The introduction of leverage on prediction-market event contracts raises three structurally distinct questions that have not been addressed jointly: how leverage changes manipulation incentives, how it interacts with informed-trading rents, and how regulatory frameworks should respond. This paper develops a theoretical framework for the first two and a synthesis of the existing regulatory landscape for the third. The principal analytical move is a two-axis manipulation taxonomy distinguishing market-price manipulation from real-world outcome manipulation, where the manipulator affects the underlying event itself. Continuous-underlying derivative markets generally do not make outcome manipulation a venue-level payoff channel; event-linked markets do. Within this taxonomy, leverage plays asymmetric roles: it scales market-price manipulation linearly but shifts the cost-benefit threshold for outcome manipulation, and it scales informed-trading rents in three ways (direct multiplication, Sharpe-ratio preservation, detection-cost amortization). Section 7 connects Paper 1's pre-emption and halt-protocol findings (CC-007b, CC-008) to three manipulation channels: pre-emption introduced by the dynamic-margin engine, halt-arbitrage introduced by the resolution-zone halt protocol, and strategic bad-debt-shifting that no engine in Paper 1's framework family addresses. The framework's manipulation-resistance contribution is a re-allocation of attack surface, not a net reduction. The regulatory synthesis covers principal jurisdictions (US, EU, UK, Singapore, offshore) and identifies three regulatory-arbitrage pathways. The paper concludes with 14 recommendations for venue operators, regulatory bodies, and the research community, separated into framework-independent and framework-conditional categories.

Agent-based models (ABMs) are increasingly used in macroeconomics, but their analysis still often relies on ad hoc Monte Carlo campaigns with heterogeneous statistical effort across parameter settings. We show how statistical model checking (SMC), implemented through MultiVeStA, can provide a principled analysis layer for a realistic macroeconomic ABM without rewriting the simulator in a dedicated formalism. Our case study is the heuristic-switching Keynes+Schumpeter(K+S) model, analysed hrough a transient sensitivity campaign over one-parameter sweeps, two macro observables (unemployment and GDP growth), and one auxiliary micro-level probe (market share) on the post-warmup phase of a 600-step horizon. The analysis is driven by reusable temporal queries, observable-specific precision targets, and confidence-based stopping rules that automatically determine the simulation effort required by each configuration. Results show a clear contrast across parameter families: macro-financial and structural sweeps produce the strongest transient effects, whereas several heuristic-rule sweeps remain much weaker under the same precision policy. More broadly, the paper shows that SMC can support reproducible and informative quantitative analysis of substantively rich economic ABMs, while making uncertainty estimates and simulation cost explicit parts of the reported results.

Paper 1 of this research programme develops a resolution-aware risk-design framework for the simplest event-linked perpetual: a contract whose underlying tracks a single binary prediction-market probability through resolution. The instrument class is broader. Variants span conditional probabilities P(A|B), spreads p^A - p^B, weighted baskets sum w_i p^(i), derivatives on variance or entropy of the probability process, contracts on liquidity itself, perpetual-on-expiring-event roll structures, and funding-only derivatives with no settlement. Each variant inherits some framework components from the single-market binary case and requires its own design adaptations. This paper develops a formal taxonomy of seven pure-form canonical variants beyond the probability-index perpetual of Paper 1, organised along four orthogonal design axes: underlying geometry, temporal structure, settlement structure, and venue composition. The list is not exhaustive; combinations are not treated separately. For each variant we provide a precise payoff definition; an inheritance map identifying which Paper 1 components carry over, are modified, or fail; variant-specific design constraints; microstructure properties; empirical evaluability on the PMXT v2 archive; and limitations. Notable findings: the conditional variant admits a candidate non-portability proposition (denominator instability as the conditioning event becomes improbable); the spread variant requires a three-channel decomposition of resolution risk; the volatility/entropy variant avoids random binary terminal-collapse but introduces estimator-convention and entropy-decay issues; the basket variant requires multi-period jump-aware margin whose aggregation is correlation-dependent. The paper is theoretical primarily; it specifies how demonstrative time series can be constructed and provides evaluability criteria to guide future work.

