Maarten P. Scholl, Mahmoud Mahfouz, Anisoara Calinescu
et al.
While investment funds publicly disclose their objectives in broad terms, their managers optimize for complex combinations of competing goals that go beyond simple risk-return trade-offs. Traditional approaches attempt to model this through multi-objective utility functions, but face fundamental challenges in specification and parameterization. We propose a generative framework that learns latent representations of fund manager strategies without requiring explicit utility specification. Our approach directly models the conditional probability of a fund's portfolio weights, given stock characteristics, historical returns, previous weights, and a latent variable representing the fund's strategy. Unlike methods based on reinforcement learning or imitation learning, which require specified rewards or labeled expert objectives, our GAN-based architecture learns directly from the joint distribution of observed holdings and market data. We validate our framework on a dataset of 1436 U.S. equity mutual funds. The learned representations successfully capture known investment styles, such as "growth" and "value," while also revealing implicit manager objectives. For instance, we find that while many funds exhibit characteristics of Markowitz-like optimization, they do so with heterogeneous realizations for turnover, concentration, and latent factors. To analyze and interpret the end-to-end model, we develop a series of tests that explain the model, and we show that the benchmark's expert labeling are contained in our model's encoding in a linear interpretable way. Our framework provides a data-driven approach for characterizing investment strategies for applications in market simulation, strategy attribution, and regulatory oversight.
Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in deep learning and large language models (LLMs) for quant finance have improved predictive modeling and enabled agent-based automation, suggesting a potential paradigm shift in this field. In this survey, taking alpha strategy as a representative example, we explore how AI contributes to the quantitative investment pipeline. We first examine the early stage of quant research, centered on human-crafted features and traditional statistical models with an established alpha pipeline. We then discuss the rise of deep learning, which enabled scalable modeling across the entire pipeline from data processing to order execution. Building on this, we highlight the emerging role of LLMs in extending AI beyond prediction, empowering autonomous agents to process unstructured data, generate alphas, and support self-iterative workflows.
The paper presents a multifaceted analysis of the strategic impact of the Georgian railway corridor on the country’s economic development through an innovative synthesis of physical, mathematical, and financial modeling frameworks. Located on the Eurasian transit axis, the corridor is a critical artery for the flows of freight, capital, and innovation that underpin economic sustainability and regional competitiveness. Yet traditional macroeconomic approaches only partially capture these processes, while the systemic nature and diversity of corridor effects call for an integrated methodology, especially under mounting socioeconomic challenges such as energy-price volatility, external shocks, and shifting regional competition. The scientific novelty of the study lies in the simultaneous application of three advanced models: (1) economics-adapted Navier–Stokes equations to simulate operational flows and systemic risks; (2) a multidimensional Ricci Flow model to assess dynamics of economic inequality and structural transformation; and (3) an EVA (Economic Value Added) framework to analyze long-term value creation, investment efficiency, and capital allocation. Drawing on a multi-year dataset for the Georgian railway corridor, the empirical results indicate that modernization and operational efficiency are associated with higher liquidity velocity and lower residual risk; stress-testing reveals the corridor’s sensitivity to external shocks; Ricci Flow curvature maps parameters driving inclusion or polarization; and the EVA matrix indicates that a sustained annual growth rate of approximately 3% or more supports a positive EVA margin. Overall, the multi-model synthesis advances applied mathematical economics and offers a transferable methodology for evaluating complex infrastructure systems amid uncertainty and rapid technological change. Policy guidance centers on institutionalizing monitoring and stress-testing, prioritizing inclusion-enhancing parameters, optimizing investments, and strengthening resilience to socioeconomic challenges.
