Search Results (1,298)

The aim of this study was to develop a model that classifies companies into high or low categories based on their return on equity (RoE), the most important indicator of financial performance, using sustainability and governance-related committee reports and reports shared with the public. As a sample, the RoE, sustainability, and governance variables of all 427 companies traded on the Istanbul Stock Exchange in 2024 were used. Using a 70:30 stratified split between the training and test sets, three tree-based models (XGBoost, LightGBM, and Random Forest) were used to perform a binary classification task. The findings show that tree-based models perform only slightly better than the naive majority class rule, and therefore, have limited overall classification power. A noteworthy finding from the study is that SHAP-based explainability analysis shows that the Corporate Governance Report (IMNG), the Integrated Report (IREP) and the existence of a Sustainability Committee (ICOM) rank higher in terms of SHAP-based global importance in the High RoE classification model, although their average contributions are small and, in the case of IMNG, predominantly negative for the probability of belonging to the High RoE class. Methodologically, the article moves away from traditional econometric methods based on ESG scores, instead combining a predictive classification structure with TreeSHAP-based explanations. These findings indicate a need for reporting practices that offer deeper content, clearer evidence of governance quality, and stronger data integrity to better support investors’ decision-making processes through sustainability and governance. Full article

As enterprises increasingly depend on software systems, security defects such as vulnerability disclosures, exploitations, and misconfigurations have become economically relevant risk events. However, their short-term impacts on capital markets remain insufficiently understood. This study examines how different types of software security defects affect short-horizon stock market behavior. Using a multi-model event-study framework that integrates the Constant Mean Return Model (CMRM), Autoregressive Integrated Moving Average (ARIMA), and the Capital Asset Pricing Model (CAPM), we estimate abnormal returns and trading-activity responses around security-related events. The results show that vulnerability disclosures are associated with negative abnormal returns and reduced trading activity, while exploitation events lead to larger price declines accompanied by significant increases in trading activity. Misconfiguration incidents exhibit weaker price effects but persistent turnover increases, suggesting that markets interpret them primarily as governance-related issues. Further analyses reveal that market reactions vary with technical severity, exposure scope, industry context, and firm role, and that cyber shocks propagate through both price adjustment and liquidity migration channels. Overall, the findings indicate that software security defects act as short-term information shocks in financial markets, with heterogeneous effects depending on event type. This study contributes to the literature on cybersecurity economics and provides insights for firms, investors, and policymakers in managing software-related risks. Full article

This article examines how the recognition, measurement, and disclosure of biological assets (BAs) under IAS 41 affect value creation in Colombian agribusinesses following IFRS adoption. Using EMIS Benchmark data for Colombia, we construct a panel of 157 agro-industrial firms that are neither subsidiaries of multinationals nor listed on the stock exchange; the panel covers 2012–2022, spanning the period before and after IFRS adoption. The database combines accounting and financial indicators with categorical variables capturing the scope of activities, valuation methods (historical cost, realisable value, present value, fair value), and disclosure policies for BAs. Value creation is proxied by EBITDA, return on equity (ROE), and return on assets (ROA). We estimate fixed-effects panel models for three IFRS groups. Results show that, in Group 1, defining the accounting scope and using fair value and present value as measurement bases are associated with higher firm value, while Groups 2 and 3 display positive but statistically weaker effects. Explicit disclosure is also associated with higher profitability, particularly for SMEs. These findings are consistent with agency and firm theories: when entrepreneurial activities are recognised, measured, and disclosed consistently and transparently, information asymmetry and agency costs fall, and accounting policies become a driver of organisational performance in agribusinesses in emerging markets. The results also support the assumptions of institutional theory, as external regulatory pressures from IFRS and internal pressures arising from relationships among firms in the agro-industrial sector shape and reinforce information disclosure practices. Full article

