Search Results (116)

Over the past decade, multifactor models have shown enhanced capability compared to single-factor models in explaining asset return variability. Given the common assertion that higher risk tends to yield higher returns, this study empirically examines the augmented human capital six-factor model’s performance on thirty-two portfolios of non-financial firms sorted by size, value, profitability, investment, and labor income growth in the Indian market over the period July 2010 to June 2023. Moreover, the current study extends the Fama and French five-factor model by incorporating a human capital proxy by labor income growth as an additional factor thereby proposing an augmented six-factor asset pricing model (HC6FM). The Fama and MacBeth two-step estimation methodology is employed for the empirical analysis. The results reveal that small-cap portfolios yield significantly higher returns than large-cap portfolios. Moreover, all six factors significantly explain the time-series variation in excess portfolio returns. Our findings reveal that the Indian stock market experienced heightened volatility during the COVID-19 pandemic, leading to a decline in the six-factor model’s efficiency in explaining returns. Furthermore, Gibbons, Ross, and Shanken (GRS) test results reveal mispricing of portfolio returns during COVID-19, with a stronger rejection of portfolio efficiency across models. However, the HC6FM consistently shows lower pricing errors and better performance, specifically during and after the pandemic era. Overall, the results offer important insights for policymakers, investors, and portfolio managers in optimizing portfolio selection, particularly during periods of heightened market uncertainty. Full article

This study comparatively evaluated the Capital Asset Pricing Model (CAPM), the Fama and French three-factor model (FF3), and the Fama and French five-factor model (FF5) in key US market sectors (finance, energy, and utilities). The goals were to optimize financial decisions and reduce valuation errors. The historical daily returns of ten-stock portfolios, selected from sectors with the highest trading volume in the S&P 500 Index between 2020 and 2024, were analyzed. Companies with the lowest beta were prioritized. Models were compared based on the metrics of the root mean square error (RMSE) and mean absolute error (MAE). The results demonstrate the superiority of the multifactor models (FF3 and FF5) over the CAPM in explaining returns in the analyzed sectors. Specifically, the FF3 model was the most accurate in the financial sector; the FF5 model was the most accurate in the energy and utilities sectors; and the FF4 model, with the SMB factor eliminated in the adjustment of the FF5 model, was the least error-prone. The CAPM’s consistent inferiority highlights the need to consider factors beyond market risk. In conclusion, selecting the most appropriate asset valuation model for the US market depends on each sector’s inherent characteristics, favoring multifactor models. Full article

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin’s Q ratio, market capitalization, return on assets, leverage, and volatility. A range of overconfidence proxies is employed, including changes in trading volume, turnover rate, changes in outstanding shares, and alternative measures of excessive trading. We observe a significant positive relationship between overconfidence (as measured by changes in trading volume) and firm valuation, suggesting that investor biases contribute to notable pricing distortions. Leverage has a significant negative relationship with firm valuation. In contrast, market capitalization has a significant positive relationship with firm valuation, implying that meme stock investors respond to both speculative sentiment and traditional firm fundamentals. Robustness checks using alternative proxies reveal that turnover rate and changes in the number of shares are negatively related to valuation. This shows the complex dynamics of meme stocks, where psychological factors intersect with firm-specific indicators. However, results from a dynamic panel model estimated using the Dynamic System Generalized Method of Moments (GMM) show that the turnover rate has a significantly positive relationship with firm valuation. These results offer valuable insights into the pricing behavior of meme stocks, revealing how investor sentiment impacts periodic valuation adjustments in speculative markets. Full article

