Shanghai Composite Index prediction has become a hot issue for many investors and academic researchers. Deep learning models are widely applied in multivariate time series forecasting, including recurrent neural networks (RNN), convolutional neural networks (CNN), and transformers. Specifically, the Transformer encoder, with its unique attention mechanism and parallel processing capabilities, has become an important tool in time series prediction, and has an advantage in dealing with long sequence dependencies and multivariate data correlations. Drawing on the strengths of various models, we propose the CNN-Transformer-LSTM Networks (CTLNet). This paper explores the application of CTLNet for Shanghai Composite Index prediction and the comparative experiments show that the proposed model outperforms state-of-the-art baselines.

We introduce a multi-agent framework intended to emulate parts of a quantitative research team and support equity factor research on large financial panel datasets. QRAFTI integrates a research toolkit for panel data with MCP servers that expose data access, factor construction, and custom coding operations as callable tools. It can help replicate established factors, formulate and test new signals, and generate standardized research reports accompanied by narrative analysis and computational traces. On multi-step empirical tasks, using chained tool calls and reflection-based planning may offer better performance and explainability than dynamic code generation alone.

We study how AI agents form expectations and trade in experimental asset markets. Using a simulated open-call auction populated by autonomous Large Language Model (LLM) agents, we document three main findings. First, AI agents exhibit classic behavioral patterns: a pronounced disposition effect and recency-weighted extrapolative beliefs. Second, these individual-level patterns aggregate into equilibrium dynamics that replicate classic experimental findings (Smith et al., 1988), including the predictive power of excess demand for future prices and the positive relationship between disagreement and trading volume. Third, by analyzing the agents' reasoning text through a twenty-mechanism scoring framework, we show that targeted prompt interventions causally amplify or suppress specific behavioral mechanisms, significantly altering the magnitude of market bubbles.

This paper asks how institutional stock-market integration reshapes the transmission of monetary policy through asset prices in small open economies. Motivated by the persistent segmentation of Western Balkan capital markets, we develop a two-stage counterfactual transmission framework to identify how stock-exchange consolidation would alter the elasticity of market valuations to monetary shocks. First, a synthetic-control simulation constructs a counterfactual integrated Western Balkan stock exchange comprising Bosnia and Herzegovina, North Macedonia, and Serbia, benchmarked to the Baltic OMX merger, thereby quantifying the structural valuation gains of institutional integration. Second, we identify exogenous monetary-policy innovations using a Taylor-rule framework augmented with inflation and output forecasts and reserve adjustments. These shocks are then embedded within a Local-Projections estimator à la Jordà (2005) to trace the dynamic responses of market capitalisation under fragmented and integrated market regimes. The results point to a systematic amplification of monetary-policy transmission through the asset-price channel once markets are unified. Following a policy tightening of about 100 basis points, equity valuations fall roughly twice as strongly under integration than under fragmented markets. Additionally, we find that integration alters the sensitivity of monetary transmission itself: the initial pass-through intensifies, but its marginal responsiveness to further integration declines over time, signalling the consolidation of a new steady-state regime.

This paper investigates the evolution of self-referentiality and knowledge flows in economics journals before and after the 2008 financial crisis. Using a multi-level approach, we analyze patterns at the discipline, cluster, and journal levels, combining citational measures with a classification of journals based on intellectual similarity and social proximity. At the aggregate level, results suggest a general decline in self-referentiality, indicating increased openness across the discipline. However, this trend conceals substantial heterogeneity. At finer levels of analysis, two clusters - CORE and Finance - emerge as persistent outliers, exhibiting very high levels of self-referentiality. While Finance experienced a gradual reduction over time, the CORE shows increasing closure. By examining reference asymmetries, we uncover a hierarchical structure of knowledge flows. The CORE operates as a central hub and net exporter of knowledge to all other clusters, particularly to the traditional core fields of economics, whereas Finance acts as a net exporter only within its own domain and remains dependent on the CORE. These asymmetries are reinforced at the level of individual journals, where a small set of top journals occupies the apex of a hierarchically ordered system of knowledge transmission. We argue that these patterns reflect the interplay between intellectual dynamics and organizational structures, particularly the role of editorial networks in shaping access to publication and visibility. The findings suggest that, following the financial crisis, economics has experienced a process of increasing epistemic and organizational closure at its core, alongside greater openness in peripheral areas. This dual dynamic raises questions about the representativeness of top journals and the evolving structure of the discipline.

