Investment Decision Model Research Articles

PENGUJIAN DECISION MAKING MODEL DENGAN MEDIASI FINANCIAL LITERACY DAN RISK TOLERANCE DALAM MEMPREDIKSI PERILAKU INVESTASI MAHASISWA

This study aims to test the investment decision model among college students by integrating financial literacy and risk tolerance as mediating variables. The sample in this study were students who had invested. The data analysis technique used in this research is Structural Equation Modeling (SEM). Testing includes measurement models (outer model) and structural models (inner model). The results of this study indicate that mental accounting, herding behavior, and risk preference directly affect investment interest. Risk tolerance can mediate the effect of herding behavior on investment interest. In addition, financial literacy can mediate the influence between mental accounting and risk preference on investment interest. Furthermore, interest is the dominant factor in driving investment decisions. This research contributes theoretically by expanding the investment decision-making model, especially among students.

BIM investment decision model (BIDM): evaluation of features and proposal of a regression model based on the LASSO method

BIM investment decision model (BIDM): evaluation of features and proposal of a regression model based on the LASSO method

The Finance of Cybersecurity: Quantitative Modeling of Investment Decisions and Net Present Value

Despite the growing literature on cybersecurity investment, significant gaps remain unaddressed. First, the existing literature does not differentiate between cybersecurity expenditures, namely investment versus spending, despite the differences in their financial treatments. Second, the literature predominantly employs economic and risk-based models, in which the incorporation of financial parameters is limited. Third, the literature focuses on quantifying market (stock) loss of cyberattacks or conducting a cost-benefit analysis of cybersecurity investments but has yet to bridge these two areas. Thus, our objective is to introduce a new financial model, the Alpha Model, that addresses these gaps. We first provide distinctions between cybersecurity investment (capital expenditure) and spending (operational expenditure). Grounded in Relative Valuation Models and Optimal Capital Structure Theory, we quantify the upper-bound of optimal cybersecurity investment by utilizing book and market values of equity and debt. We also demonstrate that the Net Present Values (NPV) of cybersecurity investment is considerably lower than its book value by incorporating financial parameters of equity and debt financing, inflation-adjusted capital cost, and tax credits of depreciation and interest. To validate our model, we empirically test it by using data from the S&P500 index during 2018–2023. We offer practical implications to key stakeholders, including: (1) industry executives to financially quantify cybersecurity investments and ascertain their actual NPVs, individually and collaboratively with supply chain partners; (2) cybersecurity solution providers to promote business investments in cybersecurity; and (3) insurance providers to price premiums with higher NPVs than the corresponding investments. We also offer several avenues for further research.

Optimization and Demonstration of PV Power Plant Investment Evaluation and Decision Model under the Background of Marketization

Based on the real option analysis method, this paper makes an empirical analysis of the evaluation and optimization of the model, and analyzes the main factors of the investment value of photovoltaic power plants in combination with the investment decision-making characteristics of photovoltaic power plant projects under the background of marketization. The electricity price and generation income, transaction price and income are all processed by geometric Brownian motion, and the random process is obtained by Monte Carlo simulation method. Based on the sensitivity analysis of the uncertain factors of the model, the investment risk of photovoltaic power station is evaluated and the suggestions are put forward. The results show that when the on-grid electricity price increases by 10%, the net present value is about 6 million yuan in the 26th year. Therefore, how to evaluate the uncertainty factors of photovoltaic power plant investment and make scientific and reasonable investment decisions is of great significance for the development of photovoltaic power generation.

The Optimal Selection of Renewable Energy Systems Based on MILP for Two Zones in Mexico

This paper presents a series of enhancements to a previously proposed mixed-integer linear programming (MILP) model for investment decisions and operational planning in distributed generation (DG) systems. The main contribution of this study consists of integrating a wind generation system and multiple loads at different buses in a network. The model considers dynamic weather data, energy prices, costs related to photovoltaic and wind systems, storage systems, operational and maintenance costs, and other pertinent factors, such as efficiencies, geographical locations, resource availability, and different load profiles. The simulation results obtained through implementation in Julia’s programming language illustrate that the MILP formulation maximizes the net present value, and four configurations for hybrid power generation systems in Mexico are analyzed. The objective is to enable profitability assessment for investments in large-capacity DG systems in two strategic zones of Mexico. The results show that the configurations in the NE zone, especially in Tamaulipas, are the most cost-effective. Case 1 stands out for its highest net present value and shortest payback time, while Case 2 offers the highest energy savings. In addition, Cases 3 and 4, which incorporate storage systems, exhibit the longest payback periods and the lowest savings, indicating less favorable economic performance compared with Cases 1 and 2. Moreover, the sales of two case studies, one without a storage system and the other with a storage system, are shown. The model also incorporates instruments for buying or selling energy in the wholesale electricity market, including variables that depict the injected energy into the electrical grid. This comprehensive approach provides a detailed overview of optimal energy management.

