Search Results (499)

Artificial intelligence (AI) refers to a set of computational techniques, including machine learning, natural language processing, deep learning, and generative AI, that enable systems to perform tasks traditionally requiring human intelligence, such as prediction, pattern recognition, and decision-making. The rapid diffusion of AI into business organizations is transforming how information is processed, decisions are made, and knowledge-intensive work is performed, creating both new opportunities for economic value and new challenges for human judgment, organizational governance, and accountability. The growing adoption of AI across accounting, finance, and management makes it increasingly important to understand not only what AI can do, but also how and under what conditions it affects individuals, organizations, and markets. This paper provides a comprehensive review of the rapidly growing literature on artificial intelligence across these three disciplines. We synthesize existing research to examine how AI is transforming information processing, decision-making, governance, and organizational performance. In accounting, AI enhances auditing, financial reporting, and fraud detection while raising concerns regarding transparency and professional judgment. In finance, AI improves asset pricing, risk assessment, and trading strategies by leveraging large-scale structured and unstructured data. In management, AI reshapes organizational design, human capital, strategic decision-making, and innovation through increasingly sophisticated human–AI collaboration. Across these disciplines, we organize the literature around several unifying themes, including information asymmetry, automation versus augmentation, decision quality, interpretability, and governance. We further identify important research gaps concerning whether AI’s predictive and analytical advantages translate into meaningful economic and organizational outcomes, how AI reshapes human judgment and skills, the emerging risks, and the need for stronger research designs. By integrating evidence across three major business disciplines, this review provides a unified framework for understanding AI’s transformative role in organizations and offers a roadmap for future interdisciplinary research on the economic, behavioral, organizational, and governance consequences of AI. Full article

Financial time-series forecasting remains challenging because of high volatility, nonlinear market dynamics, and the growing volume of heterogeneous information available to market participants. This study investigates Bitcoin forecasting in a big-data setting by combining high-frequency market data aggregated to hourly forecasting features, news headlines, Bitcoin on-chain variables, and broader macro-financial indicators. Rather than treating headline sentiment as a predefined categorical property, we construct continuous headline-conditioned market-impact labels from short-horizon Bitcoin price responses, directional volume imbalance, and volatility conditions. A chronology-controlled expanding-window FinBERT procedure generates scores without reusing each headline’s own future-derived target. The scores are integrated into multivariate Bitcoin forecasting using iTransformer, with LSTM as a benchmark. Evaluation uses repeated runs, benchmarks, Diebold–Mariano tests, backtesting, and forecast-free momentum controls. The headline-derived signal exhibits a measurable but temporally heterogeneous association with subsequent Bitcoin movements. Adding the headline score yields small, statistically non-significant error reductions for iTransformer, whereas it significantly worsens LSTM forecasts. Backtesting shows no consistent improvement in terminal portfolio value, but the headline feature alters the risk–return profile in several strategy configurations. Overall, the study provides a chronology-aware evaluation of whether headline-conditioned market-impact signals add predictive or economic value, while acknowledging residual dependence from exploratory iTransformer architecture selection. Full article

Energy transition is central to both economic development and climate-change mitigation and has become a shared global challenge. Given the close relationship between conventional energy prices and the development of the new-energy industry, this study investigates systemic risk spillovers among three crude-oil futures, two natural-gas futures, and five Chinese new-energy sector indices. We employ a quantile time-frequency-connectedness framework and an out-of-sample-validated link-prediction model to assess both realized spillovers and potential changes in the network structure. The results reveal that network connectedness is time-varying and asymmetric across quantiles, with short-horizon connectedness accounting for the majority of average system-wide connectedness. Overall connectedness also increases markedly during major crisis episodes. INE crude-oil futures and both natural-gas futures are net receivers of shocks, whereas WTI and Brent crude-oil futures consistently act as net transmitters, with Brent playing the dominant role under extreme market conditions. As the investment horizon lengthens, the solar sector shifts from a net risk receiver to a net risk transmitter. In the predicted network, the solar sector emerges as the market most likely to initiate new short-term spillover links. This finding reflects a prospective, model-implied tendency rather than a causal relationship. These findings offer useful implications for energy market policy, portfolio risk management, and investment decisions involving new-energy companies. 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 study forecasts cumulative log returns at horizons of one to twenty trading days and reconstructs future adjusted prices by exponentiating those return forecasts; it does not optimize a price-level loss. This task is difficult because financial returns are nonstationary, heavy-tailed, horizon-dependent, and subject to rapidly changing volatility. We propose FreqCast, which combines a market-conditioned spectral decomposition, a structured state-space branch for the component designated low-frequency, causal multiscale encoders for the components designated intermediate- and high-frequency, and a horizon-conditioned reliability gate. The gate uses expert representations, predictive scale, and cross-expert disagreement to fuse four cumulative-return estimates. The joint objective covers point loss, an auxiliary directional score, Laplace likelihood, ordered quantile loss, decomposition regularization, and horizon coherence. Experiments use eight large U.S. stocks and a single 2021–2024 test interval. Within that restricted benchmark, the reported point estimates favor FreqCast over the included baselines and show horizon- and volatility-dependent expert allocation. Our main contribution is the coordinated frequency-dependent assignment and reliability fusion of heterogeneous forecasting mechanisms, while broad market robustness and statistical superiority remain to be established. Full article

