Search Results (314)

This paper studies how hidden, stochastic continuation of informed participation shapes price discovery in a two-period Kyle-type market. Hidden participation separates two analytical questions that coincide in the standard Gaussian-linear model. Conditional on a specified linear continuation order, we first derive the exact Bayesian posterior mean and variance of a mixture comprising an informed-trading regime and a noise-only regime; this is a conditional-inference result, not a full nonlinear equilibrium. We then derive an equilibrium under a constrained best-linear-pricing protocol in which market makers use the minimum-mean-square-error affine projection and the insider optimizes pointwise against linear prices. The exact Bayesian posterior responds nonlinearly because order flow reveals both residual value and the likelihood of informed participation, while moderate flows can preserve substantial regime uncertainty. In the projection benchmark, a lower continuation probability accelerates first-period information revelation, shifts insider rents toward the initial round, and creates opposing early- and late-learning effects. A dimensionless analysis characterizes how inference varies with continuation probability and the informed-to-noise variance ratio and establishes scale invariance for the benchmark’s normalized comparative statics. The paper thus isolates a participation margin in price discovery and states precisely which results concern exact inference and which concern a constrained equilibrium. Full article

Software startups operate in environments characterized by rapid change, high uncertainty, and limited resources, resulting in high failure rates and challenges such as premature scaling and cash flow mismanagement. Prior research on pre-MVP validation has largely measured activity by volume rather than by the strength of evidence produced, leaving open whether evidence type, rather than quantity, is associated with startup performance. This study addresses that gap by investigating how early-stage software startups validate their initial idea before building their first Minimum Viable Product (MVP). Through 29 semi-structured interviews with founders from 16 software startups, pre-MVP validation activities were extracted and inductively coded into a six-level Validation Canvas spanning three validation stages identified in the literature: problem validation, problem-solution fit, and product-market fit. Startup performance was assessed through a composite ranking across funding, revenue, profitability, and runway indicators, and validation activities were analyzed thematically to derive the six evidence levels. No clear relationship was observed between the number of validation events and startup performance. Instead, stronger-performing startups tended to reach higher levels of evidence—particularly securing contingent investment commitments (Level 5) or paying customers (Level 6) before full MVP development. Level 6—paying customers before the full product exists—is identified as the strongest form of pre-MVP market evidence, as it directly validates willingness-to-pay without relying on investor confidence. In this study, product-market fit is operationalised as demonstrated commercial viability through external financial commitments rather than interest signals or free sign-ups alone. Based on these exploratory findings, the study proposes the Hierarchy of Validation: a staged, bidirectional process model in which bottom-up traversal from informal interest signals (L1) toward paying customers (L6) emerged as the primary pattern among stronger-performing startups. A top-down direction, in which experienced founders begin at higher evidence levels and work downward, is proposed as a hypothesis for future research. To our knowledge, this is among the first accounts of pre-MVP validation that differentiates strength of evidence rather than volume of activity, contributing the Hierarchy of Validation as an original, exploratory framework for early-stage software startups. These findings remain exploratory and require validation in larger and more diverse samples. Full article

Background: Manufacturing productivity increasingly depends on reliable interorganizational flows, yet supply chain disruptions can interrupt materials, information, finance, and efficient use of productive inputs. Although supply chain resilience is widely treated as a continuity capability, its relationship with firm-level total factor productivity remains insufficiently established. Methods: This study uses 22,509 firm-year observations for Chinese A-share listed manufacturing firms from 2009 to 2024. An entropy-weighted resilience index is constructed from adaptability, resistance, recovery capacity, human capital, institutional support. Firm-level revenue productivity is estimated using the Olley Pakes method, and the analysis employs fixed effects regressions, robustness tests, a two-step selection correction test, mechanism regressions, heterogeneity analysis, and dimension-specific tests. Results: Supply chain resilience is positively associated with firm-level total factor productivity, and this association remains robust to alternative productivity and resilience measures, sample restrictions, industry-by-year fixed effects, and selection correction. Resilience is also associated with lower financing constraints and investment inefficiency. The association is stronger for firms with higher managerial incentives, high-technology industries, and competitive markets, while recovery capacity is negatively associated with contemporaneous productivity. Conclusions: Supply chain resilience supports efficient resource utilization, but its productivity value depends on capability composition, timing, and efficient resilience investment rather than maximizing resilience resources. Full article

