Valuation Model Research Articles

Fair value estimates for illiquid cryptocurrency

To address the need for reporting and disclosure of cryptocurrency holdings in compliance with the FASB guidance for the use of fair value measurements for cryptocurrency (FASB, 2023), this paper develops a modeling process for reporting entities to measure the market value of cryptocurrencies with limited or no observable transactions. In this valuation model, we consider the last observable market information with time decay, its comparable assets market index, and dynamic real-time market participants’ sentiment and attention. Notably, the application of exogenous variables allows us to maximize the observable inputs in measuring fair value, such as asset classification based on economic traits and market participants’ attention and sentiment measurement with online media textual analytics. We propose a valuation framework and construct a prediction model that can achieve a prediction accuracy of 87 % on target asset resurging prices.

Analyzing the impacts of watershed management interventions and scenarios on sediment export reduction in yezat watershed, north west Ethiopia using InVEST model

ABSTRACT This study aimed at assessing the long term impacts of watershed management interventions and scenarios on sediment export reduction in the data-scarce region of the Yezat watershed in north-west Ethiopia. The modified ecosystem services valuation model (InVEST) was employed to evaluate the amount of sediment export in the watershed. Different watershed management intervention scenarios were applied to evaluate the effectiveness of watershed management interventions on sediment export reduction. The results of the study showed that the mean annual sediment yield was 35 tons ha yr in 20000, due to the expansion of cultivated land and built-up area. Conversely, it decreased from 35 ton ha yr in 2000 -18.4 ton ha yr in 2021 due to watershed management interventions. On the contrary, moderate, severe, and very severe categories of sediment export intensity were high in 2000. But it decreased in 2021 due to watershed management interventions. The mean annual sediment export in the baseline scenario (BS) was 35 tons per year, which was reduced to 13.39 tons per year, 3.46 tons per year, and 1.17 tons per year in the reforestation scenario, soil/stone bund, and integrated scenarios, respectively. The result revealed that areas covered by high sediment severity classes were reduced.

Utilizing Genetic Algorithms in Conjunction with ANN-Based Stock Valuation Models to Enhance the Optimization of Stock Investment Decisions

Navigating the stock market’s unpredictability and reducing vulnerability to its volatility requires well-informed decisions on stock selection, capital allocation, and transaction timing. While stock selection can be accomplished through fundamental analysis, the extensive data involved often pose challenges in discerning pertinent information. Timing, typically managed through technical analysis, may experience delays, leading to missed opportunities for stock transactions. Capital allocation, a quintessential resource optimization dilemma, necessitates meticulous planning for resolution. Consequently, this thesis leverages the optimization attributes of genetic algorithms, in conjunction with fundamental analysis and the concept of combination with repetition optimization, to identify appropriate stock selection and capital allocation strategies. Regarding timing, it employs deep learning coupled with the Ohlson model for stock valuation to ascertain the intrinsic worth of stocks. This lays the groundwork for transactions to yield favorable returns. In terms of experimentation, this study juxtaposes the integrated analytical approach of this thesis with the equal capital allocation strategy, TAIEX, and the Taiwan 50 index. The findings affirm that irrespective of the Taiwan stock market’s bullish or bearish tendencies, the method proposed in this study indeed facilitates investors in making astute investment decisions and attaining substantial profits.

Evaluation of the value of Internet platform Enterprises in the digital economy by the big data cooperation asset valuation model

With the innovation of information technology, the national industry has been adjusted and upgraded, and the development of the Internet industry has had a huge impact on economic development. The investment and financing of network enterprises and the merger and acquisition of network companies need to evaluate the value of network companies. In this regard, this paper evaluated the value of Internet platform enterprises under the digital economy based on the Big Data (BD) cooperation asset valuation model. This paper analyzed the factors affecting the value evaluation of Internet enterprises and discussed the advantages of BD cooperative asset valuation model in the value evaluation of Internet enterprises in the digital economy. The BD cooperation asset valuation model was constructed, and the value evaluation experiment of Internet platform enterprises under the digital economy was carried out. The experimental results of this paper showed that in the evaluation of the profitability value of Internet enterprises, the difference between the net sales interest rate was 0.14%–0.51 %. The difference between the net interest rate of equity was 0.09%–0.67 %, and the difference between the net interest rate of total assets was 0.19%–0.92 %; in terms of the evaluation of the operating capacity of Internet enterprises, the difference between the current asset turnover rate was 0.05–0.16. The difference of non-current asset turnover rate was 0.02–0.15, and the difference of total asset turnover rate was 0.01–0.16. The evaluation value based on the BD cooperation asset valuation model was not different from the actual enterprise value, which showed that the BD cooperation asset valuation model had good advantages in the evaluation of the value of Internet enterprises.

