Objective: to provide a comprehensive legal and economic analysis of the validity of robot taxation as a measure to protect the labor market under the increasing automation, taking into account the socio-economic realities of Nigeria’s developing economy.Methods: the research is based on doctrinal and comparative legal methodology. The author systematically analyzed scientific publications, legislative acts, statistical data and empirical materials related to the impact of robotics and artificial intelligence on global labor markets. Special attention was paid to studying tax policy in the field of automation in South Korea and the European Union, in order to identify universal patterns and specific features of automation regulation in various jurisdictions. Methodological tools include content analysis of regulatory documents, economic and statistical analysis of data from international organizations, and a critical analysis of doctrinal provisions regarding the prospects for robot taxation.Results: the research demonstrates the ambiguity of the robot taxation institute in the modern legal and economic system. It was found that the robot taxation may slow down the pace of automation, provide workers with time to adapt and retrain, compensate for the reduction in income tax revenues and ensure economic equity by redistributing corporate income from automation. At the same time, significant limitations of this concept were identified: the risk of inhibiting innovation, the lack of a unified legal definition of the “robot”, the threat of capital outflow and the shift of production to jurisdictions with a more favorable tax environment. In relation to Nigeria, the conclusion is that a robot tax is premature due to low automation, high structural unemployment, the dominance of the informal employment sector, and poor digital infrastructure.Scientific novelty: the work is a systematic study of the legal and economic aspects of robot taxation in the Nigerian legal system. The study is novel as it substantiates a contextual approach to determining the feasibility of a robot tax, taking into account the stage of economic development, the structure of the labor market and the degree of penetration of automation technologies. For the first time, the author formulates the concept of responsible automation for developing economies, which implies not punitive taxation, but a system of incentives combining moderate fees with investments in human capital and digital infrastructure.Practical significance: the research results are valuable for forming state policy in the field of labor automation regulation. The proposed recommendations include the reform of corporate tax codes taking into account responsible automation, the introduction of mandatory assessment of the impact of automation on employment, the creation of a system of tax incentives for companies retraining workers displaced by technology, and the formation of a multilateral platform for ethical automation management. They can be used by the legislative and executive authorities of Nigeria and other developing countries to create legal mechanisms for regulating the digital economy and protecting workers’ rights under the technological transformation.
Global carbon emissions are rising despite historic investments in renewables and net-zero pledges from nearly every economy. The current study examines the dynamic relationship between economic activities and environmental sustainability with reference to carbon emissions from emerging economies using the IPAT model utilizing panel data of 24 emerging economies from 2000 to 2019. Moreover, the study also used robust least square and fixed effect models for robustness purposes. The findings revealed that population (β = 0.103), economic growth (β = 1.090), financial development (β = 0.498), human capital (β = 1.073), and industrial structure (β = 0.158) are influential factors that significantly contributed to carbon emissions. In contrast, renewable energy transition (β = −0.375) has been identified as a mitigating factor that reduces emissions through the adoption of cleaner energy sources. This study provides empirical evidence to support the Environmental Kuznets Curve hypothesis, which states that economic development increases environmental pollution at the initial stage but fosters environmental improvements in later stages of economic growth. In addition, the econometric model for the moderating effect indicated that financial development reinforces the impacts of energy transition (from −0.141 to −0.201) in reducing emission levels, and significantly reduces the positive impact of human capital (from 0.931 to 0.503) and industrial structure (from 0.957 to 0.005). Therefore, green financial systems should be promoted to reduce carbon industries for sustainable economic growth and development, to assist in transitioning toward environmental sustainability in emerging economies.