Do projects learn across space and time? The Olympics, among the largest publicly funded programmes in the world, offer a unique empirical setting. Theoretically, the Games seem ideal for generating "positive learning curves," driving down costs from one iteration to the next. In practice, they do not. Drawing on the concept of "myopia of learning," we argue that spatiotemporality (geographic distance, temporal gaps, and the temporary organisational form of each host committee) combines to block higher-level learning. Our analysis of cost overruns from 1960 to 2024 reveals no sustained improvement over 64 years. Tactical learning abounds, but none aggregates into strategic improvement. We propose four strategies for overcoming the spatiotemporal barrier (incremental, centralising, decentralising, and real options), arguing that radical reform is required.

International commerce has long been seen as a key way to keep the global food system stable, allowing agricultural surpluses in some areas to compensate for shortages in others. This strategy has led to the rise of highly specialised processing hubs that combine significant industrial capacity with agricultural inputs sourced from throughout the world. Türkiye's flour sector -- currently the largest wheat flour exporter in the world -- represents one of the most prominent examples of this model. However, increasing climate variability and geopolitical fragmentation raise important questions regarding the long-term resilience of food systems that rely heavily on imported biological inputs. Recent research shows the growing probability of synchronised crop failures across multiple agricultural regions due to atmospheric circulation anomalies and climate-induced extreme weather events. The assumption that global markets can consistently rebalance supply disruptions through trade is challenged by such events. Using the flour industry of Türkiye as a case study, this paper investigates the susceptibility of globally integrated grain processing centres. In order to assess the correlation between the scope of industrial processing and the capacity of domestic agricultural production, we introduce the Biophysical Autonomy Ratio~(BAR). The analysis demonstrates that Türkiye's BAR has declined consistently over time, suggesting that its processing sector has expanded beyond the domestic production base. The results suggest that in order to enhance the resilience of the food system in the future, it may be necessary to establish a more precise alignment between biological production systems and industrial food infrastructure. The paper concludes by addressing the policy implications for national food security governance in the context of escalating climate instability.

This paper investigates whether large language models (LLMs) can generate reliable stock market predictions. We evaluate four state-of-the-art models - ChatGPT, Gemini, DeepSeek, and Perplexity - across three prompting strategies: a naive query, a structured approach, and chain-of-thought reasoning. Our results show that LLM-generated recommendations are hindered by recurring reasoning failures, including financial misconceptions, carryover errors, and reliance on outdated or hallucinated information. When appropriately guided and supervised, LLMs demonstrate the capacity to outperform the market, but realizing LLMs' full potential requires substantial human oversight. We also find that grounding stock recommendations in official regulatory filings increases their forecasting accuracy. Overall, our findings underscore the need for robust safeguards and validation when deploying LLMs in financial markets.

Regulators currently govern the AI data economy based on intuition rather than evidence, struggling to choose between inconsistent regimes of informed consent, immunity, and liability. To fill this policy vacuum, this paper develops a novel computational policy laboratory: a spatially explicit Agent-Based Model (ABM) of the data market. To solve the problem of missing data, we introduce a two-stage methodological pipeline. First, we translate decision rules from multi-year fieldwork (2022-2025) into agent constraints. This ensures the model reflects actual bargaining frictions rather than theoretical abstractions. Second, we deploy Large Language Models (LLMs) as "subjects" in a Discrete Choice Experiment (DCE). This novel approach recovers precise preference primitives, such as willingness-to-pay elasticities, which are empirically unobservable in the wild. Calibrated by these inputs, our model places rival legal institutions side-by-side to simulate their welfare effects. The results challenge the dominant regulatory paradigm. We find that property-rule mechanisms, such as informed consent, fail to maximize welfare. Counterintuitively, social welfare peaks when liability for substantive harm is shifted to the downstream buyer. This aligns with the "least cost avoider" principle, because downstream users control post-acquisition safeguards, they are best positioned to mitigate risk efficiently. By "de-romanticizing" seller-centric frameworks, this paper provides an economic justification for emerging doctrines of downstream reachability.