Crypto perpetual futures embed liquidation risk in one chain: leverage and funding move the margin boundary, the mark determines when a crossing is observed, and executable depth determines the concession paid after detection. The primary forecasting question is how to quantify both the probability of an isolated-margin boundary breach and the loss hidden by delayed or smoothed detection. This paper develops a first-passage density-forecasting framework in which the executable price is observed through a delayed or smoothed mark, funding is a persistent collateral drain, and liquidation occurs when isolated-margin surplus reaches its maintenance boundary. The output is a joint predictive distribution: a horizon-specific probability of a boundary breach and, conditional on detection, a distribution of catch-up loss. Under a fixed delay, expected overshoot is $\sigma \sqrt{\delta /2\pi }$, and bad-debt probability is a Gaussian tail governed by the latency-to-margin ratio $\sigma \sqrt{\delta }/m$. Its leverage independence is exact only in the constant-maintenance, fixed-non-price-drain benchmark. For time-weighted-average marks, fixed-time variance reduction does not imply liquidation-time safety. A stationary-downcrossing approximation size-biases the stale error in the adverse direction, producing a mean overshoot about $1.6$ times the same-window fixed-delay value. A rolling comparison with historical simulation scores model-consistent margin-breach forecasts from public price and funding paths. The structural forecast has lower Brier scores at $10\times$ over 24-, 72- and 168-hour horizons and across the 24-hour grid, but historical simulation performs better for one-week forecasts at $20\times$ and $50\times$. The evidence supports a conditional risk-forecasting use of the framework, while not establishing uniform forecast dominance. Full article

Serving large language models (LLMs) on cloud spot and preemptible instances reduces costs by 60 to 90 percent compared to on-demand pricing, but unpredictable instance preemptions cause request failures, KV-cache state loss, and degraded user experience. We present MorphCloud-LLM, an elastic LLM serving system designed to achieve the reliability properties of on-demand serving at spot-instance pricing. MorphCloud-LLM integrates three synergistic components: (1) an asynchronous incremental KV-cache checkpointing engine that streams only delta state to disaggregated persistent storage with less than 3% throughput overhead, enabling sub-second KV-cache delta streaming and reconstruction for KV-cache sizes up to 32 GB on replacement instances (total end-to-end migration latency: 1390 ms); (2) a gradient-boosted preemption prediction model trained on spot market telemetry that achieves 89% recall at a 30-s prediction horizon, providing sufficient lead time for proactive migration before forced eviction; and (3) a speculative decoding continuity engine that offloads draft model token generation to on-demand fallback nodes during migration windows, bounding the user-visible interruption to a sub-second buffering pause. MorphCloud-LLM is deployed and evaluated on AWS and GCP using LLaMA-70B and Mixtral-8x7B across 521 trace-injected preemption events, achieving up to 76% cost reduction under active-serving accounting (69.8% for LLaMA-70B; 67% including warm standby fallback capacity) with only 2.1% p99 latency overhead and zero dropped requests. Extensive ablation studies confirm the contribution of each component to overall system resilience. Note that preemption events are reproduced via a trace-driven simulation framework built on empirical AWS and GCP spot interruption traces rather than fully uncontrolled live production preemptions. Production generalizability under uncontrolled preemption—including simultaneous multi-node failures, network congestion, storage contention, and replacement-instance scarcity remains subject to future validation in sustained live deployments. Full article

