Investment Problem Research Articles

The Potential of Diaspora System-Building Investments in Homeland

The problem of Diaspora investments is of great importance in research and policy development circles, but this demand does not strengthen by comprehensive research on the problems that have arisen. In these spheres, we need to emphasize attention to the problems of targeted investments in the home state and its system-building potential. This paper discusses the opportunities and limitations that form the framework for investments from the Diaspora to Armenia. The work aims to understand whether the investments from Diaspora could have system-building potential and how it impacts the homeland. In this connection, such questions are asked as to what motivates these investments to be made, within the framework of what policy, where, and how are the investments incorporated and what future effects they have.

Solution of the Diophantine equation 22x+40y=z2

Since Diophantine equations play an important role in solving important real-world problems such as business investment problems, network flow problems, pole placement problems, and data privacy problems, researchers are increasingly interested in developing new techniques for analyzing the nature and solutions of the various Dio phantine equations. In this study we investigate the Diophantine problem 22x+40y = z2, where x, y, z are non-negative integers, and discover that it does not have a non-negative integer solution.

The Influence of Equity Incentive and its Concentration Degree on Enterprise Investment Efficiency

As an important part of China's modern industrial system, high-tech industry has long-term inefficient investment problems due to the characteristics of high investment demand, high risk and long cycle. As an incentive and constraint mechanism based on the principal-agent theory, equity incentive can effectively reduce the agency cost and improve the investment efficiency of enterprises. Based on the different perspective of equity incentive between senior executives and core technical personnel, this paper takes the sample of equity incentive in 2018-2022 to test the impact of equity incentive on the investment efficiency of enterprises. The results show that equity incentive can effectively improve the investment efficiency, alleviate the investment shortage and restrain the excessive investment; equity concentration plays a negative effect on the relationship between equity incentive and enterprise investment efficiency; compared with executive equity incentives, core technical personnel equity incentives have a more significant effect on improving investment efficiency, Therefore, enterprises should pay attention to improving the level of equity incentive; focus on the influence of equity structure on the utility of equity incentive and prevent excessive equity concentration; when designing equity incentive plans, focus on incentivizing core technical personnel.

Application of Asymptotic Analysis of a High-Dimensional HJB Equation to Portfolio Optimization

In this paper, we consider a portfolio optimization problem where the wealth consists of investing into a risky asset with a slow mean-reverting volatility and receiving an uncontrollable stochastic cash flow under the exponential utility. The Hamilton–Jacobi–Bellman equation formulated from the optimal investment problem is a high-dimensional nonlinear partial differential equation and difficult to find its analytical or numerical solutions. The paper provides a tractable asymptotic approach which treats the initial problem as a perturbation around the constant volatility problem. In this paper, we present a formal derivation of asymptotic approximation and prove the accuracy of the value function. Moreover, an illustrative example is provided to assess our approximate strategy and value function.

Green Credit Policy and Short-Term Financing for Long-Term Investment: Evidence from China’s Heavily Polluting Enterprises

In 2012, China issued the “Green Credit Guidelines” policy to guide the green transformation of companies, and at the same time, the investment and financing behaviors of heavy polluters during the green transition have received widespread attention. In the view of the investment and financing maturity structure, we take China’s A-share listed enterprises from 2009 to 2021 assamples, and construct a difference-in-differences (DID) model to examine the implication of the green credit policy on the short-term financing for long-term investment (SFLI) of heavy polluters. We found that: (1) green credit policy can reduce the level of SFLI of heavy polluters; (2) the size of short-term debt and the level of over-investment can play a mediating effect, and government subsidies can weaken the relationship between green credit policy and SFLI; (3) this effect is more significant when directors, supervisors, or senior executives have a financial institution background. (4) this effect is not significant in enterprises with bank-firm shareholding relationships and a stronger innovation intensity; (5) the effect is more significant in areas with stronger environmental regulations. This paper argues that heavily polluting enterprises should reduce short-term debt financing and over-investment, so, to solve the problem of investment and financing term mismatch under the credit risk; banks should prevent the credit rent-seeking problem caused by the equity association between banks and enterprises, and promote the consistency of green credit standards. The government can provide subsidies to enterprises in green transformation and strengthen the construction of regional environmental regulations in order to guide the smooth innovation and upgrading of heavy polluters. Our research expands the study of the micro-economic consequences of green credit policy, providing references for how to reduce maturity mismatch risk and guide the smooth transformation of heavy polluters from the multi-perspective of the government, banks, and enterprises, thus helping to promote companies’ smooth transit.

