Search Results (1,537)

This study investigates the dynamic interactions between Bitcoin, global financial indicators, and the Kazakhstan Stock Exchange (KASE) index within a VAR-based econometric framework, addressing a notable gap in the literature on emerging and shallow financial markets. While prior research predominantly focuses on developed economies, evidence suggests that cryptocurrency–stock market linkages are time-varying, crisis-sensitive, and often asymmetric. In this context, the present study examines both short-term causality structures and shock transmission mechanisms among KASE, Bitcoin (BTC), oil prices, the U.S. dollar index (DXY), and the VIX using monthly data for the period 2017M01–2026M04. Empirical findings indicate that, despite the absence of statistically significant Granger causality from individual global variables to KASE, the joint dynamics suggest a non-negligible, albeit indirect, interaction structure. Variance decomposition and impulse-response analyses further reveal that KASE dynamics are predominantly driven by its own shocks, reflecting the relatively segmented and internally driven nature of the market. Diagnostic tests confirm the robustness of the model, with no evidence of serial correlation or heteroskedasticity in residuals. These findings are consistent with the structural characteristics of the Kazakh financial system, including limited market depth, lower investor participation, and high sensitivity to domestic macroeconomic conditions. Unlike developed markets where stronger integration is observed, KASE appears only weakly connected to global financial and cryptocurrency markets. The study contributes to the literature by providing empirical evidence from a frontier market and highlights the importance of considering country-specific structural factors when evaluating financial integration. Policy implications emphasize the need to enhance market depth, transparency, and investor confidence to strengthen the responsiveness of KASE to global financial developments. Full article

We re-conceptualize corporate debt default risk (EDF) as an emergent state variable of a coupled financial system and ask how capital-market opening reshapes its equilibrium. Extending the structural credit-risk framework with three interacting subsystem channels—external financing, investment efficiency, and information disclosure—we derive a closed-form result showing that an exogenous increase in liberalization strictly reduces the system-level corporate debt default probability through three complementary channels. We then exploit the staggered roll-out of China’s Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connect (HSGT) programs as a quasi-natural experiment on a panel of 21,351 firm-year observations over 2011–2023. A difference-in-differences (DID) estimator confirms a significant stabilizing effect on the firm’s market-implied default probability that is robust to an extensive battery of identification and specification checks; mechanism regressions confirm all three model-implied channels. The stabilizing effect is further amplified in firms facing greater environmental uncertainty and greater customer concentration—precisely the regimes in which our model predicts the underlying subsystem coupling to be most fragile. Our findings recast capital-market opening as a system-level intervention that simultaneously re-balances financing, investment, and information subsystems of the financial system, with implications for financial-stability policy in emerging economies. Full article

Marine ecosystem degradation may reduce state revenues, increase recovery spending, and weaken fiscal sustainability, yet Indonesia does not yet have a routine governance mechanism that links marine natural capital valuation to fiscal-risk assessment in the State Budget Financial Note. This article develops a governance design, Blue Natural Capital Value at Risk (BNC-VaR), to translate changes in marine ecosystem conditions into fiscal-exposure signals for Indonesian public finance. Ecological condition indicators, such as fish-stock status, coral-reef condition, and mangrove extent, are converted into traceable valuation parameters and then into structured outputs, including fiscal-exposure scenarios, budget-relevance notes, and medium-term fiscal-sustainability readings across revenue, expenditure, deficit, and financing channels. The design treats ecological change as affecting the fiscal position through mediated and disclosable pathways rather than automatic causal effects. It adapts Value at Risk as a risk logic for public fiscal governance rather than as a conventional market-based probabilistic measure. Using theory synthesis and a model-paper approach across six analytical stages, the study produces five design principles, four formal propositions, and a five-component institutional architecture, with the Directorate General of State Assets Management positioned as a valuation custodian. As a conceptual contribution, BNC-VaR offers an operational architecture and implementation roadmap for future empirical testing in Indonesia and other archipelagic or marine-resource-dependent fiscal systems. Full article

