Valuation Metrics Research Articles

Design of heat-resilient housing in hot-arid regions

Extreme heat events in urban areas increasingly challenge the capacity of electrical distribution systems to serve building cooling equipment under peak loads and, when power is interrupted, the thermal response of buildings that can delay the onset of dangerously high indoor temperatures. The design of new buildings and their operation can mitigate the risks of intense heatwaves, but architects and planners face a myriad of choices about what measures to select, and how best to estimate their individual and collective performance. To aid the design and operation of heat-resilient buildings, this paper takes a multidisciplinary approach that is novel in two key aspects. First, it evaluates the individual and aggregated impact of factors associated with architectural and urban design, equipment technologies, and human behavior. Second, its valuation metrics include the magnitude of peak electrical load, appropriate for assessing active measures aimed at reducing peak power, and the time after a power outage for indoor temperatures to reach levels associated with heat stress, an indication of the efficacy of passive (no power) measures. The application of the method in the Middle East North Africa (MENA) region, where growing populations and demand for space cooling make it particularly relevant, relies on knowledge of building codes and local construction practice. Single-factor testing shows that pre-cooling produces the largest reduction in peak electrical load during a simulated four-day heatwave, followed by building adjacency, maximum temperature set point and equipment loads. A combination of all considered factors reduces peak power by 70% and shifts the reduced peak to a later hour. In response to a power outage, the incorporation of architectural factors (roof, wall and window thermal resistance above code minima, increased thermal mass, reduced glazing solar heat gain coefficient and window shading) reduces the time above a Heat Index of 28 °C (caution) in a week-long test period in which the power failure occurs at hour 40 from 119 to 53 h. The presented methodology applies broadly to other building types and to regions affected by very hot weather.

A comprehensive method for error separation in hydrological modelling

AbstractEvaluation metrics play a pivotal role in the calibration process of hydrological models, serving as objective functions that directly influence the final values of model parameters and significantly affect users' perceptions of model performance. However, the choice and interpretation of evaluation metrics are subjective; therefore, this study provides a more objective framework for assessing model performance. This paper initially explored the applicability of various commonly used evaluation metrics, providing an overview of their limitations. Following this, we decomposed errors by analysing their physical significance and geometric representation in scatter plots, categorizing them into systematic and unsystematic errors. Through the decomposition and derivation of the Nash–Sutcliffe efficiency (NSE) formula, we established the quantitative relationship among various evaluation metrics. The soil and water assessment tool (SWAT) model was utilized to simulate monthly runoff in the Baishan basin (China), for the period 1994–2017, with NSE serving as the objective function for calibration. Our findings are consistent with previous studies, indicating that the model tends to slightly underestimate high flows while significantly overestimating low flows. Further analysis through error decomposition and the examination of relationships among various evaluation metrics revealed that unsystematic errors were dominant during the spring snowmelt runoff period, while systematic errors prevailed in the dry season. By evaluating the runoff series based on the magnitude of runoff or by categorizing it according to seasons and months, a more stringent assessment of the model's performance was achieved. These findings not only highlight the necessity for careful selection of evaluation metrics but also underscore the significance of our methodological advancements in enhancing hydrological model precision and reliability.

Accuracy of Relative Valuation Methods in Predicting the Offer Price of Indonesian IPO Stocks

The increasing Initial Public Offerings (IPO) trend in Indonesia has created many investment opportunities for institutional and retail investors. However, multiple valuation methods often result in varying IPO price predictions, leading to uncertainty and confusion among investors. This study aims to assess the effectiveness of relative valuation methods in predicting IPO offer prices, focusing on widely adopted methods due to their simplicity and accessibility. The research evaluates three popular relative valuation metrics: the price-to-book (P/B) ratio, the price-to-earnings (P/E) ratio, and the price-to-sales (P/S) ratio. By analyzing companies that went public in Indonesia in 2023 and utilizing average financial ratios from the preceding year, the study seeks to determine which method provides the most accurate price predictions. The findings indicate that the P/B ratio is the most reliable method, achieving an average absolute error of 51.1%. This suggests that the P/B method minimizes valuation errors more effectively than P/E and P/S ratios. Moreover, regression models' P/B ratio demonstrates the highest adjusted R² value of 0.781, further validating its predictive accuracy. The implications of this study are significant for investors and financial analysts who rely on relative valuation methods to make informed decisions about IPO investments. By highlighting the P/B ratio's superior performance in predicting IPO offer prices, this research contributes to the ongoing discourse on valuation methodologies and their practical application in emerging markets like Indonesia.