Optimal investment in defined contribution pension schemes with forward utility preferences

Optimal investment strategies of an individual worker during the accumulation phase in the defined contribution pension scheme have been well studied in the literature. Most of them adopted the classical backward model and approach, but any pre-specifications of retirement time, preferences, and market environment models do not often hold in such a prolonged horizon of the pension scheme. Pre-commitment to ensure the time-consistency of an optimal investment strategy derived from the backward model and approach leads the supposedly optimal strategy to be sub-optimal in the actual realizations. This paper revisits the optimal investment problem for the worker during the accumulation phase in the defined contribution pension scheme, via the forward preferences, in which an environment-adapting strategy is able to hold optimality and time-consistency together. Stochastic partial differential equation representation for the worker's forward preferences is illustrated. This paper constructs two of the forward utility preferences and solves the corresponding optimal investment strategies, in the cases of initial power and exponential utility functions.

On Frontier Portfolio in Shanghai Stock Exchange Based on Mean Variance Model

In security markets, how to achieve optimal allocation of asset has become the focus of investors. The mean-variance model is a method to achieve revenue maximization and risk minimization on investing stock portfolio. This paper will research on investment problem in Shanghai Stock Exchange. It involves research result about mean variance model since 1952. To take the experiment, 10 stocks are chosen from Shanghai Stock Exchange. Organizing rates of return for 360 days and using the average daily rate of return as the expected rate of return. Applying mean variance model, selecting data, the curve of frontier portfolio is obtained by experiment. Based on the result, the investment advice is given. It shows that higher expected rate of return accompanies by higher risk. To achieve higher returns, investor could analyse the variation of each asset along with the rise of the expected rate of change. When negative weight appears, there exists short selling. If short selling is not allowed, investor could shrink the interval of the expected rate of return.

Optimal Electric Vehicle Parking Lot Energy Supply Based on Mixed-Integer Linear Programming

E-mobility represents an important part of the EU’s green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector’s development it is necessary to invest in energy infrastructure and to assure favorable conditions in terms of competitive electricity prices to make the technology even more attractive. Large peak consumption of parking lots which use different variants of uncoordinated charging strategies increases grid problems and increases electricity supply costs. On the other hand, as observed lately in energy markets, different, mostly uncontrollable, factors can drive electricity prices to extreme levels, making the use of electric vehicles very expensive. In order to reduce exposure to these extreme conditions, it is essential to identify the optimal way to supply parking lots in the long term and to apply an adequate charging strategy that can help to reduce costs for end consumers and bring higher profit for parking lot owners. The significant decline in photovoltaic (PV) and battery storage technology costs makes them an ideal complement for the future supply of parking lots if they are used in an optimal manner in coordination with an adequate charging strategy. This paper addresses the optimal power supply investment problem related to parking lot electricity supply coupled with the application of an optimal EV charging strategy. The proposed optimization model determines optimal investment decisions related to grid supply and contracted peak power, PV plant capacity, battery storage capacity, and operation while optimizing EV charging. The model uses realistic data of EV charging patterns (arrival, departure, energy requirements, etc.) which are derived from commercial platforms. The model is applied using the data and prices from the Croatian market.