Network vector AutoRegressive models play a vital role in multivariate time series analysis. However, previous research in the classic Network vector AutoRegressive (NAR) model is limited to strict assumptions of linearity and time-invariance of node-specific covariates. In this study, we propose a Semiparametric NAR (SNAR) model to broaden existing research horizons by (1) extending node-specific covariates to a nonlinear framework, (2) incorporating high-dimensional time-varying covariates for a more comprehensive analysis, and (3) maintaining the interpretability of the autoregressive effects of the NAR. A deep learning-based method is presented to simultaneously estimate the nonparametric function and the parameters in SNAR. We also provide theoretical proof for the convergence rate of the nonparametric deep neural network estimator to support linear-to-nonlinear extension and show that the proposed method is capable of avoiding the curse of dimensionality. Furthermore, we prove the asymptotic normality of the parametric estimators for autoregressive effects to demonstrate the maintenance of interpretability. Experiments on various numerical simulated data show that the proposed method can avoid the curse of dimensionality; for instance, in nonlinear settings, the SNAR model reduces the prediction MSE by approximately 69% compared to the classic NAR model (decreasing from 3.44 to 1.06). Furthermore, in real-world stock return analysis, the SNAR model achieves an MSE of 0.9930, significantly outperforming the NAR baseline (MSE 1.6540) and other state-of-the-art methods. Full article

In the context of the growing prominence of socially responsible investment, the debate over whether sustainable corporate practices translate into sustained shareholder value has intensified. As a key tool for aligning their investment portfolios with responsible/sustainable corporate practices, investors rely on listed companies’ Environmental, Social, and Governance (ESG) ratings. This study aims to investigate the long-term impact of ESG practices on the stock performance of listed companies. We perform a Q1 2000–Q1 2025 backtest to analyse the comparative performance of a Best-in-Class ESG portfolio, constructed by the top 30 listed companies with market capitalisations above USD 2 billion ranked by Morningstar Sustainalytics’ ESG Risk Ratings as of 31 March 2025 against the S&P 500 Total Return index. We found that ESG leaders exhibited superior risk-adjusted performance, outperforming the S&P 500 Total Return Index. The BiC portfolios achieved a substantially higher CAGR and Sharpe ratio, while maintaining maximum drawdowns that remained comparable to the benchmark S&P 500 Total Return index. We also found that ESG advantages were more pronounced in market downturns, with the Best-in-Class ESG portfolio showing better CAGR and Sortino ratios. The findings of this study demonstrate that responsible governance and management create benefits for all stakeholders, including investors, society and nature, in the broadest sense of these terms. Full article

Stock return prediction in emerging markets remains difficult due to the gap between theoretical efficiency and empirical irregularities. This study assesses the statistical and economic performance of Linear Regression, Ridge Regression, Random Forest, and XGBoost in forecasting 5-day and 21-day returns for six LQ45 stocks (2016–2025). Momentum, volatility, trend, and volume indicators are used as predictors, while model performance is evaluated using MAE, RMSE, R², and backtested trading metrics that include transaction costs. All models yield near-zero or negative R², directional accuracy of 49–54%, and AUC around 0.50–0.53, indicating weak signals overshadowed by noise. XGBoost offers the lowest statistical errors, but Ridge Regression achieves slightly better risk-adjusted outcomes (Sharpe 0.1232), although every strategy underperforms Buy & Hold. SHAP results show volatility and volume features as most influential, but with minimal absolute impact. Overall, the LQ45 market exhibits semi-efficiency: patterns exist but fail to translate into profitable trading once real-world frictions are considered, underscoring the gap between statistical predictability and economic viability in algorithmic trading. This research was conducted in order to support the achievement of various goals through SDG 8 (Decent Work and Economic Growth). Full article

This study employs a modern GARCH framework to conduct a comparative analysis of the volatility transmission between crude oil prices and a comprehensive set of financial assets, including sectoral equities, precious metals, and cryptocurrencies, across oil-exporting and oil-importing countries. Our central finding reveals a stark pre-pandemic dichotomy: before COVID-19, oil price volatility exhibited a significant positive correlation with nearly all sectoral stock returns in oil-exporting countries (the United States and Canada), reflecting a systemic, demand-driven linkage. In contrast, this relationship was largely insignificant in oil-importing countries (the United Kingdom, France, and Japan), with the exception of the energy sector. The COVID-19 crisis temporarily erased this fundamental distinction, as sectoral stock markets in both country groups moved in significant positive correlation with oil, driven by the synchronized global demand shock. This transition underscores that the oil–equity relationship is structurally determined by a country’s net oil trade position, a dynamic that can be overridden during systemic global crises. These findings offer crucial insights for international portfolio diversification and risk management. Full article

