The rapid advancements in Large Language Models (LLMs) have unlocked transformative possibilities in natural language processing, particularly within the financial sector. Financial data is often embedded in intricate relationships across textual content, numerical tables, and visual charts, posing challenges that traditional methods struggle to address effectively. However, the emergence of LLMs offers new pathways for processing and analyzing this multifaceted data with increased efficiency and insight. Despite the fast pace of innovation in LLM research, there remains a significant gap in their practical adoption within the finance industry, where cautious integration and long-term validation are prioritized. This disparity has led to a slower implementation of emerging LLM techniques, despite their immense potential in financial applications. As a result, many of the latest advancements in LLM technology remain underexplored or not fully utilized in this domain. This survey seeks to bridge this gap by providing a comprehensive overview of recent developments in LLM research and examining their applicability to the financial sector. Building on previous survey literature, we highlight several novel LLM methodologies, exploring their distinctive capabilities and their potential relevance to financial data analysis. By synthesizing insights from a broad range of studies, this paper aims to serve as a valuable resource for researchers and practitioners, offering direction on promising research avenues and outlining future opportunities for advancing LLM applications in finance.

The third National Charter School Study (NCSS III) aimed to test whether charter school were effective and to highlight outcomes on academic progress. The results showed that typical charter school students outperformed similar students in noncharter public schools by 6 days in mathematics and 16 days in reading. The key metric used to claim better performance in charter schools is "days of learning." In this report, the origin of the days-of-learning metric is explored with NAEP data, and the interpretation of days of learning as within-year growth is considered.

Prior literature on two-firm two-market and two-stage extended dynamic models has introduced what Guth (2016) succinctly terms a social dilemma. A state in which conglomerate firms competing in a Bertrand duopoly consider jointly optimizing profits under a tacit self-enforcing agreement to deter market entry. This theoretical article reinterprets the social dilemma highlighted by Guth (2016) not only in the context of allocation but also through the lens of competition where entry must legally be permitted even if cooperative signalling would otherwise sustain joint profitability. This study explores the significance of a sufficiency condition on each firms non-instantaneous reaction function requiring the maintenance of a stable long-run equilibrium through retaliative restraint characterized by either two negative eigenvalues or a saddle-path trajectory.

Traditional optimal commodity tax analysis, dating back to Ramsey (1927), prescribes that to maximize welfare one should impose higher taxes on goods with lower demand elasticities. Yet policy makers do not stress minimizing efficiency costs as a desideratum. In this note we revisit the commodity tax problem, and show that the attractiveness of the Ramsey inverse-elasticity prescription can itself be inverted if the tax system is chosen -- or at least strongly influenced -- by taxpayers who are overly confident of their ability, relative to others, to substitute away from taxed goods.

Safe sanitation access is commonly believed to be ubiquitous in high-income countries; however, researchers and community advocates have exposed a glaring lack of access for many low-income communities and communities of color across the U.S. While this disparity has been identified and quantified at a high level, local and household-level implications of sanitation failures remain ill-defined. We develop a set of user-based and environmental measures to assess the performance of centralized wastewater systems, septic systems, and straight-piped systems in Lowndes County, Alabama. We combine qualitative, survey, and environmental sampled data to holistically compare user experiences across infrastructure types. This integrated approach reveals new routes of exposure to wastewater through informal household maintenance and system backups and provides evidence for the spread of wastewater-like contamination throughout the community. This work elucidates the severity of sanitation failures in one rural U.S. community and provides a framework to assess sanitation quality in other contexts with limited sanitation access in high-income countries.

Financial misconduct in advisory services persists despite ongoing regulatory oversight, raising important questions about its endurance and the nature of retail investor responses. Using detailed FINRA BrokerCheck data - capturing the time from the initiation of misconduct to its detection - we document pronounced heterogeneity in detection lags. Although detection is generally delayed, our analysis reveals that investor responses are markedly swifter during market downturns, suggesting that substantial portfolio losses prompt investors to quickly assign blame to their advisors. These findings contribute to our understanding of how adverse economic environments can trigger more immediate investor reactions, even when overall detection remains sluggish.

We propose to model the records of the maximum Drawdown in capital markets by means a Piecewise Deterministic Markov Process (PDMP). We derive statistical results such as the mean and variance that describes the sequence of maximum Drawdown records. In addition, we developed a simulation study and techniques for estimating the parameters governing the stochastic process, using a practical example in the capital market to illustrate the procedure.