This study investigates whether shareholder value creation, defined as beta-adjusted outperformance relative to a market benchmark, can be effectively predicted in an emerging market using a sequential machine learning framework. While prior research has predominantly focused on profitability forecasting or stock return prediction, the prediction of risk-adjusted shareholder value creation remains relatively underexplored, particularly in emerging economies such as Morocco. To address this gap, the study develops a predictive framework that combines market-based indicators, macroeconomic variables, and accounting fundamentals using only information realistically available to investors at each decision date. These variables are organized into firm-level temporal sequences based on a monthly decision-date panel of non-financial firms listed on the Casablanca Stock Exchange over the period 2010–2024. To capture nonlinear relationships and temporal dependencies in financial data, the empirical analysis compares baseline models with deep learning architectures, including GRU, LSTM, and CNN1D. The results indicate that deep learning models consistently outperform naïve and linear benchmark models, suggesting that shareholder value creation exhibits a measurable degree of predictability. With an AUC of 0.700 and a PR-AUC of 0.727, CNN1D achieves the strongest performance in the final evaluation setting and ranks as the best-performing model according to the primary AUC criterion. The findings also reveal that macroeconomic variables generate the strongest standalone predictive signal, whereas market-based variables exhibit comparatively weaker predictive power when considered in isolation. By extending financial prediction toward a risk-adjusted, benchmark-based, and investor-oriented framework, and by providing new empirical evidence on the value of temporal modeling and multi-source financial information for forecasting shareholder value creation in an emerging market context, this study contributes to the growing literature at the intersection of financial forecasting and artificial intelligence. Full article

The paper investigates the evolution of earnings management across corporate life-cycle stages and evaluates the moderating role of leverage within an emerging market context. Utilising a dataset of 273 non-financial firms listed on Vietnamese stock exchanges over the 2012–2022 period (3003 firm-year observations), we delineate life-cycle phases—introduction, growth, maturity, and decline—using net cash flow patterns. Earnings quality is proxied via diverse discretionary accrual models. Methodologically, the study employs fixed-effects regressions with firm-clustered standard errors, incorporating interaction terms to capture stage-specific leverage dynamics. The empirical evidence reveals a non-linear and stage-dependent trajectory of earnings management, with introduction- and decline-stage firms exhibiting higher discretionary accruals compared to benchmarks. Crucially, the institutional impact of debt financing is contingent upon corporate maturity; while leverage exhibits a baseline positive association with earnings management, this relationship diminishes or reverses during the introduction and decline phases. These insights withstand rigorous robustness checks, including different discretionary accrual models and alternative life-cycle classifications. This study advances current literature by integrating capital structure into the corporate life-cycle framework, demonstrating that leverage effects are dynamic and shaped by shifting financial constraints and monitoring environments. Ultimately, the findings offer valuable insights into financial reporting incentives in emerging markets characterised by concentrated ownership and transitional corporate governance, yielding critical implications for regulators, investors, and auditors. Full article

We study dynamic hedging in an incomplete market where the underlying asset follows a stochastic-volatility process and the hedger trades only the stock and the money-market account. The hedging problem is formulated as a multi-stage stochastic control problem with a quadratic terminal-loss objective and is solved through a Hamilton–Jacobi–Bellman framework. For the Heston model, the resulting mean-variance hedge specializes to the Galtchouk–Kunita–Watanabe projection and can be written as the sum of the spot delta and a volatility-risk correction term. We emphasize that this representation is used in the paper as an implementation theorem for our setting, rather than as a new general result. On the numerical side, we compare a finite-difference alternating-direction implicit solver with a Deep Galerkin Method, providing full implementation details for both. The finite-difference solver is the preferred method for the two-state Heston problem because it is faster and more accurate on low-dimensional grids, whereas the neural solver becomes attractive only for higher-dimensional extensions where mesh-based methods become computationally burdensome. In backtests across major S&P 500 market regimes from 2006 to 2022, the stochastic-volatility-aware hedge modestly improves on Black–Scholes hedging during stress episodes, while differences are negligible in calm markets. Across the reported experiments, the PDE-optimal mean-variance hedge is numerically indistinguishable from the recalibrated Heston hedge, indicating that the main value of the framework is theoretical unification and implementation guidance rather than a materially different trading rule in the tested setting. Fixed worst-case robust hedging is overly conservative in the historical sample, although adaptive robustness remains a promising conceptual extension. The main contribution of the paper is therefore a rigorous and implementable unification of multi-stage PDE optimization with stochastic-volatility-aware hedging, together with evidence that the economic value of model sophistication is concentrated in stressed markets. Full article