Sociology (General), Economic history and conditions
As China pursues its dual carbon goals—peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, the energy sector is central to the country’s climate strategy. This study investigates the impact of Environmental, Social, and Governance (ESG) performance on firm value in China’s energy sector, an industry critical to national carbon emissions and energy consumption. Using a panel dataset of 20,225 firm-year observations from A-share listed firms between 2016 and 2023, we apply regression models to assess how ESG performance affects firm value, with controls for industry characteristics and policy effects. The results show that ESG performance significantly enhances firm value, especially among non-state-owned firms and those in high-pollution industries. ESG performance also facilitates access to green bond financing, providing firms with enhanced capital for green investments, thereby boosting market value. Furthermore, we find that firms in regions with higher green development attention benefit more from ESG practices, with local carbon trading policies playing a key role in improving firm competitiveness and market performance. This study provides critical insights into how ESG strategies and carbon governance policies influence firm performance in the energy sector. The findings offer practical implications for policymakers aiming to support low-carbon industrial transformation and for firms seeking to integrate sustainability into their long-term strategic planning. These insights are crucial for driving the successful implementation of China’s dual carbon strategy.
Underground gasification combined cycle (UGCC) with carbon capture and storage (CCS) is regarded as a promising method of carbon-neutral coal gasification power generation, capable of effectively reducing greenhouse gas emissions and environmental pollution. However, due to high capital investment and uncertainties, the UGCC-CCS project has not yet been commercially deployed. In view of this, the study adopted the real options approach to perform the investment returns analysis of UGCC-CCS. In this study, several critical uncertain factors such as carbon price, technological process and policy incentives were considered. And the critical carbon for the investment were obtained and the recommendations for future project investments were provided. The results showed that investment returns cannot be obtained for UGCC-CCS power plants when the carbon market is ignored. Once UGCC-CCS power plants are integrated into the carbon market, the critical carbon price for investment is 518.49 CNY/t and 527.93 CNY/t under carbon capture and CCS, respectively, with the optimal investment time being 2032. When the feed-in tariff subsidy is 0.5 CNY/kWh or the research and development subsidy is 3 billion CNY, the optimal timing to invest can be advanced to 2027 and 2028, respectively.
We present a computer assisted proof for a result concerning a three player betting game, introduced by Angel and Holmes. The three players start with initial capital $x, y, z > 0$ respectively. At each step of this game two players are selected at random to bet on the outcome of a fair coin toss, with the size of the bet being the largest possible, namely the total capital held by the poorer of the two players at that time. The main quantity of interest is the probability of player 1 being eliminated (reaching 0 capital) first. Angel and Holmes have shown that this probability is not monotone decreasing as a function of the initial capital $x$ of player 1. They conjecture that if $x < y < z$ then player 1 would be better off (less likely to be eliminated first) by swapping their capital with another player. In this paper we present a computer-assisted proof of this conjecture. To achieve this, we introduce the theoretical framework MeshItUp, and then perform a two-stage reduction to make MeshItUp computationally feasible, through the use of mixed-integer programming.
Companies with excess capital can opt to: pay dividends to shareholders, buy back treasury shares for short-term shareholder benefits, or pursue M&A investments for long-term shareholder returns. Using difference in differences approach of event research methods combined with unique manually collected data sets, this paper investigates the market bias for three events: M&A, share repurchase, and dividend payment. The results show that information was leaked to the outside 1 day before it was officially announced at all events. When observing the company's performance 3 years after the event announcement, we also find that the market reaction is biased in M&A and stock dividend payment events, but accurate in the cash dividend payment and share repurchases. In addition, the market has the strongest and longest reaction to the news of the company buying back shares; has the weakest reaction to the stock dividend payment; has the shortest reaction to cash dividend payment; has a negative reaction to the acquisition company's stock, and has a positive reaction to the target company's stock. Our research has provided empirical evidence on the market response to published information, and supports CEOs make the most accurate choices when the company has an excess cash flow.
Introduction
Financial development is one of the effective factors on economic growth in different countries. The relationship between financial development and economic growth is influenced by various parameters and the economic structure of countries. One of the factors that can affect this relationship is the natural resource abundance and the degree of dependence on them. According to economic literature, natural resource abundance can impact financial efficiency, capital accumulation, and the optimal allocation and effectiveness of financial resources, thereby influencing the relationship between financial development and economic growth in resource-rich countries. This study aims to explore the impact of natural resource abundance on the relationship between economic growth and financial development through productivity in Iran.