Healthcare Supply Chain Resilience Investment Strategy Analysis Based on Evolutionary Game

Healthcare is considered one of the necessities for sustaining life. However, frequent emergencies raise the risk of supply chain disruption, seriously threatening people’s lives and health security. Therefore, building a resilient healthcare supply chain is an important initiative to manage the healthcare crisis effectively. Based on the secondary supply chain formed by medical supply manufacturers and retailers, this paper constructs an evolution game model of resilience investment decisions under the non-disruption and disruption symmetry scenarios of the supply chain and analyzes the stabilization strategies employed by both parties based on their asymmetry strategy choices. Subsequently, the numerical simulation is used to analyze the impact of various parameters on the evolutionary results and their evolutionary trends. The results of the study show that additional benefits from resilience investment, potential costs, disruption losses, market encroachment revenue, “free-rider” benefits, additional benefit increase coefficient, resilience investment reduction coefficient, disruption loss reduction coefficient, additional unit cost reduction coefficient, and market encroachment revenue coefficient all influence the resilience investment decisions. Finally, based on the simulation results, specific recommendations are formulated to improve the resilience of the healthcare supply chain.

An Intelligent Financial Investment Decision Model Based on Multi-Agent Reinforcement Learning

Financial investigation decision is a complex affair that highly relies on expert knowledge. In the era of big data, it remains practical to make intelligent financial investigation decisions via computing technology. As a consequence, this work introduces reinforcement learning to search for effective decision schemes that are adaptive to the diverse financial environment. Therefore, an intelligent financial investment decision model based on multi-agent reinforcement learning is proposed in this paper. First, the long short-term memory (LSTM) model is utilized to strengthen financial investment data. Then, the hidden Markov model (HMM) is utilized to make a preliminary judgment on financial investment strategy. Next, a multi-agent deep deterministic policy gradient (MADDPG) is employed to set the goal of agent reinforcement and determine decision indicators based on the financial market environment. Multiple agents (investors) form a cooperative strategy through learning and interaction to achieve the optimal investment decision in the financial market. The experimental results on realistic data indicate that the proposal can play an important role in investment decision-making.

Assessment of the Economic Consequences of Riverbank Erosion: The Case of the South Morava River, Serbia

Landscape transformation, degradation and destruction are caused by fluvial processes as the predominant erosive processes in Serbia. The loss of arable land due to riverbank erosion is permanent, and the economic consequences are, therefore, especially pronounced. The primary aim of this study was to quantify the intensity of riverbank erosion in the lower part of the South Morava River (Serbia) during period 1924–2020, evaluate its economic consequences, and conduct a cost benefit analysis of revetment investments. The economic effects of riverbank erosion were analyzed by means of land loss and reduction in the quantity of agricultural production. An interdisciplinary research approach was applied using specific methodological procedures to calculate the riverbank erosion and soil (land) loss intensity (geographic information system-GIS), the economic consequences of riverbank erosion (ECRE), and the results of an investment decision-making model. The results showed that 202.6[Formula: see text]ha of arable land was lost during the observed period, the value of arable land loss was almost 622,000[Formula: see text]USD, and the loss in agricultural production was 7.5 million USD (discount rate 3.7%). The model is seen as the main research output and can be used for the assessment of long-term capital-intensive infrastructure projects in developing countries. The analysis identified the river segments that are economically viable for investments in riverbank revetments to preserve the largest area of fertile agricultural land. The results are especially valuable for river channel management, environmental planners and policy-makers, who deal with decisions regarding planning and the protection of bank erosion in areas of particular interest.

