Search Results (512)

RFID is usually used for product recognition, object-level tracking, inventory visibility, logistics monitoring, authentication, and automated data capture for various supply chain industries. RFID can enable traceability, authentication (anti-counterfeiting), order allocation, inventory control, warehouse efficiency, logistics monitoring, safety management and sustainability information in apparel, fashion, and product-based supply chains. The current evidence, however, is quite diverse, ranging from fashion supply chains and manufacturing analytics to inventory systems, logistics, asset tracking, environmental monitoring, worker safety, privacy, cyber and other aspects of digital transformation. The purpose of the scoping review is to chart evidence on RFID deployment in apparel, fashion, textile, and transferable product contexts, focusing on traceability, anti-counterfeiting, product visibility, and implementation conditions in apparel, fashion, and textile supply chains, as well as in sustainability contexts. The review process followed scoping review guidance and its protocol. Evidence was found in the following formats: EndNote XML/export records, Covidence screening (title/abstract), Covidence screening outputs (included study exports), and uploaded full-text PDFs. Documents were classified by source type, study design, context, technology focus, purpose, key findings, relevance to sustainability, limitations, and role in the synthesis. The results show that RFID evidence is strongest in product identification, item-level visibility, secure inventory search, manufacturing data capture, logistics tracking, asset traceability, and operational efficiency. Full article

Energy sharing among small- and medium-sized enterprises (SMEs) may improve local use of distributed renewable generation and flexibility, but its effects on distribution grids remain uncertain under participant-oriented rather than grid-optimized dispatch. This study assesses the grid impacts and economic implications of participant-oriented energy sharing for a 2024 reference scenario and a highly electrified 2045 stress-test scenario. The analysis uses measured load profiles from SMEs in sectors covering a 30-day period from 15 October to 15 November. The results therefore represent an autumn-specific comparison rather than annual estimates. For both scenarios, grid operation without energy sharing is compared with participant-oriented energy sharing, and the resulting time series are evaluated in a separate load-flow simulation. Grid impacts are assessed using asset utilization, power flows, and aging-related operating costs. In 2024, energy sharing caused no significant adverse grid effects and reduced aging-related operating costs by 5.6%. In the 2045 stress-test scenario, these costs increase by 11.7%. Nevertheless, based on an energy sharing potential of 22.76 GWh, participating companies retain an economic benefit even if additional grid costs are allocated to them. The results indicate that participant-oriented energy sharing can be economically attractive, while its grid effects depend on future electrification and system conditions. Full article

Population ageing and the growing importance of private savings in financing retirement have increased interest in identifying investment strategies that enhance portfolio sustainability and risk-adjusted performance during the retirement decumulation phase. This study examines whether threshold-based rebalancing improves portfolio sustainability relative to a buy-and-hold strategy. Using historical equity and government bond returns for Spain, the United States, and Japan, the analysis considers alternative asset allocations, sustainable withdrawal rates (SWRs), and rebalancing thresholds over a 25-year retirement horizon. Strategy performance is evaluated using two complementary indicators: the average number of years of portfolio sustainability and a risk–return ratio. The results show that intermediate rebalancing thresholds generally provide the most favourable balance between sustainability, return, and risk, although the effectiveness of rebalancing depends on the SWR, portfolio allocation, and the characteristics of the market under consideration. The study provides new empirical evidence on the effectiveness of threshold-based rebalancing during the retirement decumulation phase and offers practical insights for portfolio management and retirement financial planning. The findings are also relevant for public policymakers, providing evidence that may support the design of strategies aimed at helping individuals maintain an adequate standard of living throughout retirement decumulation phase. Full article

