Hasil untuk "Capital. Capital investments"

Alfred Marshall

Bingzheng Chen, Jan Dhaene, Chun Liu et al.

This paper develops a dynamic equilibrium model of the insurance market that jointly characterizes insurers' underwriting, investment, recapitalization, and dividend policies under model uncertainty and financial frictions. Competitive insurers maximize shareholder value under a subjective worst-case probability measure, giving rise to liquidity-driven underwriting cycles and flight-to-quality behavior. While an equilibrium typically fails to exist in such dynamic liquidity management framework with external financial investment, we show that incorporating model uncertainty restores equilibrium existence under plausible parameter conditions. Moreover, the model uncovers a novel relationship between the correlation of insurance and financial market risks and the equilibrium insurance price: negative loadings may emerge when insurance gains and financial returns are positively correlated, contrary to conventional intuition.

Yegandi Imhotep Paul Alagidede

This article documents the design, construction, cost, and productive performance of the Little Legon Mushroom (LLM) Farm, a small-scale oyster mushroom (Pleurotus ostreatus) growing facility established in Little Legon, Greater Accra, Ghana. The farm was deliberately constructed around existing Leucaena leucocephala (white lead) trees, incorporating living vegetation into the structural and environmental design of the growing house — a strategy that simultaneously eliminates structural timber costs, provides natural shade, improves microclimate conditions, and enhances the aesthetic character of the facility. The article presents the complete construction process, itemised material costs, and a comparative materials analysis contrasting conventional-timber and tree-integrated construction approaches. It develops a detailed financial analysis at current market prices of GH₵55–75 (~$5.1–6.9) per kilogram of fresh oyster mushrooms, demonstrating that the LLM facility — producing 60–85 kg per harvest cycle with four to five cycles per year — generates annual gross revenue of GH₵13,200–21,250 (~$1,214–1,955) with a conservative net return that compares favourably with land-based agricultural investments of comparable capital requirement. The article further addresses the farm's role in household nutrition, its integration with animal husbandry through by-product feed for poultry, birds, and rabbits maintained on-site, and its employment-creation potential at two persons per facility. The LLM Farm is explicitly framed as an experiment in replication: the article presents a scalable model — from the household single-unit growing house to village-cluster enterprise networks — and identifies the research gaps that must be addressed to realise mushroom cultivation's full potential as a food security and income-generation intervention in sub-Saharan Africa.

Andrew B. Abel, N. Mankiw, Lawrence H. Summers et al.

V. D. Richards, Eugene J. Laughlin

Innocentus Alhamis

This paper explores key theoretical frameworks instrumental in understanding the relationship between sustainability and institutional investment decisions. The study identifies and analyzes various theories, including Behavioral Finance Theory, Modern Portfolio Theory, Risk Management Theory, and others, to explain how sustainability considerations increasingly influence investment choices. By examining these frameworks, the paper highlights how investors integrate Environmental, Social, and Governance (ESG) factors to optimize financial outcomes and align with broader societal goals.

Giorgia Callegaro, Claudio Fontana, Caroline Hillairet et al.

We develop a continuous-time stochastic model for optimal cybersecurity investment under the threat of cyberattacks. The arrival of attacks is modeled using a Hawkes process, capturing the empirically relevant feature of clustering in cyberattacks. Extending the Gordon-Loeb model, each attack may result in a breach, with breach probability depending on the system's vulnerability. We aim at determining the optimal cybersecurity investment to reduce vulnerability. The problem is cast as a two-dimensional Markovian stochastic optimal control problem and solved using dynamic programming methods. Numerical results illustrate how accounting for attack clustering leads to more responsive and effective investment policies, offering significant improvements over static and Poisson-based benchmark strategies. Our findings underscore the value of incorporating realistic threat dynamics into cybersecurity risk management.