Stock Valuation of PT. Panca Budi Idaman, Tbk. (PBID) based on Free Cash Flow to the Firm and on Relative Valuation

The stock valuation of PT Panca Budi Idaman, Tbk. (PBID), an Indonesian plastic packaging manufacturer, is evaluated using the absolute and relative valuation. The absolute valuation is based on Discounted Cash Flow (DCF) method. In relative valuation, PBID is compared to its peers, which in this study are IPOL, PDPP, and TRST. Indonesia’s significant economic growth and the vital role of the plastic industry are noting strong demand for plastic packaging despite environmental challenges and regulatory pressures. Financial performance from 2017 to 2023 is assessed through profitability, liquidity, and solvency ratios, demonstrating PBID’s efficient operations and robust financial health. Future cash flows are projected, discounted using PBID’s Weighted Average Cost of Capital (WACC), and the terminal value is calculated. The intrinsic value is estimated at IDR 3,987 per share, indicating undervaluation compared to the current price of IDR 1,325 per share. Relative valuation compares PBID to industry peers using EV/EBITDA, P/E, and P/B ratios, reinforcing the undervaluation finding. Investment recommendations suggest PBID’s stock as a buying opportunity due to its strong market position and favourable valuation metrics.

Book value and share prices: The mediating effect of inflation in Nigeria

Purpose: This study examines the nexus between book value and the share price of listed firms in the Nigerian exchange group, considering inflation as a mediating variable. Research methodology: The study used book value per share, share prices, and inflation rate as the independent, dependent, and mediating variables, respectively. The study uses regression analysis and a structural equation model for the effect and mediating effect, respectively, to analyze data collected from a company’s financial statements and the capital market for 2011-2020. Findings: The regression and structural equation model results show that book value per share has a negative and insignificant effect on share price, and inflation has a mediating effect on the relationship between book value per share and share prices. Limitations: This study was limited to book value per share, share price, and inflation rate. The scope of this study was limited to listed firms in Nigeria from 2011 to 2020. Contribution: This study contributes to the understanding of how inflation rates influence the relationship between book value per share and share prices in financial markets. By exploring the mediating effect of the inflation rate, this study sheds light on how changes in purchasing power affect the valuation metrics of companies, providing valuable insights for investors, policymakers, and financial analysts in making informed decisions amidst varying economic conditions. Moreover, this study contributes to the body of knowledge because there are limited studies in this area.

The Influence of Capital Structure, Company Growth and Company Value on Share Prices in Coal Sub-Sector Companies Listed on the Indonesian Stock Exchange 2018-2023

This study aims to empirically investigate the impact of Capital Structure, Company Growth, and Company Value on stock prices. Employing a quantitative descriptive research method with a verificative approach, the study utilizes panel data regression analysis to examine these relationships. The research focuses on Coal Sub-Sector Companies listed on the Indonesia Stock Exchange from 2018 to 2023, encompassing a sample of 12 companies. The findings reveal that Capital Structure negatively influences stock prices, indicating that higher debt levels may detract from stock value. Conversely, Company Growth is found to have no significant effect on stock prices, suggesting that growth metrics do not necessarily translate to market valuation in this sector. In contrast, Company Value exerts a positive impact on stock prices, demonstrating that higher valuation metrics are associated with increased stock prices. These results provide valuable insights for investors and stakeholders in the coal sub-sector, highlighting the critical factors that drive stock price movements and offering a basis for more informed investment decisions.

