of the utLIFE model in the study. The utLIFE of preoperative samples could discriminate UTUC with high specificity (96.25%, 77/80), and high sensitivity (96.77%, 90/93) regardless of stage and grade. The sensitivity of utLIFE was significantly higher than urine cytology (p < 0.001) and fluorescence insitu hybridisation (FISH) (p < 0.001) (N = 19), especially in early-stage and low-grade UTUC. Postoperative utLIFE scores were significantly decreased compared with those of preoperative samples (79 vs. 36, p < 0.001), indicating its association with tumour burden. For special pathology types, utLIFE performed less well in sensitivity and perioperative alteration. In conclusion, we established a bioinformatic utDNA valuation model, utLIFE, which was validated to be a rapid and noninvasive approach with high sensitivity for early detection and MRD monitoring for UTUC.

Valuation and Returns on Stock Return Volatility

ABSTRACT This paper provides an accounting-based valuation model that predicts that cross-sectional variation in firm-level returns to investments in both stock and stock return volatility are related to cross-sectional variation in firm-level fundamentals. The model predicts that expected stock returns have a positive quadratic relation with stock return variance and a negative quadratic relation with gains to trading in stock return variance. Consistent with these predictions, firms with high model-implied expected stock returns have high future stock return variance, and the relation is roughly quadratic. In contrast, firms with high expected stock returns have low future returns to trading in stock return variance through option contracts because these firms have high option-implied variance relative to future realized variance, i.e., low variance risk premia (VRP). The study provides a framework for using fundamentals for trading in individual stocks and options. JEL Classifications: G12; G14; G17.

The Finance of Cybersecurity: Quantitative Modeling of Investment Decisions and Net Present Value

Despite the growing literature on cybersecurity investment, significant gaps remain unaddressed. First, the existing literature does not differentiate between cybersecurity expenditures, namely investment versus spending, despite the differences in their financial treatments. Second, the literature predominantly employs economic and risk-based models, in which the incorporation of financial parameters is limited. Third, the literature focuses on quantifying market (stock) loss of cyberattacks or conducting a cost-benefit analysis of cybersecurity investments but has yet to bridge these two areas. Thus, our objective is to introduce a new financial model, the Alpha Model, that addresses these gaps. We first provide distinctions between cybersecurity investment (capital expenditure) and spending (operational expenditure). Grounded in Relative Valuation Models and Optimal Capital Structure Theory, we quantify the upper-bound of optimal cybersecurity investment by utilizing book and market values of equity and debt. We also demonstrate that the Net Present Values (NPV) of cybersecurity investment is considerably lower than its book value by incorporating financial parameters of equity and debt financing, inflation-adjusted capital cost, and tax credits of depreciation and interest. To validate our model, we empirically test it by using data from the S&P500 index during 2018–2023. We offer practical implications to key stakeholders, including: (1) industry executives to financially quantify cybersecurity investments and ascertain their actual NPVs, individually and collaboratively with supply chain partners; (2) cybersecurity solution providers to promote business investments in cybersecurity; and (3) insurance providers to price premiums with higher NPVs than the corresponding investments. We also offer several avenues for further research.

Étude capacité théranostique 2023–2024 – Modélisation sur la base de la RIV-PSMA. Radiothérapie interne vectorisée (RIV), une approche théranostique à laquelle le patient doit avoir accès. Partie II : analyse approfondie

This in-depth analysis highlights the issues and perspectives regarding the deployment and optimization of vectorized internal radiotherapy (VIR), based on the VIR-PSMA model, which is the first theranostic modality that changes the scale of clinical management in nuclear medicine. It made it possible to confirm a characterization of health establishments based on their experience and their ability to integrate this new treatment modality into their care offering. Here are 8 key points and recommendations highlighted in the following pages: 1. Current and future reception capacity: “experienced” and “initiated” establishments have the greatest short-term reception capacity, while “medium-term future” and “long-term future” establishments will come, in the medium and long term respectively, strengthen the provision of care. They will be able to strengthen the healthcare offer and the territorial network if the award of a B rating allows them to do so. 2. Discrepancy between capacity and need: a gap is already observed between the number of patients who can be treated and the real needs of patients eligible for VIR-PSMA, even though capacity projections are voluntarily restricted to “experienced” and “initiated” services only (the “future” services in the medium and long term are not to date uniform in obtaining the necessary authorizations). This gap between capacity and need can be considered initially by optimizing the use of existing resources (in particular thanks to available land capacity), which remains mainly limited by the shortage of professionals. Secondly, the granting of authorizations to “future” services will make it possible to complete the system, and in particular to promote the regional distribution of care provision which shows disparities that could be sources of inequitable access to care in France. 3. Clinical pathway and harmonization of practices: the clinical pathway for patients benefiting from a VIR requires harmonization of practices and the making of recommendations by the SFMN (from the assessment of eligibility to post-dose monitoring, including the organization of treatments and the reception of patients). 4. Training and recruitment needs: the anticipated growth in demand for VIR requires strengthening teams through the recruitment of all staff involved in the patient's journey, as well as the creation of cooperation protocols between all health professionals by strengthening the initial and continuing training of all professionals involved. 5. Investments and adaptation of infrastructure: significant investments in specialized infrastructure and in the upgrading of “up-and-coming” establishments are necessary to support the increase in VIR-related activity. 6. Define a valuation model allowing these care activities to be sustained while meeting specific constraints in terms of operation, infrastructure, and regulations. 7. Role of regional health agencies (ARS): the ARS play a crucial role in coordinating the deployment of the VIR, requiring adaptation of the offer to local needs and a coherent national policy to face the challenges, particularly in the awarding of B grades, but also by ensuring that the teams of professionals are sufficient and trained and that the service meets logistical and security requirements. 8. Short, medium and long term strategies: to respond to the anticipated saturation of reception capacities, strategic actions are necessary in the short-term (less than 2 years), in the medium-term (less than 5 years), and long-term (more than 5 years), including the structuring of the organization, the expansion of recruitment, the harmonization of practices, and the strengthening of reception capacities.

