Seyyed Ali Sadat, Joseph E. B. Lemieux, Joshua M. Pearce
Canada’s fossil fuel production is highly subsidized despite the pollution. In the Province of Alberta, subsidies for oil and gas total approximately CAD 1.78 billion/year. This study develops a model to quantify the impacts of shifting fossil fuel subsidies towards solar photovoltaic (PV) capital investments. Although solar is already the lowest-cost form of electricity, such a subsidy shift would accelerate the renewable energy transition. This study found such a shift would enable the installation of 1.53 GW of new solar PV capacity annually with the current investment tax credit (ITC) or 1.07 GW without it. These new solar PV systems can generate 2.02 TWh/year of clean electricity if the ITC is applied on capital investments, and 1.41 TWh/year without it. Solar electricity is cost-competitive with natural gas generation, with levelized costs ranging from 49.01 to 61.97 CAD/MWh with the ITC, and from 63.62 to 80.45 CAD/MWh without the ITC across Alberta. High solar resource locations in Alberta, including Lethbridge (49.01 CAD/MWh) and Calgary (49.28 CAD/MWh), achieve lower costs than natural gas (51.80 CAD/MWh). This excludes carbon externalities, fuel price volatility, and the long-term operational subsidies required to maintain fossil fuel competitiveness, suggesting that solar PV systems are already an economically rational alternative. Shifting Alberta’s fossil fuel subsidies is a solution for Canada’s 2050 net-zero commitments. Solar–fossil fuel generation parity would be achieved by 2040 with the ITC credits or by 2045 without it. The subsidy redirection can reduce Alberta’s grid emission intensity from the current 450 kg-CO<sub>2</sub>e/MWh to 68.8 kg-CO<sub>2</sub>e/MWh (with the ITC) or 119.2 kg-CO<sub>2</sub>e/MWh (without the ITC) by 2050, representing reductions of 84.7% and 73.5%, respectively.
The cryptocurrency market offers significant investment opportunities but faces challenges including high volatility and fragmented information. Data integration and analysis are essential for informed investment decisions. Currently, investors use three main approaches: (1) Manual analysis across various sources, which depends heavily on individual experience and is time-consuming and prone to bias; (2) Data aggregation platforms-limited in functionality and depth of analysis; (3) Large language model agents-based on static pretrained models, lacking real-time data integration and multi-step reasoning capabilities. To address these limitations, we present Coinvisor, a reinforcement learning-based chatbot that provides comprehensive analytical support for cryptocurrency investment through a multi-agent framework. Coinvisor integrates diverse analytical capabilities through specialized tools. Its key innovation is a reinforcement learning-based tool selection mechanism that enables multi-step planning and flexible integration of diverse data sources. This design supports real-time interaction and adaptive analysis of dynamic content, delivering accurate and actionable investment insights. We evaluated Coinvisor through automated benchmarks on tool calling accuracy and user studies with 20 cryptocurrency investors using our interface. Results show that Coinvisor improves recall by 40.7% and F1 score by 26.6% over the base model in tool orchestration. User studies show high satisfaction (4.64/5), with participants preferring Coinvisor to both general LLMs and existing crypto platforms (4.62/5).
Overseas investment and trade can be daunting for beginners due to the vast amount of complex information. This paper presents a chatbot system that integrates natural language processing and information retrieval techniques to simplify the document retrieval process. The proposed system identifies the most relevant content, enabling users to navigate the intricate landscape of foreign trade and investment more efficiently. Our methodology combines the BM25 model and a deep learning model to rank and retrieve documents, aiming to reduce noise in the document content and enhance the accuracy of the results. Experiments with Thai natural language queries have demonstrated the effectiveness of our system in retrieving pertinent documents. A user satisfaction survey further validated the system's effectiveness. Most respondents found the system helpful and agreed with the suggested documents, indicating its potential as a valuable tool for Thai entrepreneurs navigating foreign trade and investment.
Does AI conform to humans, or will we conform to AI? An ethical evaluation of AI-intensive companies will allow investors to knowledgeably participate in the decision. The evaluation is built from nine performance indicators that can be analyzed and scored to reflect a technology's human-centering. The result is objective investment guidance, as well as investors empowered to act in accordance with their own values. Incorporating ethics into financial decisions is a strategy that will be recognized by participants in environmental, social, and governance investing, however, this paper argues that conventional ESG frameworks are inadequate to companies that function with AI at their core. Fully accounting for contemporary big data, predictive analytics, and machine learning requires specialized metrics customized from established AI ethics principles. With these metrics established, the larger goal is a model for humanist investing in AI-intensive companies that is intellectually robust, manageable for analysts, useful for portfolio managers, and credible for investors.