We develop and counterfactually evaluate a resolution-aware risk-design framework (PIRAP) for perpetual futures whose underlying tracks a single binary prediction-market probability through resolution. The framework specifies six components: an index estimator combining mid-price, depth-weighted mid, and time-decayed VWAP; jump-aware tiered margin sized against bounded-event terminal-collapse magnitude; leverage compression schedule contracting toward resolution; resolution-aware funding rule with boundary-aware correction; a multi-stage halt protocol; and an eligibility framework. Two formal non-portability propositions establish that standard basis-only funding paired with continuous-vol static margin fails on bounded-event underlyings. Empirical evaluation uses Polymarket's PMXT v2 archive for 2026-04-21 to 2026-04-27 (13,298-market analysis sample passing adequacy gates from 61,087 ingested; 13,115 resolved within the empirical window for E3). E1 evaluates two pre-registered stylized facts; E2 conducts counterfactual replay across three engine configurations; E3 isolates the resolution-zone protocol's contribution. Results are mixed. Five pre-registered floors: stylized-fact floors (boundary depth asymmetry, terminal-jump magnitude) PASS; welfare-side directional floors (final-hour liquidation -6%, drawdown -5.1% pooled, median PnL +14%) two FAIL one PASS; E3 mechanic floors (final-hour liquidation -80% by halt construction PASS; bad-debt frequency +2.4% FAIL). Three of five materiality floors fail: the framework as specified does not validate deployment, but the empirical record establishes a halt-versus-margin scope distinction (halt addresses execution-channel risk; terminal-jump bad-debt remains margin-side) and documents a pre-emption trade-off constraining the dynamic-margin component. The paper concludes with structural recommendations and explicit non-deployable status.

Recent advances in generative artificial intelligence (AI) are reshaping who enters entrepreneurship, but not who reaches the top of the quality distribution. Using data on over 160,000 product launches on Product Hunt, we find that entrepreneurial entry increased sharply following the public release of ChatGPT-3.5, driven disproportionately by solo entrepreneurs. This shift toward solo entry is particularly pronounced in categories that historically favored team-based ventures. However, much of this growth reflects low-commitment, experimental entry and does not translate into greater representation among the highest-quality outcomes. Team-based ventures are increasingly dominant in the top tiers of platform rankings. These findings suggest that generative AI lowers barriers to solo entrepreneurship while reinforcing team-based advantages.

This paper studies how the organization of production shapes democratic accountability. I propose a model in which learning economies make specialization productively efficient: most workers perform one-domain tasks, while a small set of integrators with cross-domain knowledge keep the system coherent. When policy consequences run across domains, integrators understand them better than specialists. Electoral competition then tilts government policies toward integrators' interests, while low aggregate system knowledge weakens governance and reduces the fraction of public resources converted into citizen-valued services. Labor markets leave these civic margins unpriced, failing to internalize the political returns to system knowledge. Broadening specialists can therefore raise welfare relative to the market allocation. The model speaks to debates on liberal arts education and the effects of AI.

We conduct a pre-registered randomized controlled trial to test for income targeting in labor supply decisions among sellers of a Swedish street paper. Unlike most workers, these sellers choose their own hours and face severe liquidity constraints and volatile incomes. Treated individuals received a 25 percent bonus per copy sold for the duration of an issue, simulating an increase in earnings potential. Consistent with standard labor supply theory, they sold more papers and, by our measures, worked longer hours and took fewer days off. These findings contrast with studies on intertemporal labor supply that find small substitution effects.

We study overpricing in a repeated game between two representative agents: a market maker, who controls market liquidity, and a market taker, who chooses trade quantities. Market prices evolve through the endogenous price impact of trades and exogenous shocks. We define overpricing relative to a counterfactual price path that holds fixed the same sequence of shocks while shutting down price impact, and characterize the set of feasible strategy profiles that generate persistent overpricing while respecting cash and inventory constraints. We provide a sufficient condition for decentralized learning to reach the overpricing region in finite time, and we show that this condition is satisfied, in particular, by projected stochastic gradient ascent. A key step in the analysis is a decomposition of the game into a competitive component, which favors zero price impact, and a collaborative component, which makes overpricing jointly profitable when aggregate inventory is positive. We further show that the same structural incentives govern both myopic and farsighted objectives. Together, these results show how decentralized learning by adaptive market agents can lead to persistent overpricing in financial markets.