Background: The transition towards a more sustainable global economy requires meaningful changes in the way organisations purchase products across industrial value chains. Whilst extensive research has explored sustainable consumption within the business-to-consumer (B2C) space, comparatively little research has examined the antecedents of sustainable purchasing intent within business-to-business (B2B) environments. Existing literature has predominantly focused on individual behavioural determinants, and has under-explored the organisational, informational and product-level antecedents that shape sustainable purchasing decisions within industrial contexts. Objectives: This research explores the antecedents of sustainable purchasing intent within the chemical and ingredients value chain, and develops a novel integrated framework, the Business-to-Business Sustainable Buying Behaviour (B2B-SBB) Model, capable of explaining sustainable purchasing intent within the B2B setting. Methods: An abductive, mixed-methods, single-case study design was adopted, set within the customer base of a global chemical and ingredient distributor. Twelve semi-structured interviews were conducted with purchasing professionals across six end-market segments, and a quantitative survey generated 57 complete responses from a global customer base. Qualitative data were analysed through thematic analysis, and quantitative data through descriptive statistics and Wilcoxon Signed Rank testing. Results: The findings indicate that organisational antecedents, including buying centre process and responsibilities, corporate sustainability commitments, and information flow, exert materially greater influence on B2B sustainable purchasing intent than individual behavioural antecedents. Product sustainability claim variables emerge as a distinct and material antecedent, with a strong and statistically significant preference for third-party assured claims over self-declared claims. A structural value–action gap is also observed between publicly stated sustainability commitments and observed purchasing behaviour. Conclusions: The findings indicate that sustainable purchasing intent within the B2B chemical value chain is best understood through an integrated framework combining individual, organisational, environmental, product sustainability and information-flow antecedents. The proposed B2B-SBB Model provides such a framework, and offers both theoretical extension of existing scholarship and practical application for organisations seeking to accelerate the adoption of more sustainable products within industrial value chains. Full article

Globally, beef customers are seeking verified information regarding the carbon footprint (CF) of the products they buy. As a major supplier of premium grass-finished and natural grain beef supplying markets world-wide, JBS Southern Australia developed a certified Farm Assured (FA) program, launched in 2013, to provide quality beef from independently audited suppliers. This study conducted a life cycle assessment (LCA) with ‘cradle to farm gate’ and ‘cradle to processor gate’ boundaries, using two reference flows—(i) one kilogram (kg) of liveweight (LW) at the farm gate, and (ii) one kg of boxed beef at the processor gate—to assess the greenhouse gas (GHG) CF for beef produced in southern Australia. This study is the first to integrate individual animal carcass characteristics with brand level CF analysis at scale. This was achieved by developing a uniquely comprehensive dataset, with primary data supplied by 200 farms and individual animal data provided for 514,922 heads of cattle. The mean farm gate CF was 11.7 (standard deviation 0.4) kg carbon dioxide equivalent (CO₂-e) kg⁻¹ LW, and the mean boxed beef CF was 24.1 kg CO₂-e kg⁻¹ boxed beef. The study’s novel approach to data collection allowed for the CF to be stratified by region, carcass characteristics, farm of origin and product brand. Analysis revealed that the lowest farm-average and individual animal CFs were 29% and 48% lower than the supply chain average, respectively. These findings indicate that the CFs of beef produced from grass and natural grain-finished production systems in southern Australia were comparable or lower than the CFs of beef entering similar markets. Full article