Water scarcity threatens global stability, with demand set to surge 20–30% by 2050, pushing withdrawals from 4600 km³ yr⁻¹ today to 5500–6000 km³ yr⁻¹ under rising demographic and climatic pressures. This study presents a sequentially coupled econometric–hydrological–reduced-form economic framework that couples water supply dynamics with demand forecasting and macroeconomic impact assessment. Agriculture dominates current withdrawals at 70% (FAO AQUASTAT), followed by industry (20%) and domestic use (10%). Monte Carlo simulations (n = 1000) identify critical regional hotspots: Asia (stress ratio = 1.06), the Middle East (1.18), and Africa (0.99). The reduced-form economic module uses a target-calibrated scarcity elasticity (ε = 0.1865) applied against a fixed economic reference threshold (4600 km³ yr⁻¹). This internally calibrated parameter yields a first-order GDP loss estimate of approximately $16.0 trillion under the high-demand (+30%) 2050 scenario (6000 km³ yr⁻¹ demand), equivalent to 5.5% of projected 2050 global GDP ($290 trillion, PwC 2017 baseline). The resulting magnitude is broadly consistent with the order of GDP impacts discussed by OECD (2012) and GCEW (2024), although neither publication reports this specific elasticity value. This is not an independently predicted outcome; it is a calibrated scenario estimate produced by a reduced-form damage function designed to reproduce first-order magnitudes consistent with published structural model results. Mitigation strategies including efficiency improvements, pricing reforms, and AI-driven allocation can reduce demand by up to 40%, which within the model’s mathematical structure reduces the calibrated economic loss to zero. Sectoral water distribution is addressed through continuous linear programming with proportional rationing. This framework advances transparent, reproducible scenario-based understanding and informs policy decisions aimed at mitigating future water scarcity challenges globally, while explicitly acknowledging limitations relative to full structural CGE models and empirically estimated panel econometric models. Full article

The rapid growth of electric-vehicle charging demand has increased the need for reliable station-level congestion monitoring and early warning. Existing studies mainly predict charging demand, load, occupancy, or availability, whereas charging congestion pressure is usually shaped by multiple operational factors. This study proposes an interpretable station-level charging congestion pressure assessment and multi-horizon early-warning framework. A Charging Congestion Pressure Index (CCPI) is constructed by integrating occupancy, arrival pressure, charging or occupation duration, service volume, and price–time context into a unified station–hour pressure representation. Based on temporally aligned current, lagged, and rolling features, future high-pressure states are predicted at 1 h, 3 h, and 6 h horizons. Using 1423 charging stations and 6,181,512 station–hour observations from September 2022 to February 2023, this study evaluates whether the proposed station–hour pressure representation can support multi-horizon high-pressure warning under temporal and station-level validation settings. Results show that current pressure is a strong short-term persistence baseline, while learning-based models provide larger F1-score gains at longer horizons. Extreme Gradient Boosting (XGBoost) achieved F1 gains of +0.022, +0.040, and +0.068 over the persistence baseline at the 1 h, 3 h, and 6 h horizons, respectively. Ablation, temporal validation, station holdout validation, and block-bootstrap tests further support the stability of the proposed framework. These findings indicate that interpretable pressure-index construction and temporally consistent multi-horizon warning can provide an engineering decision-support basis for charging-infrastructure operation, station-level congestion monitoring, and proactive resource management. Full article

