Optimal Investment Decisions Research Articles

Impact of climate change on high wind and solar optimal mixes and system costs: the case of France

Abstract. The electricity generation sector is undergoing profound transformations via the introduction of wind and solar energies. It is impacted by changing climate conditions, both on the demand and the supply side. The impact of a change in wind and solar resource coupled to the change in demand has however not been studied in regard of its effect on optimal investment decisions. We tackle this issue through the use of regional climate projections coupled to a minimalistic electricity system modeling tool. We find that for the case of France, increasing levels of climate change tend to decrease the optimal penetration of wind energy, while the optimal level of installed solar energy remains constant. We propose that this is explained by the interplay of an average effect coupled to a demand to generation correlation. To the contrary of previous literature, we find that the sole impact of climate change has no adverse consequences on system total costs, with adaptation measures being less attractive economically than their passive counterparts. This highlights the importance of specifying the working hypotheses and phenomena taken into account when issuing policymaking advice, and calls for further research exploring how combining all processes related to climate change impact all relevant elements of the electricity generation sector. We also encourage continued research at the climate and energy interface, to increase the precision and interpretability of similar studies.

A Case Study on Multi-Real-Option-Integrated STO-PF Models for Strengthening Capital Structures in Real Estate Development

This study examines the integration of multi-real-option valuation and security token offering (STO) as an innovative approach to real estate project financing. The case study of Aspen Resort Development serves to illustrate this methodology. The traditional discounted cash flow (DCF) method is frequently ill-suited to the dynamic and uncertain nature of long-term real estate projects, particularly in regard to the ability to adapt to market fluctuations. In order to address these limitations, this study employs a multi-real-option model with a binomial lattice framework, thereby facilitating flexible decision-making in various investment stages. The analysis demonstrates that the STO-based project financing (STO-PF) model offers enhanced financial performance and strategic advantages in comparison to the conventional DCF approach. Furthermore, the STO-PF model has the effect of increasing liquidity, expanding investment accessibility, and improving risk management through the utilization of digital platforms. By quantifying the project’s extended net present value (ENPV), the integration of STOs with real-options models can facilitate optimal investment decisions in the context of a high level of market volatility. Consequently, the STO-PF model is determined to yield a project value (E) of USD 7.34 million and a real-options value (ROV) of USD 3.69 million. This is markedly higher than the net present value (NPV) of USD 3.65 million derived from the traditional project finance (PF) model. Furthermore, the put option for the second investment stage contributes USD 16.45 million to the overall value of the project, thereby demonstrating the flexibility and strategic advantages of the STO framework in comparison to static NPV analysis. The Aspen project serves as a case study, demonstrating the financial viability of phased investments in dynamic market conditions. It contributes to the theoretical understanding of STO-based financing and provides practical insights for developers seeking flexible and innovative financing solutions in the real estate sector. Further research is required to confirm the applicability of STOs in diverse market environments and regulatory contexts. Additionally, in-depth research is necessary to integrate emerging technologies, such as artificial intelligence and machine learning, into multi-real-option-based financial platforms. This integration aims to enhance financial modeling and decision-making processes, as well as to facilitate the integration of digital technologies in this field. Only then can the development and implementation of smart construction development advance.

Optimal green technology investment and lot-sizing decision under carbon tax and cap-and-trade regulations considering planned shortages, outsourced repair and batch shipments

In recent years, various issues such as industrial waste and emissions of greenhouse gases have led to serious environmental pollution. Industrial managers nowadays need to regard cutting carbon emissions as one of their principal responsibilities in relation to the environment, as industry is a major source of carbon emissions. Two prominent regulatory approaches to reducing carbon emissions from operations are the carbon tax and the cap-and-trade system. The existing literature on inventory studies has often considered the market-expanding effects of greening efforts. Nevertheless, a number of additional factors exert influence on greening efforts, with the cost reduction effect representing a critical one. This paper develops an inventory system in which each time a lot of items is received, a proportion of items are found to be of imperfect quality; to identify these, the retailer carries out a 100% inspection of goods received. Following this inspection, the saleable items are added to the inventory in the warehouse in batches of equal size, rather than one by one, and the retailer allows backordering to meet demand. Carbon emissions are incurred at every stage, including ordering, purchasing, repairing, transporting, and holding, so advanced green technology is employed to reduce them. Imperfect products can be sold to a second-hand market or sent to a repair shop. The model discussed in this paper calculates, for both options, the most cost-effective lot size for orders, shortage quantity, scale of green investment and number of batches.