History of scholarship and learning. The humanities, Social Sciences
Large language models (LLMs), as a new generation of recommendation engines, possess powerful summarization and data analysis capabilities, surpassing traditional recommendation systems in both scope and performance. One promising application is investment recommendation. In this paper, we reveal a novel product bias in LLM investment recommendation, where LLMs exhibit systematic preferences for specific products. Such preferences can subtly influence user investment decisions, potentially leading to inflated valuations of products and financial bubbles, posing risks to both individual investors and market stability. To comprehensively study the product bias, we develop an automated pipeline to create a dataset of 567,000 samples across five asset classes (stocks, mutual funds, cryptocurrencies, savings, and portfolios). With this dataset, we present the bf first study on product bias in LLM investment recommendations. Our findings reveal that LLMs exhibit clear product preferences, such as certain stocks (e.g., `AAPL' from Apple and `MSFT' from Microsoft). Notably, this bias persists even after applying debiasing techniques. We urge AI researchers to take heed of the product bias in LLM investment recommendations and its implications, ensuring fairness and security in the digital space and market.
With electric power infrastructure posing an increasing risk of igniting wildfires under continuing climate change, utilities are frequently de-energizing power lines to mitigate wildfire ignition risk, which can cause load shedding. Recent research advocates for installing battery energy storage systems as well as undergrounding risky overhead lines to reduce the load shedding during such de-energizations. Since wildfire ignition risk can exhibit substantial geographic and temporal variations, it is important to plan battery installation and line undergrounding investments while considering multiple possible scenarios. This paper presents a scenario-based framework for optimizing battery installation and line undergrounding investments while considering many scenarios, each consisting of a day-long time series of uncertain parameters for the load demand, renewable generation, and wildfire ignition risks. This problem is difficult to solve due to a large number of scenarios and binary variables associated with the battery placements as well as the lines to be undergrounded. To address the computational challenges, we decompose the problem in a two-stage scheme via a Benders decomposition approach. The first stage is a master problem formulated as a mixed integer linear programming (MILP) model that makes decisions on the locations and sizes of batteries as well as the lines to be undergrounded. The second stage consists of a linear programming model that assesses these battery and line undergrounding decisions as modeled by a DC OPF formulation. We demonstrate the effectiveness of the proposed scheme on a large-scale transmission network with real world data on wildfire ignition risks, load, and renewable generation.
The relevance of the article is due to the fact that the issue of sovereignty is now firmly rooted in digital space and manifests in many areas. In particular, it is reflected in actions to relocalise production, support through the state, and attract investments. The purpose of this study is theoretical substantiation of the directions in forming a policy of technological sovereignty in the zones of advanced socio-economic development with the help of private and public capital. The following methods were used: comparative and logical analysis in studying of trends in the digital economy, synthesis and generalisation in determining the essence of technological sovereignty and formulating directions to improve these processes. The article gives reasons for the phenomenon being in fact an object of industrial policy, competition,innovative development, and geopolitical studies, which actualises the issue of its formation. Moreover, territories of advanced socio-economic development may become an effective mechanism in this case, as they have created favourable conditions to attract private and public capital. The research presents vectors of technological sovereignty establishment with consideration to the possibilities of a regional aspect (the mentioned territories) and an investment one (a combination of both types of capital).
Hiroki Sakaji, Ryotaro Kobayashi, Kiyoshi Izumi
et al.
In this paper, we attempt to summarize monthly reports as investment reports. Fund managers have a wide range of tasks, one of which is the preparation of investment reports. In addition to preparing monthly reports on fund management, fund managers prepare management reports that summarize these monthly reports every six months or once a year. The preparation of fund reports is a labor-intensive and time-consuming task. Therefore, in this paper, we tackle investment summarization from monthly reports using transformer-based models. There are two main types of summarization methods: extractive summarization and abstractive summarization, and this study constructs both methods and examines which is more useful in summarizing investment reports.