Methodology
In order to investigate and empirically analyze the long-term and short-term dynamic relationship between variables, this research employs the Autoregressive Distributed Lag (ARDL) bounding test approach. The ARDL Bounding test method was developed by Pesaran and Shin (1999) and Pesaran et al. (2001). This method offers advantages over other conventional and previous cointegration methods, such as Johansen and Toda-Yamamoto approaches. Some advantages include applicability regardless of considering the order of cointegration between variables, its ability to handle cases where variables are I(0) or I(1), suitability for limited sample sizes, obtaining efficient estimates without risk of over-specification in long-run model relationships, and presenting a reduced form single-equation form rather than a systemic one for the long-run relationship.
Results and Discussion
Based on the results obtained from the research, financial development has not shown a significant impact on economic growth in Iran during the study period. This suggests that institutions and financial entities, particularly the banking system, have not effectively channeled financial resources towards productive investments and market stimulation. However, the abundance index of natural resources has demonstrated a positive and significant influence on economic growth.
Considering the substantial portion of Iran's GDP that is attributed to oil revenues, such a finding is not unexpected. The per capita capital impact on economic growth is also positive and statistically significant. Among the effective factors on economic growth in this study, this variable has exerted the most considerable impact, indicating that capital plays a crucial role in boosting economic growth in Iran. Estimating the factors affecting total factor productivity (which is calculated using the Solow method) also indicates that financial development has had a positive impact on total factor productivity during the study period. However, the ultimate impact of financial development on productivity is influenced by oil revenues, as per the estimated model. The negative and significant coefficient of the interaction variable between financial development and natural resource abundance suggests a negative effect of oil revenues on the relationship between financial development and productivity in Iran. This result could imply an indirect impact of resource abundance on the financial development-economic growth relationship through the productivity channel during the study period in Iran.
Furthermore, it signifies that the heavy dependence on oil, one of the most vital avenues for economic growth, has eroded the relationship with financial development. Hence, the research hypothesis, suggesting that the abundance of natural resources (oil dependence) weakens the relationship between financial development and economic growth in Iran due to its negative impact on productivity, is validated, and the "resource curse" hypothesis is confirmed for the study period in Iran.
Conclusion
The results of this study indicate that financial development had a positive and significant impact on total factor productivity in Iran. However, ultimately, it did not have a significant impact on economic growth. This is due to the abundance of natural resources (oil revenues) leading to a reduction in the positive influence of financial development on total factor productivity. As a consequence, it weakens the relationship between financial development and economic growth during the examined period in Iran. Based on these findings, it is plausible to confirm the hypothesis of the "resource curse" during the examined period in Iran.
The findings can encompass a set of policy recommendations for the Iranian economy. Firstly, the government should be aware of the indirect negative impact of oil dependence on the financial system and, consequently, on investment activities. It is logical for the government to maintain the degree of oil dependence at the lowest possible level, enhance economic diversification, and increase the contribution of other sectors to GDP growth. Additionally, the financial system should engage more in productive investment activities to strengthen its role in improving economic growth. In this regard, policymakers should pursue measures that facilitate improvement of banking intermediation efficiency.
Furthermore, one of the most significant mechanisms of the resource curse in oil-dependent economies is the mismanagement of these resources and neglect of human development. Easy access to oil revenues might exempt the government from investing in human capital development, which could potentially have a negative impact on the performance of the financial sector and other sectors of the economy. Therefore, it is recommended that policymakers prioritize the necessary prerequisites for enhancing human development, which plays a crucial role in enhancing productivity and investment efficiency.
Katarzyna Cyrkun, Zbigniew Małodobry, Karolina Nastaj-Sałek
et al.
The article analyzes rapid technological changes and the dynamically evolving geopolitical environment, emphasizing the need to adapt the strategies and operational structures of modern armed forces. The use of modern weapon systems, such as artificial intelligence, drones, robotics, cyber systems and advanced sensors, is becoming a key element of military effectiveness in the 21st century. The authors point out the challenges related to the integration of these technologies, which require a comprehensive approach covering technical, operational and strategic aspects. It is also important to take into account interoperability and the ethical and legal aspects of the use of autonomous weapon systems. Cybersecurity and effective management of financial resources and cooperation with the private sector and international partners are key to success. Ultimately, the ability to quickly adapt and be flexible, investments in human capital and a comprehensive approach to technology integration are necessary to ensure the safety and effectiveness of armed forces operations in a dynamically changing world.