Predicting Stock Price Movements Using Deep Learning: An Analysis of Machine Learning Models for Portfolio Optimization

Capital markets play a strategic role in the global economy; however, high volatility often poses challenges for investors in making optimal investment decisions. Stock price prediction has become one of the key elements in effective portfolio management, but the complexity of market data demands more advanced analytical approaches. This study aims to explore the potential of Long Short-Term Memory (LSTM) models in predicting stock price movements and integrating these predictions into portfolio optimization strategies based on modern portfolio theory. The research methodology utilizes historical stock data from the S&P 500 and IDX Composite indices with daily frequency. The LSTM model is compared with Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and accuracy. The LSTM model demonstrated superior performance with an MSE of 0.0018, RMSE of 0.042, and prediction accuracy of 91.5%, significantly outperforming RNN (87.3%), SVM (80.2%), and ANN (82.7%). The prediction results were then integrated into portfolio optimization using the Mean-Variance Optimization approach, resulting in an increase in expected returns from 8.5% to 12.3%, a reduction in risk from 6.2% to 5.8%, and an improvement in the Sharpe ratio from 1.37 to 2.12. This study demonstrates that the LSTM model not only excels in stock price prediction accuracy but also contributes significantly to enhancing portfolio management efficiency. This Deep Learning-based approach offers a more adaptive, data-driven investment strategy, supporting more informed decision-making in the dynamic capital market

CEO Inside Debt and Corporate Investment Efficiency

ABSTRACTThis paper investigates whether and how the chief executive officer (CEO)'s inside debt holding influences a firm's investment policy. Our evidence on a sample of US S&P 1500 firms indicates that CEO inside debt can significantly improve investment efficiency and help to overcome both over‐investment and under‐investment, suggesting that the CEO's higher positions of inside debt can lead to optimal investment decisions. The results are robust when we use PSM, entropy balancing, the Heckman two‐stage method and the lagged value of the independent variables to mitigate the endogeneity concerns and other robustness checks. Furthermore, we document that the effectiveness of the CEO inside debt on investment is not sensitive to agency problems, as well as financial constraints. Overall, we confirm the importance of investigating the effect of managerial inside debt holdings on corporate capital investment and provide new insights into how inside debt mitigates agency problems.

Green Technology Investment and Production Decisions Considering Stochastic Demand and Firms' Decision Preferences Under Cap‐and‐Trade Regulation

ABSTRACTThis paper investigates how finite rational firms optimally respond to market potential under cap‐and‐trade regulation, considering stochastic demand. Using the mean–variance model framework, we develop an optimization model for green technology investment and production by finite rational firms with different decision‐making preferences. The main results indicate: (1) Under stable demand, firms investing in green technology gain more profits and consumer surplus, however, the effectiveness of carbon emission reduction depends on the market potential size. (2) Under stochastic demand, optimal decisions resulting from firms' varying decision preferences systematically deviate from those of risk‐neutral firms and often demonstrate firms' risk‐averse tendencies that lean towards conservatism. As external factors change, there can even be a reversal in technology investment with an increase in demand uncertainty that amplifies this systematic deviation. (3) Taking firms with different decision‐making preferences as the starting point, this study further analyses the distortion effect of the firm's risk attitude on the optimal technology investment and production decisions, enriching the related research on the joint decision‐making risk model of clean production. And it is more pertinent to the complex situation firms face in the real situation with decision‐making. (4) This study provides a theoretical basis and practical reference for the finite rational firms facing green technology investment choices. Considering the risk‐averse characteristics of firms, this study suggests that the government should establish a risk‐sharing mechanism to weaken the deviation of stochastic demand from the optimal decision‐making of firms or implement certain subsidy policies to protect the profit level of firms.