The linear fractional stable motion (LFSM) extends the fractional Brownian motion (fBm) by considering $α$-stable increments. We propose a method to forecast future increments of the LFSM from past discrete-time observations, using the conditional expectation when $α>1$ or a semimetric projection otherwise. It relies on the codifference, which describes the serial dependence of the process, instead of the covariance. Indeed, covariance is commonly used for predicting an fBm but it is infinite when $α<2$. Some theoretical properties of the method and of its accuracy are studied and both a simulation study and an application to real volatility data, with a comparison to the fBm and to the heterogeneous auto-regressive model, confirm the relevance of the approach. The LFSM-based method shows a promising performance in the forecast of time series of volatilities, decomposing properly, in the fractal dynamic of rough volatilities, the contribution of the kurtosis of the increments and the contribution of their serial dependence. Moreover, the analysis of hit ratios suggests that, beside independence, persistence, and antipersistence, a fourth regime of serial dependence exists for fractional processes, characterized by a selective memory controlled by a few large increments.

Coping with prolonged periods of low availability of wind and solar power, also referred to as variable renewable energy droughts or "Dunkelflaute", emerges as a key challenge for realizing decarbonized energy systems based on renewable energy. Here we investigate the role of long-duration electricity storage and geographical balancing through transmission in dealing with such events in Europe, combining a time series analysis of renewable availability with power sector modeling of 35 historical weather years. We find that extreme droughts define long-duration storage operation and investment. Assuming policy-relevant interconnection, the least-cost system in our model capable of coping with the most extreme event requires 351 terawatt hours long-duration storage capacity, corresponding to 7% of yearly European electricity demand. While nuclear power can partially reduce storage needs, the storage-mitigating effect of fossil backup plants in combination with carbon removal is limited. Policymakers and system planners should prepare for a rapid expansion of long-duration storage to safeguard the renewable energy transition in Europe.

Automated equity trading requires converting noisy market and news signals into executable portfolio decisions under risk, turnover, and transaction costs. We propose Hierarchical Reinforced Trader (HRT), a bi-level reinforcement learning framework for text-aware portfolio management in multi-asset equity markets. HRT separates trading into two coordinated decisions: a factorized sparse High-Level Controller (HLC) selects asset-level increase, reduce, or hold directions from compact market and text-derived signals, while a risk-aware Low-Level Controller (LLC) converts these directions into feasible portfolio weight adjustments under turnover, drawdown, and text-risk penalties. This decomposition avoids enumerating the full joint action space and makes selection and execution easier to inspect. We evaluate HRT on an open stock-news benchmark with a fixed 89-stock Nasdaq universe, using 2013--2018 for training, 2019 for validation, and 2020--2023 for final out-of-sample testing; the test horizon is restricted to 2020--2023 due to public benchmark data availability under the same timestamp-clean text-aware protocol. Across market-proxy, same-universe portfolio, alpha-only, flat-RL, and hierarchical ablation baselines, HRT delivers the strongest learning-based return--risk--cost trade-off. The full model improves Sharpe from 1.06 for HRT-Base to 1.24, reduces daily turnover from 0.112 to 0.090, and remains robust under transaction-cost stress. These results suggest that separating sparse directional selection from risk-aware execution is an effective way to incorporate market forecasts and text-derived risk signals into portfolio management.

This paper studies the well-posedness of a class of nonlocal parabolic partial differential equations (PDEs), or equivalently equilibrium Hamilton-Jacobi-Bellman equations, which has a strong tie with the characterization of the equilibrium strategies and the associated value functions for time-inconsistent stochastic control problems. Specifically, we consider nonlocality in both time and space, which allows for modelling of the stochastic control problems with initial-time-and-state dependent objective functionals. We leverage the method of continuity to show the global well-posedness within our proposed Banach space with our established Schauder prior estimate for the linearized nonlocal PDE. Then, we adopt a linearization method and Banach's fixed point arguments to show the local well-posedness of the nonlocal fully nonlinear case, while the global well-posedness is attainable provided that a very sharp a-priori estimate is available. On top of the well-posedness results, we also provide a probabilistic representation of the solutions to the nonlocal fully nonlinear PDEs and an estimate on the difference between the value functions of sophisticated and naïve controllers. Finally, we give a financial example of time inconsistency that is proven to be globally solvable.

Historical Simulation (HS) and its extensions form a popular class of methods for estimating Value-at-Risk for portfolios of financial assets based on historical data. In this note, we seek to unify several ideas and models from throughout the literature into a single modeling framework. By explicitly defining a parametric model form for the asset returns and extracting the realized increments of the driving innovation process from historical data, we are able to reproduce the Historical Simulation, filtered Historical Simulation, and displaced Historical Simulation methods. This shows beyond a doubt that these methods need more underlying assumptions than what is often alluded to.