This paper develops a theoretical framework for corporate financial resilience under incomplete-market conditions, in which firm-specific equity derivatives are structurally unavailable or only weakly developed. Using the Romanian capital market and the Bucharest Stock Exchange (BSE) as a focal context rather than as the paper’s sole relevance, the study links Tobin’s q, liquidity policy, capital structure, ESG governance, and the domestic quasi-risk-free benchmark ($R_f^{ROM}$) to explain how firms may partly support financial flexibility when direct hedging instruments are missing. This is a conceptual framework paper: it does not provide empirical tests or validated firm-level results but instead formulates empirically testable propositions (P1–P4) and a future empirical research agenda. Building on selective hedging theory, Tobin’s q investment theory ESG finance and organisational resilience research, the framework identifies six assumptions of the classical model that are violated and four limitations affecting q measurement on the BSE. Within thin and illiquid markets, Tobin’s q is treated as a noisy, imperfect valuation signal rather than as a precise decision threshold. The paper contributes by delimiting the scope conditions under which classical q-based and selective-hedging assumptions weaken in derivative-constrained markets by reframing financial flexibility as a conditional resilience mechanism rather than a hedge substitute and by specifying falsifiable propositions for future empirical testing in the Romanian capital-market context. Full article

This study examines the relationship between valuation multiples and investment performance in the U.S. stock market. Specifically, it tests whether portfolios constructed with high-multiple stocks consistently outperform portfolios with low-multiple stocks. The analysis spans the Technology, Consumer Staples, and Healthcare sectors from 2018 to 2022. A sector-based portfolio construction framework was employed using quarterly portfolio-return data. Quantitative financial modelling, including regression analysis and descriptive statistics, was applied to assess the correlation between portfolio returns and valuation multiples (P/E and EV/EBITDA), while interpreting results within the broader context of market volatility and the COVID-19 period. The results show no statistically significant relationship between valuation multiples and portfolio performance. Low-multiple portfolios demonstrated marginally higher average returns over the period, offering weak support for value-based investment strategies. Results further suggest limited standalone predictive power in high-multiple valuations. Drawing on the Efficient Market Hypothesis, Value Investing, Growth Investing, and the Fama-French Three-Factor Model, this paper empirically tests the impact of valuation multiples within a sector-based portfolio framework. Accordingly, the study adds to the asset pricing literature by offering a structured null-result framework, demonstrating that valuation multiples, when applied in isolation, may not provide sufficiently reliable standalone signals for portfolio performance. The COVID-19 period is interpreted as an economically meaningful contextual regime characterized by elevated volatility, liquidity intervention, and sectoral divergence, rather than as a formally estimated event-study framework. Full article

This study examines the impact of public attention on financial markets during the Israel–Hamas conflict, focusing on the TA35 stock index and the Israeli Shekel (ILS) exchange rate over the period October 2023 to April 2025. By distinguishing between global and domestic Google search activity, the analysis investigates whether the origin of attention differentially affects market performance and currency dynamics. Public attention is treated as a real-time proxy for investor sentiment and perceived risk. Methodologically, the study combines Google Trends data with EGARCH(1,1) models to capture both return effects and asymmetric volatility responses. To enhance robustness, Principal Component Analysis (PCA) is applied separately to global and domestic search datasets, generating latent indices that reflect conflict-related and humanitarian narratives. These indices are subsequently incorporated into the empirical models. The findings reveal that global search intensity related to conflict topics exerts a significant negative effect on stock returns and contributes to currency depreciation, reflecting heightened uncertainty and risk aversion. In contrast, domestic search activity is associated with stabilizing or positive effects, suggesting local resilience and confidence. PCA-based models improve explanatory power and confirm that the geographical origin of attention plays a crucial role in shaping financial outcomes. Additionally, the results indicate that attention-driven shocks influence volatility asymmetrically, amplifying downside risk during periods of intensified global concern. Overall, the study contributes to the literature by integrating behavioral indicators into financial risk modeling and providing a novel, real-time framework for assessing how digital attention transmits geopolitical risk into asset prices. Full article