Strategies for Investing in Big Data Information Services in Community Group Buying Supply Chains and Their Comparative Study

This study investigates the investment strategy of big data information service in community group-buying supply chain. Combined with the background of big data information service, it constructs three investment decision models, compares the optimal profits of all parties of the game under different modes, and analyzes the problem of choosing the optimal investment strategy for suppliers, community group-buying platforms and group leaders. It is found that: 1) The community group-buying supply chain invests in big data information service, which has a promotion effect on the supply chain profit, and when the supplier's marginal profit and the head's marginal profit satisfy certain conditions, the investment model is the O-mode, which can make the supply chain realize a long-term win-win situation. The profit of the supply chain is related to the consumer conversion rate, discount rate, cost coefficient of supplier's quality management efforts, cost coefficient of platform's big data marketing, and cost coefficient of group leader's marketing.

Improved ant colony optimization algorithms for multi-objective investment decision model based on intelligent fuzzy clustering algorithm

With the continuous changes and development of financial markets, it has brought many difficulties to investment decision-making. For the multi-objective investment decision-making problem, the improved Ant colony optimization algorithms was used to improve the effectiveness and efficiency of the multi-objective investment decision-making. Therefore, based on intelligent Fuzzy clustering algorithm and Ant colony optimization algorithms, this paper studied a new multi-objective investment decision model, and proved the advantages of this method through comparative analysis of experiments. The experimental results showed that the improved Ant colony optimization algorithms has significantly reduced the system’s construction costs, operating costs and financial costs, all of which were controlled below 41%. Compared with the traditional Ant colony optimization algorithms, this method had lower values in policy risk, technical risk and market risk, and can effectively control risks. Meanwhile, the environmental, economic, and social benefits of this method were all above 58%, and the average absolute return rate and success rate in this experiment were 21.5450% and 69.4083%, respectively. Therefore, from the above point of view, the multi-objective investment decision model based on intelligent Fuzzy clustering algorithm and the improved Ant colony optimization algorithms can effectively help decision-makers to find the best investment decision-making scheme, and can improve the accuracy and stability of decision-making. This research can provide reference significance for other matters in the field of investment decision-making.

Deep Transfer Learning Based Surrogate Modeling for Optimal Investment Decision of Distribution Networks

At present, the distribution network investment is faced with complicated operation strategies and various new investment elements. It's difficult to evaluate the impact of different investment measures on the final benefits due to the complicated physical modeling and potential coupling correlation, which contributes to that the traditional model-driven decision-making method may not work. This paper proposes a novel deep transfer learning based surrogate-assisted method to address the challenge. The key idea is to develop a surrogate model to replace the input-output correlation between the certain investment measure and its benefits, which can be further reformulated into mixed-integer linear constraints and embedded into the classical investment decision-making model for the target distribution network. As the model is trained through a designed two-stage adaptive transfer learning algorithm, which can extract the similar rules from other distribution networks, the dilemma of insufficient effective samples is alleviated, while a neural network constrained algorithm is designed to linear the surrogate model for simplifying the optimization. Numerical cases prove the effectiveness of the surrogate model on correlation mining and show the superiority of the surrogate-assisted method in achieving the precision investment for target distribution network.

An optimal sequential investment decision model for generation-side energy storage projects in China considering policy uncertainty

Energy storage systems (ESS) are crucial for addressing the intermittent nature of renewable energy, and improving the flexibility of power systems. However, the uncertainties in the investment decision process pose a challenge for investment evaluation of ESS. This study develops a sequential investment decision model for ESS projects based on real options, aiming to derive the optimal investment timing and value of the project under electricity price and subsidy policy uncertainties. Considering the policy uncertainty caused by the possible retraction or provision of subsidies, sequential investments under four subsidy policies of stable, retraction, provision, and transformation are analyzed separately. The model is empirically and simulatively analyzed using a generation-side ESS project in Qinghai Province, China, as an example. The results show that: First, the sequential investment strategy promotes earlier project deployment compared to the lumpy investment strategy. Second, stochastic provision of subsidies incentivizes investment, but stochastic retraction of subsidies is a greater disincentive to investment, whereas stable subsidy policies provide the best incentives. However, local subsidies still need to increase to 0.15 yuan/kWh to trigger investment. Third, under subsidy recovery uncertainty, excessive subsidy levels discourage investment; meanwhile, under subsidy provision uncertainty, low subsidy levels become almost incentive-less.