The overwhelming majority of homeless individuals are jobless, despite many expressing a willingness to work. While this strong individual-level link between homelessness and unemployment is well-documented, the broader impact of labor market dynamics on homelessness remains largely unexplored. To fill this gap, this paper investigates the impact of local labor market conditions on the duration of homelessness, using individuals' homeless shelter usage records as a proxy for measuring their homelessness duration. Specifically, drawing on Canada's National Homelessness Information System data from 2014 to 2017, we analyze how local employment growth and changes in the local employment rate affect shelter usage duration. Our findings reveal that a 1% increase in local employment is associated with a 0.11-quarter (approximately 0.33-month) reduction in the average duration of shelter usage, while a 1% rise in the local employment rate leads to a 0.34-quarter (approximately 1.02-month) reduction. These changes correspond to decreases of 2.9% and 8.9%, respectively, in the average duration of shelter stays. The findings underscore the critical role of employment opportunities in reducing homelessness and lend support to job-oriented policy interventions for the homeless. In addition, the results suggest that demographic disparities-particularly the overrepresentation of Indigenous people and men among the homeless-are partially explained by slower exit rates from homelessness within these groups.

Although Google is blocked in China, Chinese provinces export significantly more to foreign countries that recently searched for them (up to 12 months prior). This attention premium is found mainly at the extensive margin of exports, larger in products that are relatively homogeneous, substitutable, and upstream in the production process, and more pronounced during the COVID pandemic and during the holiday season. The attention premium is not found for Chinese imports from the rest of the world. Our findings attest to online attention as a scarce resource in international trade allocated by importers.

Bushmeat hunters on Bioko Island, Equatorial Guinea use shotguns and snares to capture wild arboreal and ground animals for sale in the Malabo Bushmeat market. Two tools for the analysis of economic efficiency, the production possibilities frontier and isorevenue line, can be used to explain the post hoc changing spatial distribution of takeoff rates of bushmeat. This study analyzes changes in technical efficiencies over time and in different locations for the open access wildlife hunted on Bioko for the last ten years. Due to inadequate refrigeration in the field and the bushmeat market, animals must be sold quickly. The result is a takeoff distribution that is not efficient, consequently too many of the wrong species of animals are harvested. The larger, slower-breeding mammals, such as monkeys disappear before the smaller, faster-breeding mammals, such as blue duikers and pouched rats, promoting a steepening of the production possibilities frontier, inducing a greater takeoff of monkeys than the expected efficient level. Soon after hunters penetrate into a new area, the relative selling price of monkeys exceeds the rate of transformation between ground animals and arboreal animals triggering inefficient and unsustainable harvests

Several key states in various regions have experienced recent sawtimber as well as pulp and paper mill closures, which have resulted in harmful effects to rural, natural-resource dependent communities. This raises an important research question, how will key macroeconomic and related variables for the U.S. forest sector change in the future for highly forest-dependent states? To address this, we employ a vector error correction (VEC) model to forecast economic trends in three major industries - forestry and logging, wood manufacturing, and paper manufacturing - across six of the most forest-dependent states in the U.S.: Alabama, Arkansas, Maine, Mississippi, Oregon, and Wisconsin. The forecasting results imply that the forestry and logging industry will largely experience decreases in employment and the number of firms. Wood manufacturing has similar findings, but employment is forecasted to increase in general. Paper manufacturing is forecasted to decrease employment, output, and the number of firms, while wages will remain constant. The analysis highlights how timber-based manufacturing communities may be more resilient than other forestry-based industries in the face of economic disruptions. This type of regional forecasting provides valuable insights for regional policy makers and industry stakeholders, helping them anticipate economic shifts and implement strategies to support affected communities. In addition, the methodology applied in this study can be extended to other non-forestry industries that serve as economic pillars for specific regions such as mining, agriculture, and energy production, offering a framework for assessing economic resilience in resource-dependent communities.

We estimate the variability of solar and wind energy generation potential in Europe over a 43 year period between 1980-2022 with the MERRA-2 reanalysis datasets. We compare the estimated supply potential to hourly demand data from 36 European countries to calculate the reliability of a highly renewable electricity grid in Europe. We find that in cost-optimised scenarios with onshore wind, solar and storage, but no natural gas, reliably meeting the last 1% of demand represents 36% of the entire system cost. Including small amounts of dispatchable natural gas drastically reduces the cost of a renewable, highly reliable grid: overall system costs fall by 31% when just 1% of total generation is permitted to come from natural gas. Large renewable overbuild factors (greater than $\times$4 peak demand) are required to meet modern grid reliability standards in all scenarios, and wind, rather than solar, dominates the generation mix.