This paper examines stock market reactions to corporate blockchain adoption announcements and explores whether the visibility of such initiatives shapes investor response. While prior research documents strong valuation effects during early phases of technological hype, evidence from more mature stages of diffusion remains limited. Accordingly, this study provides exploratory evidence on investor behavior in a later-stage adoption context. We construct a hand-collected dataset of 51 announcements by publicly traded firms across multiple industries and employ a standard event-study methodology to estimate abnormal returns over short announcement windows, using both market-model and Fama–French factor specifications. Adoption visibility is conceptualized as a multidimensional construct capturing (i) the intensity of communication surrounding the initiative and (ii) whether the application is customer-facing or internally oriented. The results indicate that average abnormal returns around announcement dates are positive but economically modest and statistically insignificant. These findings suggest that blockchain adoption announcements no longer trigger uniform market repricing effects. Instead, investors appear to respond more selectively, potentially differentiating based on the perceived informational content and strategic relevance of the initiatives. Overall, the analysis offers exploratory evidence consistent with a shift in investor response as emerging technologies move beyond hype-driven phases toward more mature stages of diffusion. The results should be interpreted with appropriate caution and motivate further research using larger samples and complementary empirical approaches. Full article

Decades of cultivation and the often exclusive use of mineral fertilisers as a substitute for organic inputs have reduced the soil organic carbon (SOC) content of agricultural soils, meaning they now represent a potential sink for carbon sequestration to mitigate climate change and improve soil function. As well as being a legacy of management, SOC will also be dependent on local scale climate, topography, and soil properties; accounting for this local context is important when benchmarking fields and quantifying the potential for additional carbon sequestration. We developed a landscape-scale methodology, using a handheld infrared device, for baselining SOC stocks in the top 30 cm across a 45,000 ha farm cluster in the UK. The cluster is exploring opportunities for landscape-scale environmental improvement with a focus on natural flood protection and water pollution reduction through conversion of arable land to permanent grassland. We used the baseline data to estimate additional benefits of arable reversion for soil carbon sequestration. Because all the farms in the cluster share the same pedoclimatic conditions, variance in SOC at the field scale could be confidently attributed to differences in soil type and land use. Average SOC stocks in arable and permanent pasture fields were 103.9 and 140.3 Mg C ha⁻¹, respectively. Variance in %SOC was modelled using soil series, sample depth, land use, and clay content, and fields were benchmarked based on deviation from the expected value. The fields with the largest SOC stocks were identified and used as references to predict future potential sequestration. The conversion of arable land to permanent pasture resulted in a predicted average uplift in SOC of 55.0 Mg C ha⁻¹. Our landscape-scale methodology provides robust evidence on current and future carbon stocks for public subsidy schemes and natural capital markets that account for local constraints and opportunities. Full article

Raw sentiment extracted from Chinese financial news is noisy and difficult to use directly for market prediction. This study proposes a mathematical filtering framework that converts noisy Chinese financial news sentiment into reliable quantitative signals for financial market prediction. Three daily sentiment measures were constructed from Chinese financial news: sentiment mean, sentiment dispersion, and polarity imbalance. Seven filtering methods were applied to each measure, including exponential smoothing, autoregressive filtering, ARIMA filtering, moving average smoothing, discrete wavelet transform, Savitzky–Golay filtering, and Kalman filtering. The seven filtered outputs were averaged to produce an ensemble-smoothed sentiment signal. Support vector machines and neural networks were then used to compare the predictive performance of raw and filtered signals for stock index log returns and realized volatility. Filtering reduced the standard deviation of sentiment mean by 48%, sentiment dispersion by 55%, and polarity imbalance by 50%, while mean levels remained stable. Filtered sentiment consistently outperformed raw sentiment across all model configurations. The improvement was larger for realized volatility than for returns: the best support vector machine reduced volatility prediction error by 16.9% and return prediction error by 5.8%. A moderate neural network with 20 hidden neurons achieved optimal performance for both outcomes. Mathematical filtering extracts stable and informative sentiment signals from Chinese financial news. Filtered sentiment is more useful than raw sentiment for predicting market volatility, and the improvement holds across multiple machine learning models. Full article