Average forecast accuracy is not the same as forecast reliability. I treat forecast loss differentials relative to a benchmark as a return series. I then evaluate these returns using risk-adjusted performance measures from finance, including the Sharpe ratio, Sortino ratio, Omega ratio, and drawdown-based metrics. I also introduce the Edge Ratio capturing a model's propensity to deliver uniquely informative predictions relative to the forecasting frontier. I apply this framework to U.S. macroeconomic forecasting, comparing econometric benchmarks, machine learning models, a foundation model (TabPFN), and the Survey of Professional Forecasters. While it is often feasible to beat professional forecasters in terms of average accuracy, it is much harder to beat them on a risk-adjusted basis. They rarely exhibit catastrophic failures and often achieve high Edge Ratios, plausibly reflecting the value of contextual judgment. Nonetheless, selected machine learning methods deliver attractive risk profiles for specific targets. The framework naturally extends to meta-analyses across targets, horizons, and samples, illustrated with a density forecast evaluation and the M4 competition.

ESG-aware portfolio optimization is increasingly important for sustainable capital allocation, yet most learning-based methods still operationalize ESG by appending static scores to the policy observation or reward. This creates a mismatch for sequential control: ESG scores are noisy, provider-dependent, low-frequency, and temporally misaligned with sequential portfolio decisions, while financial evidence suggests that ESG is better treated as a portfolio preference, risk-exposure, or hedge dimension than as a robust alpha factor. We propose to impose ESG constraints without modifying the financial policy's observation or reward, using a Multimodal Action-Conditioned Constraint Field (MACF) that learns mechanism-specific ESG costs from point-in-time multimodal evidence and contemplated portfolio transitions. We then introduce MACF-X, a family of optimizer-specific adapters that converts MACF costs and uncertainties into native constrained-optimization interfaces through a shared slack- and uncertainty-aware pressure layer. Across multiple constraint-integration interfaces, MACF-X reduces tail ESG budget pressure while maintaining competitive financial performance. Ablations show that this improvement depends on dynamic evidence inputs and three-head decomposition, while static ESG-score proxies are nearly indistinguishable from score-shuffled noise baselines.

Large-scale portfolio choice is highly sensitive to estimation error, making the preliminary asset selection essential in empirical implementation. Existing selection rules typically rely on scalar returns or low dimensional high frequency summaries, and thus discard intraday risk dynamics that may be relevant for risk adjusted allocation. We propose Metric Dependence Screening (MDS), an asset selection procedure that incorporates high frequency information as object valued data. Each asset day observation is represented as a point-curve object combining daily return with an intraday risk state curve, equipped with a weighted product metric that preserves both reward information and within day risk dynamics. MDS ranks assets by a Fréchet variation based dependence score, measuring how much a risk adjusted target explains the metric dispersion of the asset representations. This yields a simple two stage portfolio procedure: MDS first reduces the investable universe, and standard mean-variance or minimum variance allocation is then applied. We develop a target slicing estimator and establish concentration, sure selection, and rank consistency guarantees under $α$-mixing time series dependence and ultrahigh dimensionality. Simulations show that MDS performs well across both Euclidean and non-Euclidean settings. Using high frequency data for $2938$ Chinese A-share stocks from July 2023 to December 2025, we demonstrate that MDS improves out of sample portfolio performance over return based and scalar dependence based benchmarks, highlighting the value of preserving intraday risk dynamics.

We explore a nuance to 'no arbitrage' in relation to 'information efficiency': acting immediately on an arbitrage is sometimes suboptimal; in such cases optimised trading can suppress the anticipation of predictable risk-outcomes, thereby creating an apparent Status Quo Bias, with Momentum and Low-Risk effects. This is shown in continuous time under model- or event-risk, where, unlike existing approaches, we allow pre-horizon risk-resolution and Risk-Neutral Equivalent pricing, with the technical challenges overcome through results from the 'weak viability' and 'side/inside information' literature. Thus the tension between 'no arbitrage', 'information efficiency' and 'risk-anticipation' is exposed and treated in a practically relevant setting.