Gravity equations are often used to evaluate the effects of trade policies, such as regional trade agreements. We argue that their suitability for this purpose critically depends on their ability to produce unbiased out-of-sample predictions. We propose a methodology to evaluate the out-of-sample predictions obtained with gravity equations and with machine learning methods. We find that the 3-way gravity model is difficult to beat when the purpose is to evaluate policy interventions, further cementing its position as the predominant tool for applied trade policy analysis. However, when the goal is to predict individual flows, machine learning methods can be preferable.

Building on the task-based approach to labour markets, we develop the Generative AI Susceptibility Index (GAISI), a job-level measure of UK exposure to large language models (LLMs). Drawing on Eloundou et al. (2024), we use LLMs as probabilistic raters to classify task exposure, linking ratings to worker-reported task data from the British Skills and Employment Surveys. GAISI measures the share of job activities where LLMs can reduce task completion time by at least 25% beyond existing tools. Systematic validations demonstrate high reliability, strong validity, and predictive power over existing exposure measures. By 2023/24, nearly all UK jobs (94%) exhibited some LLM exposure, yet only 13% were heavily exposed (GAISI > 0.5), with the highest concentration in scientific and technical professions. Aggregate exposure rose 16% of one standard deviation since 2017, driven by occupational shifts rather than within-occupation task changes. The wage premium for AI-exposed tasks declined 12% between 2017 and 2023/24, and the period since ChatGPT's release has coincided with a relative contraction of job postings in more AI-exposed occupations. These findings are consistent with generative AI beginning to affect hiring and pay in exposed occupations, though causal attribution requires further research. GAISI offers policymakers and researchers a validated, replicable tool for monitoring AI exposure at the job level as this technology diffuses.

Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically secured, yield-bearing instruments issued across multi-chain Web3 infrastructures, with growing significance for transparency, accessibility, and financial inclusion. While the market has expanded rapidly, empirical analyses of transaction-level behaviours remain limited. This paper conducts a quantitative, function-level dissection of U.S. Treasury-backed RWA tokens, including BUIDL, BENJI, and USDY across multi-chain: mostly Ethereum and Layer-2s. Decoded contract calls expose core financial primitives such as issuance, redemption, transfer, and bridging, revealing patterns that distinguish institutional participants from smaller or retail users for the extent and limits of inclusivity in current RWA adoption. To infer address-level economic roles, we introduce a curvature-aware representation learning model. Our method outperforms baseline models in role inference on our collected U.S. Treasury transaction dataset and generalizes to address classification across broader public blockchain transaction datasets. The decoded transaction-level patterns in tokenized U.S. Treasuries across chains surface the degree of retail participation, and the role inference model enables the distinction between institutional treasuries, arbitrage bots, and retail traders based on behavioral patterns, facilitating future more transparent, inclusive, and accountable Web3 finance.

We propose post-screening portfolio selection (PS$^2$), a two-step framework for high-dimensional mean--variance investing. First, assets are screened by Lasso-type regression of a constant on excess returns without an intercept. Second, portfolio weights are estimated on the selected set using standard low-dimensional methods. Because strong factors can destroy sparsity in real data, we further introduce PS$^2$ with factors (FPS$^2$), which defactors returns before screening and allows factor investing in the final step. We establish theoretical guarantees, and simulations and an empirical application show competitive performance, especially when sparse screening is appropriate or strong factors are explicitly accommodated.