Әртүрлі оқиғалардың криптовалюта құнына әсерін талдау

The relevance of the research topic is due to the fact that many crypto investors are attracted by the high liquidity of cryptocurrencies, low transaction costs and ease of transactions via the Internet. In contrast from Fiat currencies, corporate stocks, and bonds, cryptocurrencies do not have an underlying value. demand therefore market cost to a greater extent depends on how wellknown and popular this cryptocurrency is. Also, the price of cryptocurrencies is influenced by market sentiment, namely the thoughts, feelings and emotions of investors regarding the asset. With the help of traditional asset valuation models, it is impossible to qualitatively explain the latest changes in the price of cryptocurrencies. However, some financial models point to that cryptocurrency is currently overvalued. Apparently, the hypothesis of financial instability is better than any of the verified economic theories suitable to explain recent changesin prices of cryptoassets. The theory suggests that because the cryptocurrency market is moving against the macroeconomic fundamentals of the economy, emotions are a major factor. determining demand on a given market. In this article, the authors examined the influence of behavioral financial factors on investment decisions in the cryptocurrency market. Multiple regression analysis was used to examine this effect. The purpose of the article is to study the development of cryptocurrency in Kazakhstan and assess the impact of events on the value of cryptocurrency. Based on this goal, the authors put forward the following tasks: to analyze and evaluate the use of cryptocurrencies in the modern economy; explore the features of cryptocurrency pricing.

The Analysis of Pricing of Non-fungible Token from a Game Theory Perspective

ith the development of the metaverse, the deals of non-fungible Token(NFT) became hot. However, it is very difficult to evaluate the value of NFT because of the influence of its own nature. Although there are many valuation models for NFT, investors still cannot get the accurate and true value of NFT. Such inaccurate estimates will influence the strategy of investors. To understand and alleviate this effect. This article will use evolutionary game theory to construct model to analyze how should sellers and purchasers choose their strategy with the help of valuation models so that they can avoid risk. The conclusion shows that for the sellers, when the earnings of pricing over valuation overcomes is higher than pricing equal to the valuation, it is better to choose the strategy of pricing over valuation. For the purchasers, when the net profit of purchase is positive, it is better to choose the strategy of purchase.

Financial risk propensity and investment decisions: An empirical analysis using behavioural biases

The study aims to determine the influence of behavioural biases on financial risk propensity. It also attempts to examine the influence of financial risk propensity on investment decisions and the mediational role of financial risk propensity on the relationship between behavioural biases and investment decisions. A survey by questionnaire method is adopted to collect data from 203 respondents using the purposive sampling technique among the investors. The study has found that prospect, herding, and heuristics dimensions of behavioural bias have a significant impact on financial risk propensity, and in turn, financial risk propensity has a significant impact on investment decisions. The results of the study can help to develop more realistic investment valuation models in light of the revised risk-return expectations of investors who act contrary to the traditional concept of rational utility maximisers.