European energy markets have experienced significant instability as a result of consecutive global systemic shocks, particularly the COVID-19 pandemic and the geopolitical conflict in Ukraine. This paper analyses the development of crude oil and natural gas prices between 2019 and 2024 and discusses their implications for sustainable logistics, energy security and supply chain resilience in Europe. The study is based on secondary data from internationally recognized sources, including the International Energy Agency, OPEC, Eurostat, the European Council, the World Bank and the U.S. Energy Information Administration. An event-based comparative approach supported by descriptive price-change calculations was applied to distinguish between the pandemic-related demand shock and the geopolitical supply-side shock after 2022. The results show that crude oil prices declined from approximately 64 USD/barrel in 2019 to 41 USD/barrel in 2020, representing a decrease of about 3f5.9%, mainly in connection with reduced mobility, lower transport activity and industrial slowdown during the COVID-19 pandemic. In contrast, crude oil prices increased to approximately 100 USD/barrel in 2022, representing an increase of about 143.9% compared to 2020, coinciding with geopolitical uncertainty and supply-side pressures. The European natural gas market appeared particularly vulnerable to the 2022 crisis because of supplier dependence, pipeline infrastructure constraints and reduced Russian gas flows. EU natural gas demand declined by 55 billion m³, or 13%, in 2022, indicating the effect of high prices, energy savings and crisis adaptation. The findings suggest that crude oil shocks are mainly related to transport costs and freight rates, while natural gas shocks may influence energy-intensive production, warehousing, cold chains and broader supply chain stability. The study highlights the need for energy diversification, renewable and low-carbon energy development, energy efficiency and more resilient logistics strategies. Full article

Quantifying the direction, strength and temporal clustering of cross-border financial risk contagion calls for methods that go beyond linear correlation. We suggest a two-layer framework that brings together transfer entropy and a multivariate Hawkes self-exciting point process on a time-varying, directed, weighted network. In the first layer, one-to-one transfer entropies of sovereign credit default swap spreads are estimated with a bias-corrected k nearest neighbour estimator, and this step detects nonlinear and directional information transfer between spreads. The second layer is a multivariate Hawkes process that models how extreme loss events arrive and mutually excite one another across countries, and it gives an excitation intensity matrix, encoding the way a tail event in one country raises the likelihood of an instantaneous hazard occurring in another. By merging these two layers, we obtain a composite, directed, weighted adjacency matrix in which the weights of the edges reflect both information flow and event clustering. We introduce a network-level contagion intensity index and split it into direct, indirect and feedback terms using the graph Laplacian spectrum. Von Neumann graph entropy together with the spectral gap ratio serve as entropy-based measures of the complexity and fragility of the evolving network. We validate the choice of Shannon-type entropy through a Tsallis q-sensitivity analysis, and we verify the nonlinear dependence structure of the data using BDS tests and maximal Lyapunov exponent estimates. Three empirical findings emerge from analysing 20 sovereign CDS markets from January 2015 to December 2025: (i) directional risk spillover signals derived based on transfer entropy are more timely than those derived from variance decomposition; (ii) the Hawkes excitation component amplifies measured contagion intensity by 35 to 58 percent during the COVID-19 shock and the 2022 European energy crisis relative to a transfer-entropy-only baseline; (iii) von Neumann graph entropy reaches historically extreme values 7 to 12 trading days before the peak drawdown in a Global Sovereign Bond Index. These results hold across rolling window lengths, significance thresholds, alternative entropy functionals and alternative Hawkes kernels. Full article

