Hasil untuk "Trade associations"

Yingyi Qian, Yingyi Qian, Gérard Roland et al.

J. Eaton, Samuel Kortum

Avi Arora, Ritesh Malpani

Prediction markets offer a natural testbed for trading agents: contracts have binary payoffs, prices can be interpreted as probabilities, and realized performance depends critically on market microstructure, fees, and settlement risk. We introduce PredictionMarketBench, a SWE-bench-style benchmark for evaluating algorithmic and LLM-based trading agents on prediction markets via deterministic, event-driven replay of historical limit-order-book and trade data. PredictionMarketBench standardizes (i) episode construction from raw exchange streams (orderbooks, trades, lifecycle, settlement), (ii) an execution-realistic simulator with maker/taker semantics and fee modeling, and (iii) a tool-based agent interface that supports both classical strategies and tool-calling LLM agents with reproducible trajectories. We release four Kalshi-based episodes spanning cryptocurrency, weather, and sports. Baseline results show that naive trading agents can underperform due to transaction costs and settlement losses, while fee-aware algorithmic strategies remain competitive in volatile episodes.

Zebiao Li, Xueying Wu, Chengyi Tu

The global food system has metamorphosed from a loose aggregation of bilateral exchanges into a highly intricate, interdependent Global Food Trade Network (FTN). This comprehensive review synthesizes the extant literature to examine the FTN through the rigorous lens of complex network science, moving beyond traditional economic trade models to quantify the system's topological architecture. We delineate the network's historical transition from a unipolar, efficiency-driven system dominated by Western hegemony to a multipolar, regionalized structure characterized by high clustering and scale-free heterogeneity. Special emphasis is placed on the dual nature of connectivity, which functions simultaneously as a buffer against local production variances and a conduit for global contagion. By conceptualizing the FTN as a multiplex system-distinguishing between the robust topology of wheat, the brittle regionalism of rice, and the polarized "dumbbell" structure of soy-we elucidate the distinct structural vulnerabilities inherent in modern food security. Furthermore, we analyze the impact of recent high-magnitude shocks, specifically the COVID-19 pandemic and the Russia-Ukraine conflict, illustrating the critical trade-off between logistical efficiency and systemic resilience. The review concludes by assessing the future trajectory of the network under anthropogenic climate change, predicting a poleward migration of comparative advantage that necessitates a paradigm shift from isolationist protectionism to cooperative network redundancy.

Ren Liu, Yun Sun, Zongming Cai et al.

Understanding the interplay between plant leaf functional traits and plant and soil factors under different soil thicknesses is significant for quantifying the interaction between plant growth and the environment. However, in the context of ecological restoration of vegetation in mining areas, there has been a lot of research on trees, shrubs, and grasses, but the characteristics and correlations of leaf functional traits of vines have not been fully studied to a large extent. Here, we report the differences in leaf functional traits of six vine plants (<i>Parthenocissus quinquefolia</i>, <i>Pueraria lobata</i>, <i>Hedera nepalensis</i>, <i>Campsis grandiflora</i>, <i>Mucuna sempervirens</i>, and <i>Parthenocissus tricuspidata</i>) with distinct growth forms in different soil cover thicknesses (20 cm, 40 cm, and 60 cm). In addition, soil factor indicators under different soil cover thicknesses were measured to elucidate the linkages between leaf functional traits of vine plants and soil factors. We found that <i>P. lobata</i> showed a resource acquisition strategy, while <i>H. nepalensis</i> demonstrated a resource conservation strategy. <i>C. grandiflora</i> and <i>P. tricuspidata</i> shifted toward more conservative resource allocation strategies as the soil cover thickness increased, whereas <i>M. sempervirens</i> showed the opposite trend. In the plant trait–trait relationships, there were synergistic associations between specific leaf area (SLA) and leaf nitrogen content (LNC); leaf moisture content (LMC) and leaf nitrogen-to-phosphorus ratio (LN/P); and leaf specific dry weight (LSW), leaf succulence degree (LSD), and leaf dry matter content (LDMC). Trade-offs were observed between SLA and LSW, LSD, and LDMC; between leaf phosphorus content (LPC) and LN/P; and between LMC, LSW, and LDMC. In the plant trait–environment relationships, soil nutrients (pH, soil total phosphorus content (STP), and soil ammonium nitrogen content (SAN)) and soil enzyme activities (cellulase (CB), leucine aminopeptidase (LAP), enzyme C/N activity ratio, and enzyme N/P activity ratio) were identified as the primary drivers of variation in leaf functional traits. Interestingly, nitrogen deficiency constrained the growth of vine plants in the mining area. Our study revealed that the responses of leaf functional traits of different vines under different soil thicknesses have significant species specificity, and each vine shows different resource acquisition and conservation strategies. Furthermore, soil cover thickness primarily influences plant functional traits by directly affecting soil enzyme activities and nutrients. However, the pathways through which soil thickness impacts these traits differ among various functional traits. Our findings provide a theoretical basis and practical reference for selecting vine plants and optimizing soil cover techniques for ecological restoration in mining areas.