Thaib Rizwan, Makwiyah A. Chaliluddin, Hayatun Nuvus
et al.
The fishing shipyard in Banda Aceh City is a privately owned shipyard and is managed in a family manner. The shipyard here is active in carrying out maintenance, repair and construction of new ships when there is demand from consumers. Shipyards in Banda Aceh City generally make ships made of wood. The problem that is currently being faced is that there are many abandoned ships due to lack of finance, natural resources, human resources and the environmental, this is an obstacle to the progress and development of shipyards. The purpose of this study is to determine the inhibiting factors that exist in shipyards in the city of Banda Aceh and find alternative solutions to these problems. The method used in this study is a survey method used to look at existing symptoms and collect data on factors related to research variables and then analyzed using the Fuzzy AHP method. The results of this study indicate that the financial inhibiting factor is the most influential factor in shipyards with a resulting value of 0.4635, the inhibiting factor of Natural Resources is worth 0.35675, the inhibiting factor of Human Resources is worth 0.2865 and the inhibiting factor from the environment is the inhibiting factor which is the lowest or less influential with a value of 0.14325. The alternative solutions to financial problems are capital loans and investments. An alternative for natural resources is the addition of a minimum stock to anticipate stock scarcity and delays in the delivery of materials and tools. The alternative for human resources is the existence of an office, organizational structure, and division of tasks as well as awareness of occupational health and safety. As for the alternatives for the environment, namely the need for buildings or installation of tarpaulins for areas where ships are built, good land management and studies of other natural impacts.
The Sustainable Development Goals (SDGs) were introduced by the United Nations in order to encourage policies and activities that help guarantee human prosperity and sustainability. SDG frameworks produced in the finance industry are designed to provide scores that indicate how well a company aligns with each of the 17 SDGs. This scoring enables a consistent assessment of investments that have the potential of building an inclusive and sustainable economy. As a result of the high quality and reliability required by such frameworks, the process of creating and maintaining them is time-consuming and requires extensive domain expertise. In this work, we describe a data-driven system that seeks to automate the process of creating an SDG framework. First, we propose a novel method for collecting and filtering a dataset of texts from different web sources and a knowledge graph relevant to a set of companies. We then implement and deploy classifiers trained with this data for predicting scores of alignment with SDGs for a given company. Our results indicate that our best performing model can accurately predict SDG scores with a micro average F1 score of 0.89, demonstrating the effectiveness of the proposed solution. We further describe how the integration of the models for its use by humans can be facilitated by providing explanations in the form of data relevant to a predicted score. We find that our proposed solution enables access to a large amount of information that analysts would normally not be able to process, resulting in an accurate prediction of SDG scores at a fraction of the cost.
Dipti Rani Hazra, Md. Shah Naoaj, Mohammed Mahinur Alam
et al.
Inspired by the recent debate on the macroeconomic implications of the new bank regulatory standards known as Basel III, we tried to find out in this study that the impact of Basel III liquidity and capital requirements in Bangladesh proposed by Basel Committee on Banking Supervision (BCBS, 2010a). A small set of macro variables, using a sample of 22 private commercial banks operating in Bangladesh for the period of 2010-2014, are used to estimate long-run relationships among the variables. The macroeconomic variables are included The profitability of banks, GDP, banks' lending to private sector, Net Stable Funding Ratio, Tier 1 capital Ratio, Interest rate spread, real interest rate. The cost is quantified using Driscoll and Kraay panel data models with fixed effect. Impact of higher capital and liquidity requirement on Interest rate spread and lending to private sector of banks were considered as the cost to the economy as a whole whereas impact of higher capital and liquidity requirement on profitability of banks(ROE) was considered as the cost of banks. Here it is found that, the interest rate level is positively affected by the tighter liquidity and capital requirements which driven toward lessen of the private sector lending of banks. The return on equity of banks varies negatively with the liquidity and capital. The economic costs are considerably below the estimated positive benefit that the reform should have by reducing the probability of banking crises and the associated banking losses (BCBS, 2010b).