Background: Economic logistics in fire and rescue services is a critical determinant of operational readiness, fiscal sustainability, and resilience to large-scale emergencies. Despite its strategic importance, logistics remains under-researched in Central and Eastern European contexts, where legacy governance structures and EU-funded modernization coexist with systemic inefficiencies. This study focuses on the Slovak Fire and Rescue Service (HaZZ) as a case to explore how economic logistics systems can be restructured for greater performance and value. Objective: The objective of this paper was to evaluate the structure, performance, and reform potential of the logistics system supporting HaZZ, with a focus on procurement efficiency, lifecycle costing, digital integration, and alignment with EU civil protection standards. Methods: A mixed-methods design was applied, comprising the following: (1) Institutional analysis of governance, budgeting, and legal mandates based on semi-structured expert interviews with HaZZ and the Ministry of Interior officers (n = 12); (2) comparative benchmarking with Germany, Austria, the Czech Republic, and the Netherlands; (3) financial analysis of national logistics expenditures (2019–2023) using Total Cost of Ownership (TCO) principles, completed with the visualization of cost trends and procurement price variance through original heat maps and time-series graphs. Results: The key findings are as follows: (1) HaZZ operates a formally centralized but practically fragmented logistics model across 51 district units, lacking national coordination mechanisms and digital infrastructure; (2) Maintenance costs have risen by 42% between 2019 and 2023 despite increasing capital investment due to insufficient lifecycle planning and asset heterogeneity; (3) Price variance for identical equipment categories across regions exceeds 30%, highlighting the inefficiencies in decentralized procurement; (4) Slovakia lacks a national Logistics Information System (LIS), unlike peer countries which have deployed integrated digital platforms (e.g., CELIS in the Czech Republic); (5) Benchmarking reveals high-impact practices in centralized procurement, lifecycle-based contracting, regional logistics hubs, and performance accountability—particularly in Austria and the Netherlands. Impacts: Four high-impact, feasible reforms were proposed: (1) Establishment of a centralized procurement framework; (2) national LIS deployment to unify inventory and asset tracking; (3) adoption of lifecycle-based and performance-based contracting models; (4) development of regional logistics hubs using underutilized infrastructure. This study is among the first to provide an integrated economic and institutional analysis of the Fire and Rescue Service logistics in a post-socialist EU member state. It offers a structured, transferable reform roadmap grounded in comparative evidence and adapted to Slovakia’s hybrid governance model. The research bridges gaps between modernization policy, procurement law, and digital public administration in the context of emergency services. Full article

Audit committees monitor the actions of managers as they pursue the goal of shareholder wealth maximization. The purpose of this study is to measure the impact of audit committee oversight on novel aspects of firm performance, including investor rationality, price expectations, human capital, and research and development expenses. It extends the literature to non-financial outcomes of audit committee oversight. The literature thus far has focused on the financial effects of audit committee oversight, such as return on assets, return on equity, risk, debt capacity, and firm value. Data was collected from 588 publicly traded firms in the U.S. pharmaceutical industry and energy industry from 2010 to 2022. Audit oversight was measured by the novel measurement of the frequency of the term ‘audit committee’ in annual reports and Form 10Ks from the SeekEdgar database. COMPUSTAT provided the remainder of the data. Panel Data fixed-effects models were used to analyze the data. Audit committee oversight significantly increased investor rationality, significantly reduced price expectations, and significantly increased human capital investment. An inverted U-shaped relationship occurred for audit committee oversight and research and development expenses, with audit oversight first increasing research and development expenses, then decreasing them. The study makes several contributions. First, the study uses a novel measure of audit oversight. Second, the study predicts the effect of audit committee oversight on unexplored non-financial measures, such as human capital and research and development expense. Third, the study offers a current test of the Miller model, as the last tests were performed over 20 years ago. Fourth, the study examines the impact of auditing on market measures that have not been explored in the literature, such as investor rationality and short selling. Full article

In this paper, we propose a linear factor model that incorporates investor preferences toward sustainability to analyze indirect effects that climate concerns may have on asset prices. Our approach is based on the relationship between environmental, social, and governance (ESG) investing and climate change considerations by investors. We use ESG scores as a part of the information set used by investors to determine the unconditional version of the conditional capital asset pricing model (CAPM). Our results show that the ESG score allows the linearized version of the conditional CAPM to greatly outperform the classic CAPM and the Fama–French three-factor model for different sorts of stock portfolios, contributing significantly to reducing pricing errors. Furthermore, we find a negative price of risk for stocks that covary positively with ESG growth, which suggests that green assets may perform better than brown ones if ESG concerns suddenly become more pressing over time. Thus, our paper constitutes a step forward in the attempt to shed light on how climate change is priced regardless of the climate risk measure used. Full article