The growth of the tech startup ecosystem in Latin America (LATAM) is driven by innovative entrepreneurs addressing market needs across various sectors. However, these startups encounter unique challenges and risks that require specific management approaches. This paper explores a case study with the Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) metrics within the context of the online food delivery industry in LATAM, serving as a model for valuing startups using the Discounted Cash Flow (DCF) method. By analyzing key emerging powers such as Argentina, Colombia, Uruguay, Costa Rica, Panama, and Ecuador, the study highlights the potential and profitability of AI-driven startups in the region through the development of a ranking of emerging powers in Latin America for tech startup investment. The paper also examines the political, economic, and competitive risks faced by startups and offers strategic insights on mitigating these risks to maximize investment returns. Furthermore, the research underscores the value of diversifying investment portfolios with startups in emerging markets, emphasizing the opportunities for substantial growth and returns despite inherent risks.
Maaike B. Elgersma, Germán Morales-España, Karen I. Aardal
et al.
Fast and accurate large-scale energy system models are needed to investigate the potential of storage to complement the fluctuating energy production of renewable energy systems. However, standard Mixed-Integer Programming (MIP) models that describe optimal investment and operation of these storage units, including the optional capacity to provide up/down reserves, do not scale well. To improve scalability, the integrality constraints are often relaxed, resulting in Linear Programming (LP) relaxations that allow simultaneous charging and discharging, while this is not feasible in practice. To address this, we derive the convex hull of the solutions for the optimal operation of storage for one time period, as well as for problems including investments and reserves, guaranteeing that no tighter MIP formulation or better LP approximation exists for one time period. When incorporating this convex hull into a multi-period formulation and including it in large-scale energy system models, the improved LP relaxations can better prevent simultaneous charging and discharging, and the tighter MIP could positively affect the solving time. We demonstrate this with illustrative case studies of a unit commitment problem and a transmission expansion planning problem.
Recently, we introduced a new paradigm for alpha mining in the realm of quantitative investment, developing a new interactive alpha mining system framework, Alpha-GPT. This system is centered on iterative Human-AI interaction based on large language models, introducing a Human-in-the-Loop approach to alpha discovery. In this paper, we present the next-generation Alpha-GPT 2.0 \footnote{Draft. Work in progress}, a quantitative investment framework that further encompasses crucial modeling and analysis phases in quantitative investment. This framework emphasizes the iterative, interactive research between humans and AI, embodying a Human-in-the-Loop strategy throughout the entire quantitative investment pipeline. By assimilating the insights of human researchers into the systematic alpha research process, we effectively leverage the Human-in-the-Loop approach, enhancing the efficiency and precision of quantitative investment research.
The paper provides a mathematical model and a tool for the focused investing strategy as advocated by Buffett, Munger, and others from this investment community. The approach presented here assumes that the investor's role is to think about probabilities of different outcomes for a set of businesses. Based on these assumptions, the tool calculates the optimal allocation of capital for each of the investment candidates. The model is based on a generalized Kelly Criterion with options to provide constraints that ensure: no shorting, limited use of leverage, providing a maximum limit to the risk of permanent loss of capital, and maximum individual allocation. The software is applied to an example portfolio from which certain observations about excessive diversification are obtained. In addition, the software is made available for public use.
This study investigates the global relationship between venture capital (VC) investments and environmental pressure in order to contribute to the literature on the influence of venture capital on sustainable development. Using a unique dataset covering VC activity and CO2 intensity in 131 countries from 2011 to 2021, the study employs a revised STIRPAT model—a stochastic model for assessing the environmental impact of human activities. The aim is to examine the potential negative correlation between VC investments and CO2 intensity. This motivation stems from previous findings, indicating that increased VC investments spur the diffusion of eco-efficient technologies. The main results affirm a significant negative correlation between VC investments and CO2 intensity, even after controlling for relevant variables and potential confounding factors (e.g., foreign direct investments), country, and year fixed effects, and addressing potential endogeneity through lagging independent variables. Exploring heterogeneity in the baseline results reveals that these findings are consistent only for VC investments in the Asia-Pacific region, in emerging and developing economies, and in areas where they can contribute more to the development of green technologies and innovations. This suggests that VC activity may impact environmental intensity primarily in countries where emission regulations are less stringent or where existing technologies exhibit lower efficiency in terms of energy consumption.