We present a novel framework for high-resolution forecasting of residential heating demand and non-heating electricity demand using probabilistic deep learning models. Because our models are trained on electricity consumption from a predominantly gas-heated region, the learned electricity demand patterns primarily reflect non-heating end uses such as lighting, appliances, and cooling. We focus specifically on providing hourly building-level electricity and heating demand forecasts for the residential sector. Leveraging multimodal building-level information -- including data on building footprint areas, heights, nearby building density, nearby building size, land use patterns, and high-resolution weather data -- and probabilistic modeling, our methods provide granular insights into demand heterogeneity. Validation at the building level underscores a step change improvement in performance relative to NREL's ResStock model, which has emerged as a research community standard for residential heating and electricity demand characterization. In building-level heating and electricity estimation backtests, our probabilistic models respectively achieve RMSE scores 18.8% and 27.6% lower than those based on ResStock, with probabilistic forecast quality measured via WIS improving by 59% for both applications. By offering an open-source, scalable, high-resolution platform for demand estimation and forecasting, this research advances the tools available for policymakers and grid planners, contributing to the broader effort to decarbonize the U.S. building stock and meeting climate objectives.

How to allocate limited resources to projects that will yield the greatest long-term benefits is a problem that often arises in decision-making under uncertainty. For example, organizations may need to evaluate and select innovation projects with risky returns. Similarly, when allocating resources to research projects, funding agencies are tasked with identifying the most promising proposals based on idiosyncratic criteria. Finally, in participatory budgeting, a local community may need to select a subset of public projects to fund. Regardless of context, agents must estimate the uncertain values of a potentially large number of projects. Developing parsimonious methods to compare these projects, and aggregating agent evaluations so that the overall benefit is maximized, are critical in assembling the best project portfolio. Unlike in standard sorting algorithms, evaluating projects on the basis of uncertain long-term benefits introduces additional complexities. We propose comparison rules based on Quicksort and the Bradley--Terry model, which connects rankings to pairwise "win" probabilities. In our model, each agent determines win probabilities of a pair of projects based on his or her specific evaluation of the projects' long-term benefit. The win probabilities are then appropriately aggregated and used to rank projects. Several of the methods we propose perform better than the two most effective aggregation methods currently available. Additionally, our methods can be combined with sampling techniques to significantly reduce the number of pairwise comparisons. We also discuss how the Bradley--Terry portfolio selection approach can be implemented in practice.

Eradicating extreme poverty and inequality are the key leverage points to achieve the seventeen Sustainable Development goals. Yet, the reduction in extreme poverty and inequality are vulnerable to shocks such as the pandemic and climate change. We find that that these vulnerabilities emerge from the interaction between individual and institutional mechanisms. Individual characteristics like risk aversion, attention, and saving propensity can lead to sub-optimal diversification and low capital accumulation. These individual drivers are reinforced by institutional mechanisms such as lack of financial inclusion, access to technology, and economic segregation, leading to persistent inequality and poverty traps. Our experiments demonstrate that addressing above factors yields 'double dividend' - reducing poverty and inequality within-and-between communities and create positive feedback that can withstand shocks.

A sustainable solution to negative externalities imposed by road transportation is replacing internal combustion vehicles with electric vehicles (EVs), especially plug-in EV (PEV) encompassing plug-in hybrid EV (PHEV) and battery EV (BEV). However, EV market share is still low and is forecast to remain low and uncertain. This shows a research need for an in-depth understanding of EV adoption behavior with a focus on one of the main barriers to the mass EV adoption, which is the limited electric driving range. The present study extends the existing literature in two directions; First, the influence of the psychological aspect of driving range, which is referred to as range anxiety, is explored on EV adoption behavior by presenting a nested logit (NL) model with a latent construct. Second, the two-level NL model captures individuals' decision on EV adoption behavior distinguished by vehicle transaction type and EV type, where the upper level yields the vehicle transaction type selected from the set of alternatives including no-transaction, sell, trade, and add. The fuel type of the vehicles decided to be acquired, either as traded-for or added vehicles, is simultaneously determined at the lower level from a set including conventional vehicle, hybrid EV, PHEV, and BEV. The model is empirically estimated using a stated preferences dataset collected in the State of California. A notable finding is that anxiety about driving range influences the preference for BEV, especially as an added than traded-for vehicle, but not the decision on PHEV adoption.

