Hasil untuk "Capital. Capital investments"

A. Taylor

T. Schultz

Clifford W. Smith

J. Stiglitz

James R. Booth, Richard L. Smith

Steven M. Fazzari, Bruce C. Petersen

Bruce A. Blonigen, R. Davies

Weimin Liu

F. Caselli, James D. Feyrer

H. Chen

I build a dynamic capital structure model that demonstrates how business-cycle variations in expected growth rates, economic uncertainty, and risk premia influence firms' financing and default policies. Countercyclical fluctuations in risk prices, default probabilities, and default losses arise endogenously through firms' responses to the macroeconomic conditions. These comovements generate large credit risk premia for investment grade firms, which helps address the "credit spread puzzle" and "under-leverage puzzle" in a unified framework. The model generates interesting dynamics for financing and defaults, including "credit contagion" and market timing of debt issuance. It also provides a novel procedure to estimate state-dependent default losses.

Zachary Elkins, A. Guzman, Beth A. Simmons

Over the past forty-five years, bilateral investment treaties (BITs) have become the most important international legal mechanism for the encouragement and governance of foreign direct investment. The proliferation of BITs during the past two decades in particular has been phenomenal. These intergovernmental treaties typically grant extensive rights to foreign investors, including protection of contractual rights and the right to international arbitration in the event of an investment dispute. How can we explain the widespread adoption of BITs? We argue that the spread of BITs is driven by international competition among potential host countries—typically developing countries—for foreign direct investment. We propose a set of hypotheses that derive from such an explanation and develop a set of empirical tests that rely on network measures of economic competition as well as more indirect evidence of competitive pressures on the host to sign BITs. The evidence suggests that potential hosts are more likely to sign BITs when their competitors have done so. We find some evidence that coercion and learning play a role, but less support for cultural explanations based on emulation. Our main finding is that the diffusion of BITs is associated with competitive economic pressures among developing countries to capture a share of foreign investment. We are agnostic at this point about the benefits of this competition for development.For useful comments on earlier drafts of this article, we thank Bill Bernhard, Bear Braumoeller, Frank Dobbin, Robert Franzese, Jeffry Frieden, Geoffrey Garrett, Tom Ginsburg, Jude Hays, Lisa Martin, Bob Pahre, Mark Ramsayer, Steven Ratner, Susan Rose-Ackerman, and John Sides. For research assistance, we thank Elizabeth Burden, Raechel Groom, and Alexander Noonan.

D. Duffie

Özgür Orhangazi

Rajarishi Nahata

H. DeAngelo, L. DeAngelo, Toni M. Whited

Emma Benjaminson

Electric utilities must make massive capital investments in the coming years to respond to explosive growth in demand, aging assets and rising threats from extreme weather. Utilities today already have rigorous frameworks for capital planning, and there are opportunities to extend this capability to solve multi-objective optimization problems in the face of uncertainty. This work presents a four-part framework that 1) incorporates extreme weather as a source of uncertainty, 2) leverages a digital twin of the grid, 3) uses Monte Carlo simulation to capture variability and 4) applies a multi-objective optimization method for finding the optimal investment portfolio. We use this framework to investigate whether grid-aware optimization methods outperform model-free approaches. We find that, in fact, given the computational complexity of model-based metaheuristic optimization methods, the simpler net present value ranking method was able to find more optimal portfolios with only limited knowledge of the grid.

Jussi Keppo, Yingkai Li

This paper studies optimal contract design in private market investing, focusing on internal decision making in venture capital and private equity firms. A principal relies on an agent who privately exerts costly due diligence effort and then recommends whether to invest. Outcomes are observable ex post even when an opportunity is declined, allowing compensation to reward both successful investments and prudent decisions to pass. We characterize profit maximizing contracts that induce information acquisition and truthful reporting. We show that three tier contracts are sufficient, with payments contingent on the agent's recommendation and the realized return. In symmetric environments satisfying the monotone likelihood ratio property, the optimal contract further simplifies to a threshold contract that pays only when the recommendation is aligned with an extreme realized return. These results provide guidance for performance based compensation that promotes diligent screening while limiting excessive risk taking.

Mohammad Hossein Poostforoush, Amirhassan Monajemi, Saeed Daei-Karimzadeh et al.

This study aims to demonstrate the performance of algorithmic trading strategies compared to traditional trading methods in artificial financial markets. This research uses a hybrid model based on agent-based modeling and machine learning methods to simulate agents' behavior in an artificial financial market. This model includes two categories, traditional agents and intelligent agents. Traditional agents are divided into three groups: liquidity providers, liquidity consumers, and noise traders. Intelligent agents are trained using deep learning techniques and recurrent neural networks. Based on the developed algorithms, the agent-based model simulates both categories of traditional and trained agents in an artificial financial market. Sensitivity analysis tests were used to test the validity and reliability of the model, and the values of the fat-tailed distribution of returns, volatility clustering, autocorrelation of returns, long memory in order flow, concave price impact, and extreme price events are calculated in the model and compared with the standardized values. Historical data was used to predict stock prices, and model simulations were used to generate trading signals and update the limited order book. The results of executing the model show the ability of intelligent agents to trade in artificial financial markets compared to traditional agents.

Leonardo Ermann, Dima L. Shepelyansky

We introduce the Ising Network Opinion Formation (INOF) model and apply it for the analysis of networks of 6 Wikipedia language editions. In the model, Ising spins are placed at network nodes/articles and the steady-state opinion polarization of spins is determined from the Monte Carlo iterations in which a given spin orientation is determined by in-going links from other spins. The main consideration is done for opinion confrontation between {\it capitalism, imperialism} (blue opinion) and {\it socialism, communism} (red opinion). These nodes have fixed spin/opinion orientation while other nodes achieve their steady-state opinions in the process of Monte Carlo iterations. We find that the global network opinion favors {\it socialism, communism} for all 6 editions. The model also determines the opinion preferences for world countries and political leaders, showing good agreement with heuristic expectations. We also present results for opinion competition between {\it Christianity} and {\it Islam}, and USA Democratic and Republican parties. We argue that the INOF approach can find numerous applications for directed complex networks.

Antony Seabra de Medeiros, Luiz Afonso Glatzl Junior, Sergio Lifschitz

This study investigates the mechanisms of Surveillance Capitalism, focusing on personal data transfer during web navigation and searching. Analyzing network traffic reveals how various entities track and harvest digital footprints. The research reveals specific data types exchanged between users and web services, emphasizing the sophisticated algorithms involved in these processes. We present concrete evidence of data harvesting practices and propose strategies for enhancing data protection and transparency. Our findings highlight the need for robust data protection frameworks and ethical data usage to address privacy concerns in the digital age.