State Council Document No. 43 and the revised Budget Law legally disclaimed Chinese local governments’ responsibility for the debts of the enterprises they own. Across 22,233 firm-year observations on 1686 listed firms from 2009 to 2023, the effective cost of debt of local state-owned firms fell by 0.33 percentage points relative to private firms, 5.5 percent of its mean. The direction was not obvious in advance: withdrawing a guarantee that lenders had been pricing should have made credit dearer, while the debt swap enacted alongside the disclaimer replaced high-cost vehicle liabilities with low-cost provincial bonds and eased the balance sheets standing behind those firms. Central state-owned enterprises, whose support the reform left untouched, serve as a falsification group and show no statistically distinguishable change. The estimate passes the joint pre-trend test, survives matching, entropy balancing and twenty specification changes, and operates through the interest paid rather than the quantity of debt, which identifies a price effect. Investment rose by 0.76 percentage points of assets, but no improvement in investment efficiency was detected, and an equivalence test cannot exclude a small one. A cross-sectional pattern consistent with fiscal relief does not survive correction for multiple testing, so that channel is reported as suggestive rather than identified. Hardening the budget constraint of local governments lowered the debt servicing burden of the firms they own without directing the freed resources toward more productive use, so the sustainability gain is fiscal rather than allocative. Full article

Portfolio reinforcement learning (RL) commonly represents each action as a complete asset-weight vector, causing the action dimension and exploration difficulty to grow with the investment universe. This study proposes FrontierStep-RL, which replaces the direct N-dimensional action with two bounded variables: a frontier coordinate and a rebalancing step. At each decision date, rolling estimates of expected returns and covariance define a regularized efficient frontier. A cost–risk-aware coordinate organizes the frontier using normalized local changes in predicted volatility and one-way turnover. The coordinate selects a frontier-supported target portfolio, while the step controls how far the pre-trade portfolio moves toward that target. We evaluate FrontierStep-RL on FF49, FF100, and FNSPID-50 against traditional strategies, controlled direct-weight RL policies, and recent portfolio-management methods. FrontierStep-RL achieves net Sharpe ratios of 0.75 on both FF49 and FNSPID-50 while maintaining comparatively low volatility, drawdown, and turnover. In the 100-asset setting, it achieves 0.68, compared with 0.52 for the strongest direct-weight baseline, and completes all runs. At a transaction cost of 50 basis points, it retains net Sharpe ratios of 0.559 and 0.568. The results support fixed-dimensional target selection and controlled execution for scalable, transaction-cost-aware portfolio RL. Full article

Environmental, social, and governance (ESG) quality cannot be directly observed because substantial disagreement across rating providers contaminates the observed ESG scores with measurement error. This paper addresses this problem by modelling the true ESG state as a latent, vector-valued Itô diffusion defined on a filtered probability space whose information set is progressively enlarged by FinTech signals. We establish the well-posedness of the latent ESG process, prove the existence of an equivalent martingale measure under an explicit exponential-moment condition, and solve an ESG-constrained portfolio problem under a wealth-scaled sustainability constraint through a Hamilton–Jacobi–Bellman verification theorem. Computationally, raw sustainability information is extracted from SEC Form 10-K filings using a FinBERT transformer architecture and incorporated into a linear Gaussian state-space model, where the latent ESG state is recovered via Kalman filtering. Theoretical results are then linked to asset pricing, portfolio allocation, and systemic risk networks through a common filtered ESG factor. Using an unbalanced panel of 1086 U.S. listed firms over 2011–2023 and ESG information from MSCI, Refinitiv, and Sustainalytics, we document substantial provider disagreement and show that the observed ESG ratings contain significant transitory measurement noise. The filtered ESG state exhibits higher reliability, lower noise, and greater persistence than individual provider scores. In asset pricing tests, the latent ESG state predicts future excess returns, whereas a composite provider-based ESG measure does not; a one-standard-deviation increase in the latent ESG state is associated with approximately 0.35 percentage points higher monthly excess returns (about 4.3% annualised). When both measures are included simultaneously, only the filtered ESG state retains explanatory power. Out-of-sample portfolio tests show that a latent ESG strategy achieves a Sharpe ratio of 0.72, significantly exceeding both an unconstrained benchmark (0.59) and a composite ESG screen strategy (0.55). At the network level, ESG-adjusted weighting attenuates systemic fragility by reducing the spectral abscissa from 0.34 to 0.21, with the mitigating effect remaining significant under permutation-based placebo tests. Overall, the evidence supports the central hypothesis that ESG measurement error attenuates the observed pricing effects and that FinTech-enabled filtering recovers economically meaningful sustainability information relevant for asset pricing, portfolio construction, and systemic risk assessment. Full article