High and continuous inflation in Iran's economy as a structural dilemma has adverse economic, political, and cultural outcomes, and to control the inflation, policymakers should employ appropriate and well-timed policies concuring to the economic structures of the country. Hence, this study points to distinguish and analyze the nature of inflation. For this reason, the present study examines the dynamics of the causal relationship between inflation and liquidity as well as the relationship between inflation and exchange rate by applying the continuous wavelet transform approach using monthly data during the years 1982 to 2021 in Iran’s economy. The results indicate: 1. Liquidity does not infulence the inflation rate in the long term and there is a reverse causality (causality from inflation to liquidity) and this result affirms the endogeneity of liquidity in the long term in Iran's economy. 2. The exchange rate growth shocks (from the supply side of the economy) affect inflation, in a way that the exchange rate altogether influences the inflation in both the short and long term.1.IntroductionAmid the last few decades, high and steady inflation has been a serious economic problem in Iran's economy. Empirical evidence suggests that in the years 1995, 1996, 2013, 2014, 2019, and 2020-21, Iran's economy has suffered from heavy and sequentional inflations. However, the perseverance of high inflation, especially since 2020, has turned into a fundamental problem. The main issue about the inflation in our country is not the inflation per se, but the critical status of it has faced development plans with great challenges for many years. Then again during the last decade, the economy tried to control inflation by restricting the growth of the money supply. But it appears that the results come to oppose established recommendations to curb the growth of liquidity. Therefore, the question raised in the present study is whether the high inflation rate in Iran's economy is due to the rise of the money supply.Although the relationship between inflation and liquidity in the economy has been examined in several studies, the significance of inflation and its relation with macroeconomic variables- the broad previous and subsequent link with other variables- exaggerates the study of the relationships among these variables and other macroeconomic variables in different time scales. In this regard, the present study examines the relationship among some key monetary and price variables in the economy (dynamics of the relationship between inflation and liquidity as well as inflation and exchange rate).2.Methodology and MethodsThere are several methods to examine the interrelationships of inflation, exchange rate, and liquidity that are commonly divided into the form of statistical methods as well as model-based methods. But, since the causal relationship between these variables is likely to change over time, so further exploration of those relationships requires techniques that consider the relationship between two variables over time and different time horizons (different friquencies). Unlike most statistical and econometric techniques, the wavelet approach does not require variables to be survivable, nor does it assume linear relationships between them. In contrast to time series techniques, the use of wavelet approaches, especially wavelet coherence and continuous wavelet transform approaches within the framework of the methodology of econophysics (econophysics), opens new horizons in the study of causality in time series, because it shows the possibility of dynamically examining effects at different frequencies by separating it to the short and long term. To this end, the present study, using the continuous wavelet transform approach, examines the dynamics of the causal relationship between inflation and liquidity and the relationship between inflation and exchange rate by applying monthly data during the years 1982:1 to 2020:12 in Iran’s economy.3. Discussion and ResultsGenerally speaking, based on what we've learned regarding the rooting of inflation in the our economy, it can be said that when the inflation rate increases and reaches a level higher than the average inflation (30 to 40 percent), such as when the average inflation rate shows lower figures, other monetary variables cannot be illustrative. Also, regarding the rooting of inflation, it can be said that in recent years, due to the adjustment policy, decrease of oil exports or sanctions, the demand for foreign currency exceeded its supply, and we witnessed instabilities in the exchange rate. Hence, the instability and fluctuations in the exchange rate and its concerned indicators do not exclusively follow monetary conditions.Therefore, the stability of exchange rates leads to the stability of prices and the limitation of monetary follow ups, and the resulting inflation itself causes more changes in the exchange rate in the next period.4. ConclusionIn the current economic situation, the appreciation of the exchange rate is the cause of inflation and high inflation is to a noteable extent the cause of the budget deficit and liquidity growth. Therefore, another factor is the supply side that causes inflation and is not a monetary factor. Therefore, in a situation where the endogenous creative forces of liquidity are active, relying on controlling the amount of money and liquidity as the goal of monetary policy and a solution to curb inflation will not work and will pave the way for speculators and unproductive agents. Therefore, in order to achieve the price stability, it is recommended that the monetary policy maker should a) avoid instant changes in relative and key prices (the most important of which is the exchange rate) and b) control the bank interest rate along with the structural reforms of the banking system in a way that the banking system moves toward optimal allocation of credit resources.