This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of algorithmic investment strategies (AIS) construction. We propose the Mean Absolute Directional Loss (MADL) function, solving important problems of classical forecast error functions in extracting information from forecasts to create efficient buy/sell signals in algorithmic investment strategies. Finally, based on the data from two different asset classes (cryptocurrencies: Bitcoin and commodities: Crude Oil), we show that the new loss function enables us to select better hyperparameters for the LSTM model and obtain more efficient investment strategies, with regard to risk-adjusted return metrics on the out-of-sample data.
Samira Khonsha, Mehdi Agha Sarram, Razieh Sheikhpour
Portfolio allocation with Deep Reinforcement Learning (DRL) has been the focus of many researchers. In investing, a portfolio optimization strategy is selecting assets that maximize return on investment while minimizing the risk. Asset optimization involves balancing risk and return, where stock returns are profits over time, and risk is the standard deviation value of the asset's return. Many of the existing methods for portfolio optimization are essentially the expansion of diversification methods for assets in the investment. Signiant drawdowns and early entry into the share are still challenging in portfolio construction. The idea is that having a portfolio based on net money flow is less risky than allocating a portfolio based on historical data only and turbulence as risk aversion. This paper proposes a profitable stock recommendation framework for portfolio construction using the DRL model based on the net money flow (MNF) indicator. We develop a new risk indicator based on the intelligent net-flow behavior of smart money to help determine the optimal market timing for buying and selling. The experimental results of real-world trading scenario validation show that the model outperforms all the considered baselines and even the conventional Buy-and-Hold strategy. Moreover, in this paper, the effect of defining different environments made of various information with hyper parameter optimization on the performance of models has been investigated, and the performance of DRL-driven models in different markets and asset positions has been investigated. The empirical results show the dominance of DRL models based on MNF indicators.
We investigate the impact of big winner stocks on the performance of active and passive investment strategies using a combination of numerical and analytical techniques. Our analysis is based on historical stock price data from 2006 to 2021 for a large variety of global indexes. We show that the log-normal distribution provides a reasonable fit for total returns for the majority of world stock indexes but highlight the limitations of this model. Using an analytical expression for a finite sum of log-normal random variables, we show that the typical return of a concentrated portfolio is less than that of an equally weighted index. This finding indicates that active managers face a significant risk of underperforming due to the potential for missing out on the substantial returns generated by big winner stocks. Our results suggest that passive investing strategies, that do not involve the selection of individual stocks, are likely to be more effective in achieving long-term financial goals.
Crime can have a volatile impact on investments. Despite the potential importance of crime rates in investments, there are no indices dedicated to evaluating the financial impact of crime in the United States. As such, this paper presents an index-based insurance portfolio for crime in the United States by utilizing the financial losses reported by the Federal Bureau of Investigation for property crimes and cybercrimes. Our research intends to help investors envision risk exposure in our portfolio, gauge investment risk based on their desired risk level, and hedge strategies for potential losses due to economic crashes. Underlying the index, we hedge the investments by issuing marketable European call and put options and providing risk budgets (diversifying risk to each type of crime). We find that real estate, ransomware, and government impersonation are the main risk contributors. We then evaluate the performance of our index to determine its resilience to economic crisis. The unemployment rate potentially demonstrates a high systemic risk on the portfolio compared to the economic factors used in this study. In conclusion, we provide a basis for the securitization of insurance risk from certain crimes that could forewarn investors to transfer their risk to capital market investors.
Mohammad Ebrahim Sadeghi, Hamed Nozari, Hadi Khajezadeh Dezfoli
et al.
