Hasil untuk "Capital. Capital investments"

Len Patrick Dominic M. Garces, Yang Shen

We investigate a continuous-time investment-consumption problem with model uncertainty in a general diffusion-based market with random model coefficients. We assume that a power utility investor is ambiguity-averse, with the preference to robustness captured by the homothetic multiplier robust specification, and the investor's investment and consumption strategies are constrained to closed convex sets. To solve this constrained robust control problem, we employ the stochastic Hamilton-Jacobi-Bellman-Isaacs equations, backward stochastic differential equations, and bounded mean oscillation martingale theory. Furthermore, we show the investor incurs (non-negative) utility loss, i.e. the loss in welfare, if model uncertainty is ignored. When the model coefficients are deterministic, we establish formally the relationship between the investor's robustness preference and the robust optimal investment-consumption strategy and the value function, and the impact of investment and consumption constraints on the investor's robust optimal investment-consumption strategy and value function. Extensive numerical experiments highlight the significant impact of ambiguity aversion, consumption and investment constraints, on the investor's robust optimal investment-consumption strategy, utility loss, and value function. Key findings include: 1) short-selling restriction always reduces the investor's utility loss when model uncertainty is ignored; 2) the effect of consumption constraints on utility loss is more delicate and relies on the investor's risk aversion level.

Huisheng Wang, H. Vicky Zhao

Decision-changing imitation is a prevalent phenomenon in financial markets, where investors imitate others' decision-changing rates when making their own investment decisions. In this work, we study the optimal investment problem under the influence of decision-changing imitation involving one leading expert and one retail investor whose decisions are unilaterally influenced by the leading expert. In the objective functional of the optimal investment problem, we propose the integral disparity to quantify the distance between the two investors' decision-changing rates. Due to the underdetermination of the optimal investment problem, we first derive its general solution using the variational method and find the retail investor's optimal decisions under two special cases of the boundary conditions. We theoretically analyze the asymptotic properties of the optimal decision as the influence of decision-changing imitation approaches infinity, and investigate the impact of decision-changing imitation on the optimal decision. Our analysis is validated using numerical experiments on real stock data. This study is essential to comprehend decision-changing imitation and devise effective mechanisms to guide investors' decisions.

Tiziano De Angelis, Caio César Graciani Rodrigues, Peter Tankov

We study a problem of optimal irreversible investment and emission reduction formulated as a nonzero-sum dynamic game between an investor with environmental preferences and a firm. The game is set in continuous time on an infinite-time horizon. The firm generates profits with a stochastic dynamics and may spend part of its revenues towards emission reduction (e.g., renovating the infrastructure). The firm's objective is to maximize the discounted expectation of a function of its profits. The investor participates in the profits, may decide to invest to support the firm's production capacity and uses a profit function which accounts for both financial and environmental factors. Nash equilibria of the game are obtained via a system of variational inequalities. We formulate a general verification theorem for this system in a diffusive setup and construct an explicit solution in the zero-noise limit. Our explicit results and numerical approximations show that both the investor's and the firm's optimal actions are triggered by moving boundaries that increase with the total amount of emission abatement.

Marco Franke, Quan Deng, Zisis Kyroudis et al.

Industry 4.0 enables the modernisation of machines and opens up the digitalisation of processes in the manufacturing industry. As a result, these machines are ready for predictive maintenance as part of Industry 4.0 services. The benefit of predictive maintenance is that it can significantly extend the life of machines. The integration of predictive maintenance into existing production environments faces challenges in terms of data understanding and data preparation for machines and legacy systems. Current AI frameworks lack adequate support for the ongoing task of data integration. In this context, adequate support means that the data analyst does not need to know the technical background of the pilot’s data sources in terms of data formats and schemas. It should be possible to perform data analyses without knowing the characteristics of the pilot’s specific data sources. The aim is to achieve a seamless integration of data as information for predictive maintenance. For this purpose, the developed data-sharing infrastructure enables automatic data acquisition and data integration for AI frameworks using interoperability methods. The evaluation, based on two pilot projects, shows that the step of data understanding and data preparation for predictive maintenance is simplified and that the solution is applicable for new pilot projects.

Kalichak Mariia, Pylypenko Liubomyr, Sorokovyi Pavlo et al.