Multi‐timescale synergistic planning for flexible regulation of thermal power to support wind‐photovoltaic‐storage

AbstractWith the proposed goal of 30•60 carbon peak and carbon neutrality, the proportion of new energy continues to increase, the proportion of conventional power supply capacity continues to decrease, and the flexibility demand of the existing power system continues to increase and the flexibility adjustment ability is insufficient. Therefore, a medium and long term planning method is proposed to flexibly adjust the multi‐time scale coordination of thermal power support wind and solar storage. Considering the long‐term investment decision and the short and medium term operation simulation, the flexible transformation cost and the penalty cost of insufficient flexibility of thermal power units are included in the planning objective, and a multi‐time scale coordinated planning model is established with wind power as the main power source and thermal power and hydrogen energy storage as the auxiliary power source. Through the multi‐stage cycle iteration of investment decision model, medium and long term production simulation and typical daily operation simulation, the flexible transformation resources of wind and solar fire storage and thermal power are optimized. Finally, a provincial power grid in northeast China is taken as an example to verify that hydrogen energy storage equipment assisting thermal power unit flexibility transformation can better support load fluctuation and large‐scale new energy access in new power systems.

Fundamental Stock Assessment Using the Dividend Discount Model and Price Earning Ratio Method for Investment Decisions in the Banking Sector Listed on the Indonesian Stock Exchange

This study was conducted to determine the fair value of shares of banking sector companies for the period 2022. We evaluated the stocks of five companies listed on the Indonesia Stock Exchange based on two approaches: the dividend discount model and the price-to-earnings ratio . The survey results of five companies show that two companies are undervalued and three companies are overvalued. If an investor is undervalued, he can decide to buy the stock, while an overvalued investor can sell the stock.

Determinan Keuangan Keperilakuan Heuristic dan Herding Analysis terhadap Keputusan Investasi Berbasis Syariah

Increasing investor interest in investing will certainly increase transaction activity in capital market, so that it will have an impact on the smooth running of company's operations. Business activities that run well and safely will increase the rate of economic growth. Relatively affordable investment capital as launched by Indonesia Stock Exchange since 2015 by holding the "Yuk Nabung Saham" campaign aimed at inviting investors to invest by buying stocks or other instruments on a regular basis at Capital Market. Throughout 2021, the growth in the number of investors has increased very significantly, especially for novice investors from millennial generation to invest in several sharia instruments. This study aims to examine heuristic bias (representativeness, overconvidence) and herding financial behavior models of sharia-based investment decisions for millennials in the West Java region. Quantitative research with a causal design was applied using a purposive sampling technique by collecting a questionnaire of 180 respondents. Meanwhile, the data analysis method uses the PLS software application. The results showed that financial variable representativeness behavior had a significant negative effect on investment decisions of millennial generation in West Java, while financial variables of overconfidence and herding behavior had a significant positive effect on investment decisions of millennial generation in the West Java region.

Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty

Given the steep rises in renewable energy's proportion in the global energy mix expected over several decades, a systematic way to plan the long-term deployment is needed. The main challenges are how to handle the significant uncertainties in technologies and market dynamics over a large time horizon. The problem is further complicated by the fast-timescale volatility of renewable energy sources, potentially causing grid instability and unfulfilled demands. As a remedy, energy storage and power-to-hydrogen systems are considered in conjunction with energy management system but doing so raises the complexity of the planning problem further. In this work, the long-term capacity planning for a hybrid microgrid (HM) system is formulated as a multi-period stochastic decision problem that considers uncertainties occurring at multiple timescales. Long-term capacity decisions are inherently linked with energy dispatch and storage decisions occurring at fast-timescale and it is best to solve for them simultaneously. However, the computational demand for solving it becomes quickly intractable with problem size. To this end, we propose to develop a Markov decision process (MDP) formulation of the problem and use simulation-based reinforcement learning for multi-period capacity investments of the planning horizon. The MDP includes the policies used for dispatch and storage operation, which are represented by linear programming as a part of the simulation model. The effectiveness of our proposed method is demonstrated with a case study, where decisions over multiple decades are considered along with various uncertainties of multi-timescales. Economic and environmental assessments are performed, providing valuable guidelines for government's energy policy.

