Market Value Research Articles

EXPRESS: Supplier Slip-Ups: How Malpractices Can Bite into Restaurant Market Value

Ethical, environmental, and human rights standards are critical issues in the tourism and hospitality industry. In this context, restaurants must choose third-party members—such as suppliers—that align with their quality and sustainability standards, especially because these third-party members may encounter challenges related to food safety, ethical misconduct, environmental matters, and human rights violations. This study analyzes whether the market holds restaurants accountable for the shortcomings of their suppliers, even when the restaurant has not directly participated in or caused these problems. Based on signaling theory and the associative network memory paradigm, this study finds that supplier issues negatively affect the market value of restaurants, with indirect linkage exerting a greater impact than direct linkage, which informs these theories by underscoring the importance of memory and associations dynamics.

Telomere-to-telomere gapless genome assembly of the giant grouper (Epinephelus lanceolatus)

The giant grouper (Epinephelus lanceolatus) is a large coral reef fish distributed in the Indian Ocean and the Pacific Ocean. With a high market value, this species can grow up to approximately 2.7 meters in length and weigh 440 kilograms. With the rapid development of bioinformatics, higher standards of genome analysis are now required compared to previous reference genomes. This study presents a gapless assembly of the giant grouper genome, which has a length of 1.03 Gb. The sequences were assembled onto 24 chromosomes with a coverage of over 99% (1.02 Gb), and telomeres were detected on 24 chromosomes. Analysis using Merqury indicated a high level of accuracy, with an average consensus quality value of 59.24. The ONT ultralong and PacBio HiFi data were aligned with the assembly using minimap2, resulting in a mapping rate of 99.9%. The study inferred 25,815 protein-coding genes. These results lay a foundation for exploring the evolution and biology of the giant grouper, and advancing molecular breeding techniques.

Exploring the Mediated-Moderated Impact in ESG-Value Nexus: The role of Cost of Debt and Firm Size in Chinese Listed Companies

Background: ESG disclosures are critical aspects of corporate reporting, reflecting an entity’s commitment towards sustainable and responsible business practices. In China, the increasing emphasis on the ESG practices tends to raise questions regarding their effect on the financial performance, specifically in case of the listed companies. Purpose: The study aims to investigate the influence of ESG disclosures and Firm Value of the Chinese listed companies, directly, as well as through the mediating effect of cost of debt and size’s moderating role. Design: The study employs a fixed-effects regression for a panel dataset, consisting of 1065 observations, developed using 213 entities and 5 fiscal years (2018 to 2022). The independent variables of the study are the individual ESG components and the aggregate ESG score while Tobin’s Q is the dependent variable. The control variables include leverage, profitability, growth, age, and liquidity. The cost of debt and size has been proxied by the interest costs and natural log of total assets respectively. Results: ESG metrics, individually or collectively, do not influence Tobin’s Q whereas the interaction effect of the size tends to influence the value negatively, only in case of the total ESG score. It states that the positive effect on market value declines as the size of the company increases. The cost of debt, on the other hand, is a significant mediator in the interaction aspect, adversely influencing Tobin’s Q. Policy Implications: The policy makers must support the smaller companies in their ESG adoption as they are more likely to benefit from the ESG practices due to their increase in the market valuation. Additionally, tailored ESG reporting as well as implementation strategies tend to maximise the financial benefits across different firm sizes. Originality/Value: The novel insights provided by the study into the complicated dynamics of the ESG and Value in case of the Chinese companies highlights varied effects. The role of firm size and the importance of cost of debt are prominent in contributing to a broader understanding of the ESG’s role in enhancing value of the enterprises.