Economic analysis of the field of housing construction indicates a certain specificity of its functioning in Ukraine, which is primarily related to the lack of opportunities for developers to invest their own resources in this construction and the need to attract financing at the early stages of the construction of residential real estate objects. The results of the empirical analysis of the dynamics of housing construction financing indicate that the main source of this financing is public funds (from 55% to more than 73% of the total volume of housing construction investments, depending on the year of their implementation). The insufficient level of quantitative and qualitative provision of housing for Ukrainian citizens provokes a constant demand for residential real estate objects, which in turn stimulates the development of housing construction. An analysis of the dynamics of residential real estate commissioning volumes and the amount of capital investments in residential buildings indicates a steady growth of these indicators over the past 10 years, with the exception of the crisis years of 2014 and 2020, in which there was a general decline in the national economy (2014 ) or even the global (2020) economy, caused by extraordinary circumstances (the Revolution of Dignity and the coronavirus epidemic). However, in subsequent years after these crises, the amount of capital investment in residential construction continued to grow.

E. V. Khudko, E. V. Dondopova

The level of accessibility to various sources of financing plays a crucial rolein formation of an investment strategy.   The purpose of the article is to identify and analyse factors influencing the volume of investment expenditures of public Russian enterprises in the real economic sector with consideration to the degree of financial constraints.   The research is based on division of companies into more and less financially constrained. The following characteristics were chosen as criteria for segmentation: size, age of the business and placement of corporate bonds on the exchange. A regression analysis specified similarities and differences among determinants of investment activity of more and less financially constrained companies. The key common factor that has an impact on this characteristic of organisations in both groups is investment expenditures of the previous period. Investments of more financially constrained companies have shown a strong dependence on indicators of current cash flow and capital structure. At the same time, the volume of expenses of less financially constrained companies is more influenced by the indicator of accumulated tangible assets. The results of the research may interest enterprises in the real sector when assessing the degree of their financial constraint and determining the volume of investment expenditures.

Carter Davis

What is the demand elasticity of statistical arbitrageurs that invest according to the advice of modern cross-sectional asset pricing models? Thirteen models from the literature exhibit strikingly inelastic demand, in contrast to classical models that rely on statistical arbitrageurs to create elastic market demand for assets. This inelasticity arises from the difficulty of trading against price changes. A quantitative equilibrium model shows that aggregate demand remains inelastic even with these statistical arbitrageurs in the market.

Chunbing Cai, Jordan Roulleau-Pasdeloup

What are the effects of investing in public infrastructure? We answer this question with a New Keynesian model. We recast the model as a Markov chain and develop a general solution method that nests existing ones inside/outside the zero lower bound as special cases. Our framework delivers a simple expression for the contribution of public infrastructure. We show that it provides a unified framework to study the effects of public investment in three scenarios: $(i)$ normal times $(ii)$ short-lived liquidity trap $(iii)$ long-lived liquidity trap. We find that calibrations commonly used lead to multipliers that diverge with the duration of the trap.

Zikai Wei, Bo Dai, Dahua Lin

Active investing aims to construct a portfolio of assets that are believed to be relatively profitable in the markets, with one popular method being to construct a portfolio via factor-based strategies. In recent years, there have been increasing efforts to apply deep learning to pursue "deep factors'' with more active returns or promising pipelines for asset trends prediction. However, the question of how to construct an active investment portfolio via an end-to-end deep learning framework (E2E) is still open and rarely addressed in existing works. In this paper, we are the first to propose an E2E that covers almost the entire process of factor investing through factor selection, factor combination, stock selection, and portfolio construction. Extensive experiments on real stock market data demonstrate the effectiveness of our end-to-end deep leaning framework in active investing.

Liuchun Deng, Minako Fujio, M. Ali Khan

We provide a complete characterization of optimal extinction in a two-sector model of economic growth through three results, surprising in both their simplicity and intricacy. (i) When the discount factor is below a threshold identified by the well-known $δ$-normality condition for the existence of a stationary optimal stock, the economy's capital becomes extinct in the long run. (ii) This extinction may be staggered if and only if the investment-good sector is capital intensive. (iii) We uncover a sequence of thresholds of the discount factor, identified by a family of rational functions, that represent bifurcations for optimal postponements on the path to extinction. We also report various special cases of the model having to do with unsustainable technologies and equal capital intensities that showcase long-term optimal growth, all of topical interest and all neglected in the antecedent literature.