Mahdiyeh Rezagholizadeh, Hossein Jafari, Narges Kafi

The resource curse hypothesis, as an important area of research, addresses the complex relationship between natural resource revenues and financial development. The impact of natural resource revenues on the financial development of any country can be influenced by factors such as technological innovation, financial market risk, and institutional quality. It is believed that these factors can influence whether natural resources act as a blessing or a curse. In this line, the present study examined the impact of natural resource revenues on financial development, the development of financial institutions, and the development of financial markets. Applying the fully modified ordinary least squares (FMOLS) model to data from 2000 to 2021, the analysis focused on the role of technological innovation, financial risk, and institutional quality in a selected group of developing countries with rich natural resources. According to the findings, natural resource revenues contribute to the development of financial markets, with technological innovation and institutional quality enhancing this positive effect, while financial market risk diminishes it. The findings showed that although the resource curse hypothesis is supported regarding the impact of natural resource revenues on overall financial development and the development of financial institutions, technological innovation and institutional quality mitigate this negative effect, thereby undermining the resource curse hypothesis. However, financial market risk intensifies the resource curse hypothesis.IntroductionNatural resources serve as a foundation for a country’s economic growth and development. The countries endowed with natural resources should leverage them to achieve sustainable economic growth. In other words, natural resources are viewed as a driving force for transforming developing and emerging economies into developed ones. Many economists support the idea that economic development significantly depends on resource abundance. However, a growing body of empirical evidence suggests that countries rich in natural resources often experience lower economic growth compared to those without such resources. This paradoxical relationship between resource abundance and slower economic progress was first introduced by Auty (2002) as the resource curse hypothesis. Despite numerous studies on natural resource wealth, a definitive answer has yet to be found regarding whether natural resources are ultimately a blessing or a curse. In the literature on the resource curse, the impact of natural resource revenues on financial development remains particularly complex. Researchers highlight several key factors-such as technological innovation (TIN), financial market risk (FMR), and institutional quality (IQ)-as critical in determining whether natural resources are a blessing or a curse. The present study aimed to examine the impact of natural resource revenues on financial development.Materials and MethodsThis study aimed to examine the impact of natural resource revenues on financial development, with a particular focus on testing the resource curse hypothesis. A fully modified ordinary least squares (FMOLS) model was applied to analyze the data from 2000 to 2021. The analysis focused on a group of resource-rich developing countries, including Iran, Russia, Saudi Arabia, China, Brazil, Argentina, Mexico, Chile, the Democratic Republic of the Congo, the Republic of the Congo, Nigeria, Egypt, India, Indonesia, Malaysia, Qatar, and Vietnam. The modeling approach is based on three key factors—technological innovation, financial market risk, and institutional quality—that may influence the relationship between natural resource revenues and financial development. These variables were incorporated into the model through interaction terms. This allows for an assessment of how each variable shapes the relationship between natural resource revenues and financial development. Based on the proposed theoretical framework and previous research, the general form of the panel data model used in this study is as follows:(1)   (2)   (3) Moreover, three financial development indicators were considered as dependent variables across Models (1) to (3). In Model (1), the dependent variable FD represents the overall financial development index; in Model (2), FI denotes the financial institutions development index; and in Model (3), FM stands for the financial markets development index. Other variables were defined as follows: NRR for natural resource revenues, TIN for technological innovation, FMR for financial market risk, IQ for institutional quality, and GDP for gross domestic product.Results and DiscussionThe results of the FMOLS model estimation indicated that natural resource revenues had a negative impact on overall financial development and the development of financial institutions in a selection of resource-rich developing countries, thereby confirming the resource curse hypothesis in these two areas. However, natural resource revenues positively influenced the development of financial markets. Furthermore, the FMOLS estimates for the technological innovation showed a positive relationship with all three indicators: financial development, financial institution development, and financial market development. This suggests that technological innovation can help reduce production costs and increase production efficiency through improved natural resource management. As a result, it enhances the profitability of manufacturing firms and the financial sustainability of businesses, thus facilitating economic and financial development. Regarding financial market risk, the FMOLS model showed a negative relationship with all three dimensions of financial development. This indicates that components of financial market risk-such as external debt, exchange rate volatility, debt service, capital account, and international liquidity-create economic uncertainty, which discourages investment and participation in financial markets. Consequently, this weakens overall financial development, financial institutions, and financial markets. Finally, institutional quality was found to have a positive effect on financial development and financial markets, but a negative effect on the development of financial institutions. This may be explained by the idea that the stronger rule of law is likely to enhance the efficiency of natural resource management within an economy.ConclusionIn sum, a lack of technological innovation, high financial risk, and weak institutional quality-along with factors such as policy imbalances and low levels of human development-contribute to the occurrence of the resource curse hpothesis in resource-rich countries.

Kalok Chan

Kamil Kashif, Robert Ślepaczuk

This study focuses on building an algorithmic investment strategy employing a hybrid approach that combines LSTM and ARIMA models referred to as LSTM-ARIMA. This unique algorithm uses LSTM to produce final predictions but boosts the results of this RNN by adding the residuals obtained from ARIMA predictions among other inputs. The algorithm is tested across three equity indices (S&P 500, FTSE 100, and CAC 40) using daily frequency data from January 2000 to August 2023. The testing architecture is based on the walk-forward procedure for the hyperparameter tunning phase that uses Random Search and backtesting the algorithms. The selection of the optimal model is determined based on adequately selected performance metrics focused on risk-adjusted return measures. We considered two strategies for each algorithm: Long-Only and Long-Short to present the situation of two various groups of investors with different investment policy restrictions. For each strategy and equity index, we compute the performance metrics and visualize the equity curve to identify the best strategy with the highest modified information ratio. The findings conclude that the LSTM-ARIMA algorithm outperforms all the other algorithms across all the equity indices which confirms the strong potential behind hybrid ML-TS (machine learning - time series) models in searching for the optimal algorithmic investment strategies.