ASSESSMENT OF THE APPROACH TO MEASURING MINERAL RESOURCES IN THE CONTEXT OF THE TRANSITION TO A SOCIO-ECONOMIC MODEL OF BUSINESS REPORTING: THE CASE OF THE REPUBLIC OF MOLDOVA

The article studies and evaluates the trend of measurement of mineral resources adopted in the Republic of Moldova for its compliance with the imperative of transition to the socio-economic model of reporting. The trend analysis has shown that in the national economy there is a tendency of dominance of the contribution of processing enterprises of non-metallic raw materials and construction industry in the formation of macroeconomic indicators, which contradicts the essence of the activity of these enterprises - their secondary character in relation to the enterprises of extractive industry. The paper utilizes quantitative and qualitative analysis using the contribution coefficient and the coefficient of contribution of one notional percent of the identified economic activity to the selected macroeconomic indicators. The aggregate data on indicators of the identified enterprises of the real sector of the economy used in the calculations were obtained from the official database of the National Bureau of Statistics. The obtained results allowed to reveal an asymmetric approach in structuring the contribution to macroeconomic indicators of the identified branches of the real sector of the economy of the Republic of Moldova. The reason for this phenomenon is the adopted model of valuation based on the cost of mineral resources, which ignores their value under constrained conditions.

Addressing Methodological Challenges in the Mass Real Estate Valuation Process, Specifically Within the Context of the Republic of Moldova

This research investigates the precision of mass real estate valuation models, particularly focusing on the methodology of mathematical value modeling. The discussed challenges are particularly relevant to nations employing an ad-valorem taxation system. The Republic of Moldova's mass appraisal system for real estate serves as an illustrative case. Through a comparative analysis of the performance metrics between existing models and those refined based on the proposed recommendations, the study illustrates the improved efficiency of the advocated methodology.

A mortgage valuation model: a tale of two equations

ABSTRACT In the Journal of Financial Economics, Gerardi, Willen, and Zhang (2023) examine the relationship between prepayment rates, race, and monetary policy. In this article, we identify an anomaly in GWZ‘s valuation equation that relates to the effect of prepayment rates on the ratio of market price to initial loan balance. We then show the mathematical steps to correct this anomaly.

Efficient likelihood estimation of Heston model for novel climate-related financial contracts valuation

We propose novel Bitcoin-denominated derivatives contracts on carbon bonds. We consider a futures contract on carbon bonds where its price is expressed in terms of bitcoins. Then, we put forward options on a futures contract of the former type. Governments can use such contracts to hedge climate change and influence the prices of carbon bonds and cryptocurrencies. We show how these derivatives transfer volatility to the bitcoin market without a negative effect in the carbon bonds market. Since the aforementioned options are not yet traded in the market, we price them by assuming that the underlying is driven by the well-known Heston model, where the model parameters are estimated by a novel method based on Shannon wavelets. Heston model belongs to the class of stochastic volatility (SV) models. The discrete observations from the SV model can be seen as a state-space model, that is, a stochastic model in discrete-time which contains two sets of equations, the state equation and the observation equation. While the first describes the transition of a latent process in time, the second shows how an observer measures the latent process at each time period. We infer the properties of the latent variable by means of a filtering algorithm, and we estimate the parameters of the model via maximum likelihood. The evaluation of the likelihood function is a time-consuming task that involves updating and prediction steps of the state variable, leading to the computation of complicated integrals. We calculate these integrals by means of an integration method based on Shannon wavelets, and compare the root mean square error (RMSE) of the estimation with state-of-the-art methods. The results show that the RSME is dramatically reduced in a short CPU time with the use of wavelets.

The main aspects of building a financial model for Russian public companies valuation

The main aspects of building a financial model for Russian public companies valuation

Financial Investment Valuation Models for Photovoltaic and Energy Storage Projects: Trends and Challenges

Energy production through non-conventional renewable sources allows progress towards meeting the Sustainable Development Objectives and constitutes abundant and reliable sources when combined with storage systems. From a financial viewpoint, renewable energy production projects withstand significant challenges such as competition, irreversibility of investments, high uncertainty levels, and considerable investment amounts. These facts make their financial valuation fundamental for all the agents involved. Using the Web of Science (WoS) and Scopus databases, a scientometric analysis was carried out to understand the methods that have been used in the financial appraisal of photovoltaic energy generation projects with storage systems. The present research project was developed from 268 studies published between 2013 and 2023; tools such as Bibliometrix 4.1.3, VOSViewer 1.6.19, and Tree of Science 0.0.1a9 were used. Two main findings stand out: (i) the most used methods in the literature are the traditional ones, and within them, the levelized cost of energy has been used with greater frequency; and (ii) there is an interest in analyzing the investments of these systems for residences within the framework of distributed energy generation. Two gaps were found in the literature: (i) the studies that were carried out have not comprehensively incorporated the financial challenges faced by these investments; and (ii) the evaluation of these projects has not been addressed from the perspective of a utility-based power generator.

