Hasil untuk "Finance"

Oliver Slumbers, Benjamin Patrick Evans, Sumitra Ganesh et al.

Game theory has traditionally had a relatively limited view of risk based on how a player's expected reward is impacted by the uncertainty of the actions of other players. Recently, a new game-theoretic approach provides a more holistic view of risk also considering the reward-variance. However, these variance-based approaches measure variance of the reward on both the upside and downside. In many domains, such as finance, downside risk only is of key importance, as this represents the potential losses associated with a decision. In contrast, large upside "risk" (e.g. profits) are not an issue. To address this restrictive view of risk, we propose a novel solution concept, downside risk aware equilibria (DRAE) based on lower partial moments. DRAE restricts downside risk, while placing no restrictions on upside risk, and additionally, models higher-order risk preferences. We demonstrate the applicability of DRAE on several games, successfully finding equilibria which balance downside risk with expected reward, and prove the existence and optimality of this equilibria.

Hoyoung Lee, Junhyuk Seo, Suhwan Park et al.

In finance, Large Language Models (LLMs) face frequent knowledge conflicts arising from discrepancies between their pre-trained parametric knowledge and real-time market data. These conflicts are especially problematic in real-world investment services, where a model's inherent biases can misalign with institutional objectives, leading to unreliable recommendations. Despite this risk, the intrinsic investment biases of LLMs remain underexplored. We propose an experimental framework to investigate emergent behaviors in such conflict scenarios, offering a quantitative analysis of bias in LLM-based investment analysis. Using hypothetical scenarios with balanced and imbalanced arguments, we extract the latent biases of models and measure their persistence. Our analysis, centered on sector, size, and momentum, reveals distinct, model-specific biases. Across most models, a tendency to prefer technology stocks, large-cap stocks, and contrarian strategies is observed. These foundational biases often escalate into confirmation bias, causing models to cling to initial judgments even when faced with increasing counter-evidence. A public leaderboard benchmarking bias across a broader set of models is available at https://linqalpha.com/leaderboard

Abdulrahman Alhaidari, Bhavani Kalal, Balaji Palanisamy et al.

Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users' trust and drain liquidity from token pools on Decentralized Exchanges (DEXs), leaving users with worthless tokens. Although rug pulls in Ethereum and Binance Smart Chain (BSC) have gained attention recently, analysis of rug pulls in Solana remains largely under-explored. In this paper, we introduce SolRPDS (Solana Rug Pull Dataset), the first public rug pull dataset derived from Solana's transactions. We examine approximately four years of DeFi data (2021-2024) that covers suspected and confirmed tokens exhibiting rug pull patterns. The dataset, derived from 3.69 billion transactions, consists of 62,895 suspicious liquidity pools. The data is annotated for inactivity states, which is a key indicator, and includes several detailed liquidity activities such as additions, removals, and last interaction as well as other attributes such as inactivity periods and withdrawn token amounts, to help identify suspicious behavior. Our preliminary analysis reveals clear distinctions between legitimate and fraudulent liquidity pools and we found that 22,195 tokens in the dataset exhibit rug pull patterns during the examined period. SolRPDS can support a wide range of future research on rug pulls including the development of data-driven and heuristic-based solutions for real-time rug pull detection and mitigation.

Ashraf Ghiye, Baptiste Barreau, Laurent Carlier et al.

Graph Neural Networks have significantly advanced research in recommender systems over the past few years. These methods typically capture global interests using aggregated past interactions and rely on static embeddings of users and items over extended periods of time. While effective in some domains, these methods fall short in many real-world scenarios, especially in finance, where user interests and item popularity evolve rapidly over time. To address these challenges, we introduce a novel extension to Light Graph Convolutional Network (LightGCN) designed to learn temporal node embeddings that capture dynamic interests. Our approach employs causal convolution to maintain a forward-looking model architecture. By preserving the chronological order of user-item interactions and introducing a dynamic update mechanism for embeddings through a sliding window, the proposed model generates well-timed and contextually relevant recommendations. Extensive experiments on a real-world dataset from BNP Paribas demonstrate that our approach significantly enhances the performance of LightGCN while maintaining the simplicity and efficiency of its architecture. Our findings provide new insights into designing graph-based recommender systems in time-sensitive applications, particularly for financial product recommendations.