Fang Wang, Jiayi Li, Ting Xiao et al.

Abstract Background Cycas bifida is an endemic species distributed in limited areas of Yunnan and Guangxi, with a notably small population size. Under the context of global climate warming, this species may require introduction to colder habitats. Previous studies have confirmed its weak cold tolerance. To thoroughly investigate the molecular and metabolic mechanisms underlying its response to low temperature, this study employed low-temperature stress treatments combined with transcriptome sequencing and metabolomics technologies for systematic comparative analysis. Results Through multi-omics analysis, this study identified 1,960 differentially expressed genes in Cycas bifida under cold stress, showing an overall upregulation trend. Pathway enrichment analysis confirmed galactose metabolism and flavonoid biosynthesis as key pathways responding to cold stress. Co-expression network analysis revealed significant associations between the MEyellow module and melibiose, and between the MEgreen module and luteolin. Additionally, the core turquoise module was found to mediate a “metabolic trade-off” mechanism through the key gene pair CYCAS_003438/CYCAS_003468, promoting osmoprotectant production via galactose metabolism while suppressing flavonoid synthesis. These results systematically elucidate the molecular mechanisms by which Cycas bifida coordinates multiple gene modules to regulate metabolic pathways in response to cold stress. Conclusion This study identified a key gene module in Cycas bifida that coordinates carbon allocation through a metabolic trade-off mechanism, promoting osmoprotectant synthesis while suppressing flavonoid accumulation. This finding first reveals the suppression of flavonoid metabolism in cycads under cold stress, elucidating a novel mechanism of resource optimization through gene module reprogramming in plants, which provides a theoretical foundation for cold-tolerant breeding.

Aleksei P. Klementyev

Master agreements produced by trade associations - standardized framework contracts used in international markets - have largely replaced tailor-made documentation in the contemporary financial world. These agreements provide a reliable foundation for structuring obligations across a broad range of financial instruments, including forwards, options, swaps, repurchase agreements (repos), and securities lending transactions. Despite being developed in diverse regions, standard contracts for cross-border financial products exhibit several common features. A key feature is the “single agreement” concept, and its corresponding contractual clause. The article provides an overview of “single agreement” clauses found in international master agreements and examines two critical implications of this fundamental aspect of standard documentation. First, it explores the enforceability of close-out netting, a mechanism vital for managing counterparty risk in the event of default. Second, it discusses the application of a unified governing law to all elements of the standard documentation, which might otherwise be subject to various laws determined by conflict-of-laws rules. Beyond these legal applications, the single agreement also serves a technical function by uniting numerous schedules, annexes, confirmations, protocols, and other components of standard documentation withing a single legal framework.

Lewen Yan, Jilin Mei, Tianyi Zhou et al.

LLM-based trading agents are increasingly deployed in real-world financial markets to perform autonomous analysis and execution. However, their reliability and robustness under adversarial or faulty conditions remain largely unexamined, despite operating in high-risk, irreversible financial environments. We propose TradeTrap, a unified evaluation framework for systematically stress-testing both adaptive and procedural autonomous trading agents. TradeTrap targets four core components of autonomous trading agents: market intelligence, strategy formulation, portfolio and ledger handling, and trade execution, and evaluates their robustness under controlled system-level perturbations. All evaluations are conducted in a closed-loop historical backtesting setting on real US equity market data with identical initial conditions, enabling fair and reproducible comparisons across agents and attacks. Extensive experiments show that small perturbations at a single component can propagate through the agent decision loop and induce extreme concentration, runaway exposure, and large portfolio drawdowns across both agent types, demonstrating that current autonomous trading agents can be systematically misled at the system level. Our code is available at https://github.com/Yanlewen/TradeTrap.