This paper proposes a novel robustness framework for studying the unit elasticity of intertemporal substitution (EIS) assumption based on the Perron-Frobenius sieve estimation model by Christensen, 2017. The sieve nonparametric decomposition is a central model that connects key strands of the long run risk literature and recovers the stochastic discount factor (SDF) under the unit EIS assumption. I generate various economies based on Epstein–Zin preferences to simulate scenarios where the EIS deviates from unity. Then, I study the main estimation mechanism of the decomposition as well as the time discount factor and the risk aversion parameter estimation surface. The results demonstrate the robustness of estimating the average yield, change of measure, and preference parameters but also reveal an “absorption effect” arising from the unit EIS assumption. The findings highlight that asset pricing models assuming a unit EIS produce distorted parameter estimates, caution researchers about the potential under- or over-estimation of risk aversion, and provide insight into trends of misestimation when interpreting the results. I also identify an additional source of failure from a consumption component, which demonstrates a more general limit of the consumption-based capital asset pricing model and the structure used to estimate relevant preference parameters. Full article

This study examines the dynamic relationship between changes in construction contract amounts across the diversified business areas within the portfolios of Korean construction firms and their overall business performance using a vector error correction model. It aims to provide a detailed evaluation of the effectiveness and characteristics of diversification strategies in the construction industry. This analysis employs key variables, including the debt ratio, return on total assets, diversification index, and construction contract amounts in domestic and overseas building, civil engineering, and plant construction projects. Two distinct models are used: Model A investigates the relationship between the debt ratio and diversification while Model B explores the relationship between the return on total assets and diversification. The time series data for the analysis spans from Q1 2002 to Q4 2021 on a quarterly basis. The results indicate that Korean construction firms have actively expanded into overseas markets to enhance their financial soundness. However, while such diversification efforts facilitate short-term capital acquisition, they have a negative impact on long-term business performance. When technological capabilities remain constant, lowering prices to increase contract volume may ultimately erode long-term profitability. Therefore, rather than focusing solely on expanding contract volumes through diversification, it is essential to first objectively assess the strengths of each business sector and focus on strengthening core competencies and expertise before pursuing further diversification. Full article

The capital asset pricing model (CAPM) is a foundational asset pricing model that is widely applied and holds particular significance in the globally influential Chinese stock market. This study focuses on the banking sector, enhancing the performance of the CAPM and further assessing its applicability within the Chinese stock market context. This study incorporates a heterogeneous autoregressive (HAR) component into the CAPM framework, developing a CAPM-HAR model with time-varying beta coefficients. Empirical analysis based on high-frequency data demonstrates that the CAPM-HAR model not only enhances the capability of capturing market fluctuations but also significantly improves its applicability and predictive accuracy for stocks in the Chinese banking sector. Full article

Computing optimal-solution sets has long been a topic in multiple-objective optimization. Despite substantial progress, there are still research limitations in the multiple-objective portfolio optimization area. The optimal-solution sets’ structure is barely known. Public-domain software for even three objectives is absent. Alternatively, researchers scrutinize equality-constraint-only models and analytically resolve them. Within this context, this paper extends these analytical methods for nonnegative constraints and thus theoretically contributes to the literature. We prove the existence of positive elements and negative elements for the optimal-solution sets. Practically, we prove that non-negative subsets of the optimal-solution sets can exist. Consequently, the possible existence endorses these analytical methods, because researchers bypass mathematical programming, analytically resolve, and pinpoint some non-negative optima. Moreover, we elucidate these analytical methods’ alignment with capital asset pricing models (CAPMs). Furthermore, we generalize for k-objective models. In conclusion, this paper theoretically reinforces these analytical methods and hints the optimal-solution sets’ structure for multiple-objective portfolio optimization. Full article