The impact of environmental, social and governance disclosure on stock prices: Empirical research in Vietnam

The aims of this research is to evaluate the impact of Environmental, Social and Governance (ESG) information disclosure (using both financial and non-financial information) on stock prices of businesses in Vietnam; From there, propose recommendations to promote transparent practices and information disclosure about ESG of businesses related activities; Contribute to developing sustainable investment goals in businesses, and the financial market in general. Based on the (Ohlson, 1995) [40] valuation model, and developed by Barth and Clinch (2009) [41] the research was conducted empirically in 30 listed companies in Vietnam in the period 2019–2022. Research results based on the GLS method show that ESG positively impacts stock prices. Additionally, this paper conducted an in-depth analysis of each individual pillar and found that the environmental pillar was considered the most meaningful to market participants. However, the correlation between ESG and stock prices is weakened for environmentally sensitive businesses. This study also makes a valuable contribution to the current literature on non-financial information reporting by using manually collected data and investigating the effects of ESG reporting on market participants in the public system voluntary information disclosure system of an emerging market like Vietnam.

The Credit Spread: Risk-Free Rate in the Model

This paper proposes a parsimonious credit spread estimation model for valuation of corporate bonds in data-scarce markets. We emphasize the importance of incorporating the risk-free rate directly into credit spread determination. Our model aligns with established literature and demonstrates the ability to capture the observed influence of risk-free rates on credit spreads across economies. We posit that models omitting the risk-free rate component may underestimate credit spreads, particularly impactful in emerging markets with elevated default probabilities and high risk-free rates. Finally, we discuss practical applications of the model, including exchange rate premium calculations, policy analysis, and negative yield spread analysis.

Mapping the landscape: A systematic literature review on automated valuation models and strategic applications in real estate

In the rapidly evolving real estate industry, integrating automated valuation models (AVMs) has become critical for improving property assessment accuracy and transparency. Although there is some research on the subject, no thorough qualitative systematic review has been done in this field. This paper aims to provide an up-to-date and systematic understanding of the strategic applications of AVMs across various real estate subsectors (i.e., real estate development, real estate investment, land administration, and taxation), shedding light on their broad contributions to value enhancement, decision-making, and market insights. The systematic review is based on 97 papers selected out of 652 search results with an application of the PRISMA-based method. The findings highlight the transformative role of AVMs approaches in streamlining valuation processes, enhancing market efficiency, and supporting data-driven decision-making in the real estate industry, along with developing an original conceptual framework. Key areas of future research, including data integration, ethical implications, and the development of hybrid AVMs approaches are identified to advance the field and address emerging challenges. Ultimately, stakeholders can create new avenues for real estate valuation efficiency, accuracy, and transparency by judiciously utilizing AVMs approaches, leading to more educated real estate investment decisions.

Tobin’s q Revisited: A Theoretical and Empirical Framework for Accurate Business Valuation.

Objective - This study revisits Tobin’s q by offering a theoretically derived empirical model based on the Ohlson framework, aiming to correct the misapplications found in existing literature. Methodology/Technique – While Tobin’s q has been extensively used as a measure of firm value, the traditional empirical models often assume a linear relationship with variables such as growth, leverage, and profitability. Finding – This paper demonstrates that even in a basic model, such as the Gordon growth model, this linearity does not hold. In fact, this paper shows that a linear relationship will hold only under restricted conditions and only with certain explanatory variables. Novelty – By introducing a theoretically sound equation for Tobin’s q, this study highlights the limitations of current empirical methods and provides a new perspective for firm valuation research. The results contribute significantly to improving the accuracy of business valuation models, particularly in settings with varying capital structures and market dynamics. Type of Paper: Theoretical JEL Classification: G12, G19. Keywords: Tobin’s q; Ohlson model; Firm value; Valuation Reference to this paper should be referred to as follows: Tharavanij, P. (2024). Tobin’s q Revisited: A Theoretical and Empirical Framework for Accurate Business Valuation, GATR-Global J. Bus. Soc. Sci. Review, 12(3), 125–133. https://doi.org/10.35609/gjbssr.2024.12.3(3)

Real Estate Automated Valuation Model with Explainable Artificial Intelligence Based on Shapley Values

Real Estate Automated Valuation Model with Explainable Artificial Intelligence Based on Shapley Values

Economic valuation of changes in ecosystem services of 77 Ramsar wetlands in West Asia over 37 years (1984–2021)

Summary In the West Asia region, the vulnerability of Ramsar Convention wetlands due to unsustainable utilization driven by water scarcity continues to grow. Here, a global surface water product generated by the European Joint Research Centre was used to assess changes in surface water in 77 wetlands listed under the Ramsar Convention over a 37-year period (1984–2021). By combining this product with a quantitative valuation model, estimates were made of the economic value of the ecosystem services provided by these wetlands, enabling the determination of the economic losses resulting from any reduction in surface water. We show that 20% (7550 km2) of permanent surface waters in Ramsar sites have disappeared or are no longer classified as permanent. Based on this, USD 106 billion of the economic value of wetlands ecosystem services have been lost. Additionally, 33% (12 100 km2) of seasonal surface waters in these wetlands have experienced a decrease in area. Iran and Iraq account for 90% of water losses, primarily in 34 wetlands (30 in Iran and 4 in Iraq). These findings underscore the urgent need for water management policies and conservation strategies in the West Asia region.