Modification and Use of Naturally Renewable Cardanol-Type Prepolymers in One-Component Silyl-Terminated Prepolymer Sealant Systems

The current research is devoted to integrating naturally renewable cardanol derivatives into one-component silyl-terminated-polyether-based prepolymer systems to improve climatic resistance and obtain materials with versatile mechanical properties that could be significant to various sectors of the economy. Various cardanol-type products are used in industries that require high climatic resistance, and thus combining cardanol with commercially available silyl-terminated polyether prepolymers would improve its material climatic resistance, maintaining its market and application value as well as improving material sustainability. The results obtained in this work show that depending on how the cardanol prepolymer Ultra Lite 513 is modified, it is possible to increase the elasticity (670%) or tensile strength (104%) of the material as well as significantly increase the climatic resistance of the material, thus improving the quality and sustainability of the adhesive compared to existing silyl-terminated-prepolymer-based adhesives on the market.

Bond Ratings and Company Value: Insights from Indonesia's Real Estate Sector

This study investigates the impact of bond ratings on company value, mediated by investment decisions, funding decisions, and corporate governance disclosure, focusing on property and real estate companies listed on the Indonesia Stock Exchange (IDX). Using data from 45 companies over a five-year period (2016-2020), this research employs WarpPLS, a variance-based structural equation modeling (SEM) technique, to analyze both direct and indirect effects between the variables. The findings reveal that while investment decisions have a significant positive effect on company value, funding decisions, bond ratings, and corporate governance disclosure do not show direct impacts. However, bond ratings significantly influence company value indirectly through investment decisions. These results highlight the critical role of strategic investment management in enhancing firm value within Indonesia's property and real estate sector, suggesting that companies should focus on improving bond ratings to facilitate more favorable investment opportunities. The practical implications of these findings suggest that firms should prioritize capital allocation decisions that align with long-term growth objectives to optimize market valuation.

The Rise of Scientific Research in Corporate America

It is widely believed that university and corporate research are complementary: companies invest in research in part to develop the capacity to absorb the knowledge emerging from universities. However, as we show in this paper, corporate research in the United States emerged when American universities were behind the world frontier in scientific research. Why, then, did for-profit businesses choose to invest in creating new knowledge, much of which could spill over to rivals, and whose conduct presented many managerial challenges? We argue that corporate research in America arose in the 1920s to compensate for weak university research, not to complement it. Using newly assembled firm-level data from the 1920s and 1930s, we find that companies invested in research because inventions increasingly relied on science but American universities were unable to meet their needs. Large firms close to the technological frontier and operating in concentrated industries were likely to invest in research, especially in scientific disciplines where American universities lagged behind the scientific frontier. Corporate science seems to have paid off, resulting in novel patents and high market valuations for firms engaged in research. Funding: A. Arora, S. Belenzon, and J. Suh acknowledge support from the Fuqua School of Business, Duke University. S. Belenzon and Y. Yafeh gratefully acknowledge financial support from the Israel Science Foundation [Grant No. 963-2020]. K. Kosenko is grateful for support from the Bank of Israel. Y. Yafeh acknowledges support from the Krueger Center at the Hebrew University of Jerusalem School of Business. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.18053 .

EnConv: enhanced CNN for leaf disease classification

Detecting leaf diseases in plants is essential to maintain crop yield and market value. Machine learning has shown promise in detecting these diseases as it can group data into predetermined categories after examining it from various angles. However, machine learning models require a thorough knowledge of plant diseases, and processing time can be lengthy. This study proposes an enhanced convolutional neural network that utilizes depthwise separable convolution and inverted residual blocks to detect leaf diseases in plants. The model considers the morphological properties and characteristics of the plant leaves, including color, intensity, and size, to categorize the data. The proposed model outperforms traditional machine learning approaches and deep learning models, achieving an accuracy of 99.87% for 39 classes of different plants such as tomato, corn, apple, potato, and more. To further improve the model, global average pooling was used in place of the flatten layer. Overall, this study presents a promising approach to detect leaf diseases in plants using an enhanced convolutional neural network with depthwise separable convolution and inverted residual blocks. The results show the potential benefits of using this model in agriculture to improve the early detection of plant diseases and maintain crop yield and market value.