This paper examines global equity market integration, commodity-price exposure, and volatility spillovers in Ghana’s frontier equity market. Using daily data from January 2011 to December 2025, we estimate a multi-factor asset pricing model nested within a GARCH framework for the Ghana Stock Exchange Composite Index (GSECI) and the Financial Sector Index (GSEFSI). The model jointly estimates first-moment return exposures and second-moment volatility spillovers from a global equity market and three key global commodity markets: gold, crude oil, and cocoa, while controlling for asymmetric volatility, return serial dependence, and domestic macro-financial shifts associated with banking sector recapitalization and the Domestic Debt Exchange Programme (DDEP). The Ghanaian equity market is exposed to the global equity market, indicating measurable but economically modest global integration, with stronger exposure in the financial sector. Commodity-price exposures are selective, with gold and crude oil exposures concentrated in the financial sector, whereas the cocoa factor is negatively associated with returns on both indices. The variance results show persistent volatility, inverse asymmetric volatility responses, and differentiated volatility spillovers from global equity and commodity markets. The DDEP period is associated with significant equity market repricing, particularly in the financial sector. These findings indicate that Ghana’s equity market dynamics are shaped jointly by global equity and commodity market information, frontier market frictions, and sovereign–bank conditions. Full article

This article analyses trading-performance patterns in a stock market simulation conducted with 134 second-year students at the University of Mons (Belgium) on 11 December 2025. Participants had a virtual capital of 100,000 euros and were free to trade CAC 40 securities without any restrictions on the number or volume of transactions. An academic incentive scheme, combining a participation bonus and bonuses for the three best portfolios, created a tournament-style environment with continuous ranking feedback. This feature is considered as part of the experimental context rather than as a separately identified causal mechanism. We estimate a quadratic model linking performance to activity, measured by the number of mean-centered transactions to reduce the collinearity between the first-degree term and its square, and control exposure via the average percentage of cash in the portfolio, portfolio variability (measured as the standard deviation of portfolio value) and the average trade size. Breusch–Pagan and White tests indicate heteroscedasticity, justifying a robust inference. The results highlight a convex relationship between activity and performance: the marginal association is initially negative but becomes positive above a model-implied upper-tail level corresponding to approximately 46 transactions. This value should not be interpreted as a behavioral level or as a trading rule. The percentage of cash in the portfolio and the average trade size are negatively associated with performance, while the portfolio variability does not show a statistically significant association with performance. Overall, the results indicate heterogeneous trading patterns rather than a single activity–performance profile. Full article

This study investigates the determinants and predictability of stock return volatility by integrating firm-specific financial characteristics with advanced econometric and volatility modeling techniques. Using an unbalanced panel dataset comprising 1596 firms and 19,752 firm-year observations from MENA stock markets over the period 2010–2024, the analysis employs fixed-effects panel regression models, conditional volatility models, and machine learning-based forecasting approaches. Following extensive diagnostic testing, including tests for heteroskedasticity, serial correlation, cross-sectional dependence, and model specification, a two-way fixed-effects model with Driscoll–Kraay standard errors is adopted as the preferred estimation framework. The results indicate that liquidity ratio, cash ratio, sales growth, firm age, lagged volatility, and lagged returns are significant determinants of stock return volatility, whereas leverage, tangibility, board independence, firm size, Tobin’s Q, and profitability do not exhibit statistically significant effects after controlling for firm-specific and time-specific heterogeneity. The volatility analysis reveals substantial persistence in stock return volatility, with the EGARCH-t specification providing the best fit among the competing GARCH-family models according to the Akaike Information Criterion. The estimated asymmetry parameters indicate that volatility responds differently to positive and negative shocks, supporting the presence of asymmetric volatility dynamics and the suitability of asymmetric volatility models. The forecasting analysis shows that advanced machine learning and deep learning models achieve competitive predictive performance; however, differences in predictive accuracy across models are generally modest. Full article