We analyze an irreversible investment decision for a project which yields a flow of future operating profits given by a geometric Brownian motion with unknown drift. In contrast to similar optimal stopping problems with incomplete information, the agent's payoff now depends directly on the unknown drift and not only indirectly through the underlying dynamics. Hence, many standard arguments are not applicable. Nonetheless, we show that it is optimal to invest in the project if the current profit level exceeds a threshold depending on the current belief for the true state of the unknown drift. These thresholds are described by a boundary function, for which we establish structural properties like monotonicity and continuity. To prove these, we identify a central class of stopping times with useful features. Moreover, we characterize the boundary function as the unique solution of a nonlinear integral equation. Building on this characterization we compute the boundary function numerically and investigate the value of information.
Nouman Afgan, Muhammad Zubair Mumtaz, Robert M. Kunst
We apply panel estimation techniques to full population of Austrian corporations from 2007-2020 in order to analyze the impact of ownership concentration on performance. Return on investment (ROI) is lower than cost of capital, which insinuates that managers invest beyond optimal investment level instead of maximizing shareholders’ wealth. ROI for pyramidal firms is 35% lower than cost of capital implying that managers pursue their objectives. State-owned firms’ ROI is 28% lower than cost of capital showing that discretionary investments lead to sub-optimal performance. An inverted U-curve is estimated with a turning point at 69% voting rights, beyond which entrenchment effect dominates the incentives effect for 37% firms. This evidence confirms minority shareholders’ expropriation, which has repercussions for efficient governance in Austrian corporations.
In this paper, we study an optimal mean-variance investment-reinsurance problem for an insurer (she) under a Cramér-Lundberg model with random coefficients. At any time, the insurer can purchase reinsurance or acquire new business and invest her surplus in a security market consisting of a risk-free asset and multiple risky assets, subject to a general convex cone investment constraint. We reduce the problem to a constrained stochastic linear-quadratic control problem with jumps whose solution is related to a system of partially coupled stochastic Riccati equations (SREs). Then we devote ourselves to establishing the existence and uniqueness of solutions to the SREs by pure backward stochastic differential equation (BSDE) techniques. We achieve this with the help of approximation procedure, comparison theorems for BSDEs with jumps, log transformation and BMO martingales. The efficient investment-reinsurance strategy and efficient mean-variance frontier are explicitly given through the solutions of the SREs, which are shown to be a linear feedback form of the wealth process and a half-line, respectively.
The socio-economic conditions in the North of Russia exert a significant influence on financial relations. Of particular importance is the unique budgetary mechanism within the complex constituent entities of the Russian Federation, exemplified by the Tyumen region and its autonomous districts. This macro-region presents a distinctive case of financial redistribution among subjects, which is governed by the Cooperation program. The objective of this article is to elucidate the nuances of financial redistribution among the constituent entities of Russia within the 2015-2025 timeframe under the framework of horizontal budgetary control outlined in the Cooperation program. The primary redistribution trajectories include the reallocation of financial resources, specifically corporate income tax, from the North, where it is generated, to the South, where it is utilized, and their partial return in the form of budgetary investments, provision of public services, and other intragovernmental transactions to the budgets of autonomous districts. In recent years, a notable shift has been observed, involving the abandonment of spending on capital construction and an increase in other intragovernmental transactions. A direct correlation has been identified between the funding allocated to the program and the redistribution coefficient. This coefficient is proposed to be calculated as the ratio of other intragovernmental transactions to the corporate income tax going into the Tyumen region’s budget from the territories of autonomous districts. Should the trend of substituting budgetary investments and public services provision with other intragovernmental transactions persist, it raises the possibility of a modification in the program’s format or a reassessment of its funding source. The presented case study of horizontal financial redistribution among regions may offer valuable insights for Far East regions with a similar sectoral structure, characterized by a predominant extractive sector in the economy. Elements of this financial redistribution mechanism could prove beneficial within the integration processes in the new regions of Russia.