We examine how career concerns influence the behavior and mobility of financial advisers. Drawing on a uniquely comprehensive matched panel that combines employer-employee data with a longstanding national ranking, our study tests predictions from classic career concerns models and tournament theory. Our analysis shows that, in the early stages of their careers, advisers destined for top performance differ significantly from their peers. Specifically, before being ranked, these advisers are twice as likely to obtain a key investment license, experience customer disputes at rates up to seven times higher, and transition to firms with 80% larger total assets. Moreover, we find that top advisers mitigate the potential costs of their higher risk-taking by facing reduced labor market penalties following disciplinary actions. Leveraging exogenous variation from the staggered adoption of the Broker Protocol through an event-study framework, our results reveal dynamic sorting: firms attract high-performing advisers intensely within a short post-adoption period. These findings shed new light on the interplay between career incentives, risk-taking, and labor market outcomes in the financial services industry, with important implications for both firm performance and regulatory policy.

This study empirically investigates firms' incentives on the choice of product durability, and its social optimality, by developing a dynamic structural model of durable goods with forward-looking consumers and oligopolistic multi-product firms. Based on the observations of the light bulb market, it specifies a model where firms produce multiple products with different durability levels and set product prices based on dynamic incentives. It proposes and applies novel estimation algorithms that alleviate the computational burden and data requirement for estimating demand and marginal cost parameters of dynamic demand models. Using light bulb market data in Japan, structural parameters are estimated. This study obtains the following results. First, large firms have incentives to collude to eliminate high durability incandescent lamps, though it is profitable to sell them for each firm. In contrast, when they can collude on prices, they don't have incentives to eliminate high durability bulbs. Second, eliminating high durability incandescent lamps leads to larger producer and total surplus, though it leads to lower consumer surplus.

The deployment of Automated Vehicles (AVs) is expected to address road transport externalities (e.g., safety, traffic, environmental impact, etc.). For this reason, a legal framework for their large-scale market introduction and deployment is currently being developed in the European Union. Despite the first steps towards road transport automation, the timeline for full automation and its potential economic benefits remains uncertain. The aim of this paper is twofold. First, it presents a methodological framework to determine deployment pathways of the five different levels of automation in EU27+UK to 2050 under three scenarios (i.e., slow, medium baseline and fast) focusing on passenger vehicles. Second, it proposes an assessment of the economic impact of AVs through the calculation of the value-added. The method to define assumptions and uptake trajectories involves a comprehensive literature review, expert interviews, and a model to forecast the new registrations of different levels of automation. In this way, the interviews provided insights that complemented the literature and informed the design of assumptions and deployment trajectories. The added-value assessment shows additional economic activity due to the introduction of automated technologies in all uptake scenarios.

The European Union's Biodiversity Strategy sets an ambitious goal to increase the area of protected land and sea to 30% with 10% devoted to strict protection by 2030. The large land areas required to fulfil the conservation target and the quick schedule of implementation challenge both the current policy instruments and public funding for conservation. We introduce a deferrence mechanism for forest conservation by using procurement auctions. Deferring the conservation payments allows the government to conserve large areas in a quicker schedule and distributing the financial burden of conservation cost for a longer period of time. The deferred payments are paid an interest. The interest earning and an auction mechanism for downpayments strengthens the incentives for landowners to take part in conservation. We characterize the general properties of the mechanism and run numerical simulations to find that the deferrence mechanism facilitates a quick conservation of stands and thereby minimizes the loss of ecologically valuable sites caused by harvesting risks. The analysis suggests that keeping the lending period no longer than 10 years and paying a 3% interest rate provides a compromise that works rather well and outperforms the up-front mechanism in most cases.

Loss Given Default (LGD) is a key risk parameter in determining a bank's regulatory capital. During LGD-estimation, realised recovery cash flows are to be discounted at an appropriate rate. Regulatory guidance mandates that this rate should allow for the time value of money, as well as include a risk premium that reflects the "undiversifiable risk" within these recoveries. Having extensively reviewed earlier methods of determining this rate, we propose a new approach that is inspired by the cost of capital approach from the Solvency II regulatory regime. Our method involves estimating a market-consistent price for a portfolio of defaulted loans, from which an associated discount rate may be inferred. We apply this method to mortgage and personal loans data from a large South African bank. The results reveal the main drivers of the discount rate to be the mean and variance of these recoveries, as well as the bank's cost of capital in excess of the risk-free rate. Our method therefore produces a discount rate that reflects both the undiversifiable risk of recovery recoveries and the time value of money, thereby satisfying regulatory requirements. This work can subsequently enhance the LGD-component within the modelling of both regulatory and economic capital.

