Investment Constraints Research Articles

Robust and Sparse Portfolio: Optimization Models and Algorithms

The robust and sparse portfolio selection problem is one of the most-popular and -frequently studied problems in the optimization and financial literature. By considering the uncertainty of the parameters, the goal is to construct a sparse portfolio with low volatility and decent returns, subject to other investment constraints. In this paper, we propose a new portfolio selection model, which considers the perturbation in the asset return matrix and the parameter uncertainty in the expected asset return. We define three types of stationary points of the penalty problem: the Karush–Kuhn–Tucker point, the strong Karush–Kuhn–Tucker point, and the partial minimizer. We analyze the relationship between these stationary points and the local/global minimizer of the penalty model under mild conditions. We design a penalty alternating-direction method to obtain the solutions. Compared with several existing portfolio models on seven real-world datasets, extensive numerical experiments demonstrate the robustness and effectiveness of our model in generating lower volatility.

The (Lack of) Anticipatory Effects of the Social Safety Net on Human Capital Investment

How does the expectation that a child will receive government benefits in adulthood affect parental investments in the child’s human capital? Most parents whose children receive Supplemental Security Income (SSI) benefits overestimate the likelihood that their child will receive SSI benefits in adulthood. We present randomly selected families with the predicted likelihood that their child will receive SSI benefits in adulthood. Reducing parents’ expectations that children will receive benefits in adulthood does not increase investments in children’s human capital. This zero effect is precisely estimated. Likely explanations include parents working more themselves, nonfinancial goals influencing investment, and families facing investment constraints. (JEL G52, I26, I38, J13, J24, J31)

Practice of Alternative Assessment at Tribhuvan University

Alternative assessment is a recent trend in the field of education. It helps the teachers and students to be more self reflective and responsible towards their tasks and activities. In this light, the present study aimed to explore the practice of alternative assessment system at Tribhuvan University. To accomplish the objective, the researcher adopted interpretivism as philosophical approach and narrative inquiry as research design. This research was based on the experience of university level teachers teaching at semester level. The total population of this study comprised all the teachers teaching at Tribhuvan University. However, the researcher selected only seven English language teachers from Tribhuvan Multiple Campus, Palpa using purposive sampling procedure. Similarly, the researcher made an interview guide, and interviewed with the sampled teachers using recording devices to obtain in-depth information. The results revealed that most of the teachers prefer to use traditional paper pencil test rather than using alternative assessment tools for student evaluation. They had their reluctance to use alternative assessment tools because of the time, resource and investment constraints. They were not properly updated and trained towards alternative assessment. However, they highlighted the importance of such tools for their students’ evaluation. The present study can be useful to the teachers of university level for gaining insights of the alternative assessment tools for student evaluation.

Robust portfolio selection for sparse index tracking under no short-selling and full investment constraints

For financial index tracking, it is desirable to build a sparse portfolio of a small number of assets to save transaction costs. For all we know, a majority of the pertinent literatures on sparse index tracking are mainly concentrated on the penalized least squares estimation under the cardinality and no short-selling constraints. Nevertheless, the return series of financial index often exhibit outliers, and thus the above literatures may fail to produce a robust solution for index tracking. In this paper, we indeed to provide a general procedure to build robust portfolio that can undertake stock selection and capital allocation for financial index tracking. To be more realistic, we further take the full investment constraint (or budget constraint) into consideration. Numerical simulations indicate that the proposed method has good resistance to heavy-tailed error and outlier contamination. Finally, the out-of-sample performance of the new portfolios is compared empirically by tracking the SSE 50 index and FTSE China A50 index.