Optimal investment, consumption, and life insurance decisions for households with consumption habits under the health shock risk

. This article investigates the optimal investment, consumption, and life insurance strategies for households under the impact of health shock risk. Considering the uncertainty of the future health status of family members, a non homogeneous Markov process is used to model the health status of the wage earner. Drawing upon the theory of habit formation, we investigate the influence of different consumption habits on households’ investment, consumption, and life insurance strategies. Based on whether the wage earner is alive or not, we formulate and solve the corresponding Hamilton-Jacobi-Bellman (HJB) equations for the two scenarios of wage earner survival and wage earner’s demise, respectively, and obtain explicit expressions for the optimal investment, consumption, and life insurance strategies of households. Through sensitivity analysis of important parameters, it has been shown that the presence of health shocks within households has a negative impact on investment and consumption decisions, while the formation of consumption habits increases household propensity for precautionary savings.

Optimal investment in artificial intelligence-driven production activities considering socio-economic concerns

This study provides valuable insights into the theoretical aspects of AI adoption risks and offers practical implications for real-world scenarios. We examined the impact of AI adoption risks, including job displacement and regulatory constraints, on capital investment in high-technology production to provide actionable recommendations for businesses. We investigated the effects of random fluctuations in a firm’s Total Factor Productivity (TFP) due to challenges in AI integration or skilled labour acquisition. We also studied the impact of background risks, such as regulatory policies, on optimal investment decisions. Our research assessed how changes in expected value and volatility in a firm’s TFP and policy-related background risk affect optimal investment. Additionally, we examined the impact of covariance changes between these risks on optimal investment. Using a mean-variance utility model, our study found that AI integration and improved skilled labour acquisition lead to significant productivity gains and increased capital investment, while increased TFP volatility reduces optimal investment. Risk-averse entrepreneurs may not definitively forgo profitable opportunities to avoid increased background risk. Improved alignment between these risks may prompt firms to invest more to mitigate their combined impact.

Real Options Analysis of Constructed Wetlands as Nature-Based Solutions to Wastewater Treatment Under Multiple Uncertainties: A Case Study in the Philippines

Constructed wetlands (CWs) are man-made ecosystems that mimic the properties of natural wetlands. They are being utilized to treat various types of wastewater, from domestic to agricultural, municipal, commercial, and industrial effluents. Despite their economic viability and environmental benefits, their widespread adoption is challenged with several uncertainties, including public support, technology learning, and the impacts of climate change. This study proposes a valuation framework that considers these uncertainties to analyze the feasibility of CWs. Using existing CWs in the Philippines as a case, this study employs the real options approach to (1) evaluate the feasibility of CW projects using cost–benefit analysis, (2) calculate the value of postponing decisions to implement CWs projects using real options analysis, and (3) identify the optimal investment decisions for CWs considering the opportunity costs of waiting and uncertainties in public support and the impacts of climate change. Results found that the project is feasible with a net present value of USD 88,968. Yet, the real options value at USD 208,865 indicates that postponing the project may be a more optimal decision. Considering the cost of waiting, the valuation identified the threshold at 5.56% to immediately implement the project. The calculated values increase with uncertainty in public support but decrease with uncertainty in climate change’s impacts. Yet, these uncertainties prolong the decision to implement CW projects until they are resolved. The findings from this case study provide a basis for recommendations to support the adoption of CWs as nature-based water treatment for a more sustainable future.