Agostino Capponi, Zhaonan Qu

We show that Poisson regression, though often recommended over log-linear regression for modeling count and other non-negative variables in finance and economics, can be far from optimal when heteroskedasticity and sparsity -- two common features of such data -- are both present. We propose a general class of moment estimators, encompassing Poisson regression, that balances the bias-variance trade-off under these conditions. A simple cross-validation procedure selects the optimal estimator. Numerical simulations and applications to corporate finance data reveal that the best choice varies substantially across settings and often departs from Poisson regression, underscoring the need for a more flexible estimation framework.

Ting Miao, Pathairat Pastpipatkul, Xinhua Liu et al.

This study employs the stochastic frontier model (SFM) to analyze trade potential and efficiency in wheat and maize among Belt and Road Initiative (BRI) countries from 2002 to 2021, encompassing 45 countries for wheat trade and 55 for maize trade. The empirical findings reveal that economic development level, population growth, government efficiency, political stability, and regulatory quality are critical determinants of trade efficiency. Notably, World Trade Organization (WTO) membership exhibits a negative correlation with trade efficiency, potentially reflecting challenges in rule implementation and opportunity utilization among member states. In the context of maize trade, increased arable land area is inversely associated with efficiency, suggesting potential issues in managing large-scale agricultural regions or optimizing land use. The BRI’s impact on trade efficiency varies across countries, with Turkey and Hungary showing improved wheat trade efficiency, while Ethiopia and Georgia experienced declines. During the COVID-19 pandemic, effective disease management strategies and diversified trade mechanisms significantly influenced trade efficiency. Furthermore, the study reveals that larger economies do not necessarily outperform small and medium-sized economies in terms of trade potential. These findings contribute significantly to the literature on agricultural trade and offer valuable insights for policymakers, emphasizing the importance of enhancing government efficiency, political stability, and regulatory quality in the context of regional economic development initiatives such as the BRI. This research underscores the need for tailored approaches to trade policy and agricultural management, considering the unique characteristics and challenges faced by different economies along BRI.

David Hengsbach

Huned Materwala, Shraddha M. Naik, Aya Taha et al.

Decentralized Finance (DeFi) leverages blockchain-enabled smart contracts to deliver automated and trustless financial services without the need for intermediaries. However, the public visibility of financial transactions on the blockchain can be exploited, as participants can reorder, insert, or remove transactions to extract value, often at the expense of others. This extracted value is known as the Maximal Extractable Value (MEV). MEV causes financial losses and consensus instability, disrupting the security, efficiency, and decentralization goals of the DeFi ecosystem. Therefore, it is crucial to analyze, detect, and mitigate MEV to safeguard DeFi. Our comprehensive survey offers a holistic view of the MEV landscape in the DeFi ecosystem. We present an in-depth understanding of MEV through a novel taxonomy of MEV transactions supported by real transaction examples. We perform a critical comparative analysis of various MEV detection approaches, evaluating their effectiveness in identifying different transaction types. Furthermore, we assess different categories of MEV mitigation strategies and discuss their limitations. We identify the challenges of current mitigation and detection approaches and discuss potential solutions. This survey provides valuable insights for researchers, developers, stakeholders, and policymakers, helping to curb and democratize MEV for a more secure and efficient DeFi ecosystem.

Meisin Lee, Soon Lay-Ki

This paper presents our participation under the team name `Finance Wizard' in the FinNLP-AgentScen 2024 shared task #2: Financial Text Summarization. It documents our pipeline approach of fine-tuning a foundation model into a task-specific model for Financial Text Summarization. It involves (1) adapting Llama3 8B, a foundation model, to the Finance domain via continued pre-training, (2) multi-task instruction-tuning to further equip the model with more finance-related capabilities, (3) finally fine-tuning the model into a task-specific `expert'. Our model, FinLlama3\_sum, yielded commendable results, securing the third position in its category with a ROUGE-1 score of 0.521.