Swati Tyagi

This paper examines two different yet related questions related to explainable AI (XAI) practices. Machine learning (ML) is increasingly important in financial services, such as pre-approval, credit underwriting, investments, and various front-end and back-end activities. Machine Learning can automatically detect non-linearities and interactions in training data, facilitating faster and more accurate credit decisions. However, machine learning models are opaque and hard to explain, which are critical elements needed for establishing a reliable technology. The study compares various machine learning models, including single classifiers (logistic regression, decision trees, LDA, QDA), heterogeneous ensembles (AdaBoost, Random Forest), and sequential neural networks. The results indicate that ensemble classifiers and neural networks outperform. In addition, two advanced post-hoc model agnostic explainability techniques - LIME and SHAP are utilized to assess ML-based credit scoring models using the open-access datasets offered by US-based P2P Lending Platform, Lending Club. For this study, we are also using machine learning algorithms to develop new investment models and explore portfolio strategies that can maximize profitability while minimizing risk.

Tolga Buz, Gerard de Melo

The recent hype around Reddit's WallStreetBets (WSB) community has inspired research on its impact on our economy and society. Still, one important question remains: Can WSB's community of anonymous contributors actually provide valuable investment advice and possibly even outperform top financial institutions? We present a data-driven empirical study of investment recommendations of WSB in comparison to recommendations made by leading investment banks, based on more than 1.6 million WSB posts published since 2018. %enriched with stock market data. To this end, we extract and evaluate investment recommendations from WSB's raw text for all S&P 500 stocks and compare their performance to more than 16,000 analyst recommendations from the largest investment banks. While not all WSB recommendations prove profitable, our results show that they achieve average returns that compete with the best banks and outperform them in certain cases. Furthermore, the WSB community has been better than almost all investment banks at detecting top-performing stocks. We conclude that WSB may indeed constitute a freely accessible, valuable source of investment advice.

Laura Chémali, Camille Souffron

Despite the amount of savings available and the money supply managed by financial institutions, significant market failures and the failure of carbon pricing strategies prevent sufficient financing of the transition, notably through bank credit. Aware of the links between natural, monetary and productive aggregates, we propose the development of ''eco-systemic'' prudential policies by exposing the interdependence between macro, micro and environmental prudential measures. These would be based on a reorientation of corporate accounting standards towards the concept of socio-environmental solvency, notably the CARE-TDL model (integration of human and natural capital alongside financial capital on the liabilities side of the balance sheet). In an ecosystemic framework, this solvency of virtuous companies would compensate in accounting terms for the lack of financial solvency. The State would then be the guarantor in order to facilitate their access to financing, also reduced by Basel III and Solvency II. This policy develops a system of reallocation of financing capacities from non-virtuous companies to the most virtuous ones with public guarantees, aiming to reduce the debt ratio while increasing green investments, with monetary policies of rates but also of volumes and ratios differentiated according to the types of assets and the greening of bank balance sheets, and finally forms of public-private partnership. Facilitating the financing of green companies would green capital but increase it, partly neutralising the positive environmental impact. It is therefore necessary to limit the credit expansion of ''brown'' companies. This would reduce risky operations and favour less leveraged investments more connected to the real economy, reducing systemic financial risk. -- The Agenda 2030 Policy Briefs series (PoCFiN Kedge Business School - SDSN France - Institut Rousseau) mobilises economists and practitioners to identify an agenda of economic and financial reforms to achieve the 2030 Agenda, at territorial, national and supranational levels. These are published after peer review.

Yuri G. Binatov, Marina V. Krayushkina, Vladislav S. Krayushkin

The article presents recommendations about formation of complex options of development of a field are made, economical and ecological regulation during the developing and operation of oil fields locates.