Granger-Causal Effects of Consumer Behavior on NFT Sales

An understanding of what influences the NFT market is valuable in such a speculative space, and predictors of directional shifts in NFT sales are beneficial to NFT users and investors alike. For these reasons, research was undertaken to determine if metrics relating to consumer behavior could predict NFT market sales. To begin, a Buyer Activity Metric and a Buyer Valuation Metric were calculated using open-access data regarding the NFT market. A three-variable vector autoregression (VAR) model was then constructed using these metrics and NFT sales data. Changes in monthly NFT sales were found to be Granger-caused by changes in both the Buyer Activity Metric and the Buyer Valuation Metric. Changes in each metric were determined to precede changes in NFT sales by up to four months. The associations were also determined to be unidirectional, indicating a clear cause-and-effect style relationship. Questions about these predictive abilities were then theoretically explored.

Tendering for Procurement Contracts in Case of Uncertainty about a Bidimensional Bid Valuation Metric: A Decision Analytic Methodology

Introduction: In some instances, when tendering for supplying goods to a purchaser, the bidders know that bid evaluation is done in a multicriteria way but ignore the precise metric used. This is the case, for example, of acquisitions by small and medium-sized businesses carried out by inviting a handful of potential suppliers to advance a proposal for fulfilling some need and informing them of criteria hurdle levels and worded statements of criteria preferred directions. Method: The uncertainty about bid assessment is compounded with that about competing bids, making bid design increasingly difficult. This work presents a Decision Analysis-based methodology for developing a model for bid preparation useful under said circumstances, aiming to take advantage of the available knowledge of competitors’ capabilities and client’s preferences through subjective probabilities. objective: This work presents a Decision Analysis-based methodology for developing a model for bid preparation useful under said circumstances, aiming to take advantage of the available knowledge of competitors’ capabilities and client’s preferences through subjective probabilities Result: A key feature of the methodology is the idea that the model of the bidder’s knowledge of the client’s preferences for different criterion levels should separately consider the criterion type and the likely size of its variation among bids. This allows basing the model on the assumption that, to the client’s decision, a criterion becomes more important if the size of its variation among bids grows, while it is rendered unimportant if it does not vary a lot among bids. After outlining the general steps of the methodology, a simple, hypothetical case study is numerically worked out, illustrating how the methodology is operationalized for a problem with two criteria and two levels. Conclusion: Finally, closing remarks on the potential practical usefulness of the presented framework are provided.

A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.

To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphasized the significance of Support Vector Regression (SVR) and SHapley Additive exPlanations (SHAP) in predicting soybean branching. By using branching data (phenotype) of 1918 soybean accessions and 42k SNP (Single Nucleotide Polymorphism) polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN (deep belief network) regression, ANN (artificial neural network) regression, Autoencoders regression, and MLP (multilayer perceptron) regression) and seven machine learning models (e.g., SVR (support vector regression), XGBoost (eXtreme Gradient Boosting) regression, Random Forest regression, LightGBM regression, GPs (Gaussian processes) regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, Polynomial Regression, DBN, and Autoencoder outperformed other models and could obtain a better prediction accuracy when they were used for phenotype prediction. In the assessment of deep learning approaches, we exemplified the SVR model, conducting analyses on feature importance and gene ontology (GO) enrichment to provide comprehensive support. After comprehensively comparing four feature importance algorithms, no notable distinction was observed in the feature importance ranking scores across the four algorithms, namely Variable Ranking, Permutation, SHAP, and Correlation Matrix, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The results of this study offer valuable insights into AI-mediated plant breeding, addressing challenges faced by traditional breeding programs. The method developed has broad applicability in phenotype prediction, minor QTL (quantitative trait loci) mining, and plant smart-breeding systems, contributing significantly to the advancement of AI-based breeding practices and transitioning from experience-based to data-based breeding.