Huynh Ngoc Chuong, Nguyen Chi Hai

AbstractThe concept of social capital has gained significant attention in recent years due to its potential for improving individual and collective well-being, and for its significance in shaping social, economic, and political structures. This study aims to measure the social capital of rural Vietnam households with data from 2008 to 2016. The authors identified different aspects of household social capital as well as social capital proxies from livelihood papers. This paper applied the fundamental theories (the resource theories and network theories to measure the household social capital in Vietnam. We propose to apply the MIMIC model (multiple indicator multiple cause model) to construct the household social capital along with integrating the indicators in both views of household social capital. Results highlight the importance of understanding the multifaceted nature of social capital, which includes different forms of social networks, social participation, and social costs. The findings suggest that participation in diverse organizations plays a vital role in the formation of household social capital. In addition, the MIMIC model shows that participation in social networks is the most important factor in the formation of household social capital. Therefore, we give some implications for the measurement as well as characteristics in the social capital of households in Vietnam. The study contributes to the existing literature on social capital by emphasizing the importance of understanding the different aspects of social capital and how they interact with each other in shaping the livelihoods of rural Vietnamese households.

N. N. Shelomentseva, O. V. Grushina, T. A. Krasnoshtanova

In the present paper, the consequences of the introduction of project financing against the backdrop of crises in 2020 and 2022 are analyzed. The subject interactions in the course of housing construction under the conditions of project financing are considered. A multi-criteria economic-mathematical model for the interest coordination of economic subjects in housing construction has been proposed. The model permits to understand and evaluate the economic consequences of choosing the possible options from the standpoint of each of the economic subjectss. The numerical calculations of choosing two (in pairs), and all three (developer, bank and consumer) economic subjects were performed using the proposed multi-criteria model with the stated limitations. The MATLAB software was employed to solve optimization problems and plotting. The solutions acceptable to the subjects were chosen from a set of Pareto-optimal alternatives. Despite the fact that all subjects of housing construction are involved in the interaction, this interaction does not occur simultaneously, but in a complex subordinate manner: the bank took the dictating position in project financing, and the consumer pays for everything. The state should play a role of the subject, which should coordinate the interests of the developer, the bank and the population. The task of the state is to create such conditions in the housing construction market so that economic subjects are interested in coordination of their interests to find a compromise. This opens routes for further research.

Mianhao Hu, Juhong Yuan

To address the dual constraints of resource shortages and environmental degradation, the water resource green efficiency (WRGE) concept, which takes into account socioeconomic and green development, has been adopted as a basis for implementation of cleaner production strategies and sustainable economic development. In the present study, the meta-frontier undesirable super-efficiency slack-based measure (Meta-US-SBM) model, which allows for technological heterogeneity across regions, was employed to estimate WRGE in 38 regions in the four-city area in middle China in 2010–2019, and the technology gaps of different regions and categories were discussed. Subsequently, the improvement potential of WRGE (WEIP) in different regions was mapped using the slacks of water resource ecological footprint input and GDP output obtained using the Meta-US-SBM model. According to the results, the regions with the highest average WRGE under group-frontier and meta-frontier groups were Huangshi and Qianjiang, respectively, whereas the category with the highest average WRGE was EOU (regions where economic benefits outmatch urbanization benefits). Surprisingly, the WRGE technology gaps among different regions and categories showed considerable differences. We observed a negative correlation between WEIP and WRGE. Moreover, there were obvious differences in water resource ecological footprint improvement potential among different regions and categories. HIGHLIGHTS The meta-frontier undesirable super-efficiency slack-based measure model was used.; Water resource green efficiency (WRGE) was estimated in 38 regions in the four-city area.; Improvement potential of WRGE (WEIP) in different regions was mapped.; EOU (economic benefits outmatch urbanization benefits) had highest average WRGE.; Negative correlation was observed between WRGE improvement potential and WRGE.;

Shimin Zhu, Yuxi Hu, Di Qi et al.