A point process for event arrivals in high frequency trading is presented. The intensity is the product of a Hawkes process and high dimensional functions of covariates derived from the order book. Conditions for stationarity of the process are stated. An algorithm is presented to estimate the model even in the presence of billions of data points, possibly mapping covariates into a high dimensional space. The large sample size can be common for high frequency data applications using multiple liquid instruments. Convergence of the algorithm is shown, consistency results under weak conditions is established, and a test statistic to assess out of sample performance of different model specifications is suggested. The methodology is applied to the study of four stocks that trade on the New York Stock Exchange (NYSE). The out of sample testing procedure suggests that capturing the nonlinearity of the order book information adds value to the self exciting nature of high frequency trading events.

Typically options with a path dependent payoff, such as Target Accumulation Redemption Note (TARN), are evaluated by a Monte Carlo method. This paper describes a finite difference scheme for pricing a TARN option. Key steps in the proposed scheme involve tracking of multiple one-dimensional finite difference solutions, application of jump conditions at each cash flow exchange date, and a cubic spline interpolation of results after each jump. Since a finite difference scheme for TARN has significantly different features from a typical finite difference scheme for options with a path independent payoff, we give a step by step description on the implementation of the scheme, which is not available in the literature. The advantages of the proposed finite difference scheme over the Monte Carlo method are illustrated by examples with three different knockout types. In the case of constant or time dependent volatility models (where Monte Carlo requires simulation at cash flow dates only), the finite difference method can be faster by an order of magnitude than the Monte Carlo method to achieve the same accuracy in price. Finite difference method can be even more efficient in comparison with Monte Carlo in the case of local volatility model where Monte Carlo requires significantly larger number of time steps. In terms of robust and accurate estimation of Greeks, the advantage of the finite difference method will be even more pronounced.

Daniel Kahneman and Amos Tversky argued for challenging inside views (informed by contextual specifics) with outside views (based on historical "base rates" for certain event types). A reasonable inside view of the prospects for the global economy in this century is that growth will converge to 2.5%/year or less: population growth is expected to slow or halt by 2100; and as more countries approach the technological frontier, economic growth should slow as well. To test that view, this paper models gross world product (GWP) observed since 10,000 BCE or earlier, in order to estimate a base distribution for changes in the growth rate as a function of the GWP level. For econometric rigor, it casts a GWP series as a sample path in a stochastic diffusion whose specification is novel yet rooted in neoclassical growth theory. After estimation, most observations fall between the 40th and 60th percentiles of predicted distributions. The fit implies that GWP explosion is all but inevitable, in a median year of 2047. The friction between inside and outside views highlights two insights. First, accelerating growth is more easily explained by theory than is constant growth. Second, the world system may be less stable than traditional growth theory and the growth record of the last two centuries suggest.

Volatility is the language in which finance often describes risk, but it is not the language in which institutions experience risk. Allocators live through drawdowns, liquidity needs, spending rules, rebalance decisions, board oversight, and the interval between a prior high-water mark and full recovery. This paper develops a path-dependent framework for asymmetric volatility management. The arithmetic of recovery is nonlinear: after a drawdown of depth $D$, the required gain is $R=\frac{1}{1-D}-1$. Lower volatility can improve geometric compounding through the familiar small-return approximation $g \approx μ-\frac{1}{2}σ^2$, but symmetric de-risking can also impair recovery if it sacrifices too much upside participation. The relevant design problem is therefore not volatility reduction in isolation; it is conditional exposure shaping. Skew engineering is defined here as the portfolio construction discipline of reducing harmful downside participation more than productive upside participation, controlling submergence, and preserving enough recovery participation to sustain compounding under adverse regimes. The resulting Recovery-Efficiency Protocol links drawdown depth, time underwater, recovery burden reduction, and rebound participation into an allocator-facing reporting discipline. Machine learning and AI methods are framed as tools for conditional estimation, regime mapping, robustness testing, and model-risk governance, not as market prediction.

Accurate and efficient imbalance electricity price forecasting is critical for industrial energy trading systems, especially as battery assets and automated bidding pipelines increasingly participate in balancing markets. However, real-time forecasting is complicated by nonlinear market-rule-based price formation, heterogeneous input signals, and incomplete data availability caused by communication delays, publication lags, and measurement outages. This paper proposes a market-rule-informed neural forecasting framework that embeds imbalance price formation rules into the latent space of an expressive neural network. The proposed framework preserves raw signal information while exploiting transparent market-rule priors. We further analyze operational robustness by removing price-component information and characterize how forecasting performance scales with input length and forecasting horizon. Experimental results show that the proposed model achieves competitive forecasting performance with substantially fewer trainable parameters and shorter training time than generic deep learning baselines. Experimental results show that the proposed model achieves competitive forecasting performance with substantially fewer trainable parameters and shorter training time than generic deep learning baselines, demonstrating that market-rule priors and expressive neural networks should be jointly used for accurate and computationally sustainable forecasting in industrial energy trading applications. The implementation is publicly available at https://runyao-yu.github.io/MRINN/.