Valuation and Financial Performance Analysis of Pt. Indika Energy

According to a coal market update study by the International Energy Agency, coal prices have been highly volatile during the previous five years, significantly influencing Indonesian coal companies’ operations and substantially impacting income and stock values. Furthermore, the increased global attention to addressing climate change is anticipated to lead to stricter enforcement of carbon emissions laws by 2030. PT. Indika Energy is an energy company that highly relies on coal prices, accounting for 75% to 88% of its revenue. Indika has stated four strategies, two of which are Efficiency and Synergy Optimization, and Prudent management. However, the result of profitability performance is not aligned with the company strategy. This research proposes to evaluate the comprehensive financial performance, assess the intrinsic value using the DCF-FCFF approach, and estimate PT’s price-to-earnings (P/E) ratio and Market to Book (M/B) ratio. Indika Energy. This study uses several ratios, including liquidity, profitability, activity, and leverage ratio. The study revealed that the company demonstrates the worst liquidity performance and is the least profitable among its peers. However, the company has managed to maintain the most significant leverage ratio. According to the DCF valuation, the company appears to be undervalued, with an intrinsic value of IDR 3,247, while the market value is IDR 1,540. The company is also undervalued based on its relative valuation metrics. It has a price-to-earnings ratio (PER) of 1.06 and a price-to-book value (PBV) of 0.45, compared to the industry average PER of 5.15 and PBV of 5.27. Hence, the company should minimize its reliance on subcontractors by optimizing its cost structure.

Video saliency-detection using custom spatiotemporal fusion method

There have been several researches done in the field of image saliency but not as much as in video saliency. In order to increase precision and accuracy during compression, reduce coding complexity and time consumption along with memory allocation problems with our proposed solution. It is a modified high-definition video compression (HEVC) pixel based consistent spatiotemporal diffusion with temporal uniformity. It involves taking apart the video into groups of frames, computing colour saliency, integrate temporal fusion, pixel saliency fusion is conducted and then colour information guides the diffusion process for the spatiotemporal mapping with the help of permutation matrix. The proposed solution is tested on a publicly available extensive dataset with five global saliency valuation metrics and is compared with several other state-of-the-art saliency detection methods. The results display and overall best performance amongst all other candidates.

The Implementation of Value Investment Based on Various Indicators for the Building Materials Industry

With the advent of post COVID-19 era, value investing strategy has popularized again in the global stock markets as the changing economic conditions, rising interest rates and different risk appetites. Therefore, it is reasonable to know how to distinguish between value stocks and growth stocks before investing. This paper aims to examine the application of value investment strategies based on the indicators’ performance of five companies selected from the building materials industry. Besides, valuation metrics are derived from three primary dimensions, i.e., growth, profitability and value per unit of growth. This study first identifies the classification and recent performance of value stocks and then compares the financial indicators of companies with value stocks. According to the analysis, the suitable P/E range of value stocks is from 10 to 20, with returns beginning to rise in 2021. By means of this method (utilizes one indicator to identify value stocks), researchers engaged in the financial industry may discover more characteristics about them. Moreover, results also illustrate that value stocks with excellent indicators are more endurable in preventing outside shocks, whereas growth stocks can outperform in profitability as long as they possess ever-increasing revenue and EBITDA. Thus, value investing still remains profitable and practical in the real world.

How Come Teslas Market Cap is Greater than other Companies?

Teslas rapid growth has made it in a dominant position in the entire auto industry, and it has successfully unveiled the start of electric vehicles market and played the leading role ever since the beginning.The prevalence and success of electric vehicles have caught the attention of conventional automakers who used to place their focus on producing petrol cars,trying to replicate the Teslas success in producing all electric vehicles.with no doubt,Tesla is sure to have the absolute advantage over the entire auto industry,which is generally reflected by its huge market cap compaired to others.In this study, the main focus is the analysis on the reason why Teslas market cap is greater than its competitors ,including introduction about Tesla itself and its CEO Elon Musks early lifetime and entrepreneurial history , as well as the the efforts Tesla made in developing the most advanced battery technology. The paper will also illustrate the comparison with four competitive automakers Ford, Volkswagen, Toyota and Niousing different valuation metrics such as price earnings ratio, price book ratio and stock price.