Short-term financial risk in intelligent trading systems is reflected not only in prices, trading volumes, and textual sentiment but also in infrastructure operating states, including server workload, device power consumption, network latency, and packet loss rate. We propose HSF-LLMNet, a hardware sensor and large language model semantic fusion network for jointly modeling external information shocks and infrastructure responses. A large language model extracts event category, sentiment polarity, risk intensity, and semantic uncertainty from financial texts. Reliability-aware temporal modeling handles sensor missingness, drift, and abnormal noise, while asynchronous soft alignment, bidirectional cross-attention, and reliability-aware gated fusion integrate irregular textual events with continuous hardware signals. The model jointly predicts market direction, realized volatility, and three-level risk over the subsequent 30 min. Experiments were conducted on eight Chinese A-share indices: the SSE Composite Index (000001.SH), SSE 50 Index (000016.SH), CSI 300 Index (000300.SH), STAR 50 Index (000688.SH), CSI 500 Index (000905.SH), CSI 1000 Index (000852.SH), Shenzhen Component Index (399001.SZ), and ChiNext Index (399006.SZ). The common observation period for market, textual, and hardware data extended from 1 March 2024 to 30 June 2025. After data cleaning, timestamp matching, and multimodal temporal alignment, 169,208 aligned asset–time prediction windows were retained for the 30 min forecasting task. Realized volatility was defined as the square root of the sum of squared one-minute log returns over the future 30 min interval. The three-level risk label was constructed from future realized volatility, absolute 30 min return, and liquidity stress, with all thresholds estimated exclusively from the training portion of each fold. A sample was labeled high risk when at least two of the three indicators exceeded their 85th-percentile thresholds or when any indicator exceeded its 95th-percentile threshold. It was labeled medium risk when, after excluding high-risk samples, at least two indicators exceeded their 60th-percentile thresholds or any indicator exceeded its 85th-percentile threshold; all remaining samples were labeled low risk. Results showed that HSF-LLMNet achieved an accuracy of 78.62%, a precision of 78.14%, a recall of 77.83%, an F1-score of 77.98%, an area under the receiver operating characteristic curve of 84.91%, and a Matthews correlation coefficient of 57.36% for directional prediction. For realized-volatility regression, the MAE, RMSE, MAPE, and $R^2$ were 0.0089, 0.0135, 9.21%, and 0.812, respectively. For high-risk-event warning, the mean effective warning time, defined as the interval between the first valid alarm and the corresponding event, was 15.37 min; the false-alarm rate and missed-alarm rate were 6.82% and 8.14%, respectively. Ablation experiments showed performance reductions after removing semantic encoding, sensor-reliability estimation, asynchronous alignment, bidirectional cross-attention, gated fusion, or multi-task learning. These results indicate that textual events and infrastructure operating states provide complementary information for quantitative risk analytics and fintech applications. Full article

Modern energy systems, including those supporting transportation electrification, are increasingly exposed to volatile market conditions and external events. Effective decision-making therefore requires the integration of structured operational data with unstructured contextual information. Existing studies on Large Language Model (LLM)-assisted energy systems have mainly applied LLMs to individual tasks such as forecasting, scheduling, or decision support, while forecasting and control are typically treated separately. As a result, semantic information extracted from external events is not consistently propagated from market prediction to operational decision-making. This paper proposes a unified dual-stage framework in which the LLM functions as a shared semantic information processor, converting raw event data into structured representations used by both forecasting and control modules. In the forecasting stage, these representations improve price prediction under non-stationary conditions. In the control stage, the same information provides an event-aware contextual action prior for reinforcement learning-based energy management. This design allows external event information to inform both future-state estimation and subsequent control decisions, establishing a consistent connection between prediction and decision-making. The framework is evaluated using real-world electricity market data and a battery energy management environment. The results show that the proposed framework achieves the highest average cumulative reward among the evaluated methods while maintaining greater robustness than the forecasting-only LLM configuration. Overall, this work demonstrates the benefit of consistently propagating structured semantic information across forecasting and control and provides a viable approach to event-aware intelligent energy management, with potential extensions to multi-energy transportation systems and electrified mobility applications. Full article

Forecasting agricultural futures prices across multiple commodities remains highly challenging due to nonlinear price dynamics, strong market volatility, and complex cross-market and cross-commodity interactions. To address these issues, this paper proposes an explainable ensemble forecasting framework that integrates cross-market feature construction and interpretation, a benchmark forecasting model pool, and reinforcement learning-based ensemble optimization. A diverse set of deep learning models generate benchmark forecasts using cross-market features that contain effective information, and a reinforcement learning-guided chaotic evolutionary optimization algorithm is employed to dynamically determine the ensemble weights under a multi-objective criterion that balances prediction accuracy and stability. Meanwhile, explainable artificial intelligence techniques, including SHAP and LIME, are incorporated to analyze the contribution of cross-market features to price predictions. The proposed framework is applied to eight major agricultural futures markets, including wheat, corn, and soybean-related commodities. Empirical results show that the ensemble model consistently outperforms individual forecasting models in terms of prediction accuracy and stability. Moreover, in backtesting, most agricultural commodities achieve positive returns under controlled risk levels. The findings indicate that the proposed framework not only improves predictive performance and generates substantial returns for most commodities, but also provides interpretable insights into the mechanisms linking financial markets and agricultural commodity prices, demonstrating its potential value for forecasting, trading, and risk management applications. Full article