This paper investigates the impact of corporate financial asset holdings on green innovation and explores the internal mechanisms through which financialization shapes firms’ sustainability-oriented technological strategies. Using a comprehensive panel dataset of Chinese A-share non-financial firms from 2010 to 2023, the analysis confirms that corporate financialization significantly inhibits green innovation, supporting the crowding-out hypothesis. Mechanism tests reveal three economic channels: reduced innovation capability (measured by R&D staffing), diminished innovation willingness (proxied by environmental investment), and a deteriorated innovation environment (captured through financial constraints). Heterogeneity analysis further shows that this negative effect is particularly pronounced among non-state-owned enterprises, firms operating in low-pollution industries, and those facing high market competition. These findings highlight how financialized capital allocation undermines long-term sustainable investment, especially where institutional protections are weak or absent. The study offers important policy implications for regulating excessive financialization, enhancing green financing mechanisms, and designing targeted incentives to foster sustainability-driven innovation across different industry and ownership structures. Full article

This paper examined whether portfolio-objective choice or forecasting complexity was more consequential in heavy-tailed commodity ETF allocation. Using daily Bloomberg data for 30 U.S.-listed commodity ETFs from 12 December 2018 to 16 December 2024, we compared historical and dynamic mean–variance and conditional value-at-risk (CVaR) portfolios under long-only and long–short constraints. The dynamic framework combined ARMA–GARCH marginals, Student-t innovations, and copula dependence. Minimum-variance and minimum-CVaR portfolios generally produced higher Sharpe, Calmar, and STARR ratios than tangent portfolios across the baseline and robustness analyses. Dynamic estimation produced higher Sharpe, Calmar, and ${\mathrm{STARR}}_{0.95}$ point estimates for C99 and the tangent portfolios, but none of the historical–dynamic differences remained significant after max-t adjustment. Hill estimates indicated heavy downside tails, while VaR and Expected Shortfall backtests showed no calibration rejections for the minimum-risk portfolios and revealed 95% VaR failures for dynamic tangent portfolios. The long-only minimum-variance and 95% minimum-CVaR portfolios recorded higher Sharpe, Calmar, and ${\mathrm{STARR}}_{0.95}$ ratios than the 60/40 SPY–AGG benchmark, whereas the benchmark had a smaller maximum drawdown and lower VaR and Expected Shortfall. Overall, portfolio-objective choice was more consistently associated with realized performance than additional forecasting complexity. Full article

This paper develops a generalized autoregressive conditional heteroskedasticity–mixed data sampling model with continuous and jump components (GARCH-MIDAS-CJ). It aligns the continuous–jump (CJ) decomposition of realized volatility with the long-run–short-run multiplicative structure. The model places block-smoothed daily continuous variation in the long-run mixed-data-sampling term and demeaned jump variation in the short-run Glosten–Jagannathan–Runkle GARCH (GJR-GARCH) recursion. Using high-frequency data for the Standard & Poor’s 500 (S&P 500), we find a positive jump coefficient under both the Corsi–Pirino–Renò (CPR) and Andersen–Bollerslev–Dobrev (ABD) decompositions, supported by one-sided Wald, boundary-adjusted likelihood-ratio, and parametric-bootstrap tests. Relative to return- and realized-measure benchmarks, the forecasting value of the CJ allocation is competitive for multi-day and cumulative variance forecasts and in high-volatility periods, whereas established benchmarks can perform better for one-day and low-volatility forecasts. Under the CPR decomposition, the $h=22$ average cumulative forecasts reduce the mean squared error (MSE) and QLIKE relative to the aggregate-realized-variance benchmark by 8.2% and 3.3%, respectively. One-day-ahead value-at-risk (VaR) results are strongest at the 1% and 0.5% tails; the 5% forecasts have exceedance rates above the nominal level. Across alternative information-timing conventions, forecast windows, and United States equity assets, the clearest gains remain concentrated at multi-day horizons, although their magnitude varies across settings. The findings support a persistence-based allocation of continuous and jump variation within the GARCH-MIDAS structure. Full article