This study examines the impact of foreign direct investment (FDI) on job creation across 109 regions in the old EU member states from 2012 to 2023. Using dynamic and spatial econometric models combined with a unique dataset of FDI projects, we find that increased FDI inflows significantly enhance regional job creation, but the relationship is nonlinear. Sectoral specialization plays a crucial role, as more concentrated FDI inflows lead to higher employment growth. Furthermore, FDI-driven job creation exhibits significant spatial spillover effects. However, regions attracting high-value FDI jobs, such as those in R&D and management, tend to experience slower overall employment growth.

We propose a surge sourcing approach to address occasional synchronous high demand (surge demand) in sharing economy systems, providing a socio-economically progressive alternative to surge pricing. Instead of suppressing demand among disadvantaged consumers, our scheme increases supply by involving privileged consumer-providers (prosumers) who under-utilize their resources. This hybrid supply approach maintains high quality-of-service (QoS) for both consumers and prosumers in both normal and surge demand situations without surge pricing. To ensure prosumer QoS, we reserve a small portion of the primary supply to meet their needs if their resources become unavailable during surge periods. As the probability of such events is low compared to that of the surge demand itself, the reserved resources required are minimal. The resulting scheme is resource-efficient, socially progressive, and sustainable, exploiting under-used resources. We illustrate our scheme through two applications: high-range car sharing for owners of small EVs, and shared charging points for EV drivers.

We study decades-long historic distributions of accumulated S\&P500 returns, from daily returns to those over several weeks. The time series of the returns emphasize major upheavals in the markets -- Black Monday, Tech Bubble, Financial Crisis and Covid Pandemic -- which are reflected in the tail ends of the distributions. De-trending the overall gain, we concentrate on comparing distributions of gains and losses. Specifically, we compare the tails of the distributions, which are believed to exhibit power-law behavior and possibly contain outliers. Towards this end we find confidence intervals of the linear fits of the tails of the complementary cumulative distribution functions on a log-log scale, as well as conduct a statistical U-test in order to detect outliers. We also study probability density functions of the full distributions of the returns with the emphasis on their asymmetry. The key empirical observations are that the mean of de-trended distributions increases near-linearly with the number of days of accumulation while the overall skew is negative -- consistent with the heavier tails of losses -- and depends little on the number of days of accumulation. At the same time the variance of the distributions exhibits near-perfect linear dependence on the number of days of accumulation, that is it remains constant if scaled to the latter. Finally, we discuss the theoretical framework for understanding accumulated returns. Our main conclusion is that the current state of theory, which predicts symmetric or near-symmetric distributions of returns cannot explain the aggregate of empirical results.

The state of economic theory and accumulated facts from the different branches of the economic science require to analyze the concept of the description of economy systems. The economic reality generates the problems the solution of that is only possible by a new paradigm of the description of economy system. The classical mathematical economics is based on a notion of the rational consumer choice generated by a certain preference relation on some set of goods a consumer wanted and the concept of maximization of the firm profit. The sense of the notion of the ratio- nal consumer choice is that it is determined by a certain utility function, defining the choice of a consumer by maximization of it on a certain budget set of goods. More- over, choices of consumers are independent. In the reality choices of consumers are not independent because they depend on the firms supply. Except the firms supply, the consumer choice is also determined by information about the state of the economy system that the consumer has and respectively eval- uates at the moment of the choice. In turn, the firms supply is made on the basis of needs of the consumers and their buying power. By information about the state of the economy system we understand a certain information about the equilibrium price vector and productive processes realized in the economy system under the equilibrium price vector.

In online advertising systems, publishers often face a trade-off in information disclosure strategies: while disclosing more information can enhance efficiency by enabling optimal allocation of ad impressions, it may lose revenue potential by decreasing uncertainty among competing advertisers. Similar to other challenges in market design, understanding this trade-off is constrained by limited access to real-world data, leading researchers and practitioners to turn to simulation frameworks. The recent emergence of large language models (LLMs) offers a novel approach to simulations, providing human-like reasoning and adaptability without necessarily relying on explicit assumptions about agent behavior modeling. Despite their potential, existing frameworks have yet to integrate LLM-based agents for studying information asymmetry and signaling strategies, particularly in the context of auctions. To address this gap, we introduce InfoBid, a flexible simulation framework that leverages LLM agents to examine the effects of information disclosure strategies in multi-agent auction settings. Using GPT-4o, we implemented simulations of second-price auctions with diverse information schemas. The results reveal key insights into how signaling influences strategic behavior and auction outcomes, which align with both economic and social learning theories. Through InfoBid, we hope to foster the use of LLMs as proxies for human economic and social agents in empirical studies, enhancing our understanding of their capabilities and limitations. This work bridges the gap between theoretical market designs and practical applications, advancing research in market simulations, information design, and agent-based reasoning while offering a valuable tool for exploring the dynamics of digital economies.