The ability to predict the stock market and analyze market trends is invaluable to researchers and anyone interested in investing. However, this task is a challenging problem due to a large number of parameters and unpredictable noise that may affect the stock price. To overcome this issue, researchers have employed numerous approaches such as Moving Average (MA), Support Vector Machine (SVM), and Neural Networks. With technological advances, deep learning methods have become popular in processing time-series data. In this paper, we compare two recently introduced deep learning models, namely a Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting daily movements of the Standard & Poor (S&P 500) index using the daily closing price of this index from 14/5/1991 to 14/5/2021. Results show that both models are effective and accurate in stock market prediction. In this case study, the mean squared error (MSE) and mean absolute error (MAE) for the GRU model are slightly lower than the LSTM model; hence, GRU outperformed the LSTM model despite its simpler structure. The results of this study are applicable in various instances where it is challenging to identify patterns among large volumes of unstructured data, such as medical data analysis, text mining, and financial time series modeling.
Land-use frontiers are territories with abundant land for agriculture and forestry, availability of natural resources relative to labor or capital and predisposed to rapid land-use change, often driven by large-scale land investments and capitalized actors, producing commodities for distal markets. Strategic spatial planning (SSP) represents a consolidated long-term governance practice across high- and low-income countries. One of the objectives of SSP processes is to articulate a more coherent and future-oriented spatial logic for the sustainability of land-use patterns and typologies, natural-resources protection, and investments. SSP may thus constitute a useful approach in addressing some of the challenges affecting land governance in frontier settings; to date, its potential contribution to land-use frontiers lacks explicit exploration. In this paper, we examine how SSP can play a role in governing land-use frontiers through a case-study analysis of Mozambique as an emerging frontier, located on the southeast coast of Africa. We gathered empirical evidence by interviewing experts involved in resource management, territorial planning, and development in the country. The theoretical spine of the paper builds on the literature focusing on land-use challenges and SSP. We show that emerging land-use frontiers face several challenges, such as transnational land deals and the intensification of commercial plantations. Interview data show that several structural factors are hindering the establishment of a long-term territorial development strategy. These are, among others, the short-termism of political cycles and the absence of a long-term strategic vision. Our analysis reveals that SSP processes could contribute to addressing land-use challenges in frontier contexts, such as poverty traps and land degradation spirals, should various local and distant actors join forces and marry interests. We conclude by presenting a systematic rationale, explaining how SSP could play a role in governing land-use frontiers, with a view to promoting the well-being and sustainability of rural communities.
Brazil's adherence to the OECD Code of Liberalisation of Capital Movements may increase its capacity to attract foreign investments - at least, this is the official discourse. Nevertheless, the Code's binding structure may reduce its power to secure legal mechanisms for managing capital movements, especially in times of crisis. For a country placed on the periphery of the international monetary system hierarchy, there are institutional challenges to be considered. The purpose of this article is to discuss the legal structure of the OECD's Code, in contrast to "institutional view" on capital flows of the IMF, of which Brazil has been a member since 1946. This article also proposes institutional approaches for national diplomatic actions, which can contribute to the construction of the OECD's decisions related to the Code to meet the demands of emerging economies such as Brazil.
Cristina Vasilica ICOCIU, Ana-Maria IFRIM, Cătălin Ionuț SILVESTRU
et al.