Examining the trend of the global economy shows that global trade is moving towards high-tech products. Given that these products generate very high added value, countries that can produce and export these products will have high growth in the industrial sector. The importance of investing in advanced technologies for economic and social growth and development is so great that it is mentioned as one of the strong levers to achieve development. It should be noted that the policy of developing advanced technologies requires consideration of various performance aspects, risks and future risks in the investment phase. Risk related to high-tech investment projects has a meaning other than financial concepts only. In recent years, researchers have focused on identifying, analyzing, and prioritizing risk. There are two important components in measuring investment risk in high-tech industries, which include identifying the characteristics and criteria for measuring system risk and how to measure them. This study tries to evaluate and rank the investment risks in advanced industries using fuzzy TOPSIS technique based on verbal variables.
Major events like natural catastrophes or the COVID-19 crisis have impact both on the financial market and on claim arrival intensities and claim sizes of insurers. Thus, when optimal investment and reinsurance strategies have to be determined it is important to consider models which reflect this dependence. In this paper we make a proposal how to generate dependence between the financial market and claim sizes in times of crisis and determine via a stochastic control approach an optimal investment and reinsurance strategy which maximizes the expected exponential utility of terminal wealth. Moreover, we also allow that the claim size distribution may be learned in the model. We give comparisons and bounds on the optimal strategy using simple models. What turns out to be very surprising is that numerical results indicate that even a minimal dependence which is created in this model has a huge impact on the control in the sense that the insurer is much more prudent then.
This article highlights the problems of determining infrastructural factors in the system of socioeconomic and cultural development of regions. Using systemic and synergetic approaches, methods of analysis and synthesis, induction and deduction, comparative analysis, it is justified that a term “infrastructure” in its modern sense is the basis of the economic system and its components, its internal organization, which guarantees its integrity. At the same time, the socio-economic meaning of the concept of “regional infrastructure” is to provide the necessary conditions for economic and social development of territories. The important role of regional infrastructure stems from its functions, which can be divided into internal (specialized) and external (regional). Internal functions are a number of important economic and social functions performed by each enterprise, and element, and subdivision of the regional infrastructure. At the same time, external ones are to ensure a comprehensive and properly balanced development of the region in accordance with its resource potential and certain specialization. In order to unconditionally improve the situation in the regional infrastructure, today many hopes of the regional and local authorities are placed on attracting external investment capital as the most promising area for financing its modernization. It is worth noting that a number of powerful foreign and domestic companies that have the appropriate experience in solving such large-scale problems are ready to become investors and implement the best achievements of organizational and technical experience in the relevant regions of Ukraine. At the same time, it is established that at this time the regions of Ukraine are trying to use their resources as efficiently as possible for further development, are looking for new opportunities to use their potential. Its disclosure and effective use, taking into account the defining regional features and the corresponding economic specialization, require a proper increase in the efficiency of all elements of the regional infrastructure. However, whatever the features of its classification, it is undeniable that it plays a key role in implementing the strategy implementation of appropriate processes of modernization of the regional economy. So, the term “infrastructure” in the modern sense is the basis of the economic system and its components, its internal organization, which guarantees its integrity. Meanwhile, the socio-economic meaning of the concept of “regional infrastructure” is to provide appropriate conditions for economic and social development of the region. Simultaneously, the more developed and modernized it is, the more attractive any unit of local self-government becomes for various investors, in particular, from the outside, which creates new jobs, reduces unemployment and increases consumer demand. In addition, today the regions of Ukraine are trying to make the most efficient use of their resources for further development, are looking for new opportunities to use their full potential. Its disclosure and effective use, taking into account regional characteristics and economic specialization, need to increase the efficiency of all elements of regional infrastructure. Contemporarily, despite the diversity of approaches to understanding its essence and the existence of different concepts, it is logical to highlight its specific levels, each of the elements of which performs its function in the economic system of the region. Therefore, the classification of regional infrastructure on a functional basis into production, social and market is the most accurate. As the analysis of its selected elements presented in this work shows, the close interrelation between them and economic development of regions is traced. The higher level of its development causes the growth of foreign investments and increase of labour resources, acceleration of economic development and growth of living standards of the population of the region. Conversely, lowering the level of infrastructural development slows down production and may lead to a decline in living standards. However, whatever the features of its classification, it is undeniable that it primarily plays one of the most important roles when it comes to implementing a strategy for modernization of the regional economy.