A data-driven approach to assist offensive and defensive players in optimal decision making

Among all the popular sports, soccer is a relatively long-lasting game with a small number of goals per game. This renders the decision-making cumbersome, since it is not straightforward to evaluate the impact of in-game actions apart from goal scoring. Although several action valuation metrics and counterfactual reasoning have been proposed by researchers in recent years, assisting coaches in discovering the optimal actions in different situations of a soccer game has received little attention of soccer analytics. This work proposes the application of deep reinforcement learning on the event and tracking data of soccer matches to discover the most impactful actions at the interrupting point of a possession. Our optimization framework assists players and coaches in inspecting the optimal action, and on a higher level, we provide for the adjustment required for the teams in terms of their action frequencies in different pitch zones. The optimization results have different suggestions for offensive and defensive teams. For the offensive team, the optimal policy suggests more shots in half-spaces (i.e. long-distance shots). For the defending team, the optimal policy suggests that when locating in wings, defensive players should increase the frequency of fouls and ball outs rather than clearances, and when located in the centre, players should increase the frequency of clearances rather than fouls and ball outs.

Why we must move beyond LCOE for renewable energy design

• Wind and solar generation devaluation occurs for grids with high renewable shares • This effect, due to intermittency, dramatically reduces the value of generated energy • Cost of Valued Energy can be used to account for this intermittency penalty • Minimizing COVE can improve VRE system design to better meet demand The inherent intermittency of wind and solar energy challenges the relevance of Levelized Cost of Energy (LCOE) for their future design which neglects the time-varying price of electricity. The Cost of Valued Energy (COVE) is an improved valuation metric that takes into account time-dependent electricity prices. In particular, it integrates short-term (e.g., hourly) wind and solar energy “generation devaluation”, whereby high wind and/or solar energy generation can lead to low, and even negative, energy prices for grids with high renewable penetration. These aspects are demonstrated and quantified using examples of two large grids with high renewable shares with three approaches to model hourly price: 1) residual demand, 2) wind and solar generation, and 3) statistical price-generation correlation. All three approaches indicate significant generation devaluation. The residual demand approach provides the most accurate price information while statistical correlations show that generation devaluation is most pronounced for the Variable Renewable Energy (VRE) that dominates market share (e.g., solar for California and wind for Germany). In some cases, the cost of valued energy relative to levelized cost can be 43% higher for solar (CAISO) and 129% higher for wind (ERCOT). This indicates that COVE is a much more relevant metric than LCOE in such markets. In particular, COVE is based on the annualized system costs relative to the annualized spot market revenue, and thus considers economic effects of costs vs. revenue as well as those of supply vs. demand. As such, COVE (instead of LCOE) is recommended to design and value next-generation renewable energy systems, including storage integration tradeoffs. However, more work is needed to develop generation devaluation models for projected grids and markets and to better classify grid characteristics as we head to a carbon-neutral energy future.

Unemployment and its Negative Consequences in Georgia

The paper covers the effect of the unemployment on Social-Economic development of  Georgia. In light of the Globalization and International Intergration chronic unemployment, reduction of poverty and improvement of living standards are the primary issues that Georgia faces. Significant part of the country’s populations still leaves beyond the official limit of poverty that is primary caused by the unemployment. The study aims to measure unemployment, as a macroeconomic problem analysis, to distinguish its basic forms, consider unemployment valuation metrics, as well as the factors influencing it, besides to study the negative effects caused by an  unemployment and the magnitude of unemployment, dynamics and structure in Georgia.