Most Transmission System Operators (TSOs) rely on seasonally static line rating models based on extreme weather conditions to determine the transmission capacity of power lines. These conservative rating approaches constrain grid capacity, limiting the integration of new renewable energy sources and delaying the transition to a more sustainable power system. Furthermore, they restrict cross-border transmission capacity between market zones, leading to “false” congestion and unnecessary market splitting. Market splitting can result in economic losses for market participants due to price differences between market zones and the potential curtailment of renewable generation. The adoption of Dynamic Line Rating (DLR) models can help avoid the need for new transmission infrastructure, reduce market splitting and false congestion, and mitigate line degradation in a cost-effective manner. The OptiRES.Lines tool integrates several DLR models, enabling their simulation and visualization through a Geographic Information System (GIS) interface. These dynamic rating models are combined with an Optimal Power Flow (OPF) model to assess: (1) the long-term potential for integrating new power plants at different grid locations; (2) the available cross-border transmission capacity between market zones; and (3) short-term grid congestion. The tool was tested in two regions of Portugal and demonstrated a significant increase in the grid’s capacity to accommodate additional renewable generation, thereby contributing to a more sustainable power system. The results showed that, although DLR alone indicated an increase in transmission line capacity during approximately 70% of the analysed period, the inclusion of OPF analysis revealed that DLR reduced line loading factors during 95% of the time analysed, highlighting its broader system-level benefits. Full article

This study investigates the flow and economic valuation of recyclable materials across fifteen Secondary Transfer Stations (STSs) in Dhaka North City Corporation (DNCC), Bangladesh, with the aim of understanding how market dynamics influence material recovery and circular economy potential. Primary data were collected through field surveys, structured questionnaires administered to 50 waste workers, five waste dealers and five local traders. Recovered materials were classified into six major categories, paper, plastic, metal, glass, electronic waste (e-waste), and other materials with further sub-categorization, to capture price variations and material characteristics. The results reveal a highly differentiated recycling market, with prices varying by more than an order of magnitude across materials. High-value materials, including copper wire, scrap aluminum, scrap metal, and electrical wire, are consistently recovered, whereas medium-value materials such as HDPE, PET, polypropylene, PVC, LDPE, Styrofoam, plastic crates, and mixed plastics are recovered selectively depending on market demand. In contrast, low-value materials, including packaging cartons, white paper, glass items, mixed LDPE, damaged LED lights, and mixed wastepaper, are frequently discarded due to weak economic incentives. Despite spatial and socioeconomic differences across STSs, price variability remains relatively low, indicating an integrated city-wide recycling market driven by active trader networks and broader commodity trends. To the best of our knowledge, this is one of the first studies to integrate recyclable material flow with economic valuation across multiple STSs in Dhaka. The findings highlight that recovery efficiency is strongly governed by economic value and informal practices, with limited segregation efficiency and inadequate occupational safety conditions. Enhancing source segregation, improving storage infrastructure, and introducing targeted market and policy interventions for low-value materials are critical for advancing Dhaka’s transition toward a circular and resource-efficient urban waste management system. Full article

Persistent inter-provincial market segmentation restrains China’s long-term sustainable domestic circulation and balanced regional development, which conflicts with the country’s SDG-aligned coordinated growth goals. Digital transformation cuts cross-border transaction frictions, while targeted fiscal and tax tools adjust local development incentives to realize sustained, inclusive market integration. Drawing on balanced panel data of 30 Chinese provinces from 2009 to 2024, this paper constructs two multi-dimensional composite indices via entropy weighting. We build a trade-flow theoretical framework embedded with fiscal incentive parameters, then design benchmark, dual mediation, and interaction-moderating panel models. System GMM and lagged variable regressions mitigate endogeneity risks, and a full suite of robustness tests validates the reliability of empirical outputs. The results show digital expansion significantly alleviates market fragmentation and fuels sustainable unified market construction. Information transparency improvement and transportation cost reduction serve as two parallel sustainable transmission paths. Obvious regional differentiation exists in inland provinces with underdeveloped market systems, which harvest larger balanced development dividends from digital upgrades. Fiscal and tax policies exert significant positive moderating effects; standardized fiscal allocation can amplify digitalization’s capacity to deliver long-term coordinated regional circulation. This study supplements institutional sustainability logic for digital-market linkage research and delivers differentiated fiscal and digital policy portfolios to narrow inter-regional development gaps and advance sustainable economic balance. Full article