Comparative Analysis of Machine Learning Algorithms in Stock Price Prediction

Stock is part of a company's principal. A person who buys stock of a company shares the profit or loss of this company. Large volume transactions are made on stock exchanges where stocks are traded. Stock prices are difficult to predict because they are affected by many variables, but when they can be predicted, great benefits are provided. Prediction of stock prices is possible with today's computers using machine learning algorithms. Machine learning provides more successful results than fundamental and technical analysis in stock price prediction. In our study, daily closing price predictions were made by collecting approximately 5-years data of the top 5 stocks with the highest market value traded in BIST 100 between 2016 and 2020. Multiple linear regression, Bayesian regression, random forest, decision trees, support vector machines, artificial neural networks algorithms were applied to include maximum 22 features and the results were compared. The most successful result was obtained in the artificial neural networks algorithm. To achieve the highest success, data pre-processing, normalization, cross-validation, parameter optimization and feature selection were applied. It has been observed that using these methods together increases the success.

«Smart» agriculture of the Community of Latin American and Caribbean States

The article analyzes the state of smart agriculture of both the world community and the Community of Latin American and Caribbean States. Quantitative indicators characterizing the shares of leading agricultural technology innovations in the agricultural solution market are given. Quantitative data on the forecast market value of smart agriculture worldwide from 2020 to 2026 and the forecast market value of smart agriculture worldwide by application in 2020 and 2026 are also provided. The author investigates the role of agriculture in the Latin American region's economy and its contribution to global agricultural production. The main trends in the implementation of smart agriculture solutions and the main challenges hindering their widespread deployment in Latin America and the Caribbean are examined. Furthermore, the author considers separately the main solutions that are already in use in the regional countries and gives a brief description of their intended use and of the objects their influence is directed to. Moreover, the results of their use are stated. In conclusion, recommendations for the implementation of smart agriculture projects in the Latin American region are given.

Predicting Used Car Prices Using Machine Learning: A Comparative Analysis of Regression and Ensemble Models

The globe is expanding daily, and with it are everyone's expectations. Purchasing an automobile is one of the demands out of all of them. However, not everyone can afford a new car, so they will purchase a used one. However, newcomers are often unaware of the market value of their ideal vehicle for an old car. That's why we require a platform that assists new users in estimating car prices. We propose that platform in this work, which is built with machine learning technology. Let's attempt to create a statistical model that can forecast the cost of a used car using supervised machine learning techniques including linear regression, KNN, Random Forest, XG boost, and decision trees. We will be assisted in this endeavor by prior customer data and a certain set of characteristics. In order to choose the best model, we will also compare the forecast accuracy of different models.For buyers, this system helps assess whether the asking price of a car is fair based on market trends. Sellers can use the predictions to set competitive prices for their vehicles, ensuring better market positioning. This predictive capability ultimately enhances transparency, allowing for more informed and confident decision-making in the automotive industry. With continuous advancements in machine learning, the accuracy and efficiency of car price predictions will continue to improve, offering even greater market insights.

Factors affecting the market value of football players in the Indonesian League

The development of accounting science, especially in human resource accounting (HRA), has progressed rapidly. The understanding of human resources (HR) as a vital asset that has an important role in the success of an organization is growing. This research focuses on measuring the market value of professional football players in the Indonesian League by considering individual performance, especially for players who play as central midfielders. Football players, recognized as intangible assets, have a market value that is closely related to their contribution on the field. The research method used is quantitative with a descriptive approach, examining the relationship between performance variables (goals, assists, number of appearances, yellow cards, and red cards) to player market value. The research data was obtained through secondary data sources from the transfermarkt.co.id website with a sample of 52 Indonesian central midfield players in the 2023 season. The results showed that goals, assists, and number of appearances had a significant influence on the player's market value. Assists and number of appearances show a very strong influence on player prices, while yellow cards and red cards do not show a significant influence. This research provides important insights for football club management in determining the market value of players as part of financial reporting and investment decision-making, and enriches the study of human resource accounting in the football industry. The findings also show the importance of players' positive on-field contributions, such as assists and performances, in determining their market value, beyond disciplinary factors such as yellow and red cards.