We introduce joint exclusivity (JE), a form of extremal negative dependence that extends the classical notion of mutual exclusivity. The JE structure is analytically tractable and is defined by the exclusion of the interior of the non-negative orthant. We establish a sharp necessary and sufficient condition for the existence of a JE random vector with prescribed marginals, namely $\sum_{i\in N} \overline{F}_i(0) \leq n - 1$. We propose a canonical construction that distributes probability mass on lower-dimensional faces of the support, while allowing flexible copula specifications within each face. The framework is further extended to a generalized class (G-JE) via marginal distortion functions. Finally, we identify a correspondence between the support structures of JE and joint mixability, revealing a structural link between the two concepts.

We present the first portfolio-level validation of MarketSenseAI, a deployed multi-agent LLM equity system. All signals are generated live at each observation date, eliminating look-ahead bias. The system routes four specialist agents (News, Fundamentals, Dynamics, and Macro) through a synthesis agent that issues a monthly equity thesis and recommendation for each stock in its coverage universe, and we ask two questions: do its buy recommendations add value over both passive benchmarks and random selection, and what does the internal agent structure reveal about the source of the edge? On the S&P 500 cohort (19 months) the strong-buy equal-weight portfolio earns +2.18%/month against a passive equal-weight benchmark of +1.15% (approximating RSP), a +25.2% compound excess, and ranks at the 99.7th percentile of 10,000 Monte Carlo portfolios (p=0.003). The S&P 100 cohort (35 months) delivers a +30.5% compound excess over EQWL with consistent direction but formal significance not reached, limited by the small average selection of ~10 stocks per month. Non-negative least-squares projection of thesis embeddings onto agent embeddings reveals an adaptive-integration mechanism. Agent contributions rotate with market regime (Fundamentals leads on S&P 500, Macro on S&P 100, Dynamics acts as an episodic momentum signal) and this agent rotation moves in lockstep with both the sector composition of strong-buy selections and identifiable macro-calendar events, three independent views of the same underlying adaptation. The recommendation's cross-sectional Information Coefficient is statistically significant on S&P 500 (ICIR=+0.489, p=0.024). These results suggest that multi-agent LLM equity systems can identify sources of alpha beyond what classical factor models capture, and that the buy signal functions as an effective universe-filter that can sit upstream of any portfolio-construction process.

U.S. dollar stablecoins are increasingly used as payment and settlement instruments beyond cryptocurrency markets. With the enactment of the GENIUS Act in 2025, the United States established the first comprehensive federal framework governing their issuance, backing, and supervision. This paper evaluates the financial, technological, and regulatory risks that may arise as GENIUS-compliant stablecoins scale into mainstream use. We show that maintaining par-value redemption may depend not only on backing-asset quality, but also on the functioning of Treasury and repo markets, the balance-sheet capacity of broker-dealers, and the operational reliability of blockchain-based transaction rails. Even conservatively backed stablecoins can face stress from redemption surges, market-intermediation bottlenecks, or technological disruptions. We argue that durable stability will likely require an integrated approach spanning financial-market infrastructure, prudential regulation, and software governance. While grounded in U.S.\ law, the analysis identifies principles that are relevant for regulators in other jurisdictions developing stablecoin regimes.

Sparsity or complexity? In modern high-dimensional asset pricing, these are often viewed as competing principles: richer feature spaces appear to favor complexity, while economic intuition has long favored parsimony. We show that this tension is misplaced. We distinguish capacity sparsity-the dimensionality of the candidate feature space-from factor sparsity-the parsimonious structure of priced risks-and argue that the two are complements: expanding capacity enables the discovery of factor sparsity. Revisiting the benchmark empirical design of Didisheim et al. (2025) and pushing it to higher complexity regimes, we show that nonlinear feature expansions combined with basis pursuit yield portfolios whose out-of-sample performance dominates ridgeless benchmarks beyond a critical complexity threshold. The evidence shows that the gains from complexity arise not from retaining more factors, but from enlarging the space from which a sparse structure of priced risks can be identified. The virtue of complexity in asset pricing operates through factor sparsity.