This paper examines portfolio optimization and tail-risk analytics for a heterogeneous universe of 30 actively managed investment funds using daily Bloomberg data from 2020 to 2025. The study compares buy-and-hold, mean–variance, conditional value-at-risk (CVaR)-based, and tangency-type portfolio strategies under long-only and long–short constraints. The sample consists predominantly of actively managed exchange-traded funds, with PTTRX retained as an actively managed fixed-income mutual-fund comparator. The results show substantial heterogeneity across thematic equity, fixed-income, income-oriented, multi-asset, and alternative strategies, creating both diversification opportunities and meaningful differences in volatility, drawdown behavior, downside exposure, and tail risk. Historical results indicate that tangency-type portfolios are generally the strongest competitors to the buy-and-hold benchmark, while minimum-variance and CVaR-minimizing portfolios provide more conservative downside control. Dynamic allocation improves performance selectively: TVP records the strongest risk-adjusted performance, followed by TC95 and TC99, but these strategies are more sensitive to turnover, transaction costs, and implementation frictions, especially under long–short constraints. Tail-risk diagnostics based on empirical VaR, Expected Shortfall, maximum drawdown, left-tail Hill estimators, and peak-over-threshold generalized Pareto distribution (POT–GPD) methods show that downside tail exposure remains meaningful after portfolio aggregation. Overall, the findings suggest that actively managed ETFs are best evaluated as components of a joint investment opportunity set in which dependence structure, portfolio design, dynamic allocation, implementation frictions, and tail-risk exposure jointly shape performance. Full article

As China’s rural poverty governance shifts from eliminating absolute poverty to preventing relative poverty risks, the sustainability of rural household livelihoods increasingly depends on whether labor migration can strengthen long-term welfare resilience beyond short-term income gains. Existing studies have extensively examined the effects of migration on income, employment, and current poverty status but have paid insufficient attention to whether migration reduces households’ future exposure to poverty. This study reframes rural labor migration as a household-level risk-restructuring strategy and examines its relationship with household poverty and vulnerability. Using rural household data from the 2022 China Family Panel Studies, we construct a Vulnerability as Expected Poverty measure to estimate the probability that household income will fall below a relative poverty line. We then examine the baseline association between the migrant worker share and poverty vulnerability, its heterogeneity across household structures, and the accompanying pathways related to income composition, information access, and asset-based buffering. The results show that a higher migrant worker share is negatively associated with poverty vulnerability in the baseline sample, but this relationship is conditional rather than homogeneous. The negative association is more evident among households with lower household-head education, lighter elderly dependency burden, and moderate labor-allocation constraints. Further evidence shows that migrant households tend to have a higher wage-income share, better internet access, and greater durable asset holdings, all of which are associated with lower vulnerability. By distinguishing income improvement from poverty-risk reduction, this study advances a vulnerability-based framework for evaluating rural migration and highlights that its contribution to sustainable poverty prevention depends on households’ capacity to convert migration-related gains into stable income structures, improved information access, and stronger asset buffers. Full article

Black-box portfolio models can produce allocation weights without a reconstructable account of how market signals, constraints, and risk controls shaped the decision. This study introduces the Constraint-Aware Portfolio Reasoning Network (CAPRN), a neuro-symbolic-inspired framework for intrinsic, decision-level explainability in portfolio allocation. Its implemented configuration uses a one-layer long short-term memory encoder and exposes factor relevance, constraint pressure, temporal state, rule-bias effects, and asset-level preference scores before producing long-only, fully invested weights. Conditional value-at-risk, a shuffled mini-batch wealth-path surrogate, and an equal-weight-deviation regularizer connect these variables to risk controls, while chronological drawdown and realized turnover are evaluated separately out of sample. CAPRN is evaluated on a ten-asset universe using strict walk-forward testing, performance measures, ablations, decision narratives, deletion and insertion diagnostics, counterfactual constraint tests, and explanation-quality metrics. CAPRN remains economically viable out of sample but neither uniformly outperforms equal-weight and mean–variance benchmarks nor exhibits statistically significant return or Sharpe-ratio dominance. Its internal variables are inspectable and stress-testable, although its factor-indicator fidelity is weaker than the marginal attribution performance of SHAP and LIME. CAPRN should therefore be viewed as an auditable allocation and governance layer rather than a benchmark-dominant production optimizer. Its principal contribution is a reproducible reasoning pathway connecting market information, constraint responses, risk controls, and final portfolio weights for practitioner oversight and regulatory reporting. Full article