Abstract Background The university years are a developmentally crucial phase and a peak period for the onset of mental disorders. The beliefs about the changeability of negative emotion may play an important role in help-seeking. The brief digital growth mindset intervention is potentially scalable and acceptable to enhance adaptive coping and help-seeking for mental health needs in university students. We adapted the Single-session Intervention on Growth Mindset for adolescents (SIGMA) to be applied in university students (U-SIGMA). This protocol introduces a two-armed waitlist randomized controlled trial study to examine the effectiveness and acceptability of U-SIGMA in promoting help-seeking among university students in the Greater Bay Area. Methods University students (N = 250, ages 18–25) from universities in the Greater Bay Area will be randomized to either the brief digital growth mindset intervention group or the waitlist control group. Participants will report on the mindsets of negative emotions, perceived control over anxiety, attitude toward help-seeking, physical activity, hopelessness, psychological well-being, depression, anxiety, and perceived stress at baseline and the 2-week and 8-week follow-ups through web-based surveys. A 30-min digital intervention will be implemented in the intervention group, with a pre- and post-intervention survey collecting intervention feedback, while the control group will receive the link for intervention after 8 weeks. Discussion This protocol introduces the implementation plan of U-SIMGA in multi-cities of the Greater Bay Area. The findings are expected to help provide pioneer evidence for the effectiveness and acceptability of the brief digital intervention for university students in the Chinese context and beyond and contribute to the development of accessible and effective prevention and early intervention for university students’ mental health. Trial registration HKU Clinical Trials Registry: HKUCTR-3012; Registered 14 April 2023.  http://www.hkuctr.com/Study/Show/7a3ffbc0e03f4d1eac0525450fc5187e .

Dangxing Chen, Luyao Zhang

Algorithm fairness in the application of artificial intelligence (AI) is essential for a better society. As the foundational axiom of social mechanisms, fairness consists of multiple facets. Although the machine learning (ML) community has focused on intersectionality as a matter of statistical parity, especially in discrimination issues, an emerging body of literature addresses another facet -- monotonicity. Based on domain expertise, monotonicity plays a vital role in numerous fairness-related areas, where violations could misguide human decisions and lead to disastrous consequences. In this paper, we first systematically evaluate the significance of applying monotonic neural additive models (MNAMs), which use a fairness-aware ML algorithm to enforce both individual and pairwise monotonicity principles, for the fairness of AI ethics and society. We have found, through a hybrid method of theoretical reasoning, simulation, and extensive empirical analysis, that considering monotonicity axioms is essential in all areas of fairness, including criminology, education, health care, and finance. Our research contributes to the interdisciplinary research at the interface of AI ethics, explainable AI (XAI), and human-computer interactions (HCIs). By evidencing the catastrophic consequences if monotonicity is not met, we address the significance of monotonicity requirements in AI applications. Furthermore, we demonstrate that MNAMs are an effective fairness-aware ML approach by imposing monotonicity restrictions integrating human intelligence.

Santanu Ganguly

Quantum machine learning (QML) is a cross-disciplinary subject made up of two of the most exciting research areas: quantum computing and classical machine learning (ML), with ML and artificial intelligence (AI) being projected as the first fields that will be impacted by the rise of quantum machines. Quantum computers are being used today in drug discovery, material & molecular modelling and finance. In this work, we discuss some upcoming active new research areas in application of quantum machine learning (QML) in finance. We discuss certain QML models that has become areas of active interest in the financial world for various applications. We use real world financial dataset and compare models such as qGAN (quantum generative adversarial networks) and QCBM (quantum circuit Born machine) among others, using simulated environments. For the qGAN, we define quantum circuits for discriminators and generators and show promises of future quantum advantage via QML in finance.

Bingqiao Luo, Zhen Zhang, Qian Wang et al.

In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralized applications. However, the growing popularity of DeFi has made it a target for fraudulent activities, resulting in losses of billions of dollars due to various types of frauds. To address these issues, researchers have explored the potential of artificial intelligence (AI) approaches to detect such fraudulent activities. Yet, there is a lack of a systematic survey to organize and summarize those existing works and to identify the future research opportunities. In this survey, we provide a systematic taxonomy of various frauds in the DeFi ecosystem, categorized by the different stages of a DeFi project's life cycle: project development, introduction, growth, maturity, and decline. This taxonomy is based on our finding: many frauds have strong correlations in the stage of the DeFi project. According to the taxonomy, we review existing AI-powered detection methods, including statistical modeling, natural language processing and other machine learning techniques, etc. We find that fraud detection in different stages employs distinct types of methods and observe the commendable performance of tree-based and graph-related models in tackling fraud detection tasks. By analyzing the challenges and trends, we present the findings to provide proactive suggestion and guide future research in DeFi fraud detection. We believe that this survey is able to support researchers, practitioners, and regulators in establishing a secure and trustworthy DeFi ecosystem.