Abstract Background Breastfeeding is the biological norm for feeding infants and young children. When mothers’ breastmilk is unavailable, donor human milk (DHM) from a human milk bank (HMB) becomes the next option for small vulnerable newborns. A comprehensive cost analysis is essential for understanding the investments needed to establish, operate, and scale up HMBs. This study aims to estimate and analyze such costs at the first facility established in Vietnam. Methods An activity-based costing ingredients (ABC-I) approach was employed, with the cost perspective from service provision agencies (specifically, the project conducted at Da Nang Hospital for Women and Children and Development Partners). Estimated financial costs, based on actual expenditures, were measured in 2023 local currency and then converted to 2023 US dollars (USD). We examined three scenarios: 1) direct start-up costs + indirect start-up costs + implementation costs, 2) direct start-up costs + implementation costs, and 3) capital costs + implementation costs over the 6.5 years of operation. Results The total start-up cost was USD 616,263, with total expenditure on direct activities at USD 228,131 and indirect activities at USD 388,132. Investment in equipment accounted for the largest proportion (USD 84,213). The monthly costs of Da Nang HMB were USD 25,217, 14,565, and 9,326, corresponding to scenarios 1, 2, and 3, respectively. Over HMB's 6.5 years of operation, on average, the unit costs were USD 166, USD 96, and USD 62 for DHM received and USD 201, USD 116, and USD 74 for pasteurized DHM meeting specified criteria in the corresponding scenarios. Unit costs were highest in the initial six months, decreased, and reached their lowest levels after a year. Then, the unit costs experienced an increase in late 2020 and early 2021. Conclusion Although the unit cost of DHM in Da Nang HMB is comparable to that in certain neighboring countries, intentional measures to reduce disposal rates, improve HMB efficiency, motivate more community-based donors, and establish an HMB service network should be implemented to lower costs.
Stakeholders are interested in creating an institutional environment in regions that promotes green economic growth. However, the increased risks of failing to achieve sustainable development goals are shifting stakeholders’ focus towards strategic evidence-based and data- driven approaches to environmental, social, and governance (ESG) strategies. This article aims to identify the drivers and barriers to green ESG strategies in Russian regions. Using regression analysis, institutional factors affecting the green index, calculated based on national sustainable development indicators, were identified. The green index proposed by the author includes indicators of water quality, green urbanization, and local climate impact. The results showed that the drivers of effective green ESG strategies are environmental investments, activities of small and medium- sized enterprises in the region, high-quality urbanization, and the development of public transportation infrastructure. Barriers to green performance include regional unemployment and a low level of health capital, reflected through mortality from cardiovascular diseases and cancers. The findings of this study can be used for green monitoring of the effectiveness of regional ESG strategies.
This study investigates the effects of innovation on global competitiveness within the framework of the EU13 and EU15 countries. Using a Pooled Driscoll and Kraay regression analysis that takes into account unit and time effects, the research illuminates the relationship between the Human Development Index (HDI), which represents Global Competitiveness, Research Expenditure, the Number of Researchers, the Number of Patents, and the Human Capital Index. The findings reveal that while the number of researchers and research expenditure lack a statistically significant effect on HDI in both groups, a significant positive correlation was found between HDI and both the number of patents and human capital. More specifically, for EU13 countries, an increase in the number of patents and human capital leads to respective increases of 0.005 and 0.04 in the HDI, while for EU15 countries, these figures stand at 0.0008 and 0.03 respectively. The study concludes that investment strategies aimed at enhancing human capital and increasing the number of patent applications can notably improve global competitiveness. For EU13 countries, in particular, greater effort in the mentioned areas is needed to narrow the gap with their EU15 counterparts. In addition, despite the indirect impact on competitiveness, it is recommended for EU13 countries to boost their R&D investments and foster technological transfer to enhance their innovation capabilities. The findings from this study underscore the pivotal role of innovation in achieving global competitiveness and suggest a need for stronger collaboration within the European Union, particularly in scientific and technological fields, to facilitate knowledge and skill exchange.
Abdulrahamn Naser, A. N. Bany-Ariffin, Bolaji Tunde Matemilola
This paper investigates the direct association between cash flow volatility and capital structure (i.e. debt ratio). This study further examines the moderating role of fixed assets on the association between cash flow volatility and capital structure in the Middle East and North Africa (MENA) and African markets. This study applies a two-step system generalized method of moment regression as the main estimation technique to minimize endogeneity concern. The data consist of non-financial listed firms in 20 MENA and African countries covering 2011 to 2020. The results reveal that cash flow volatility is significantly and positively related to capital structure of MENA and African firms. The results also reveal that fixed assets have a negative moderating impact on the relationship between cash flow volatility and the capital structures of MENA and African firms. The results are robust to different estimation techniques. The findings inform managers to consider cash flow stability as a major factor in corporate risk management and strategic decision making and consider fixed asset investment decisions and the quality of fixed assets as a significant factor in debt choice. Moreover, policymakers should formulate efficient capital structure policies that consider cash flow stability factors and encourage fixed asset investments.
Capital One Financial Corp. has announced what it calls a “community benefits plan” that includes commitments of more than $265 billion in lending, investment, and philanthropy over five years as part of its proposed acquisition of Discover Financial Services. The plan was developed in partnership with the National Association for Latino Community Asset Builders, NeighborWorks America, the Opportunity Finance Network and the Woodstock Institute, each of which will serve as philanthropic partners as the company implements its commitments.