China’s Carbon Emissions Trading (CET) Pilot Policy may alter corporate information environments and demand for auditors with industry-specific experience. Using the staggered launch of regional carbon markets as a quasi-natural experiment, this study examines whether policy exposure changes auditor industry specialization among Chinese A-share listed firms from 2010 to 2022. Specialization is measured by the logged share of an auditor’s client assets in a given industry; it does not represent an audit-fee premium or direct carbon-assurance expertise. Staggered difference-in-differences estimates show that CET pilot exposure increases this specialization measure. The result is robust to event-study, placebo, propensity-score-matched, and Callaway–Sant’Anna specifications. Channel regressions are consistent with financing-constraint and leverage pathways, while evidence for controlling-shareholder fund occupation is weak and not interpreted as causal mediation. Pairwise tests identify regional differences but no statistically significant ownership or pollution-intensity differences. By linking carbon-market policy to auditor-client matching, the study shows that sustainability policy can reshape the professional-service infrastructure supporting firms’ low-carbon transition. Allocating industry-experienced auditors to policy-exposed firms may strengthen the credibility and comparability of sustainability-related financial information and support regulatory monitoring. These implications concern the institutional capacity for sustainable development; the study does not directly measure sustainability performance or carbon-assurance quality. Full article

High renewable penetration makes imbalance settlement inseparable from the physical decisions governing reserve procurement and flexibility activation. This paper develops a decision-focused learning-based optimization framework that trains renewable-deviation and flexible-resource deliverability representations through downstream dispatch, reliability, and settlement consequences. The mathematical contribution is a settlement-aware learning objective that couples learned uncertainty, resource-time credible-capacity certification, network-constrained multi-stage dispatch, and counterfactual marginal-contribution allocation while retaining an exact revenue-adequacy identity. The 33-node Zhangjiakou-type regional case uses 15 min intervals and comprises five resource classes: independent storage, data-center flexibility, industrial adjustable load, commercial demand response, and electric-vehicle aggregation. Relative to a fixed-ratio reserve rule, the proposed method lowers the regional balancing cost from 950 to 618 thousand USD (34.9%), achieves 97.8% renewable accommodation, limits the shortage probability to 0.7%, and attains a settlement-fairness index of 0.92. The framework solves a 500-asset instance in 118 s. External validation uses 4027 half-hour observations from the 2025 Elexon/BMRS market, including measured wind and solar output, day-ahead forecasts, load, imbalance prices, and procured-reserve prices. On the 1487-interval December test set, the proposed model reduces the replay cost from 2953.3 to 2598.2 thousand GBP (12.0%), decreases the shortage-interval frequency from 4.64% to 1.28%, and reaches 99.74% renewable accommodation. Comparisons with forecast-then-optimize, Wasserstein distributionally robust optimization, off-policy reinforcement learning, and graph-based behavioral cloning establish that the improvement comes from jointly learning which uncertainty matters for dispatch and which flexible capacity is deliverable. Full article

The rapid expansion of the digital currency market and the growing role of stablecoins as potential intermediaries have brought its interconnectedness with traditional financial markets to the forefront of global financial research. Using daily data from 4 January 2021 to 30 September 2025, this study constructs a variable system with the price indices of USDT and USDC as core digital currency proxies, alongside traditional financial asset indices for stocks, bonds, and gold derived via the entropy weight method. We employ a comprehensive set of econometric techniques, including static correlation analysis, vector autoregression (VAR), impulse response functions, and extreme-event shock tests, to systematically investigate the interdependence structure, risk spillover dynamics, time-varying co-movements, and structural changes between the two markets during extreme risk episodes. The findings reveal an overall weak and asymmetric bidirectional spillover relationship between the cryptocurrency and traditional financial markets. Volatility in the digital currency market is found to be largely endogenous, with a limited capacity to transmit shocks externally. Conversely, traditional financial markets—particularly the equity market—exert a more pronounced influence on the digital currency market. Critically, under the impact of extreme risk events, the cross-market linkages exhibit structural breaks; the direction and intensity of correlation can strengthen significantly or even reverse, demonstrating a clear state-dependency. This research provides empirical evidence for understanding the functional role of digital assets within the macro-financial system, their risk transmission pathways, and their implications for systemic financial stability. The findings offer valuable theoretical and practical insights for financial regulators in designing robust cross-market risk prevention frameworks and for investors seeking to optimize asset allocation strategies. Full article