Employee Performance Assessment Model in Survey Service Companies Using Analytical Hierarchy Process and Naive Bayes

Assessment of the performance of employees of a survey service company produces a final score that is not in accordance with the company's transformation strategy in responding to external challenges and demands. The variable assessment process still uses 180 Degree Feedback which is one-way from superiors to employees. This makes the company's learning and growth activities, especially in the human resources department, not occur. Therefore, companies should improve the assessment model to become a tool for acquiring highly qualified performing employees. This study contributes to obtain a more comprehensive employee performance assessment model proposal and evaluate the model so that it can be implemented. The research limitation lies in the use of data, namely contract employees with the position of Project Manager. This study uses the Analytical Hierarchy Process and Naïve Bayes methods to obtain variable weights. Based on the results of the analysis, it was found that SOE (State Owned Enterprises) AKHLAK was a new variable. The classification of the assessment variables is divided into Performance and Behavioral aspects based on the Minister of SOE Regulation. Changes that occur include formulas, mechanisms, and rating scales. The results of the verification and validation show that the model is in accordance with the design so that it can be implemented. Model trials with 360 DF show that PM's final score has a good chance of being assessed as a full-time employee. This valuation model can change along with company policies in responding to internal and external changes.

Bridging theory and practice: International valuation standards and asset valuation in the telecommunications sector

In the rapidly evolving telecommunications industry, accurate valuation of tangible assets remains a critical challenge that requires adherence to universally recognized standards. This study addresses the pressing need for transparent and precise asset valuation methodologies that are pivotal for informed investment decisions and financial reporting. It aims to bridge the theoretical and practical divide in asset valuation by applying International Valuation Standards (IVS) 300 and 400 to Asiacell Communications PJSC, a leading entity in the sector. Focusing on five key tangible assets from 2018 to 2022 – lands, buildings, plant and equipment, means of transport and transfer, and furniture and office equipment – the study employs the income approach, augmented by a random walk model for future cash flow forecasting and the weighted average cost of capital for discounting. This innovative methodology offers a comprehensive valuation framework, revealing that despite Asiacell’s consistent growth rate of 4.63%, all asset categories experienced a depreciation upon revaluation. The study’s findings underscore the significance of implementing IVS in elucidating the asset valuation process, demonstrating the potential discrepancies between book values and standards-based valuations. The application of IVS 300 and IVS 400 not only enhances the transparency and accuracy of asset valuation but also provides valuable insights into the contributions of tangible assets to future income within the prevailing market conditions. By offering a replicable model for asset valuation in dynamic industries, this study contributes to the broader discourse on financial reporting and investment analysis, setting a foundation for future exploration in the field.

Neobanks in emerging markets: a risk assessment

Neobanks have risen as popular digital challengers to incumbent banks, especially in emerging markets (EMs) where the banking sector is often characterized by oligopolies. This article first estimates the value of Nubank and StoneCo in Brazil, two of the fastest-growing neobanks in Brazil. Normalizing revenue growth assumptions from their financial data, valuation models uncover that Nubank could be overvalued by about two times, whereas StoneCo could be undervalued by 80% of its fair value, as of March 19, 2024. This article then discusses firm-specific (high cumulative net operating losses, low long-term operating margins, high borrowing costs) and adherent macroeconomic (political uncertainty, foreign exchange risks, high equity risk premia) risks in EMs that explain the volatility in Nubank’s and StoneCo’s stock prices. These risks are instructive for understanding the challenges facing the business model of neobanks in EMs.