On-site workshop investment problem: A novel mathematical approach and solution procedure

In real-world construction sites, On-Site Workshops (OSW) are installed to accelerate construction activities and facilitate the material handling process. These temporary OSWs are cost-effective, leading to decreasing the material handling cost and project makespan, which indicates their important role as a part of a construction project. However, considering the OSW, which has not been addressed in the project scheduling problems, requires the construction site to have a space capacity constraint while considering the workshop size, availability level, and other project-related constraints. In the present work, by considering the OSWs, a real construction project scheduling problem is studied as a Multi-Mode On-Site Workshop Investment Problem with Tardiness (MOSWIPT) while finding the installation/dismantling time of the OSWs. Two new (linear) mathematical programming models are proposed for MOSWIPT. Next, due to the NP-hardness of the problem, an enhanced Genetic Algorithm (GA)-based metaheuristic with efficient problem-specific improvement rules as local search and effective crossover and mutation operators is proposed. Computational experiments show that the proposed method has solved most of the instances of the addressed problem to optimality and outperformed the existing metaheuristics, e.g., Simulated Annealing (SA) and Particle Swarm Optimization (PSO). Finally, conclusions and suggestions for future studies are stated.

Analysis of factors affecting the transformation of scientific and technological achievements in universities based on structural equation modeling

Abstract Based on structural equation modeling, this paper measures and evaluates the transformation of scientific and technological achievements of colleges and universities in terms of implicit and explicit variables. The PLS algorithm is used to measure the intrinsic factor, which is designed to follow the Gaussian distribution and reach the expected value through iterative loops. The indicators and parameters of the explicit variables are evaluated by combining the principal component analysis and the great likelihood method. According to the results, the factors that are influenced by the policy environment account for 16.5%, while the problem of capital investment accounts for 36.3%. The number of invention patents increased by 8.93% after measuring and assessing them using the method presented in this paper. It demonstrates that universities can explore their technological achievements in depth and enhance their scientific research level.

Private Equity and LBOs: An Efficient Investment Tool Needing Regulation

Private equity funds are becoming more and more significant in the national economy and their role in the development of a country's economy has become increasingly important as a promising new financial draw in the global financial markets in recent years. Private equity firms were established in the US and expanded quickly behind the decade's brisk economic growth. This paper analyzes the pros and cons of private equity and leveraged buyouts, combining case studies, the legal basis and experience of various countries and government regulation, to discuss the problems of private equity investment, clarify the responsibilities of the government, improve relevant laws and regulations, and encourage the private equity market's growth.

Reinforcement learning for continuous-time mean-variance portfolio selection in a regime-switching market

We propose a reinforcement learning (RL) approach to solve the continuous-time mean-variance portfolio selection problem in a regime-switching market, where the market regime is unobservable. To encourage exploration for learning, we formulate an exploratory stochastic control problem with an entropy-regularized mean-variance objective. We obtain semi-analytical representations of the optimal value function and optimal policy, which involve unknown solutions to two linear parabolic partial differential equations (PDEs). We utilize these representations to parametrize the value function and policy for learning with the unknown solutions to the PDEs approximated based on polynomials. We develop an actor-critic RL algorithm to learn the optimal policy through interactions with the market environment. The algorithm carries out filtering to obtain the belief probability of the market regime and performs policy evaluation and policy gradient updates alternately. Empirical results demonstrate the advantages of our RL algorithm in relatively long-term investment problems over the classical control approach and an RL algorithm developed for the continuous-time mean-variance problem without considering regime switches.

The pseudo-information entropy of Z-number and its applications in multi-attribute decision-making

In order to find a reasonable and effective approach to describe the information contained in a specific Z-number, we introduce information entropy into Z-number environment in this paper, and investigate its applications in multi-attribute decision-making (MADM) issues. Moreover, aiming at the problem with the weighting indicated by Z-number values, we propose two novel weighting methodologies based on conditional entropy and sigmoid function, respectively. Firstly, on the basis of the maximum entropy principle, the optimization model to calculate the underlying probability distribution of Z-number is introduced. And then, we define Z-number pseudo-information entropy, and a novel Z-VIKOR method is proposed to solve a selecting regional circular economy development plan issue from the perspective of information entropy. Furthermore, we propose Z-number pseudo-conditional entropy, and the relationship between Z-number pseudo-information entropy and Z-number pseudo-conditional entropy is investigated. Subsequently, a weighting method based on information entropy of Z-number is proposed. In addition, we also introduce the weighting approach based on the sigmoid function decision-making method. Finally, we introduce a government new energy investment problem to verify and compare the effectiveness of the new weighting approaches. The new method gives a solution to the problem related to Z-number from the perspective of information entropy.