Smart investing: Unveiling key drivers of strategic investment for investors in the Indonesia Stock Exchange

To achieve the optimal investment decision, people should have better financial literacy. A better understanding of stock investment can be obtained through having experience investing in the stock market. Besides experience, influences from financial socialization agents such as family, friends, education, and media will improve financial knowledge. Hence, it will determine investments wisely. The aim of this study is to determine the degree to which financial experience, family, peers, formal education, media, and financial literacy have direct on impact investment decisions and furthermore to determine the role of financial literacy as mediating variable between financial experience, family, peers, formal education, media, and investment decisions. Investors who have already registered on the Indonesia Stock Exchange are the respondents to this study. Only those investors are allowed to invest in the Indonesian stock market. There are 716 respondents who were analyzed using self-administered questionnaires and structural equation modeling (SmartPLS). Findings show that peers and financial literacy have a direct positive impact (p<0.05) on investment decisions, while financial experience, family, education, and media do not (p>0.05). Additionally, financial experience, peers, education, and media have a significant positive effect on financial literacy (p<0.05), while family does not (p>0.05). Financial literacy is shown to mediate the relationship between financial experience, peers, education, media, and investment decisions (p<0.05) but not with family (p>0.05). This implies that having financial experience improves financial literacy, which leads to better investment decisions. Furthermore, peers, education, and media all play an important role in increasing financial literacy to make optimal investment decisions. AcknowledgmentWe want to express our sincere gratitude to Universitas Multimedia Nusantara for providing the grant to support this project. Without the generous support, the successful completion of this project would not have been possible. Additionally, we extend our appreciation to the editors and reviewers for their valuable input, which significantly contributed to enhancing the quality of this paper.

Building cybersecurity resilience: integrating defense and recovery investment strategies in an expected resilience framework

PurposeTo remain competitive in an unpredictable environment where the complexity and frequency of cybercrime are rapidly increasing, a cyber resiliency strategy is vital for business continuity. However, one of the barriers to improving cyber resilience is that security defense and accident recovery do not combine efficaciously, as embodied by emphasizing cyber security defense strategies, leaving firms ill-prepared to respond to attacks. The present study thus develops an expected resilience framework to assess cyber resilience, analyze cyber security defense and recovery investment strategies and balance security investment allocation strategies.Design/methodology/approachBased on the expected utility theory, this paper presents an expected resilience framework, including an expected investment resilience model and an expected profit resilience model that directly addresses the optimal joint investment decisions between defense and recovery. The effects of linear and nonlinear recovery functions, risk interdependence and cyber insurance on defense and recovery investment are also analyzed.FindingsAccording to the findings, increasing the defense investment coefficient reduces defense and recovery investment while increasing the expected resilience. The nonlinear recovery function requires a smaller defense investment and overall security investment than the linear one, reflecting the former’s advantages in lowering cybersecurity costs. Moreover, risk interdependence has positive externalities for boosting defense and recovery investment, meaning that the expected profit resilience model can reduce free-riding behavior in security investments. Insurance creates moral hazard for firms by lowering defensive investment, yet after purchasing insurance, expanded coverage and cost-effectiveness incentivize firms to increase defense and recovery spending, respectively.Originality/valueThe paper is innovative in its methodology as it offers an expected cyber resilience framework for integrating defense and recovery investment and their effects on security investment allocation, which is crucial for building cybersecurity resilience but receives little attention in cybersecurity economics. It also provides theoretical advances for cyber resilience assessment and optimum investment allocation in other fields, such as cyber-physical systems, power and water infrastructure – moving from a resilience triangle metric to an expected utility theory-based method.

Employer-provided parking: Departure time choice, investment decision, and welfare effects

Employer-provided parking (EP) has become a prevalent way to reduce employees' parking delays and late arrivals through offering them free or low-price parking spaces at the workplace. This paper explores the EP effects on employees' trip scheduling, employer's EP investment decision, and commercial parking operator's pricing decision. An analytical trip scheduling equilibrium model is first presented to model the interaction between EP provision and employees' departure time choices during morning commute. A profit maximization model incorporating the employee productivity is then developed to determine the employer's optimal EP investment decision. A competitive game between employer's investment decision and commercial parking operator's parking pricing decision is analytically investigated, together with the effects of EP investment on social welfare. The results show that the EP investment can lead to a win-win situation with decreased employee commuting cost and increased firm production output; and the employer would like to provide only part of the employees with EP services. The competitive game solutions depend very much on the marginal costs of EP and commercial parking spaces. The EP investment with an excessively high commercial parking fee may hurt the society due to decreased social welfare.