Petri P. Karenlampi

This paper investigates the financial economics of simple periodic systems. Well-established financial procedures appear to be complicated, and lead to partially biased results. Probability theory is applied, and the focus is on the finances of simple periodic growth processes, in the absence of intermediate divestments. The expected value of the profit rate, derived from accounting measures on an accrual basis, does not depend on the capitalization path. The expected value of capitalization is path dependent. Because of the path-dependent capitalization, the return rate on capital is path-dependent, and the time-average return rate on capital differs from the expected value of the return rate on capital for the growth cycle. The internal rate of return, defined through a compounding equation, is path-independent, thereby differing from the expected value of the rate of return on capital. It is shown that within a production estate, the area-average of internal rate of return is not representative of the rate of return on capital. The growth cycle length maximizing the return rate on equity is independent of market interest rate. Leverage effect enters the microeconomics of the growth processes through a separate leverage equation, where the leverage coefficient may reach positive or negative values. The leverage effect on the internal rate of return and the net present value are discussed. Both effects are solvable, resulting in incorrect estimates.

Xuewen Han, Neng Wang, Shangkun Che et al.

In recent years, the application of generative artificial intelligence (GenAI) in financial analysis and investment decision-making has gained significant attention. However, most existing approaches rely on single-agent systems, which fail to fully utilize the collaborative potential of multiple AI agents. In this paper, we propose a novel multi-agent collaboration system designed to enhance decision-making in financial investment research. The system incorporates agent groups with both configurable group sizes and collaboration structures to leverage the strengths of each agent group type. By utilizing a sub-optimal combination strategy, the system dynamically adapts to varying market conditions and investment scenarios, optimizing performance across different tasks. We focus on three sub-tasks: fundamentals, market sentiment, and risk analysis, by analyzing the 2023 SEC 10-K forms of 30 companies listed on the Dow Jones Index. Our findings reveal significant performance variations based on the configurations of AI agents for different tasks. The results demonstrate that our multi-agent collaboration system outperforms traditional single-agent models, offering improved accuracy, efficiency, and adaptability in complex financial environments. This study highlights the potential of multi-agent systems in transforming financial analysis and investment decision-making by integrating diverse analytical perspectives.

Filip Staněk

This article investigates the influence of luck and strategic considerations on performance of teams participating in the M6 investment challenge. We find that there is insufficient evidence to suggest that the extreme Sharpe ratios observed are beyond what one would expect by chance, given the number of teams, and thus not necessarily indicative of the possibility of consistently attaining abnormal returns. Furthermore, we introduce a stylized model of the competition to derive and analyze a portfolio strategy optimized for attaining the top rank. The results demonstrate that the task of achieving the top rank is not necessarily identical to that of attaining the best investment returns in expectation. It is possible to improve one's chances of winning, even without the ability to attain abnormal returns, by choosing portfolio weights adversarially based on the current competition ranking. Empirical analysis of submitted portfolio weights aligns with this finding.

Hamid Jamshidi, Alimohammad Ghanbari

One of the main concepts in finance is portfolio diversification and optimization. Typically, investors use the risk and return approach to diversify their portfolios. However, risk spillovers and market connectivity should also be considered when making investment decisions, especially during times of crisis. The TVP-VAR approach is used in this study to analyze risk spillovers and connectivity between the S&P 500 index, green bond, real estate, oil market, and dollar index in the USA from 2016 to July 2022. The TVP-VAR model is a time-varying model that may consider current political and economic circumstances. As a result, investors can choose wisely when it comes to their portfolios. According to comparisons with other markets, the S&P 500 index and the real estate market are the two most significant sources of volatility in the system. In fact, they not only transmit greater volatility, but they also take it in more. After 2020, there will likely be a significant increase in the volatility of the real estate market and the S&P 500 index due to the COVID-19 epidemic. Additionally, as anticipated, other markets have an impact on the green bond market. It does not, however, transmit them.

Leokadia Oręziak

The UK’s exit from the European Union has had seriously negative consequences for the British economy and society. These consequences can be taken as a serious warning to other EU countries that may wish to follow Britain’s chosen path. The warning is particularly important as regards Poland, where the far right has long been openly calling for a so-called “Polexit”. The aim of this article is to analyse and assess both the current and expected effects of Brexit, as well as the conclusions that Poland can draw from them. After presenting the background of Brexit, this analysis focuses primarily on the effects that Great Britain has experienced in the areas of trade with the EU, along with Brexit’s effects on investment, inflation, immigration, and deregulation. Public opinion survey results are also shown, indicating a negative assessment of Brexit by the majority of British society, which is increasingly demanding the restoration of the country’s ties with the EU. Next, the threats to Poland’s further participation in the EU that have arisen since 2015 are shown. Based on Britain’s experience, it can be predicted that a possible Polexit may hit the Polish economy particularly hard, primarily due to the loss of free access to the EU’s single market and a possible collapse with regard to Polish exports, as well as the predicted massive outflow of capital abroad and decline in the inflow of foreign investments. A Polexit would also mean a loss of EU funding. All this would drastically limit Poland’s development possibilities and would trigger a civilisational degradation of the country.

C. Morrison, A. Schwartz

O. Blanchard, C. Rhee, Lawrence H. Summers

Andrew B. Abel, Andrew B. Abel, Olivier J. Blanchard et al.

David O. Cushman

Russell Cooper, J. Haltiwanger, Laura Power