Decision Support Systems in Stock Investment Problems

This study compiles decision support systems that aim to optimize financial decision processes by examining the literature studies targeting stock investments. The review encompasses a range of methodologies and applications, from traditional approaches such as Markowitz’s Modern Portfolio Theory, Black-Litterman, and Single Index models to artificial intelligence-based techniques. In detail, the contributions of Decision Support Systems to stock portfolio construction and portfolio optimization processes along with comparative analyses between these systems are scrutinized. The review also aims to enable researchers and practitioners to be engaged in portfolio optimization with a framework for future investigations in areas such as historical data analysis, future price movement prediction, assessment of risk factors, and determination of optimal portfolio distribution. Furthermore, it seeks to enhance the understanding of decision support systems employed in portfolio optimization, facilitating a more comprehensive grasp of their utility within stock investments.

An optimization method to solve a fully intuitionistic fuzzy non-linear separable programming problem

This paper presents an optimization method to solve a non-linear separable programming problem with coefficients and variables as generalized trapezoidal intuitionistic fuzzy numbers. Such optimization problems are known as fully intuitionistic fuzzy non-linear separable programming problems. The optimization method is based on the linear approximation of fully intuitionistic fuzzy non-linear separable functions. The concept of an intuitionistic fuzzy line segment between two intuitionistic fuzzy points is introduced to find the required linear approximation. In this way, a fully intuitionistic fuzzy non-linear programming problem is converted into an intuitionistic fuzzy linear programming problem. The defuzzification and component-wise comparison techniques are then used to convert the fully intuitionistic fuzzy linear programming problem to a linear programming problem with crisp coefficients which can then be solved by using traditional optimization techniques. The application of the proposed approach in an investment problem faced by a businessman has been presented.

Crafting investment models through contradictory value and momentum investment strategies by artificial intelligence

Crafting investment models through contradictory value and momentum investment strategies by artificial intelligence Professor Chien-Feng Huang discusses how Artificial Intelligence could provide innovative models and strategies to solve investment problems. Over the past decade, Professor Chien-Feng Huang has been working on several investment problems using Artificial Intelligence (AI). Big Data technology, data mining and machine learning play crucial roles in his research, where he hopes to discover niches and innovative solutions to bring about blue ocean strategies for investment. One major line of research Professor Huang has been pursuing is to combine seemingly contradictory strategies to generate emerging solutions for investment. For example, the momentum investing strategy might advise buying a stock, yet the value investment strategy advises against it. So, which one is right?

ESG-інвестування у критичну інфраструктуру в період повоєнного відновлення України

The problem of investment is one of the urgent issues for our country. Ensuring investment in critical infrastructure objects in conditions of hostilities and against the backdrop of the destruction of this infrastructure is associated not so much with economic growth as with the current survival of the country , burdened by continuous military actions. The need for more dynamic development of investment processes aimed at attracting ESG investments in critical infrastructure has been emphasized. The aim of the article is to explore the prospects of ESG investment in critical infrastructure during the post-war recovery period of Ukraine. It has been established that the ESG concept is a modern trend in the investment space that needs to be considered and implemented in practice. The importance of implementing ESG criteria has significantly increased in recent decades and is expected to grow even faster in the near future. Most infrastructure funds view ESG as a driving force that enhances the value and stability of portfolio companies against the backdrop of constant global changes. To benefit from the opportunities provided by ESG investment, it is necessary to consider ESG criteria (environmental, social, and governance) at every stage of the investment life cycle. ESG is increasingly becoming a requirement for investment compliance. It has been established that ESG investment, as the implementation of the ESG concept, contributes to positive changes in society, preserves the environment, etc. However, in the case of the urgent need for rapid and effective post-war recovery of critical infrastructure in Ukraine, it will significantly complicate the process since compliance with ESG criteria, which could be ignored earlier, will have to be followed. The need to change the regulatory framework regarding the inclusion of ESG investment specifics and to transform approaches to investment in the domestic business community is mentioned because the international business community seeks to invest its capital in enterprises and territories that meet ESG criteria. For Ukraine, transformation aimed at meeting the requirements for ESG investment is both a challenge and a necessity for integration into global investment processes.