Anna Lunghi, Matteo Castiglioni, Alberto Marchesi

Bilateral trade is a central problem in algorithmic economics, and recent work has explored how to design trading mechanisms using no-regret learning algorithms. However, no-regret learning is impossible when budget balance has to be enforced at each time step. Bernasconi et al. [Ber+24] show how this impossibility can be circumvented by relaxing the budget balance constraint to hold only globally over all time steps. In particular, they design an algorithm achieving regret of the order of $\tilde O(T^{3/4})$ and provide a lower bound of $Ω(T^{5/7})$. In this work, we interpolate between these two extremes by studying how the optimal regret rate varies with the allowed violation of the global budget balance constraint. Specifically, we design an algorithm that, by violating the constraint by at most $T^β$ for any given $β\in [\frac{3}{4}, \frac{6}{7}]$, attains regret $\tilde O(T^{1 - β/3})$. We complement this result with a matching lower bound, thus fully characterizing the trade-off between regret and budget violation. Our results show that both the $\tilde O(T^{3/4})$ upper bound in the global budget balance case and the $Ω(T^{5/7})$ lower bound under unconstrained budget balance violation obtained by Bernasconi et al. [Ber+24] are tight.

Salam Rabindrajit Luwang, Anish Rai, Md. Nurujjaman et al.

Statistical analysis of high-frequency stock market order transaction data is conducted to understand order transition dynamics. We employ a first-order time-homogeneous discrete-time Markov chain model to the sequence of orders of stocks belonging to six different sectors during the USA-China trade war of 2018. The Markov property of the order sequence is validated by the Chi-square test. We estimate the transition probability matrix of the sequence using maximum likelihood estimation. From the heat-map of these matrices, we found the presence of active participation by different types of traders during high volatility days. On such days, these traders place limit orders primarily with the intention of deleting the majority of them to influence the market. These findings are supported by high stationary distribution and low mean recurrence values of add and delete orders. Further, we found similar spectral gap and entropy rate values, which indicates that similar trading strategies are employed on both high and low volatility days during the trade war. Among all the sectors considered in this study, we observe that there is a recurring pattern of full execution orders in Finance & Banking sector. This shows that the banking stocks are resilient during the trade war. Hence, this study may be useful in understanding stock market order dynamics and devise trading strategies accordingly on high and low volatility days during extreme macroeconomic events.

Stefan Wahl, Armand Rousselot, Felix Draxler et al.

Modeling distributions that depend on external control parameters is a common scenario in diverse applications like molecular simulations, where system properties like temperature affect molecular configurations. Despite the relevance of these applications, existing solutions are unsatisfactory as they require severely restricted model architectures or rely on energy-based training, which is prone to instability. We introduce TRADE, which overcomes these limitations by formulating the learning process as a boundary value problem. By initially training the model for a specific condition using either i.i.d.~samples or backward KL training, we establish a boundary distribution. We then propagate this information across other conditions using the gradient of the unnormalized density with respect to the external parameter. This formulation, akin to the principles of physics-informed neural networks, allows us to efficiently learn parameter-dependent distributions without restrictive assumptions. Experimentally, we demonstrate that TRADE achieves excellent results in a wide range of applications, ranging from Bayesian inference and molecular simulations to physical lattice models.

Starodubova Anna, Iskhakova Dinara

There is no universal innovative strategy that could fit every digital enterprise in the regions. When forming an innovative strategy of digital enterprises, regions should take into account market demand generated by small, medium-sized (SMEs) and large enterprises. The share of large enterprises (5%) is much smaller in the world in relation to the number of SMEs (95%). In most countries (38 out of 43), the number of large enterprises is less than 0.01%. Correlation coefficients (calculated according to the Kendall method and the Spearman method) showed an average level of associations between innovation activity of digital enterprises in the region and the size of enterprises (small and medium-sized enterprises and large enterprises). Deviations from the supply and demand in the market of innovations of digital enterprises are determined using the scoring method in 43 regions. The researchers proposed a classification of five strategies of innovative activity of digital enterprises in the region. The criteria for classifying strategies included: priority of innovations of digital enterprises in the national market; deviation in demand and supply in the market of technologies for the digital enterprises; class of innovative activity of the digital enterprises in the region. Regions of the high level of innovation activity apply a strategy of introducing innovations at SMEs and large enterprises in the domestic market. Most regions have a 74% shortage of demand in the domestic market (except for China, India) and apply an implementation strategy based on the external market, without priority to a certain size of the enterprise. A well-chosen strategy contributes to the growth of the introduction of patents of digital enterprises, without disturbing the balance between SMEs and large enterprises to achieve sustainable development goals in the regions. Starting from 2022, regions are recommended to revise the innovation strategies. This is necessary due to the transition of regions to the development of domestic markets due to the reduction in foreign trade.