Capacity market design considering comprehensive revenue and investment decision of generations

The reasonable design of the capacity market parameters in power systems is an essential problem. Being a key element for the capacity market design, an appropriate demand curve of capacity must stimulate sufficient but not excessive capacity investments to satisfy system adequacy requirements. This paper proposes a framework to determine the parameter settings of the demand curve in the capacity market by comprehensively evaluating the investment decision of generations. The joint revenues of generators in both capacity market and energy market are calculated to assess the effect of market for incentivizing investment. The mutual effects of the revenues from capacity and energy markets are modeled using the prospect theory. An investment decision model of generators is developed based on the joint revenues and total costs. The performance of the capacity market is evaluated through dynamic simulation of generator capacity investments. An algorithm is presented to determine the desired parameter settings of capacity market that meet the capacity stimulation requirements without causing redundant capacity. A case study is used to verify the effectiveness of the proposed framework.

A stock-flow consistent model of inventories, debt financing and investment decisions

The paper develops a Kalecki–Steindl–Minsky stock-flow consistent (SFC) model by including inventories, as well as firm’s deposits and debt financing into the investment function. The role of investment decisions in shaping aggregate fluctuations over the business cycle is assessed by considering a model populated by five types of economic actors: workers, rentiers, firms, commercial banks and the central bank. Business cycle fluctuations are investigated assuming a deterministic steady growth path in the long period, in line with recent developments in heterodox growth theory. Next, we simulate the model, calibrating it for the US economy. Our main results confirm the relevance of inventory dynamics in economic cycles, similar to Metzler’s (1941, 1947) analysis. First, inventory investment propagates shocks and affects the length of waves, deviation size and time to convergence. Second, expectations may determine whether there are oscillations or a steady convergence to the long-term trend. Third, we suggest that inventory investment accommodates rapid aggregate demand variations, although not presenting a multiplier effect.

Building and testing a digital model for effective investment decisions to form strategies for development of economic entities

Subject. Sanction restrictions sharply decreased possibilities to attract external borrowings and substantiated the expediency of internal investments. This necessitated software tools enabling calculations and investment decisions. The developed a digital model of ISPI (Information System Portfolio Investor) will help make smart investment decisions, including at the government level. Objectives. The study aims to build a digital model to identify the most attractive investment areas at the regional, country, and cross-country level. Methods. The ISPI model is based on the Markowitz portfolio theory, the Profitability-Risk Model (PRM), and optimization methods. For our calculations, we used yields on Major and Sector Indices of the UK, India, China, USA, France, South Africa for 2014–2021, in one-month increments. Results. Using the developed ISPI model, we constructed scatter plots of leading stock market indices and identified the leading sectors of national economies of the studied countries and the most attractive investment areas. We solved the problem of finding a global optimum for the studied countries, differentiated the leading economic sectors by the level of investment risk, determined that an international portfolio is the most preferable for investment. Conclusions. Our ISPI model helps investors identify a region or country for smart investments. The model enables to determine industries in which investing is most justified within the nearest investment horizon. The model also helps identify the most appropriate financial instruments for investing in individual countries.

The Mechanism and Countermeasures of the Impact of State Subsidy Backslide on the Efficiency of Waste-to-Energy Enterprises—A Case Study in China

The scientific investment decision model of waste incineration power generation is helpful in providing a scientific basis for the government and environmental protection enterprises to formulate reasonable waste prices. The waste incineration power generation project revenue and cost composition framework, based on the project net present value of factors affecting causality analysis, the construction of a waste incineration power generation PPP project net present value system dynamics model, and the use of Vensim PLE software, version 7.3.5, combined with the garbage power generation of listed companies, was built, and we put into use the enterprise’s financial data and the author’s research of the case of the BOT (Build–Operate–Transfer) project data to examine the validity of the model test, simulation, and sensitivity analysis. The results show that the regression of a national subsidy does not necessarily lead to a price adjustment of the waste disposal fee, and when a change in tariff subsidy occurs, the loss brought by the reduction in a feed-in tariff can be compensated by increasing the income from carbon sinks, decreasing the intensity of investment through technological advancement, improving the coefficient of waste power generation through garbage classification, and increasing the utilization of production capacity through the treatment of multiple wastes in a single or a combination of ways.