Classic options can no longer meet the diversified needs of investors; thus, it is of great significance to construct and price new options for enriching the financial market. This paper proposes a new option pricing model that integrates the logarithmic investment strategy with the classic Black–Scholes theory. Specifically, this paper focus on put options, introducing a threshold-based strategy whereby investors sell stocks when prices fall to a certain value. This approach mitigates losses from adverse price movements, enhancing risk management capabilities. After deriving an analytical solution, we utilized mathematical software to visualize the factors influencing new option prices in three-dimensional space. The findings suggest that the pricing of these new options is influenced not only by standard factors such as the underlying asset price, volatility, risk-free rate of interest, and time to expiration, but also by investment strategy parameters such as the investment strategy index, investment sensitivity, and holding ratios. Most importantly, the pricing of new put options is generally lower than that of classic options, with numerical simulations demonstrating that under optimal parameters the new options can achieve similar hedging effectiveness at approximately three-quarters the cost of standard options. These findings highlight the potential of logarithmic investment strategies as effective tools for risk management in volatile markets. To validate our theoretical model, numerical simulations using data from Shanghai 50 ETF options were used to confirm its accuracy, aligning well with theoretical predictions. The new option model proposed in this paper contributes to enhancing the efficiency of resource allocation in capital markets at a macro level, while at a micro level, it helps investors to apply investment strategies more flexibly and reduce decision-making errors. Full article

We show that a model of “the spirit of capitalism”, or the concern for social status, can generate a high degree of international risk sharing as measured by asset prices, even when consumption and portfolio holdings exhibit “home bias”. We also show how portfolio externalities can arise in the model and highlight the caution that one needs in interpreting asset-price-based measures of international risk sharing: in the presence of portfolio externalities, even when the measured degree of risk sharing is perfect, it is still possible for government policies to induce investors to hold better-diversified portfolios and attain higher welfare. Full article

The main objective of this article is to present the formulation of a Capital Asset Pricing Model ordered weighted average CAPM_OWAand its extensions, called CAPM-induced OWA (CAPM_IOWA), CAPM Bonferroni OWA (CAPM_Bon-OWA), and CAPM Bonferroni-induced OWA CAPM_Bon-IOWA. A step-by-step process for applying this new proposal in a real case of formulating investment portfolios is generated. These methods show several scenarios, considering the attitude, preferences, and relationship of each argument, when underestimation or overestimation of the information by the decision maker may influence the decision-making process regarding portfolio investments. Finally, the complexity of the method and the incorporation of soft information into the modeling process lead to generating a greater number of scenarios and reflect the attitudes and preferences of decision makers. Full article

This study investigates the ESG performance–systemic risk (SR) nexus among Indian companies. Using the beta coefficient from the Capital Asset Pricing Model (CAPM) and statistical analysis, it explores how ESG performance affects SR. The findings reveal that firms with higher ESG scores have lower SR sensitivity. Notably, there is a significant difference in risk sensitivity between high- and low-ESG-rated companies, with ESG effects being less pronounced in high-cap firms compared to low-cap firms. Conversely, large firms, older firms, and those with lower borrowing costs show a diminished effect of ESG ratings on their SR sensitivity. These results underscore the importance of firm-specific characteristics in determining the efficacy of ESG strategies in risk mitigation. This study reveals that ESG performance reduces SR, with market valuation affecting this relationship. Full article

This survey offers a succinct overview of the General Framework of Portfolio Theory (GFPT), consolidating Markowitz portfolio theory, the growth optimal portfolio theory, and the theory of risk measures. Central to this framework is the use of convex analysis and duality, reflecting the concavity of reward functions and the convexity of risk measures due to diversification effects. Furthermore, practical considerations, such as managing multiple risks in bank balance sheets, have expanded the theory to encompass vector risk analysis. The goal of this survey is to provide readers with a concise tour of the GFPT’s key concepts and practical applications without delving into excessive technicalities. Instead, it directs interested readers to the comprehensive monograph of Maier-Paape, Júdice, Platen, and Zhu (2023) for detailed proofs and further exploration. Full article