Stock trading faces significant challenges due to market volatility and the complexity of integrating diverse data sources, such as financial texts and numerical market data. This paper proposes an innovative automated trading system that integrates advanced natural language processing (NLP) and deep reinforcement learning (DRL) to address these challenges. The system combines two novel components: PrimoGPT, a Transformer-based NLP model fine-tuned on financial texts using instruction-based datasets to generate actionable features like sentiment and trend direction, and PrimoRL, a DRL model that expands its state space with these NLP-derived features for enhanced decision-making precision compared to traditional DRL models like FinRL. An experimental evaluation over seven months of leading technology stocks reveals cumulative returns of up to 58.47% for individual stocks and 27.14% for a diversified portfolio, with a Sharpe ratio of 1.70, outperforming traditional and advanced benchmarks. This work advances AI-driven quantitative finance by offering a scalable framework that bridges qualitative analysis and strategic action, thereby fostering smarter and more equitable participation in financial markets. Full article

This study employs a quantile-on-quantile connectedness approach to analyze the asymmetric, distribution-dependent, and time-varying spillovers between FinTech indices and traditional financial markets. The results show that spillovers are concentrated in the distribution tails, with FinTech indices exhibiting strong co-movements with equities and Bitcoin under extreme conditions, while linkages with U.S. Treasury bonds are weaker and often inverse. Net connectedness analysis reveals that the S&P 500 and Bitcoin act as the primary transmitters of shocks into FinTech indices, whereas Treasuries generally serve as receivers, except during stress episodes when safe-haven flows or heightened credit risk reverse the direction of spillovers. The dynamic ∆TCI (Difference between the total direct connectedness and the reverse total connectedness) further demonstrates that FinTech indices serve as net transmitters in stable markets but become receivers during crises such as the COVID-19 pandemic, the Federal Reserve’s tightening cycle of 2022–2023, and the FTX-driven crypto collapse. Segmental heterogeneity is also evident: distributed ledger firms are highly sensitive to cryptocurrency dynamics, alternative finance providers respond strongly to both equity and bond markets, and digital payments firms are primarily influenced by equity spillovers. Overall, the findings underscore FinTech’s dual role—transmitting shocks during tranquil periods but amplifying systemic vulnerabilities during crises. For investors, diversification benefits are state-dependent and largely disappear under adverse conditions. For regulators and policymakers, the results highlight the systemic importance of FinTech–equity and crypto–ledger linkages and the need to integrate FinTech exposures into macroprudential surveillance to contain volatility spillovers and safeguard financial stability. Full article

Corporate social responsibility (CSR) plays a growing role in fostering transparency, stakeholder trust, and long-term firm sustainability, particularly in emerging markets. Firms that actively engage in CSR are more likely to disclose credible financial information, which can reduce the incentive to withhold adverse news and thereby limit stock price crash risk (SPCR). This study investigates the impact of CSR on SPCR, while also examining whether this relationship varies across different stages of the firm life cycle (FLC). The analysis is based on an unbalanced panel of listed non-financial firms from the Pakistan Stock Exchange (PSX), covering the period from 2009 to 2023. Financial data were obtained from the State Bank of Pakistan (SBP) and Securities and Exchange Commission of Pakistan (SECP), while market data were collected from the PSX. Employing fixed-effects robust regression models and two crash risk proxies, negative conditional skewness (NCSKEW) and down-to-up volatility (DUVOL), the results reveal a consistent and significant negative association between CSR and SPCR. This suggests that firms with stronger CSR engagement are less prone to extreme negative stock returns. However, the moderating effect of FLC is only evident at the introduction and decline stages, indicating that the effectiveness of CSR in reducing crash risk depends on a firm’s position in its organizational life cycle. These findings contribute to the literature on CSR and financial stability in emerging markets and offer practical implications for investors, managers, and policymakers seeking to promote risk-aware, socially responsible corporate strategies. Full article