Standard macroeconomic frameworks have correctly identified Japan's government debt - now exceeding 240% of GDP - as carrying substantial fiscal risk. Yet FRED data from 2013 to 2026 present an empirical record inviting a complementary perspective: debt ratios have stabilized, nominal GDP has exceeded 670 trillion yen (SAAR), and unemployment has remained near 2.6-2.7%. This paper formalizes these channels through the Japanese Financial Repression r-g (JFR-rg) model. Building on Blanchard (2019), the framework incorporates a financial repression bias (epsilon_t = pi_t - r^n_t, directly observable from FRED) and a non-linear exchange-rate channel. Three theoretical contributions extend the literature: (i) the Debt Sustainability Corridor, a characterization of stability in (epsilon_t, g^n*_t) space; (ii) the Normalization Ratchet, a path-dependence theorem showing that temporary policy errors generate persistently higher debt trajectories; and (iii) the Captive Financial System Parameter (phi_t), which endogenizes the institutional precondition for JFR-rg stability. Appendices H-L provide supporting empirical evidence (VAR, ARDL, Local Projections) showing the framework's claims are empirically disciplined and falsifiable. The core debt-dynamics propositions are anchored in the consolidated government budget identity (Layer L1), while selected propositions additionally rely on minimal structural assumptions; identification concerns apply only to the empirical Layer L2. Counterfactual simulations illustrate a Normalization Trap: aggressive rate hikes can produce counterproductive debt dynamics. For high-debt, low-growth economies sharing Japan's institutional characteristics, strategically deploying the resulting Repression Dividend into productivity-enhancing investment may represent a regime-contingent equilibrium possibility, conditional on the captive system condition being maintained.

Organizations have widely deployed generative AI tools, yet productivity gains remain uneven, suggesting that how people use AI matters as much as whether they have access. We conducted a field experiment with 388 employees at a Fortune 500 retailer to test two scaffolding interventions for human-AI collaboration. All participants had access to the same AI tool; we varied only the structure surrounding its use. A behavioral scaffolding intervention (a structured protocol requiring joint AI use within pairs) was associated with lower document quality relative to unstructured use and substantially lower document production. A cognitive scaffolding intervention (partnership training that reframed AI as a thought partner) was associated with higher individual document quality at the top of the distribution. Treatment participants also showed greater positive belief change across the session, though sensitivity analyses suggest this likely reflects recovery from carry-over effects rather than genuine training-induced shifts. Both findings are subject to design limitations including an AM/PM session confound, differential attrition, and LLM grading sensitivity to document length.

This paper develops a computational framework for Multi-Period Martingale Optimal Transport (MMOT), addressing convergence rates, algorithmic efficiency, and financial calibration. Our contributions include: (1) Theoretical analysis: We establish discrete convergence rates of $O(\sqrt{Δt} \log(1/Δt))$ via Donsker's principle and linear algorithmic convergence of $(1-κ)^{2/3}$; (2) Algorithmic improvements: We introduce incremental updates ($O(M^2)$ complexity) and adaptive sparse grids; (3) Numerical implementation: A hybrid neural-projection solver is proposed, combining transformer-based warm-starting with Newton-Raphson projection. Once trained, the pure neural solver achieves a $1{,}597\times$ online inference speedup ($4.7$s $\to 2.9$ms) suitable for real-time applications, while the hybrid solver ensures martingale constraints to $10^{-6}$ precision. Validated on 12,000 synthetic instances (GBM, Merton, Heston) and 120 real market scenarios.

This article explores the potential of generative AI (GenAI) to support actuarial practice through four implemented case studies. It situates these case studies within the broader evolution of artificial intelligence in actuarial science, from early neural networks and machine learning to modern transformer-based GenAI systems. The first case study illustrates how large language models (LLMs) can improve claim cost prediction by extracting informative features from unstructured text for use in the underlying supervised learning task. The second case study demonstrates the automation of market comparisons using Retrieval-Augmented Generation to identify, extract, and structure relevant information from insurers' annual reports. The third case study highlights the capabilities of fine-tuned vision-enabled LLMs in classifying car damage types and extracting contextual information from images. The fourth case study presents a multi-agent system that autonomously migrates actuarial legacy code from R to Python and validates the translation against the original code's outputs. In addition to these case studies, we outline further GenAI applications in the insurance industry. Finally, we discuss the regulatory, security, dual-use and fraud, reproducibility, privacy, governance, and organisational challenges associated with deploying GenAI in regulated insurance environments.