ESG scores and target price accuracy: Evidence from sell-side recommendations in BRICS

ESG profiling of a firm reflects its exposure to various environmental, social, and governance factors, which influence the business dynamics and impact the valuation metrics. In this paper, we evaluate the relationship between ESG scores and the target price precision of sell-side analysts. We employ four different constructs of forecast accuracy on a comprehensive sample of firms with analyst coverage in the BRICS between 2011 and 2021. The results demonstrate that the ESG score positively impacts the target price accuracy, and firms with higher ESG scores have lower forecast errors. The findings remained robust even after segregating the sample based on buy, hold, and sell recommendations. Finally, we report that within ESG, environmental and governance factors largely explain the forecast accuracy while the social aspects were insignificant. The results also suggest that the precision of sell-side analysts is persistent across periods. These findings have important implications for investors.

Exploring the Predictive Power of Valuation Metrics on Subsequent Market Returns

This article analyzes several valuation metrics’ predictive power on subsequent 1- and 10-year total returns across multiple periods and developed market countries. I evaluated the ratio of market capitalization to gross domestic product, dividend yield, dividend discount model, and Shiller’s CAPE for US data. Succinctly, this research has demonstrated predictive power at the 10-year horizon across countries and valuation metrics after adjusting for overlapping observations. We highlight that there is no strong predictive power across the entirety of the data set for countries with multiple centuries of data. In fact, there is a definitive structural shift in the data across countries within the 20th and 21st centuries. Detractors have focused on the recent failure of these models to predict subsequent returns, often citing that the shift from dividends to share repurchases has caused a structural shift in the data, and that lower interest rates warrant higher equity multiples. I acknowledge the recent failures, but I find these criticisms to be overstated.

Do Blockchain Stocks Exhibit More Herding Behaviour than Traditional Stocks?

While the volatile behaviour of cryptocurrency is extensively studied, the stock market’s blockchain sector, which has not been given much attention in the academic world, operates very differently from traditional stock industries. The paper hypothesizes that blockchain stocks exhibit more herding behaviour than traditional stocks and uses quantitative data analysis techniques to study it. The automotive industry is taken as a representative of traditional stocks. Cross-Sectional Absolute Deviation, the academic standard for herding behaviour, is used as the primary comparative measure between blockchain and automotive stocks. It reveals that blockchain industry has significant herding, while rational pricing mechanisms prevail in the automotive industry. Supporting this conclusion, a correlation matrix of stock prices of small market capitalisation firms in each industry is constructed, analysing how closely stock price movements in an industry are related. The correlation coefficient for blockchain stocks is 20% higher than the coefficient for automotive stocks. This indicates that blockchain stocks likely exhibit higher levels of herding. The impact of social media on stock price movements in the two industries is analysed by conducting a correlation study between Google Trends data for industry-related keywords and individual stock returns. The blockchain industry saw a significantly higher correlation, likely suggesting that social media has a stronger influence on blockchain stock price movements. Finally, the paper provides possible explanations for why herding behaviour is more prominent in the blockchain stocks compared to traditional stocks. These include absence of traditional stock valuation metrics, lack of financial knowledge and role of social media.

Intangible Ironies: Investor Mispricing of Company Assets On and Off its Balance Sheet

We examine how investors worldwide evaluate the mix of company assets both on and off its balance sheet. On aggregate, they appear to correctly value tangible assets but misprice intangible assets, with the possible exception of emerging market stocks. Investments in stakeholder capital—such as innovation, brand, and employees—often go unrecognized both on the balance sheet and by investors, despite their increased economic importance. In contrast, the premium paid for past acquisitions that is included on financial statements as goodwill generally fails to deliver on expectations, being written down too slowly by management and shareholders alike. These results are not explained by exposure to known systemic risk premia, limits to arbitrage, or data mining. Corroborating a recent surge of articles on this topic, we find that adjusting valuation metrics for the actual benefit of such intangibles improves performance in global equity markets. More impactfully, investors can diversify value exposure by targeting companies with latent such growth assets.