Fakhri Mirshojaee, Nasser Elahi, Mohsen Seighali

An important subject in the field of global economy is the financial crisis contagion on various markets. Given the expansion of trade relationships among different countries, proving the existence of contagion will facilitate policymaking in times of crisis. The present article tries to find the answer to the question of whether the Iranian foreign exchange market is affected by certain global crises. The answer may initially seem to be obvious; nevertheless, the channels of contagion or its share in market fluctuations cannot be confirmed if the existence of the phenomenon is not proved at first place. This study reviews the contagion effects of financial crises in selected crisis-stricken countries and those of oil and gold markets on Iran's free foreign exchange market, covering four crises including the US stock market crash, the Mexican financial crisis, SAARC, and the US subprime mortgage crisis during 1987-2008. For each crisis, stability periods were identified and using daily data and the Copula-GARCH model, the existence of contagion effects was studied. Findings indicated the contagion effects of the crises in the mentioned markets on the foreign exchange market. This was specifically witnessed in the case of the 2008 crisis with effects larger than others, manifesting themselves in the foreign exchange as well as the oil and gold markets. Therefore, part of the fluctuations in the market may be attributed to external factors, requiring the policymaker to avoid any intervention during global financial crisis or turbulence in the oil and gold markets.

Luis Lopez, Alvaro Gonzalez-Castellanos, David Pozo

This paper presents an analysis of the stability and quality of the distributed generation planning problem's investment solution. The entry of distributed generators power based on non-conventional energy sources has been extensively promoted in distribution grids. In this paper, a two-stage stochastic programming model is used to find the optimal distributed generators' installed capacities. We emphasize the design of scenarios to represent the stochasticity of power production on renewable sources. In the scenario generation, a method is proposed based on the clustering of real measurements of meteorological variables. We measure the quality and stability of the investment solution as a function of the number of scenarios. The results show that a reduced selection of scenarios can give an inadequate solution to distributed generators' investment strategy.

Tal Miller

It is common knowledge that leverage can increase the potential returns of an investment, at the expense of increased risk. For a passive investor in the stock market, leverage can be achieved using margin debt or leveraged-ETFs. We perform bootstrapped Monte-Carlo simulations of leveraged (and unleveraged) mixed portfolios of stocks and bonds, based on past stock market data, and show that leverage can amplify the potential returns, without significantly increasing the risk for long-term investors.

Changkuk Im, Jinkwon Lee

Experimental studies regularly show that third-party punishment (TPP) substantially exists in various settings. This study further investigates the robustness of TPP under an environment where context effects are involved. In our experiment, we offer a third party an additional but unattractive risky investment option. We find that, when the dominated investment option irrelevant to prosocial behavior is available, the demand for punishment decreases, whereas the demand for investment increases. These findings support our hypothesis that the seemingly unrelated and dominated investment option may work as a compromise and suggest the fragility of TPP in this setting.

Bisengaliev M.D., Zholbasarova A.T., Togasheva A.R. et al.

The technology of depressant utilization, which essentially depends on the method of its addition to oil, has a substantial impact on the efficiency of the main pipeline. Direct injection of a pour point depressant into the reservoir enables to maintain optimal conditions for oil processing, namely, to cool the oil at an optimal rate and, which is especially important, in static conditions. But in this case, significant capital investments will be required for mixing devices, devices for cooling oil, for the construction of additional tanks, etc. The addition of pour point directly into the pipeline (before or after the head pumping and heating stations) cannot provide optimal cooling conditions. This process does not significantly improve the rheological properties of oil as compared to the introduction of a depressant directly into the reservoir. The introduction of a pour point depressant directly into the pipeline will significantly reduce capital costs for processing highly paraffinic oil and the advantages of this method are obvious. The proposed method of depressant utilization will reduce their consumption, and energy consumption for heating oil. This goal is achieved by the fact that the pour point depressant is not added to the entire volume of highly paraffinic oil pumped through the pipeline, but only to the annular near-wall layer of oil in the pipe, thus creating a low-viscosity near-wall layer of the pumped highly paraffinic oil. This paper presents a scheme for the implementation of the proposed method and formulas for thermal calculations of the pipeline sections considering depressant addition to oil.

Nairobi Nairobi, Riana Respitasari

This study aims to analyze the effect of public infrastructure on economic growth in Lampung Province. The data used are time series and cross sections for the period 2012-2018 and 14 districts/cities. The method applied is the panel data model with the random effect model method. The results showed that Infrastructure, Irrigation Infrastructure, Health Infrastructure, Investment, Labor, and Gini Growth had a significant and positive effect on economic growth, while capital expenditures insignificant effect on economic growth. The implications of these findings indicate that public sector investments such as road infrastructure, bridges and other infrastructure facilities are important.