Impact of Earnings Management on Market Value in Light of Dividend Policy: an Econometric Study of a Sample of Saudi Stock Exchange-Listed Companies During the Period 2010–2021

This study aimed at examining the impact of earnings management on market value under a dividend policy for a sample of institutions listed on the Saudi Stock Exchange and registered in TASI during the period (2010–2021). A descriptive analytical approach and a quantitative econometric analysis using One-Step System GMM Dynamic Panel Data Model were employed. The study reported that all the institutions performed a short-term earnings management oscillating up and down by different proportions during the study period, and that there is an impact of earnings management on value market in both the absence and presence of the dividend policy. The study therefore points to the need for investors education about accounting manipulation in general and earnings management in particular as the latter has an impact on the market value of institutional shares and accordingly on their investment decisions.

Changing economic environment and expected return proxies

PurposeWe aim to examine two issues. First, we intend to identify the best performing expected return proxies. Second, we investigate whether the expected return proxies for individual stocks can track the corresponding realized returns during extremely good or extremely bad times of the economic environment related to business conditions, stock market valuation and broad market performance.Design/methodology/approachWe construct four sets of expected return proxies, including: (1) characteristic-based proxies; (2) standard risk-factor-based proxies; (3) risk-factor-based proxies that allow betas to vary with firm characteristics and (4) macroeconomic-variable-based proxies. First, we estimate expected returns for individual stocks using newly developed methods and evaluate the performance of these expected return proxies based on the minimum variance criterion of Lee et al. (2020). Second, we regress expected return proxies and realized returns on indicator variables that capture the extreme phases of the economic environment. Then we compare the estimated coefficients from these two sets of regressions and see if they are similar in magnitude via formal hypothesis testing.FindingsWe find that characteristic-based proxies and risk-factor-based proxies that allow betas to vary with firm characteristics are the two best performing proxies. Therefore, it is important to allow betas to vary with firm characteristics in constructing expected return proxies. We also find that model-based expected return proxies do a reasonably good job capturing actual returns during extremely bad and extremely good phases of business cycles measured by leading economic indicators, consumer confidence and business confidence. However, there is a large gap between the adjustment of model-based expected returns and realized returns during extreme episodes of stock market valuation or broad market performance.Originality/valueWe examine four types of expected return proxies and use the newly developed methodology as in Lee et al. (2020) to see which one is the best. In addition, we document whether model-based expected returns from individual stocks adjust partially or fully to keep pace with actual returns in response to changing economic conditions. No prior studies have examined these two issues.

The Test of CAPM and Fama-Franch Three-factor Model on China A-shares

William Sharpe et al. proposed the CAPM model in 1964, but subsequent empirical studies showed that the CAPM model still had limitations in practical application. In 1993, Fama and Franch proposed the three-factor model, which can explain the future return of the stock portfolio through the company size, the BM ratio of the companies, and express the excess return of a portfolio in the form of three factors. Based on the CAMP and Fama-French three-factor model, this study will use daily trading data of the constituents of the SSE 50 Index to analyze the A-share market by employing the market value factor, company size factor, and BM factor as explaining variables, so as to verify whether the CAPM and Fama-Franch three-factor model is suitable for China A-shares market. The final empirical results show that the CAPM model is applicable to China A-share market, but the validity test of the Fama-Franch three-factor model has not passed completely, that is to say, only in some industries, the Fama-Franch three-factors model can be effectively used.

Development of 3D Printable Silver Carp (Hypophthalmichthys molitrix) Surimi Gel with Dynamic High-Pressure Microfluidization-Modified Pea Protein Isolate and Microcrystalline Cellulose.