Background: Trapeziometacarpal osteoarthritis (TMC OA) is a prevalent degenerative disorder that causes considerable pain and functional limitations, especially in older individuals, whose ideal treatment is still debated in the literature. Various treatments are described to restore a good functional outcome of the thumb; over the past 50 years, biological arthroplasties have been considered the gold standard for treating advanced stages of TMC OA. However, in the last decade, the use of dual mobility cup prostheses has significantly increased, with numerous studies reporting excellent clinical outcomes. In this case report, we show the results of a patient treated on the left hand with suspension arthroplasty and on his right hand with dual mobility arthroplasty in one-stage surgery. The aim of this case report is to directly compare outcomes between trapeziometacarpal prosthesis and suspension arthroplasty performed simultaneously in the same patient. Case Presentation: The present case reports a 71-year-old male patient with bilateral TMC osteoarthritis, referred to our clinic in May 2024. His medical history included hypertension, hypertriglyceridemia, paroxysmal atrial fibrillation, and benign prostatic hyperplasia. On examination, the right hand showed grade 3 osteoarthritis according to the Eaton–Littler classification, with the trapezium maintaining adequate bone stock, making the patient eligible for trapeziometacarpal prosthesis implantation. Conversely, the left hand demonstrated scaphotrapezoid arthritis with a slight reduction in trapezial bone stock, indicating the need for trapeziectomy followed by suspension arthroplasty. Both procedures were performed during the same surgical session by the same experienced hand surgeon using a lateral approach. On the right side, the trapeziometacarpal joint surfaces were resected and replaced with a dual mobility prosthesis, while on the left side, the trapezium was excised, and suspension arthroplasty was performed using a slip of the flexor carpi radialis (FCR) tendon. Methods: The patient underwent simultaneous treatment with a dual mobility trapeziometacarpal prosthesis on the right hand and trapeziectomy with suspension arthroplasty on the left hand. Clinical outcomes (grip and pinch strength, pain, QuickDASH, satisfaction, and range of motion) were evaluated at 1, 3, 6, and 12 months. Paired comparative statistics were applied with significance set at p < 0.05. Results: At all follow-up intervals (1, 3, 6, and 12 months), the hand treated with a trapeziometacarpal prosthesis demonstrated superior grip and pinch strength compared to the hand treated with trapeziectomy and suspension arthroplasty, with the greatest difference observed at 3 months. At 12 months, grip strength increased from 28 kg to 40 kg in the prosthesis-treated hand and from 25 kg to 33 kg in the suspension arthroplasty hand. Paired comparisons were performed at each follow-up interval up to 12 months, confirming a significant difference for grip strength. Pain levels (VAS, Visual Analogue Scale) decreased progressively in both hands, with a more rapid reduction in the hand treated with a trapeziometacarpal prosthesis, reaching statistical significance. QuickDASH scores indicated an earlier return to daily activities in the hand treated with the prosthesis, although this difference was not statistically significant. Patient satisfaction was consistently higher for the hand treated with a trapeziometacarpal prosthesis, with the patient reporting a ‘very satisfied’ rating at all timepoints. Range of motion recovery, assessed through the Kapandji score and measurements of thumb abduction and extension, also favored the hand treated with the prosthesis, with statistically significant differences for abduction and extension, whereas the hand treated with trapeziectomy and suspension arthroplasty demonstrated more gradual improvement over time. Conclusions: This case highlights the functional efficacy of both surgical approaches—biological arthroplasty and trapeziometacarpal prosthesis—in the treatment of TMC osteoarthritis. Both procedures resulted in a good clinical outcome and high patient satisfaction. However, recovery was noticeably faster in the hand treated with a trapeziometacarpal prosthesis, which is consistent with findings previously reported in the literature. These observations suggest that, while both techniques are valid and effective, trapeziometacarpal prosthetic replacement may offer a quicker return to function in appropriately selected patients. Full article