Navoda Nirmani Liyanapathirana, Amanda Grech, Mengyu Li et al.

Abstract Objective: To quantify the full life cycle impacts of ultra-processed foods (UPF) for key environmental, economic and nutritional indicators to identify trade-offs between UPF contribution to broad-scope sustainability. Design: Using 24-h dietary recalls along with an input–output database for the Australian economy, dietary environmental and economic impacts were quantified in this national representative cross-sectional analysis. Food items were classified into non-UPF and UPF using the NOVA system, and dietary energy contribution from non-UPF and UPF fractions in diets was estimated. Thereafter, associations between nutritional, environmental and economic impacts of non-UPF and UPF fractions of diets were examined using a multi-dimensional nutritional geometry representation. Setting: National Nutrition and Physical Activity Survey 2011–2012 of Australia. Participants: Respondents (n 5344) aged > 18 years with 1 d of 24-h dietary recall data excluding respondents with missing values and outlier data points and under reporters. Results: Australian diets rich in UPF were associated with reduced nutritional quality, high greenhouse gas emissions, energy use, and increased employment and income associated with the food supply chains. The environmental and economic impacts associated with the UPF portion of diets become more distinct when the diets are standardised to average protein recommendation. Conclusion: Increased consumption of UPF has socio-economic benefits, but this comes with adverse effects on the environment and public health. Consideration of such trade-offs is important in identifying policy and other mechanisms regarding UPF for establishing healthy and sustainable food systems.

A. M. Titovich, A. V. Toropygin

This study discusses the extra-regional influence and scenarios for the development of regional integration of the countries of the Association of States Southeast Asia (ASEAN), in particular, the role of the future free trade agreement between the EAEU and Indonesia in maintaining the effect of convergence in Southeast Asia (Southeast Asia) in the horizon of 2025.Aim. To Determine the main trends in the development of extra-regional influence and forecasts for regional economic convergence in ASEAN countries by 2025.Tasks. Determine the trends and perspective scenarios for the development of the integration of the countries of Southeast Asia.Methods. Both the descriptive method and the system analysis method are used. Also, this study uses the approaches of the Eurasian Development Bank to assess the disproportion in the level of development of the ASEAN countries (macroeconomic indicators; in the analysis of time series of GDP, GDP per capita for ASEAN countries, the Box-Jenkins methodology (ARIMA) is used. The key concept in this study is convergence. Convergence is seen as the process of approaching a certain level or decreasing the difference between two values over time [7]; real convergence contributes to the convergence of the economic level of countries within an integrated group (differences in the economic levels of development of countries hinder the process of integration). The effect of real economic convergence for ASEAN countries in the future 2025 can take on different meanings depending on the implementation of the initiatives proposed by non-regional actors (for example, Indo-Pacific Economic Structure and FTA between the EAEU and Indonesia).Results. The results of the study present four probability-ranked scenarios for the development of regional economic convergence in the ASEAN countries. It has been established that the most likely scenario is one in which the initiative of the EAEU and the Republic of Indonesia will take place, while the Indo-Pacific Economic Structure proposed by the United States will not be implemented by 2025. In this case, regional economic convergence in the ASEAN countries may take on the most favorable meaning.Conclusions. Along with the global trend, the expansion of economic integration through the creation of free trade zones with other integration associations, the development of economic integration in the region is influenced by the rivalry between the People’s Republic of China and the United States of America. Looking forward to the mid-second half of the 2020s. years as a result, the adoption of a free trade agreement between the EAEU countries and the Republic of Indonesia will have a positive impact on the increase in the pace of regional economic convergence in Southeast Asia.

Dalia AIELLO, Carlo BREGANT, Antonia CARLUCCI et al.

Many fungi belonging to Botryosphaeriaceae are well-known as causal agents of diseases in economically and ecologically important agricultural crops and forest trees. In Italy, the high diffusion of Botryosphaeriaceae infections observed over the last decade, has shown the importance of this group of fungi, which are becoming limiting factors for plant production in agricultural systems, nurseries and natural and urban landscapes. Global warming and stress factors such as occasional extreme climatic events can affect the susceptibility of host plants, as well as fungus behaviour, increasing the risk of future infections. Available reports of Botryosphaeriaceae in Italy have been examined, focusing on wood and fruit pathogens, resulting in a list of ten genera and 57 species. Diplodia is the most widespread genus in Italy with 76 records on 44 hosts, while at species level, Neofusicoccum parvum, Botryosphaeria dothidea and Diplodia seriata show the widest host ranges and many records. The ability of the pathogens to remain latent on asymptomatic plants, and uncontrolled trade of plant materials among countries, facilitate the dissemination and potential introduction of new Botryosphaeriaceae species. Preventive detection and adequate control strategies are always needed to limit the potential damage caused by Botryosphaeriaceae. This review had particular emphasis on host-pathogen associations, disease symptoms, geographic distribution, metabolite production, and accurate pathogen identification.