Sliver carp is a nutritious and abundant species in China, but its low market value stems from its thin meat, small bones and strong odor. Processing it into surimi enhances its economic value, though surimi typically has low gel strength and is prone to deterioration. Recently, three-dimensional (3D) printing has gained attention as an innovative additive manufacturing technique for personalization and process simplification requiring high-performance materials. This study intended to develop an optimized surimi formula for 3D printing with dynamic high-pressure microfluidization (DHPM)-modified pea protein isolate (PPI) and microcrystalline cellulose (MCC). Firstly, the effect of DHPM on PPI properties was evaluated, followed by the optimization of the surimi gel formula (72.093% water content, 3.203% PPI, 1.728% MCC, 1% salt, 1% collagen peptide and 20.976% sliver carp paste) and 3D printing parameters (2000 mm/min at 25 °C with a 1.5 mm nozzle). Rheological comparisons between the optimized surimi, surimi with commercial antifreeze and surimi with only PPI or MCC indicated that the optimized formulation exhibited clearer 3D printing outlines and reduced stickiness due to a higher recovery and lower loss modulus. These results demonstrated that DHPM-treated PPI and MCC enhanced the 3D printability of silver carp surimi gel, providing a new idea for a surimi product and supporting its potential applications in food 3D printing.

Using Technological Advancements - Changing the Banking Sector's Face Value in World Market

This research paper analyses the outcome of the continuous evolvement of banking industry, it is undergoing significant change because of technological advancements that are improving client satisfaction, efficiency, and security while also changing conventional financial practices. The use of cutting-edge technologies like cloud computing, blockchain, big data analytics, and artificial intelligence (AI) has completely changed how banksfunction and engage with their clientele. These developments make it possible for smooth online transactions, individualized financial services, and enhanced decision-making. Moreover, risk management, fraud detection, and regulatory compliance are all improved by the combination of automation and machine learning. Traditional banks are under more and more pressure to compete by utilizing technology to fulfil changing client expectations as fintech and digital banking platforms gain traction. The industry is embracing change to guarantee accessibility, scalability, and resilience through projects like decentralized finance (DeFi), improved mobile banking apps, and biometric security measures. In addition to increasing operational efficiency, the ongoing advancement of technology enables banks to make substantial contributions to global financial inclusion and economic growth. This essay emphasizes how important technology breakthroughs are to promoting creativity, resolving business obstacles, and opening the door to a financial environment that is more effective, safe, and customer focused.

Determinants of the Financial Impact of Orphan Drugs in Italy: Differences Between Expected and Observed Pharmaceutical Expenditure.

The increasing demand for orphan drugs and the financial challenges associated with their reimbursement highlights the need to understand the dynamics between expected and actual pharmaceutical expenditures, particularly in the context of pricing and reimbursement negotiations. The study aims to identify the potential determinants of the difference in expected pharmaceutical spending after negotiation and the actual expenditure incurred (ΔS), as well as the difference in expected pharmaceutical spending before and after the price and reimbursement negotiation process (ΔSn) for rare disease drugs in Italy. The analysis focused on orphan drugs authorised by European Medicines Agency, with reimbursement applications to the Italian Medicines Agency from January 2013 to January 2019. Expected post-negotiation spending was estimated by applying negotiated discounts and financial-based market entry agreements to the expected pharmaceutical expenditure. The actual expenditure was approximated through company turnovers. Beta regression models were applied to identify potential determinants of ΔS and ΔSn. The study analysed 52 reimbursed orphan drugs, with 41 (78.8%) with a single indication, 25 (48.1%) antineoplastics and immunomodulators and 18 (34.6%) conditionally/fully innovative. The median expenditure in the first 3 years post-commercialisation was 7.6% lower than expected post-negotiation. The reduction was less pronounced for innovative drugs (p = 0.011), drugs with a prices and reimbursement procedure in the subsequent 3 years (p = 0.007) and those with multiple indications (p = 0.021). Payment-by-result was the only significant variable associated with the expected spending ratio before and after negotiation (p = 0.002). The actual expenditure for orphan drugs aligns with the expected post-negotiation. Yet, innovative orphan drugs exhibit a higher actual expenditure than estimated, suggesting the market values their added therapeutic value or the actual therapeutic need they meet, and configuring innovativeness status as the main determinant of the orphan drugs financial impact in the multiple regression analysis.