Payroll-anchored retail borrowers—individuals whose monthly remuneration is routed into an account at the lending institution through a salary-project arrangement—constitute the volume backbone of unsecured consumer lending in Kazakhstan, generating the largest origination flow, the lowest realized default rate, and the majority of the systemic regulatory and capital sensitivities of second-tier banks. Payroll anchoring also changes the lender’s information set, which motivates a study of how that advantage translates into model performance and borrower outcomes. We design and internally validate an explainable hybrid artificial-intelligence framework stratified by client tenure into two production models: a Weight-of-Evidence (WOE) logistic-regression scorecard for new salary-project applicants, and a hybrid scorecard for repeat applicants, in which a stacked ensemble of LightGBM, CatBoost and a multi-head self-attention neural network contributes a single WOE-encoded predictor to a second-stage L2-regularized logistic regression. The hybrid recovers a substantial share of the ensemble’s discriminatory lift while preserving an auditable, monotone scorecard at the point of decision, and isotonic recalibration restores the predicted probabilities of default to the empirical bad-rate scale required for IFRS 9 expected-credit-loss accrual and risk-based pricing. We report discrimination, calibration and stability evidence under a strict anti-leakage protocol and set out the structural preconditions under which the architecture transfers to other emerging-market payroll-anchored portfolios. We are explicit about scope: a true out-of-time validation and a full group-conditional fairness audit are identified as required next steps rather than claimed here. The contribution is a reproducible, interpretable scoring design that exploits payroll visibility while retaining full coefficient interpretability inside the production decision engine. Full article

This study investigates and explores the volatility dynamics of the South African rand against the US dollar (ZAR/USD) using the Family GARCH (fGARCH) model and the First-Order Beta-Skew-T-Generalised Autoregressive Conditional Heteroskedasticity (Beta-Skew-T-EGARCH) model. Currency volatility across the globe, uncertainties, and instability in emerging markets have become increasingly consequential for trade flows, investment allocation, and macroeconomic management. The ZAR/USD serves as a benchmark of South Africa’s economic wealth and vulnerability to external shocks and is one of the most valued, significant, and heavily traded pairings of emerging market currencies. Simple standard GARCH (sGARCH) is one of the most useful models for exchange rate volatility; however, the sGARCH model has some limitations: it fails to accommodate or allow the long memory effects, skewness distribution, and leverage dynamics consistently observed in emerging-market currency returns. This study addresses these limitations by using the fGARCH model, which includes the most popular GARCH models and Beta-Skew-T-EGARCH for daily ZAR/USD returns ranging from 5 January 2000 to 1 October 2024. Five innovation distributions are used for evaluation and comparison under fGARCH and sGARCH, namely generalised hyperbolic (GH), generalised error (GED), skewed Student’s t (SSTD), skewed generalised error (SGED), and Student’s t (STD), with model fitness criteria assessed using the Shibata criterion (SIC), Hannan–Quinn criterion (HQ), Bayesian information criterion (BIC), and Akaike information criterion (AIC), choosing the specification with the lowest overall penalty. It is found that the fGARCH(1,1) model fitted to return-frequency data under the SSTD achieves the lowest AIC, outperforming sGARCH. The study also includes an analysis among covariates, which are day, month, trend, oil, and platinum; the trend variable is a statistically significant predictor, with p = 0.007, showing a positive influence on ZAR/USD volatility. The Beta-Skew-T-EGARCH model with two components divides volatility into long-run and short-run components, which is found to deliver a superior fit over the one-component variant, evidenced by a lower BIC (3.068435) and a higher log-likelihood (−748.464826). The two components confirm that the model captures declining conditional volatility, whereas the one-component model sustains persistence in the evaluated estimates. Full article