In the current context, in which the pandemic crisis is wreaking havoc in all socio-economic fields, causing both loss of life and immeasurable economic losses, fundamental research conducted to identify directions for rapid and sustainable socio-economic recovery is of great importance. Over time, the agricultural sector has played a particularly important role in accelerating the expansion and economic development of the least developed countries and their integration into world trade. The basic hypothesis is that, over time, agriculture has been the premise for the economic development of the world's states, the agricultural transition being quite well correlated with general growth processes. By improving their competitiveness and strengthening their agricultural production capacities, the countries of the world can recover from any crisis, especially after a health crisis. The work aims to present the possible impact that agriculture can have on economic development by applying known principles and foundations. Making investments in agriculture, increasing the level of employability leads to the development of this branch of the economy and implicitly of the other branches of industry that are connected with agriculture, such as the food industry. Using a mathematical algorithm for studying the impact of the evolution of the agricultural labour (salaried) in the context in which it is desired that agriculture be a sustainable branch from the point of view of employability at the level of the European Union. The results of this study can then be materialized in strategic directions of development of every country, in the hope of moving to another level of development. In the context in which agriculture is the basis for the horizontal development of other industries (such as the food industry) it is necessary to carry out an investment program and research programs in the agricultural field. Applying Lewis's model of development, governments must strive to increase agricultural productivity while stimulating capital accumulation and in the other sectors of the economy.
Reddit's WallStreetBets (WSB) community has come to prominence in light of its notable role in affecting the stock prices of what are now referred to as meme stocks. Yet very little is known about the reliability of the highly speculative investment advice disseminated on WSB. This paper analyses WSB data spanning from January 2019 to April 2021 in order to assess how successful an investment strategy relying on the community's recommendations could have been. We detect buy and sell advice and identify the community's most popular stocks, based on which we define a WSB portfolio. Our evaluation shows that this portfolio has grown approx. 200% over the last three years and approx. 480% over the last year, significantly outperforming the S&P500. The average short-term accuracy of buy and sell signals, in contrast, is not found to be significantly better than randomly or equally distributed buy decisions within the same time frame. However, we present a technique for estimating whether posts are proactive as opposed to reactive and show that by focusing on a subset of more promising buy signals, a trader could have made investments yielding higher returns than the broader market or the strategy of trusting all posted buy signals. Lastly, the analysis is also conducted specifically for the period before 2021 in order to factor out the effects of the GameStop hype of January 2021 - the results confirm the conclusions and suggest that the 2021 hype merely amplified pre-existing characteristics.
Spatial analyses for the City of Zagreb are mostly produced by the Department for Spatial Information and Research of the Zagreb City Office for Strategic Planning and Development, which is also the coordinator of Zagreb Infrastructure Spatial Data (Croatian acronym: ZIPP). Based on an extensive database, spatial research, analyses, indicators and analytical bases can be accessed for the needs of strategic planners and other users.
Examples from practice are described which are publicly available on the web pages of the City of Zagreb, and which were produced at the request of city administrative bodies or private use, from analyses of population density, access to public transport, access to public green spaces, the network of preschool and primary school facilities, strategic city projects, capital investments in buildings for social activities, and public architecture-urbanism tenders, to registering damage after the earthquakes in Zagreb.
Spatial analyses provide data which encourage the rational use of spatial resources and informed city administration. New features are interactive web applications with publicly available data which achieve transparency on the part of the city administration. One of the advantages is that they can be refreshed in real time.
The classic economy indicates the traditional production factors (labour, nature and technical capital) as responsible for continuing improvement of the economy, the currency theories emphasize the financial capital, so that the ideology of knowledge society would identify these factors in human capital and its creative, innovative potential. Financing education followed up by training in the professional field in order to increase the skills and knowledge of an individual supports economic growth, thus the need to develop a national strategies of the educational capital. Ongoing spending toward education and health are investments made specifically for the growth of economy and prosperity of the citizens. Studies confirm the strong links bounded by human capital and labour income and that there are variations of incomes according to the degree reached by an individual during the years spent for education and professional practice, the earnings increase according to educational degree.