Analysis of Financial Distress and Bankruptcy Using the Altman Z-Score Method on the Indonesia Stock Exchange (IDX) in 2020 - 2022

This research analyzes financial distress and bankruptcy using the Altman Z-Score method and its influence on share prices in construction service companies listed on the Indonesia Stock Exchange (IDX) for 2020 - 2022. The variables tested in this research consist of working capital to total assets, retained earnings to total assets, earnings before interest and tax to total assets, market value of equity to book value of debt, sales to total assets as independent variables, and share prices as dependent variables. The population in this research is construction services companies listed on the Indonesia Stock Exchange for 2020 - 2022. The sampling technique used in this research is purposive sampling, and a sample of 63 data from 21 construction services companies listed on the Indonesia Stock Exchange in 2020-2022 that meet specific criteria was obtained. The data analysis technique used in the research is panel data regression analysis and classical assumption testing using EViews version 13 statistical software. This research shows that in 2020, 18 construction service companies were in the distress zone and 3 companies were in the gray zone. In 2021, 17 construction service companies will be in the distress zone and 4 in the gray zone. In 2022, 17 construction service companies will be in the distress zone and 4 in the gray zone. The earnings before interest and tax to total assets have a significant effect and positive relationship with share prices. Working capital to total assets, retained earnings to total assets market value of equity to book value of debt, and sales to total assets do not have a significant effect and have a negative relationship to stock prices.

Wpływ pandemii COVID-19 na sektor ubezpieczeń GPW w Warszawie S.A.

Purpose: The article aims to assess the impact of the COVID-19 pandemic on the insurance sector of the Warsaw Stock Exchange S.A. in 2019–2021. Design/Methodology/Approach: Quarterly financial reports were used to conduct the study. The economic condition was examined using elements of financial analysis, and the valuation was based on the analysis of market rates of return. Findings: The results allowed us to conclude that the COVID-19 pandemic harmed the insurance sector of WSE. Due to the differences in the parameters of the size of capitalization, the scope of services provided, the territory of operation and the structure of sources of income, the effects of the pandemic are significantly different. The pandemic had a minor impact on the financial condition of PZU S.A., however, the economic condition of the remaining analysed entities deteriorated. The market valuation in the analysed period also varied two companies recorded a significant decline in rates of return compared to the rate of return of the WSE benchmark, while the remaining two achieved rates of return higher than the rate of return of the WIG index. Research limitations/implications: In the conducted research, the limitations were: a small research sample – a small number of studied companies and the specific nature of the analysed industry insurance companies are characterized by a different nature compared to the others, which resulted in the specific selection of elements of financial analysis in the conducted study. Originality/value: The article raises the issue of the impact of the COVID-19 pandemic on the financial condition and market valuation of a specific industry.

The Influences of Corporate Governance on Corporate Social Responsibility and Firm Performance of the Listed Companies in Thailand Sustainability Investment

This study investigates the impact of corporate governance and social responsibility on the financial performance of 149 sustainable stocks listed on the Stock Exchange of Thailand using secondary data from 56-1 reports and employing structural equation modeling (SEM) to assess model fit, the analysis identified several governance factors, such as the proportion of independent directors, board size, and board meeting frequency, as significantly influencing market value. The SEM model demonstrated a strong fit with empirical data (CMIN/df=1.504, CFI=0.990, RMSEA=0.058), with board meeting frequency showing the most significant impact on financial performance at the 0.05 level. The effective corporate governance, particularly in terms of board structure and oversight, is crucial for enhancing the market value of sustainability-focused firms.