Growing network models can potentially be a useful tool in the development of economic theory. This work introduces an "opportunistic attachment" mechanism where incoming nodes, in deciding where to join a network, consider features of the entry points available to them. For example, an entrepreneur looking to start a thriving business might consider the expected revenue of many hypothetical businesses. This mechanism is explored, in isolation, via a minimal model where PageRank serves to score the available opportunities. Despite its simplicity, this model gives rise to rich node dynamics, path-dependence, and an unexpected degenerate structure. We go on to argue that this model might be useful to theoretical development as a maximally stylised model of entrepreneurial growth. Central to the argument is an alternative set of microfoundations introduced in Leontief & Brody (1993) whereby the steady state of a random walk is a notion of economic equilibrium. To the extent this argument holds, our findings suggest that entrepreneurs face a shifting "opportunity space" where the number of potential business opportunities is effectively unbounded. Opportunistic attachment is thus a candidate mechanism for relating the structure of an economic system to its future growth.

Circulation is the characteristic feature of successful currency systems, from community currencies to cryptocurrencies to national currencies. In this paper, we propose a network analysis approach especially suited for studying circulation given a system's digital transaction records. Sarafu is a digital community currency that was active in Kenya over a period that saw considerable economic disruption due to the COVID-19 pandemic. We represent its circulation as a network of monetary flow among the 40,000 Sarafu users. Network flow analysis reveals that circulation was highly modular, geographically localized, and occurring among users with diverse livelihoods. Across localized sub-populations, network cycle analysis supports the intuitive notion that circulation requires cycles. Moreover, the sub-networks underlying circulation are consistently degree disassortative and we find evidence of preferential attachment. Community-based institutions often take on the role of local hubs, and network centrality measures confirm the importance of early adopters and of women's participation. This work demonstrates that networks of monetary flow enable the study of circulation within currency systems at a striking level of detail, and our findings can be used to inform the development of community currencies in marginalized areas.

AI stocks trade at extraordinary valuations. We develop an asset pricing model in which investors use AI stocks to hedge against an AI singularity that displaces their consumption. Because markets are incomplete -- investors cannot trade private AI capital -- AI stocks command a premium. Market incompleteness distorts both valuations and the efficient development of AI, creating a rationale for government transfers that becomes compelling when singularity-driven growth overwhelms deadweight costs. This paper was generated by AI, using https://github.com/chenandrewy/ralph-wiggum-asset-pricing/.

We develop \emph{Topological Risk Parity} (TRP), a tree-based portfolio construction approach intended for long/short, market neutral, factor-aware portfolios. The method is motivated by the dominance of passive/factor flows that naturally create a tree-like structure in markets. We introduce two implementation variants: (i) a rooted minimum-spanning-tree allocator, and (ii) a market/sector-anchored variant referred to here as \emph{Semi-Supervised TRP}, which imposes SPY as the root node and the 11 sector ETFs as the second layer. In both cases, the key object is a sparse rooted topology extracted from a correlation-distance graph, together with a propagation law that maps signed signals into portfolio weights. Relative to classical Hierarchical Risk Parity (HRP), TRP is non-binary and designed for signed cross-sectional signals and hedged long-short portfolios: it preserves signal direction while using return-dependence geometry to shape exposures. It accounts for the fact that there is imperfect correlation between parent and child nodes, and thus does not propagate weights entirely to the children. We can also impose economically motivated hierarchy that involves industries, sub-industries or factors, etc. This makes it much more robust to macroeconomic shocks and crises, where within-cluster correlations might spike. These features make TRP well suited for market-neutral, equity stat-arb or L/S trend-type strategies, where enforcing neutrality or limiting exposures at the market, sector or factor level is extremely important.