Malliavin Calculus and Its Application to Robust Optimal Investment for an Insider

In the theory of portfolio selection, there are few methods that effectively address the combined challenge of insider information and model uncertainty, despite numerous methods proposed for each individually. This paper studies the problem of the robust optimal investment for an insider under model uncertainty. To address this, we extend the Itô formula for forward integrals by Malliavin calculus, and use it to establish an implicit anticipating stochastic differential game model for the robust optimal investment. Since traditional stochastic control theory proves inadequate for solving anticipating control problems, we introduce a new approach. First, we employ the variational method to convert the original problem into a nonanticipative stochastic differential game problem. Then we use the stochastic maximum principle to derive the Hamiltonian system governing the robust optimal investment. In cases where the insider information filtration is of the initial enlargement type, we derive the closed-form expression for the investment by using the white noise theory when the insider is ’small’. When the insider is ’large’, we articulate a quadratic backward stochastic differential equation characterization of the investment. We present the numerical result and conduct an economic analysis of the optimal strategy across various scenarios.

Optimal investment problem under behavioral setting: A Lagrange duality perspective

In this paper, we consider the optimal investment problem with both probability distortion/weighting and general non-concave utility functions with possibly finite number of inflection points, and apply a Lagrange duality based relaxation approach for solving this problem. Existing literature has shown the equivalent relationships (strong duality) between the relaxed problem and the original one by either assuming the presence of probability weighting or the non-concavity of utility functions, but not both. In this paper, combining both factors, we prove that the absence of concavity in the utilities may result in strictly positive gaps, thus the strong duality may not hold unconditionally. The necessary and sufficient conditions on eliminating such gaps have been provided under this circumstance. We have applied the solution method to obtain closed-form solutions for the optimal terminal wealth and corresponding investment strategies in a special cumulative prospect theory (CPT) example with an appropriately selected inverse S-shaped probability distortion function. Based on the solutions, the joint effects of non-concave utilities combined with the probability weighting on the trading behaviors can be explicitly characterized. We show that, under this particular example, the co-existence of both factors may water down the loss aversion effect induced by only S-shaped utility or probability distortion when the agents are more cash-strapped in the initial budgets. In addition, we find that the optimal strategies derived from distorted beliefs shall converge to a constant that can be expressed by the standard Merton ratio multiplied by an inflation factor which we name as “distorted” Merton ratio. More importantly, the inflation factor is solely dependent on the probability distortion rather than the features of the utility function.

A hybrid reinsurance-investment game with delay and asymmetric information

In this paper, we investigate the optimal reinsurance and investment problem in the framework of the hybrid stochastic differential game, which includes a stochastic Stackelberg differential subgame and a non-zero-sum stochastic differential subgame. The stochastic Stackelberg differential subgame considers the leader–follower relationship between a reinsurer and two insurers. The reinsurer, as the leader, prices reinsurance premium and invests its wealth in a financial market that contains a risk-free asset and a risky asset whose price process is described by the constant elasticity of variance (CEV) model. On the other side, the two insurers, as the followers, purchase proportional reinsurance from the reinsurer and invest in the same financial market. Also, the competitive relationship between the two insurers is characterized by the non-zero-sum stochastic differential subgame in which each insurer considers the relative performance. By introducing the delay feature into the hybrid game, we are able to obtain the wealth processes depicted by the stochastic delay differential equations. Considering the effect of asymmetric information, we assume the reinsurer and insurers have different levels of information about the financial market. The objective of the reinsurer is to maximize the mean–variance functional of its terminal surplus with delay, while each insurer is to maximize the mean–variance functional of its terminal surplus with delay relative to that of its competitor. By applying techniques in stochastic control theory, we obtain the extended Hamilton–Jacobi–Bellman equations, and then by using the idea of backward induction, we establish the equilibrium strategies and the corresponding value functions. Finally, we include some numerical examples and sensitivity analysis to illustrate the effects of model parameters on the equilibrium strategies.