Can randomness be better than scheduled practices, for securing an event at a large venue such as a stadium or entertainment arena? Perhaps surprisingly, from several perspectives the answer is "yes." This note examines findings from an extensive study of the problem, including interviews and a survey of selected venue security directors. That research indicates that: randomness has several goals; many security directors recognize its potential; but very few have used it much, if at all. Some fear they will not be able to defend using random methods if an adversary does slip through security. Others are concerned that staff may not be able to perform effectively. We discuss ways in which it appears that randomness can improve effectiveness, ways it can be effectively justified to those who must approve security processes, and some potential research or regulatory advances.

We study the competition between random multiplicative growth and redistribution/migration in the mean-field limit, when the number of sites is very large but finite. We find that for static random growth rates, migration should be strong enough to prevent localisation, i.e. extreme concentration on the fastest growing site. In the presence of an additional temporal noise in the growth rates, a third partially localised phase is predicted theoretically, using results from Derrida's Random Energy Model. Such temporal fluctuations mitigate concentration effects, but do not make them disappear. We discuss our results in the context of population growth and wealth inequalities.

During the 20th Century, aerial surveys captured hundreds of millions of high-resolution photographs of the earth's surface. These images, the precursors to modern satellite imagery, represent an extraordinary visual record of the environmental and social upheavals of the 20th Century. However, most of these images currently languish in physical archives where retrieval is difficult and costly. Digitization could revolutionize access, but manual scanning is slow and expensive. Here, we describe and validate a novel robot-assisted pipeline that increases worker productivity in scanning 30-fold, applied at scale to digitize an archive of 1.7 million historical aerial photographs from 65 countries.

As data is increasingly acknowledged as a highly valuable asset, much effort has been put into investigating inter-organisational data sharing, aiming at utilising the value of formerly unused data. Moreover, most researchers agree, that trust between actors is key for successful data sharing activities. However, existing research oftentimes focus on trust from a data provider perspective. Therefore, our work highlights the unbalanced view of trust, addressing it from a data consumer perspective. More specifically, our aim is to investigate trust enhancing measures on a data level, that is data trustworthiness. We found, that existing data trustworthiness enhancing solutions do not meet the requirements of the domain of inter-organisational data sharing. Therefore, our study addresses this gap. Conducting a rigorous design science research approach, this work proposes a new Levels of Assurance for Data Trustworthiness artifact. Built on existing artifacts, we demonstrate, how it addresses the identified challenges within the domain appropriately. We found that our novel approach requires more work to be suitable for adoption. Still, we are confident that our solution can increase consumer trust. We conclude by contributing to the body of design knowledge and emphasise the need for more attention to be put into consumer trust.

Agricultural price volatility challenges sustainable finance, planning, and policy, driven by market dynamics and meteorological factors such as temperature and precipitation. In India, the Minimum Support Price (MSP) system acts as implicit crop insurance, shielding farmers from price drops without premium payments. We analyze the impact of climate on price volatility for soybean (Madhya Pradesh), rice (Assam), and cotton (Gujarat). Using ERA5-Land reanalysis data from the Copernicus Climate Change Service, we analyze historical climate patterns and evaluate two scenarios: SSP2.4.5 (moderate case) and SSP5.8.5 (severe case). Our findings show that weather conditions strongly influence price fluctuations and that integrating meteorological data into volatility models enhances risk-hedging. Using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, we estimate conditional price volatility and identify cross-correlations between weather and price volatility movements. Recognizing MSP's equivalence to a European put option, we apply the Black-Scholes model to estimate its implicit premium, quantifying its fiscal cost. We propose this novel market-based risk-hedging mechanism wherein the government purchases insurance equivalent to MSP, leveraging Black-Scholes for accurate premium estimation. Our results underscore the importance of meteorological data in agricultural risk modeling, supporting targeted insurance and strengthening resilience in agricultural finance. This climate-informed financial framework enhances risk-sharing, stabilizes prices, and informs sustainable agricultural policy under growing climate uncertainty.