This paper develops a geospatial framework for climate risk stress testing in California with applications to banking and climate-exposed sectors such as agriculture, real estate, and tourism. The study integrates physical hazard mapping, sector-specific exposure analysis, and scenario-based financial risk assessment to evaluate how wildfires, drought, flooding, extreme heat, and transition risks may affect regional economic activity and financial stability. The framework is intended to support portfolio monitoring, climate scenario analysis, and institutional readiness under emerging disclosure and risk-management standards. In addition, the paper provides a survey-based implementation guide for benchmarking current climate-risk practices and data needs across industry and academic stakeholders.

Bilateral bargaining under incomplete information provides a controlled testbed for evaluating large language model (LLM) agent capabilities. Bilateral trade demands individual rationality, strategic surplus maximization, and cooperation to realize gains from trade. We develop a structured bargaining environment where LLMs negotiate via tool calls within an event-driven simulator, separating binding offers from natural-language messages to enable automated evaluation. The environment serves two purposes: as a benchmark for frontier models and as a training environment for open-weight models via reinforcement learning. In benchmark experiments, a round-robin tournament among five frontier models (15,000 negotiations) reveals that effective strategies implement price discrimination through sequential offers. Aggressive anchoring, calibrated concession, and temporal patience correlate with the highest surplus share and deal rate. Accommodating strategies that concede quickly disable price discrimination in the buyer role, yielding the lowest surplus capture and deal completion. Stronger models scale their behavior proportionally to item value, maintaining performance across price tiers; weaker models perform well only when wide zones of possible agreement offset suboptimal strategies. In training experiments, we fine-tune Qwen3 (8B, 14B) via supervised fine-tuning (SFT) followed by Group Relative Policy Optimization (GRPO) against a fixed frontier opponent. These stages optimize competing objectives: SFT approximately doubles surplus share but reduces deal rates, while RL recovers deal rates but erodes surplus gains, reflecting the reward structure. SFT also compresses surplus variation across price tiers, which generalizes to unseen opponents, suggesting that behavioral cloning instills proportional strategies rather than memorized price points.

Chitra et al. (2025) claim that Target Weight Mechanism (TWM) in Perpetual Demand Lending Pools (PDLPs) can lower the delta of the portfolio under certain condition. We prove that their condition is self-contradictory. Furthermore, we prove an impossibility result that no TWM can lower the delta uniformly.

Healthcare productivity is shaped not only by clinical complexity but by the costs of coordinating work under uncertainty. Transaction-cost economics offers a theory of these coordination frictions, yet has rarely been operationalised at task level across health occupations. Using task statements and frequency weights from the O*NET occupational database, we characterised healthcare work at task granularity and coded each unique task using a constrained large language model into one dominant transaction-cost category (information search, decision and bargaining, monitoring and enforcement, or adaptation and coordination) together with an overall transaction-cost intensity score. Aggregating to the occupation level, clinician roles exhibited substantially higher transaction-cost intensity than non-clinician roles, driven primarily by greater burdens of information search and decision-related coordination, while dispersion of transaction costs within occupations did not differ. These findings demonstrate systematic heterogeneity in the nature of coordination work across healthcare roles and suggest that the opportunities for digital and AI interventions are unevenly distributed, shaped less by technical task complexity than by underlying coordination structure.

This paper develops an axiomatic framework for ranking metrics, a general class of functionals for evaluating and ordering financial or insurance positions. Unlike traditional risk-adjusted performance measures-such as the Sharpe ratio, RAROC, or Omega-that express reward per unit of risk, ranking metrics assign each position a performance level rather than a normalized return. Relying on monotonicity and a new property called cash-quasiconcavity, we derive representation results linking ranking metrics to families of acceptance sets and risk measures, extending the theory of acceptability indices. Classical ratios arise as special cases, while new examples-based on expected-loss, Lambda-quantile, and bibliometric indices-illustrate the framework's flexibility. Empirical applications to portfolio ranking and climate-risk insurance demonstrate its practical relevance.