Tatyana Dmitrievna Sinyavets

Subject. Remote work as a form of work management has become a fact of life for education, consulting and IT companies, banks, government agencies, trading companies, and other areas. The issue of managing employees’ values by shaping, maintaining, and developing organisational culture in Russian companies has become acute. The fact that managers from all levels appeared to be unprepared for the realities of remote work aggravated the problem of work motivation and reduced the loyalty of the personnel to the employer. Objectives. The purpose of the research is to study the influence of remote work management on the components of organisational culture. The study is important and timely since it identifies the most problematic components of organisational culture which require further maintenance and development. Methodology. The research methodology is based on rational cognition (concept, judgement, inference) and procedures of logical inference and conceptual framework for value management. To achieve the set goal the following methods of scientific knowledge were used: analysis, synthesis, and comparison. The results of the study were systematised using the structure of organisational culture components proposed by F. Harris and R. Moeran. The attitude of employees to various aspects of organisational culture was studied by conducting an online survey among university educators in Omsk and IT specialists from a Russian commercial bank. Conclusions: The results of the study showed a negative impact of remote work on half of the components of organisational culture, 30% of the components were neutral to the impact of remote employment and 10% experienced a positive impact. The obtained results should be taken into consideration by managers from all management levels since organisational culture is an effective tool for strategic management that allows maintaining employee loyalty to the corporate brand.

E. A. Evdokimova, A. Yu. Fomin, M. S. Yumatov

Currently, the external conditions for the functioning of industrial enterprises are formed in such a way that the achievement of their goals is seen impossible without the implementation of innovative activities. Due to the high cost of innovations, as well as the need to conduct these processes on an ongoing basis, industrial enterprises lack their own funds to finance innovation activities. The state is interested in the innovative development of industrial enterprises. This is one of the reasons why it participates in the formation of their funding sources. The article substantiates the need for state support, as well as analyzes the options for its provision by type of financing sources. For each type of sources, options for providing large-scale support, implying a large coverage area, as well as point-directed support, are also highlighted.

Yanli Hu, O. V. Plebanek

The article compares the theoretical foundations of ethno-cultural policy in the Eurasian region.Aim. The goal set by the authors is to compare two approaches in the theory of social dynamics, on the basis of which the ethno-cultural policy of the modern states of the region — Russia (USSR) and China — is based.Tasks. Identification of fundamental differences in the geopolitical strategies developed by the theorists of Eurasianism and Chinese scientists.Methods. In the context of the implementation of this task — a comparison of the methodological foundations of real political projects in the Eurasian space, through logical analysis, differences are established in the theories of geopolitical dynamics proposed in the Eurasian concept and in Chinese science. The method of comparative analysis of ethno-cultural and ethno-economic policies in the Eurasian region of the two most influential powers allows us to conclude that alternative theoretical concepts and their paradigmatic limitations are adequate to real geopolitical processes.Results. The study showed that the concept of Eurasian geocivilization, which was formed as overcoming the limitations of the Slavophile version of Russian civilization in the context of the collapse of traditional approaches in social theory and in the context of historical collisions of the early twentieth century, had a positive potential, partially realized in the policy of the Soviet state. But the conceptual limitations imposed by the inadequate theoretical basis — the theory of civilizations existing at that time — did not allow the formation of a single Eurasian cultural space to be completed. The Eurasian unity represented by the Soviet Union was consolidated by institutional means, but it was not realized as a cultural synthesis. Chinese scientists have proposed an alternative project for the integration of the Eurasian space — the “One Belt — One Road”, which began to be implemented already in the XXI century. This project is based on Marxist theory and concepts of the second half of the twentieth century, complimentary to Marxism or being neo-Marxist.Conclusions. The incompleteness and instability of the Eurasian project in the Soviet version is a consequence of the limitations inherent in the geopolitical theory itself, which does not take into account the positive potential of Marxist theory and social concepts developed in the second half of the twentieth century. Chinese scientists use the scientific potential of Eurasianism in interpreting the Russian mentality and consider the politics of modern Russia through the prism of Eurasian connotations, but Eurasianism as a geopolitical theory, in their opinion, has not passed the test of history. Therefore, Chinese policy in the Eurasian space is based on other theoretical foundations — in addition to classical Marxism, neo-Marxist approaches. The Chinese authors conclude, in this regard, that Russia is still facing the problem of choosing a geopolitical strategy, which in turn is due to the paradigmatic uncertainty of Russian science.