Data-driven robust optimisation of hydrogen infrastructure planning under demand uncertainty using a hybrid decomposition method

In the race towards “Net-zero”, hydrogen has emerged as one of the key alternatives to carbon-based fossil fuels for a sustainable decarbonisation. This work studies the spatially explicit multi-period hydrogen infrastructure planning under demand uncertainty that contributes to the heat decarbonisation in Great Britain. Demand uncertainty surrounding future hydrogen supply chains poses challenges to cost optimisation and system security, so uncertainty-resilient policies are required to ensure robust operations. In this work, we employ data-driven robust optimisation to develop a framework for uncertainty-aware representative days explicitly characterised by polyhedral uncertainty sets. The proposed framework is applied on a multi-period mixed-integer linear model with dual temporal resolution which aims to determine the optimal yearly investment decisions and hourly operational decisions for the hydrogen infrastructure planning under demand uncertainty. To efficiently solve the large-scale two-stage adaptive robust optimisation problem, a hybrid decomposition algorithm is developed based on a two-step hierarchical procedure and the column-and-constraint generation method, which can significantly reduce the computational complexity. The optimisation results highlight how uncertainty can result in the total cost increase, and verify the advantages on controlling solution conservatism in the adaptive robust optimisation compared to the static robust optimisation.

Benchmark of mixed-integer linear programming formulations for district heating network design

Optimal routing and investment decisions are key design criteria to reduce the high investment costs of district heating systems. However, these optimization problems have prohibitively high computational costs for large districts. Four different mixed-integer linear optimization frameworks are benchmarked in this study in order to compare their computational scaling. The frameworks exhibit significant differences in solving times for synthetic benchmarks and real-world urban districts of up to 9587 potential edges. The new open-source framework topotherm, developed for this work, exhibits the best computational performance when only one time step is optimized. The comparison between the models Résimont, DHmin, DHNx, and topotherm shows two main trends. First, fewer integer variables do not necessarily translate to lower solving times, and second, using redundant binary variables, which introduce symmetries into the constraints, leads to higher solving times. None of the considered optimization frameworks is able to solve the largest benchmark problems for five time steps within the allowed time limit and tolerance. These findings highlight the challenges of and pressing need to develop efficient models for simultaneous optimization of district heating network topology, pipe sizing, and operation.

The Role of Technology in Transforming Real Estate Transactions in Uganda

Technology is significantly transforming real estate transactions in Uganda, addressing inefficiencies and modernizing the market. Digital platforms and marketplaces, such as Jumia House and Lamudi, have revolutionized property listings and accessibility, while blockchain technology enhances transaction security and reduces fraud. Mobile money and digital payments streamline financial transactions, and Geographic Information Systems (GIS) and drones improve property surveying and valuation. Artificial Intelligence (AI) and Big Data provide valuable insights and predictive analytics, optimizing investment decisions. These technological advancements have increased transparency, efficiency, and market reach, reduced costs, and improved transaction experiences. Despite challenges such as the digital divide and evolving regulatory frameworks, ongoing investment in technology and infrastructure promises further growth and innovation in Uganda’s real estate sector. Keywords: Digital Platforms, Blockchain Technology, Mobile Payments, Geographic Information Systems (GIS), Artificial Intelligence (AI)

The Role of Technology in Transforming Real Estate Transactions in Uganda

Technology is significantly transforming real estate transactions in Uganda, addressing inefficiencies and modernizing the market. Digital platforms and marketplaces, such as Jumia House and Lamudi, have revolutionized property listings and accessibility, while blockchain technology enhances transaction security and reduces fraud. Mobile money and digital payments streamline financial transactions, and Geographic Information Systems (GIS) and drones improve property surveying and valuation. Artificial Intelligence (AI) and Big Data provide valuable insights and predictive analytics, optimizing investment decisions. These technological advancements have increased transparency, efficiency, and market reach, reduced costs, and improved transaction experiences. Despite challenges such as the digital divide and evolving regulatory frameworks, ongoing investment in technology and infrastructure promises further growth and innovation in Uganda’s real estate sector. Keywords: Digital Platforms, Blockchain Technology, Mobile Payments, Geographic Information Systems (GIS), Artificial Intelligence (AI)