As global super-large open-pit mines expand in scale and extraction depth, conventional single-stage planning cannot meet the combined demands of productivity and resource recovery, making the shift to underground block caving inevitable. This study outlines the systemic challenges of block-scale extraction and the rationale for adopting multi-level spatial design decision-making. Four core model categories are briefly proposed: ultimate pit limit optimization, gravity flow simulation for draw strategy, long-term production scheduling for large-scale computation, and probabilistic frameworks addressing geological and market uncertainty. A Bayesian network-based block decision model is then proposed and decoupled into three physical decision tiers. The first tier incorporates energy prices, transport costs, and ore prices to establish an economic boundary rating robust to market volatility. The second tier aggregates mining units with discrete-event perturbations to produce a reliability-oriented production rating. The third tier integrates rock mechanics parameters with in situ monitoring data to derive a physics-informed safety rating. The three ratings are synthesized via Bayesian inference and evaluated within a multi-attribute utility function encompassing net present value, safety index, downside risk, and information risk. A feedback module quantifies the economic benefit of uncertainty reduction, yielding a closed-loop intelligent system spanning macroeconomic boundary definition to operational safety alerting. Finally, the main conclusion of this study is that integrating macro-economic volatility with rock mechanics through a dynamic Bayesian framework is essential for managing the open-pit to underground transition. The results indicate that leveraging the Value of Information for real-time risk diagnosis significantly reduces conservative design losses, providing a quantifiable and robust decision-making paradigm for super-large mining systems. Full article

Nickel has become a strategic mineral for the energy transition, yet its supply chain is increasingly shaped by a compound risk regime involving resource nationalism, processing concentration, geopolitical compliance rules, carbon-footprint requirements, and commodity-market volatility. This study develops NiChain-LLM-ABM, a large-language-model-empowered agent-based model for simulating nickel supply chain resilience under semantically rich policy shocks. The framework uses a policy semantic parsing module to transform official policy texts into structured shock parameters, a multi-agent strategy generation module to represent adaptive decisions by seven agent classes, a calibrated supply chain network module to simulate material, financial, and information flows, and a four-dimensional resilience assessment module. The model is anchored in observed nickel production, price, trade, and technology data from USGS, IEA, UN Comtrade, LME, and official legal sources, and its scenario outputs are generated through 100 Monte Carlo replications over 2025–2035. Results show that the baseline Comprehensive Resilience Index (CRI) declines from 0.620 in 2025 to 0.547 in 2035. Indonesian policy tightening causes the sharpest near-term deterioration, with CRI falling to 0.445 in 2028 and the simulated supply deficit reaching 24.5 kt Ni equivalent. A geopolitical compliance shock produces the lowest terminal resilience (CRI = 0.472 in 2035). A green-compliance scenario is disruptive in the short run but exceeds the baseline by 2035, while a coordinated policy portfolio raises the terminal CRI to 0.744, a 36.0% improvement over the baseline. Compared with a conventional rule-based ABM, the LLM-ABM reduces extreme-event backcasting error by 57%, improves policy-response fidelity by 53%, and more than doubles agent heterogeneity differentiation. The results support portfolio-based critical-mineral governance combining strategic reserves, overseas equity investment, recycling, technology substitution, and international cooperation. Full article

The study investigates the state-dependent dynamics of overconfidence in the Bangladesh equity market by exploring the relationship between market returns and trading volume within a nonlinear information-theoretic framework. Building up on the traditional return–volume literature, the study differentiates between total market returns and unexpected returns, with the latter representing unexpected information shocks obtained using the Market Index Model. Transfer Entropy with bootstrap inference estimates the directional and asymmetric information flows across five different market states, namely: bullish, bearish, crisis, extended crisis, and COVID-19. The evidence suggests that the overconfidence biases in aggregate market returns are small and intermittent and are reflected in poor and unstable information flow between market returns and trading volume. In comparison, unexpected market returns have a directionally significant impact on trading behavior, which supports the behavior of state-dependent overconfidence. The findings also reveal that overconfidence is higher in normal and bullish market situations but drops significantly in crisis-based situations. The asymmetric analysis indicates increased trading responses to negative returns shocks, as it is more evident that investors are more sensitive to losses and recovery expectations. The research adds to behavioral finance literature on frontier markets through an unexpected return decomposition with nonlinear causality model. The results have serious implications on market surveillance, assessment of investor behavior and design of regulatory policies. Full article