The Spanish Price Revolution is usually treated as a classic case in which American bullion inflows expanded the money supply and generated inflation. This view captures the first phase of the episode but fails to explain why the same monetary expansion did not continue to produce proportional price growth after 1600. We develop a two-phase Money Phase Transition Theory (MPTT) model in which the classical monetary relation is recovered before a transition point, while a second-phase correction term modifies the money-price transmission coefficient after the transition. Using annual Spanish CPI and reconstructed money-supply data, we show that 1500-1600 was a high-transmission metallic inflationary phase: CPI increased approximately 3.35-fold while money supply increased approximately 3.73-fold. After 1600, money supply continued to rise, increasing approximately 1.82-fold during 1600-1650, while CPI rose only approximately 1.22-fold. A classical one-phase model fitted on 1500-1600, therefore, overpredicts post-1600 prices when extrapolated forward. The MPTT two-phase model with transition point tau=1600 estimates beta_1=0.949, gamma=-0.812, and beta_2=beta_1+gamma=0.137, indicating a sharp post-transition weakening of monetary transmission. An unrestricted break scan identifies a deeper BIC-minimizing break around 1636. These results suggest that the Spanish Price Revolution was not a single monotonic bullion-inflation process but the rise and exhaustion of high-transmission metallic money inflation.

This study investigates how cross-stock information diffusion, driven by both retail and institutional investors, influences excess comovement in the Chinese retail-dominated market and the U.S. institution-dominated market. Using data from 4,533 Chinese stocks and 4,517 U.S. stocks from 2010 to 2022, we identify three key findings. First, the dominant investor group in each market significantly drives excess comovement. Specifically, in China, compared with institution-driven diffusion, retail-driven information diffusion has a notably stronger effect on excess comovement. In contrast, in the U.S., institution-driven diffusion is the primary driver of excess comovement, surpassing the influence of retail-driven diffusion. Second, we identify investors' trading behavior as the underlying mechanism through which information diffusion affects excess comovement. Third, we observe a lead-lag relationship: stocks with faster retail-driven information diffusion exhibit comovement that precedes those with slower diffusion. Based on this finding, we further demonstrate that the predictive power of information diffusion varies across markets. In China, retail-driven diffusion shows strong and persistent predictability for excess comovement, whereas in the U.S., institution-driven diffusion exhibits similarly robust predictive capacity.

We study the single-period portfolio selection problem under Constant Relative Risk-Aversion (CRRA) utility through the information-theoretic lens. Assuming only that the market payoff vector has finite support, we show that the Certainty-Equivalent (CE) growth rate under CRRA utility can be decomposed into a portfolio-induced Rényi divergence term, a Rényi entropy term of the risk-tilted market law, and a log-partition term. In this setting, the Rényi order has a clear operational meaning: it exactly coincides with the investor's coefficient of relative risk aversion. We further show that CRRA portfolio selection is equivalent to a Rényi information-projection problem. Using a variational representation of Rényi divergence, we obtain a Blahut-Arimoto-style alternating optimization with a closed-form auxiliary update and a KL-type portfolio step. In the low risk-aversion regime, this method empirically requires fewer iterations than both direct CRRA utility optimization and Cover's method.

Behavioral parameters such as loss aversion, herding, and extrapolation are central to asset pricing models but remain difficult to measure reliably. We develop a framework that treats large language models (LLMs) as calibrated measurement instruments for behavioral parameters. Using four models and 24{,}000 agent--scenario pairs, we document systematic rationality bias in baseline LLM behavior, including attenuated loss aversion, weak herding, and near-zero disposition effects relative to human benchmarks. Profile-based calibration induces large, stable, and theoretically coherent shifts in several parameters, with calibrated loss aversion, herding, extrapolation, and anchoring reaching or exceeding benchmark magnitudes. To assess external validity, we embed calibrated parameters in an agent-based asset pricing model, where calibrated extrapolation generates short-horizon momentum and long-horizon reversal patterns consistent with empirical evidence. Our results establish measurement ranges, calibration functions, and explicit boundaries for eight canonical behavioral biases.