This exploratory study examines which investing characteristics determine success in an equity market. Based on data from 403 respondents, exploratory factor analysis results in 13 factors: middle/long time horizon, qualitative analyst, open-minded/disciplined, organized, emotional stability, naïve, growth stock, concentrated portfolio, contrarian, value stock, globalized, intrinsic value, and price-independent. Multiple linear regression of individual investors' excess return on these factors show statistically significant relationship. These results deepen our knowledge on what sort of investing characteristics are required to survive in equity markets.
Mario Kendziorski, Leonard Göke, Christian von Hirschhausen
et al.
In this paper, we explore centralized and more decentral approaches to succeed the energiewende in Germany, in the European context. We use the AnyMOD framework to model a future renewable-based European energy system, based on a techno-economic optimization, i.e. cost minimization with given demand, including both investment and the subsequent dispatch of capacity. The model includes 29 regions for European countries, and 38 NUTS-2 regions in Germany. First the entire energy system on the European level is optimized. Based on these results, the electricity system for the German regions is optimized to achieve great regional detail to analyse spatial effects. The model allows a comparison between a stylized central scenario with high amounts of wind offshore deployed, and a decentral scenario using mainly the existing grid, and thus relying more on local capacities. The results reveal that the cost for the second optimization of these two scenarios are about the same: The central scenario is characterized by network expansion in order to transport the electricity from the wind offshore sites, whereas the decentral scenario leads to more photovoltaic and battery deployment closer to the areas with a high demand for energy. A scenarios with higher energy efficiency and lower demand projections lead to a significant reduction of investment requirements, and to different localizations thereof.
This paper builds a core-satellite model of semi-static Kelly betting and log-optimal investment. We study the problem of a saver whose core portfolio consists in unlevered (1x) retirement plans with no access to margin debt. However, the agent has a satellite investment account with recourse to significant, but not unlimited, leverage; accordingly, we study optimal controllers for the satellite gearing ratio. On a very short time horizon, the best policy is to overbet the satellite, whereby the overriding objective is to raise the aggregate beta toward a growth-optimal level. On an infinite horizon, by contrast, the correct behavior is to blithely ignore the core and optimize the exponential growth rate of the satellite, which will anyways come to dominate the entire bankroll in the limit. For time horizons strictly between zero and infinity, the optimal strategy is not so simple: there is a key trade-off between the instantaneous growth rate of the composite bankroll, and that of the satellite itself, which suffers ongoing volatility drag from the overbetting. Thus, a very perspicacious policy is called for, since any losses in the satellite will constrain the agent's access to leverage in the continuation problem. We characterize the optimal feedback controller, and compute it in earnest by solving the corresponding HJB equation recursively and backward in time. This solution is then compared to the best open-loop controller, which, in spite of its relative simplicity, is expected to perform similarly in practical situations.
This paper studies the transmission of US monetary policy shocks into Emerging Markets emphasizing the role of investment and financial heterogeneity. First, we use a panel SVAR model to show that a US interest tightening leads to a persistent recession in Emerging Markets driven by a sharp reduction in aggregate investment. Second, we study the role of firms' financial heterogeneity in the transmission of US interest rate shocks by exploiting detailed balance sheet dataset from Chile. We find that more indebted firms experience greater drops in investment in response to a US tightening shock than less indebted firms. This result is at odds with recent evidence from US firms, even when using the same identification strategy and econometric methods. Third, we rationalize this finding using a stylized model of heterogeneous firms subject to a tightening leverage constraint. Finally, we present evidence in support of this hypothesis as well as robustness checks to our main results. Overall, our results suggests that the transmission channel of US monetary policy shocks within and outside the US differ, a result novel to the literature.
Mohammad Hossein Amjadi, Ali Reza Shakibaei, Sayyed Abdolmajid Jalaee
The purpose of the study is to portray the effect of exchange rates, its uncertainty and covid-19 pandemic on house prices in Tehran using the monthly data from Mar, 2016 to Mar, 2021. In order to calculate the uncertainty, IGARCH model and to estimate the mean equation, the ARDL method have been used. According to research results, the effect of exchange rate and exchange rate uncertainty index on housing prices as the objectives of this study, are positive and significant. Accordingly, a 100% increase in the exchange rate and the exchange rate uncertainty index will cause a 14% and 6% increase in housing prices in Tehran, respectively. Therefore, any action that reduces uncertainty in the future situation of the foreign exchange market can be effective in reducing the negative effects on housing supply and demand. Also, the results of model estimation show that the outbreak of Corona virus has acted as a shock and increased housing prices in Tehran.