A wide range of real-world multivariate time series encountered in practice exhibit two simultaneous and interacting seasonal cycles, for example hourly electricity demand, intraday financial prices, and sub-daily traffic volumes. Existing Bayesian frameworks for vector autoregressive (VAR) processes accommodate at most a single seasonal periodicity, leaving no established methodology for the double seasonal case commonly observed in high-frequency multivariate data. This paper bridges that gap by introducing the double seasonal VAR (DSVAR) models, which extend the univariate double seasonal literature to a coherent multivariate setting. These models are defined through a multiplicative triple autoregressive operator that naturally accommodates the second seasonal cycle. Under a Gaussian error assumption, we derive a comprehensive and analytically convenient Bayesian framework for both modeling and forecasting of DSVAR processes. We consider two prior families: a conjugate matrix normal-Wishart prior which yields exact closed-form inference, and a Jeffreys’ non-informative prior. Under each prior, we derive the marginal posterior distribution of the coefficient matrix as a matrix-t distribution and the marginal posterior of the precision matrix as a Wishart distribution. Moreover, we derive the predictive distribution of future observations as a multivariate-t with an exact analytic form, together with its highest predictive density regions. The methodology is validated through a Monte Carlo simulation experiment and applied to hourly electricity loads in Czech Republic and Germany, two physically interconnected markets with pronounced intraday and intraweek seasonal cycles. Benchmark comparisons against standard VAR, single-seasonal VAR, and univariate seasonal AR models confirm the substantial forecasting gains delivered by the proposed DSVAR framework at both short and long horizons. Full article

To improve the long-term adaptability of photovoltaic-storage-charging systems under rapidly increasing electric vehicle charging demand, this study develops a multi-stage dynamic programming and life-cycle economic assessment framework for the Hasuhai expressway service area in Inner Mongolia, China. A SARIMA-Holt-Winters hybrid forecasting model was used to predict future load growth, and a phased capacity expansion scheme was established for photovoltaic generation, energy storage, and charging facilities. The results show that the annual average load is projected to increase by approximately 11.22 times from 2026 to 2045, indicating that the one-time construction scheme may face long-term supply insufficiency despite its favorable short-term economic performance. Compared with the initial scheme, the multi-stage planning scheme improves long-term adaptability by dynamically expanding photovoltaic capacity, energy storage capacity, and charging ports according to load growth. The optimized scheme increases the net present value from 33.094 million CNY to 57.347 million CNY, reduces the levelized cost of charging to 0.171 CNY/kWh, and increases the 25-year carbon emission reduction to 62,490 tCO₂. Sensitivity analysis indicates that the charging service fee has the greatest influence on project economics, followed by the grid electricity price and discount rate. The proposed framework provides a practical reference for the phased construction and investment decision-making of photovoltaic-storage-charging systems in expressway service areas with rapidly growing charging demand. Full article

The transport sector has emerged as a significant source of greenhouse gases and airborne pollutants. However, few studies have carried out a comprehensive assessment of the synergistic benefits between air pollutant and carbon emission reductions that account for the unique regional characteristics of individual provinces. In this study, we establish nonlinear prediction models for future activity levels of on-road mobile sources by integrating economic and demographic drivers. We then dynamically quantify the co-benefits of simultaneous air pollutant and CO₂ abatement under diverse mitigation scenarios. Environmental tax rates and carbon trading prices are combined with conventional elasticity coefficients and coordinate-based methodologies to convert emission cuts into economic advantages. The results show that under the most optimistic scenario, 47.86% of the CO₂ emissions could be mitigated, while emissions of NO_X may increase by 60.49% in the absence of mitigation strategies (BAU scenario). Marginal CO₂ emissions under the ELC scenario indicates a peak around 2027. Elasticity coefficients for all pollutants gradually converge toward 1, indicating that more stringent mitigation efforts enhance co-benefits. Findings in this study could provide essential insights for the co-management of CO₂ and air pollutants from road mobile sources in Guangxi and other key regions along the Belt and Road Initiative. Full article