Accurate prediction of shrimp body weight is critical for optimizing harvest timing, feed management, and stocking density decisions in intensive aquaculture. While prior studies emphasize environmental factors, operational management variables—particularly harvesting metrics—remain understudied. This study quantified the predictive importance of harvesting-related variables using 5 years of industrial-scale operational data from 12 ponds (5479 cleaned records, 34.94% retention rate). We trained seven machine learning models and applied three independent feature importance methods: consensus importance ranking, SHAP explainability analysis, and Pearson correlations. Main findings: Operational variables (days of culture: 2.833 SHAP, stocking density: 1.871, cumulative feed: 1.510) ranked substantially above environmental variables (temperature: 0.123, pH: 0.065, dissolved oxygen: 0.077). Partial harvest frequency showed bimodal clustering, indicating two distinct viable operational strategies. The Weighted Ensemble model achieved the highest performance (R² = 0.829, RMSE = 4.23 g, MAE = 3.12 g). Model stability analysis via 10-fold GroupKFold cross-validation showed that the Artificial Neural Network (ANN) exhibited the tightest confidence bounds (0.708 g width, 27.7% coefficient of variation), indicating exceptional consistency. This is the first study to systematically analyze the importance of harvesting variables using SHAP explainability, revealing that operational management decisions may yield greater returns than marginal environmental control investments. Our findings suggest that operational optimization may be more impactful than environmental fine-tuning in well-managed systems. Full article

This study examines the heterogeneous timing of market responses to financial news and its implications for informational uncertainty in price-adjustment dynamics. Empirically, stocks do not incorporate positive and negative sentiment at the same speed; instead, they exhibit asset-specific delays that stem from differences in investor attention, cognitive processing, and microstructural constraints. These unequal reaction windows increase the entropy of the information-transmission process, as sentiment shocks diffuse across assets in a dispersed and temporally misaligned manner. To quantify this heterogeneity, we develop a framework that integrates FinBERT-based sentiment classification, Bollinger Bands signal identification, and a Genetic Algorithm (GA) to estimate stock-specific sentiment reaction windows. Using S&P 500 data from 2021 to 2024, with 2022 to 2024 reserved for out-of-sample validation, the results show that GA-derived windows capture actual price-adjustment lags more accurately and significantly improve trading performance compared with fixed-window and technical-only benchmarks. In particular, incorporating news headline sentiment into the Bollinger Bands framework increases the win rate by approximately 5% over the testing period and leads to a significant improvement in overall returns. These findings demonstrate that the assimilation of sentiment is a time-dependent and non-uniform process shaped by behavioral and structural factors, offering new evidence that informational entropy—arising from delayed and heterogeneous reactions—plays a meaningful role in market efficiency and return dynamics. Full article

Since its launch in 2009, Bitcoin has become a market disruptor due to its primary function as a virtual currency supported by blockchain technology and the high volume of economic transactions it facilitates. This article examines the key theoretical principles that have contributed to Bitcoin’s recognition as a cryptocurrency. It assesses whether Bitcoin meets the criteria for being considered a form of money and evaluates its importance as a financial asset. This analysis of Bitcoin from 2014 to 2025 reveals that it does not sufficiently fulfill all the typical functions of money, such as serving as an internationally accepted means of payment, a unit of account, a securities depository, and a standard for deferred payments. Despite its usual close correlation with stock indices in financial markets, a decentralized digital currency like this still does not meet the requirements of fundamental analysis. In practice, this leads to its exclusion as a currency, since it does not fulfill the functions of money nor fully qualify as a crypto asset, as its value is primarily based on investors’ expectations of high returns. Apart from a lack of foundation in tangible goods or services that justifies their value and dependence on new investors, the findings do not indicate conditions typical of a developed pyramidal model. Nevertheless, this does not prevent future technological innovations from responding positively to the functions of money or from offering real money services, especially those related to service innovation and the digital economy. Full article

This study examines whether the external financing anomaly exists in an emerging-market setting. Using data on Korean listed firms from 1994 to 2023, we find that firms with higher net external financing subsequently earn significantly lower stock returns, consistent with behavioral misvaluation and market-timing explanations. A hedge portfolio long in net repurchasers and short in net issuers delivers an average annual return of about 12 percent. Decomposing financing flows show that both equity and debt issuance predict lower future returns, and further separating debt into bonds and loans reveals a stronger negative return association for bond-financed firms, consistent with greater sentiment sensitivity in market-based financing. We also document subsequent declines in operating performance, indicating that external financing aligns with temporary overvaluation rather than growth opportunities. Overall, our findings extend evidence on the external financing anomaly to an emerging market and provide further support for the behavioral interpretation of corporate financing decisions. Full article