Time-to-build, regulation, and investment

This study investigates the effects of uncertainty in time-to-build and regulation, which hinders immediate revenue generation after investment, on a firm’s optimal investment decision. We show that in the absence of regulation, uncertainty in time-to-build always accelerates investment and enhances pre-investment firm value. We also show that in the absence of time-to-build, uncertainty in regulation can mitigate the distortion of investment induced by regulation. Furthermore, in the presence of both time-to-build and regulation, there can exist harmless regulation that does not induce any distortion in the investment decision and does not harm firm value. Lastly, in the presence of both time-to-build and regulation, not only uncertainty in time-to-build but also its presence can accelerate investment.

Performance pressure and annual report text manipulation: Evidence from China

AbstractBuilding on attribution theory, this study investigates the antecedents of corporate annual report text manipulation from the perspective of managerial performance pressure. Using a data set comprising 15,076 samples from companies listed on China's Shanghai and Shenzhen A‐share markets between 2009 and 2021, we reveal that as management faces performance pressure, they tend to increase content about external environmental risk and policy uncertainty in annual reports due to actor–observer bias. When incorporating Confucian cultural factors into the research framework, the study found that the strength of Confucian culture mitigates the correlation between the pressure of management performance and annual report manipulation. However, the level of marketization where a corporation is located can undermine the ethical norms upheld by Confucian culture. In additional analysis, we also discovered that the longer the duration of performance pressure, the greater the extent of manipulation within the annual reports initiated by the management. Interestingly, the social performance pressure generated from ESG performance also positively influences annual report textual manipulation, compared to operational performance pressure. This study explores the impact of management performance pressure on information disclosure decisions from the perspective of performance attribution, not only extending the boundaries of attribution theory but also providing guidance for policymakers to enhance the supervision of annual report disclosures and for capital market investors to optimize investment decisions.

Financial Literacy and Risk Perception to Maximize Crypto Asset Investment Decisions

This study aims to improve investment decisions in crypto assets by optimizing the integration of financial literacy and risk perception through behavioral finance, emphasizing an in-depth understanding of the psychological and behavioral factors that influence financial decision-making. This study used a quantitative approach with primary data sourced from online questionnaires distributed via the Google Forms platform. The Slovin technique was used for sampling, resulting in a sample of 300 respondents. The analysis technique used by SEM-PLS is the Structural Equation Model Partial Least Square with the help of the SmartPLS program. The research was conducted at PT Indodax Nasional Indonesia or “INDODAX – Indonesia Bitcoin & Crypto Exchange”. The results find that the integration of financial literacy, understanding of risk through behavioral finance, and the use of technology can significantly improve investment decisions in crypto assets. This research provides in-depth insights into the relationship between financial literacy, risk perception, and investment decisionmaking in crypto assets. The theoretical implications of the study confirm that financial literacy, understanding of risk, and related education contribute to improving optimal investment decisions in crypto assets as well as shaping financial behavior. The practical implication is the need for financial organizations and educational institutions to focus on financial literacy education and risk perception training to support optimal investment decision-making in crypto assets. Keywords: financial literacy-1, risk perception-2, behavioral finance-3

Research on optimal investment decision of distribution network projects under the construction of the new power system

Abstract This article establishes a ranking model of distribution network projects to make optimal investment decisions. The project optimization ranking model consists of the LCC model and the project comprehensive evaluation model. When constructing the evaluation model of distribution network projects, the evaluation index system is first set, and then the weight allocation for each index is calculated based on the G1-antientropy weight comprehensive integrated weighting method, so as to calculate the comprehensive benefit of the unit cost of a project for optimal sorting. Taking a distribution network planning project in Sichuan Province as an example, the empirical analysis is conducted, and the ranking results are obtained by using the above optimization ranking model for distribution network projects depending on the comprehensive benefit index of unit cost of the project A-E, which confirms the reasonability and practicability of the decision-making system and realizes the optimization of investment decisions for distribution network projects.