How to Make Long-Term Investments in a Stock Market? A Generic Strategy for Investors

Against the backdrop of increasingly fierce industrial competition nowadays, firms tend to have substantive business risk and/or information risk, increasing the estimation risk and limit of arbitrage for investors in their short-term investments in a stock market. It is thus important for investors to hold a long-term horizon for at least part of their investments in the stock market. This paper aims to introduce a long-term investment strategy that is practically feasible and potentially valuable to investors. To this end, we first develop a parsimonious model in which we identify the major determinants of a firm’s value. This model is used to select high-value firms from each industry for further fundamental analysis and valuation. We next expatiate on how to perform strategy analysis, accounting analysis, financial analysis, and prospective analysis, and therein apply the residual operating income valuation model, in the best possible manner to further value the selected firms and their long-term investment potential. Lastly, we expound the strategy of forming and adjusting a long-term investment portfolio in a way that potentially maximizes long-term portfolio return.

On sustainable land rent

IntroductionThis paper introduces the concept of Sustainable Land Rent (SLR), providing a comprehensive, multidimensional exploration anchored in the dynamics of its origin, separability, mobility, valuation, and the imperative for equitable distribution. SLR capitalizes on the economic mobility of land’s value to enhance community welfare and promote environmental sustainability. Advocating for the systematic institutionalization of SLR, the research tackles the complex challenge of distinguishing land value from improvements.MethodsEmploying traditional Price, Cost, and Income (PCI) methods as practiced in North America, the study addresses the technical challenge of inseparability by estimating and integrating the SLR value within each of these methods. The methodology clarifies the valuation process and establishes an objective framework for resource allocation and negotiation between public and private sectors.Results and discussionFurthermore, our findings highlight SLR’s vital role in advancing public revenue generation and underscore its function as an innovative catalyst for integrating sustainability into economic valuation models and practices in real estate development and urbanization.

Industry-specific stock valuation methods – a literature review

PurposeSurprisingly little is known of the various methods of security analysis used by financial analysts with industry-specific knowledge. Financial analysts’ industry knowledge is a favored and appreciated attribute by fund managers and institutional investors. Understanding analysts’ use of industry-specific valuation models, which are the main value drivers within different industries, will enhance our understanding of important aspects of value creation in these industries. This paper contributes to the broader understanding of how financial analysts in various industries approach valuation, offering insights that can be beneficial to a wide range of stakeholders in the financial market.Design/methodology/approachThis paper systematically reviews existing research to consolidate the current understanding of analysts’ use of valuation models and factors. It aims to demystify what can often be seen as a “black box”, shedding light on the valuation tools employed by financial analysts across diverse industries.FindingsThe use of industry-specific valuation models and factors by analysts is a subject of considerable interest to both academics and investors. The predominant model in several industries is P/E, with some exceptions. Notably, EV/EBITDA is favored in the telecom, energy and materials sectors, while the capital goods industry primarily relies on P/CF. In the REITs sector, P/AFFO is the most commonly employed model. In specific sectors like pharmaceuticals, energy and telecom, DCF is utilized. However, theoretical models like RIM and AEG find limited use among analysts.Originality/valueThis is the first paper systematically reviewing the research on analyst’s use of industry-specific stock valuation methods. It serves as a foundation for future research in this field and is likely to be of interest to academics, analysts, fund managers and investors.

Improvement of DEVA Model in Valuation Research of Internet Financial Information Service Companies

With the development of the digital economy and the internet, the internet financial information service industry has shown strong momentum. Different from traditional enterprises, internet financial information service companies possess characteristics of low assets, high risks, and difficulties in measuring non-financial indicators. As a result, traditional valuation methods exhibit inaccuracies in application and require the introduction of non-financial valuation models for validation. Therefore, considering the industry's characteristics from a user perspective, we introduce the Metcalfe valuation modelthe DEVA valuation model. Simultaneously, we improve this model and, using Hithink Royalflush Information Network Co., Ltd. as a case study, estimate the value at the evaluation point using both the original DEVA model and the revised new model. Finally, we analyze and compare the differences between the various valuations and Royalflush's total market value at the evaluation point, concluding that the improved DEVA model yields more fair and accurate valuation results.