Jai Pal

This research paper focuses on the integration of Artificial Intelligence (AI) into the currency trading landscape, positing the development of personalized AI models, essentially functioning as intelligent personal assistants tailored to the idiosyncrasies of individual traders. The paper posits that AI models are capable of identifying nuanced patterns within the trader's historical data, facilitating a more accurate and insightful assessment of psychological risk dynamics in currency trading. The PRI is a dynamic metric that experiences fluctuations in response to market conditions that foster psychological fragility among traders. By employing sophisticated techniques, a classifying decision tree is crafted, enabling clearer decision-making boundaries within the tree structure. By incorporating the user's chronological trade entries, the model becomes adept at identifying critical junctures when psychological risks are heightened. The real-time nature of the calculations enhances the model's utility as a proactive tool, offering timely alerts to traders about impending moments of psychological risks. The implications of this research extend beyond the confines of currency trading, reaching into the realms of other industries where the judicious application of personalized modeling emerges as an efficient and strategic approach. This paper positions itself at the intersection of cutting-edge technology and the intricate nuances of human psychology, offering a transformative paradigm for decision making support in dynamic and high-pressure environments.

Ruyi Liu, Jingzhi Tie, Zhen Wu et al.

The focus of this paper is on identifying the most effective selling strategy for pairs trading of stocks. In pairs trading, a long position is held in one stock while a short position is held in another. The goal is to determine the optimal time to sell the long position and repurchase the short position in order to close the pairs position. The paper presents an optimal pairs-trading selling rule with trading constraints. In particular, the underlying stock prices evolve according to a two dimensional geometric Brownian motion and the trading permission process is given in terms of a two-state {trading allowed, trading not allowed} Markov chain. It is shown that the optimal policy can be determined by a threshold curve which is obtained by solving the associated HJB equations (quasi-variational inequalities). A closed form solution is obtained. A verification theorem is provided. Numerical experiments are also reported to demonstrate the optimal policies and value functions.

Tommi Kotonen

The Nordic Resistance Movement (NRM) was banned in Finland in 2020 after a court process lasting more than two and half years. This article details how effective the ban has been and how the organization has adapted to the ban, both during the process and after the verdict. The NRM has followed strategies similar to previous proscription cases, especially National Action in the UK in 2016, with whom NRM members discussed and shared experiences before the banning process began. Adaptation has meant new organizational forms and the founding of new associations. Before the ban, some commentators argued that it would only radicalize NRM members and that they might move to clandestine actions. Based on court records, police investigation files, and materials gathered from registries for trade and associations, this article covers these and other concerns as well as explores whether the goals set for the ban by the authorities have been realized. Along with perspectives on radicalization and adaptation within and around the NRM, a short analysis of the financial activities of the NRM before and after the ban has also been conducted.

Keenan Amer, Karla Saavedra-Rodriguez, William C. Black et al.

The study of fitness costs of insecticide resistance mutations in <i>Aedes aegypti</i> has generally been focused on life history parameters such as fecundity, mortality, and energy reserves. In this study we sought to investigate whether trade-offs might also exist between insecticide resistance and other abiotic stress resistance parameters. We evaluated the effects of the selection for permethrin resistance specifically on larval salinity and thermal tolerance. A population of <i>A. aegypti</i> originally from Southern Mexico was split into two strains, one selected for permethrin resistance and the other not. Larvae were reared at different salinities, and the fourth instar larvae were subjected to acute thermal stress; then, survival to both stresses was compared between strains. Contrary to our predictions, we found that insecticide resistance correlated with significantly enhanced larval thermotolerance. We found no clear difference in salinity tolerance between strains. This result suggests that insecticide resistance does not necessarily carry trade-offs in all traits affecting fitness and that successful insecticide resistance management strategies must account for genetic associations between insecticide resistance and abiotic stress resistance, as well as traditional life history parameters.

Pengcheng Xia, Haoyu wang, Bingyu Gao et al.

The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap V2 exchange and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least \$16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem, and our approach can act as a whistleblower that identifies scam tokens at their early stages.