Sustainable livelihood capital and climate change adaptation in Pakistan's agriculture: Structural equation modeling analysis in the VIABLE framework

This study aims to assess the role of sustainable livelihood capital, the mediation of investments and farming purposes, and the moderation of climatic and non-climatic factors in the adaptation process, particularly in the aspects of Crop, Farm, Irrigation, and Economic Management. Moreover, guided by the VIABLE (Values and Investments for Agent-Based Interaction and Learning in Environmental Systems) framework, we analyze stakeholders' actions, priorities, and goals in the climate change adaptation process. A structured questionnaire was designed based on a five-point Likert scale covering the concepts of livelihood capital, climate change adaptation, investment priorities, farming constraints, and farmers' decision-making factors. Field data were collected from 800 farmers during December 2021 to February 2022 in the irrigated agricultural regions in the Indus Plain of the Punjab and Sindh provinces, Pakistan. We employed the Partial Least Square Structural Equation Modeling approach to the VIABLE framework (VIABLE-SEM) to analyze the collected data. The results confirm livelihood capital as the most significant determinant (beta = 0.57, effect size = 0.503) for farmers’ adaptation strategies in the Indus plain. Other variables, such as the principal purpose of farming, available investment options, natural and human constraints, appear less important. We identified 13 significant viability pathways that show investment priorities, farming purposes, and constraints faced by the farmers in climate change adaptation. The study also found that non-climatic factors negatively influence (beta = −0.156) the relationship between capital and adaptation, while climatic factors positively influence (beta = 0.050) this relationship. Interestingly, the presence of these influencing factors increases the adaptive capacity of farmers. These findings have important implications for policymakers and researchers in designing and implementing effective climate change adaptation strategies in the agricultural sector of Pakistan.

Unraveling the impact of climate policy uncertainty on corporate default risk: Evidence from China

This paper examines the effect of climate policy uncertainty (CPU) on corporate default risk based on the comprehensive dataset of Chinese A-listed firms. Based on the empirical study, we document a significant positive impact of China’s CPU on the corporate default risk, and the impact sounds systematically aggravating with the increasing term structure of default investigation. Our research also reveals that R&D investment, ESG performance and firm financial constraints function as effective channels between climate policy uncertainty and firm default risk. Also, better local development and higher managerial climate attention have mitigating effects on the relationship between CPU and corporate default risk, while state ownership intensifies the effect of CPU on corporate default risk. These findings highlight the externality of climate policy risk on corporate default risk profile from the empirical perspective.

The Influence Of The Development Of Artificial Intelligence Technology In The Industrial Field

Artificial Intelligence (AI) has become a significant development of information and communication technology in the last ten years. However, its implementation in Indonesia is still relatively low due to constraints on worker skills and insufficient infrastructure investment. Nonetheless, AI has the potential to improve business performance, such as in the use of cloud computing. The application of AI in the industry requires changes and adjustments to technological developments. The use of AI is significantly affecting various sectors and people's lifestyles, and governments have an important role to play in providing technology-based services. The purpose of this study is to examine the influence of AI in the industrial sector, understand its development in Indonesia, and analyze the positive and negative impacts of using AI in the future. This study uses a descriptive method combined with qualitative content analysis techniques and field reviews. The descriptive method is a research method that aims to obtain an objective picture of the situation. Thus, the implementation of AI in Indonesia still needs to be improved by overcoming skills and investment constraints. Governments should play an active role in supporting the development of AI in the industry while considering its positive and negative impacts in the future

Across-time risk-aware strategies for outperforming a benchmark

We propose a novel objective function for constructing dynamic investment strategies with the goal of outperforming an investment benchmark at multiple points of evaluation during the investment time horizon. The proposed objective is intuitive, easy to parameterize, and directly targets a favorable tracking difference of the actively managed portfolio relative to the benchmark. Under stylized assumptions, we derive closed-form optimal investment strategies to guide the intuition in more realistic settings. In the case of discrete rebalancing with investment constraints, optimal strategies are obtained using a neural network-based numerical approach that does not rely on dynamic programming techniques. Compared to the targeting of a favorable tracking difference relative to the benchmark only at some fixed time horizon, our results show that the proposed objective offers a number of advantages: (i) The associated optimal strategies exhibit potentially more attractive asset allocation profiles, in that less extreme positions in individual assets are taken early in the investment time horizon, while achieving a similar terminal terminal wealth distribution. (ii) Across-time risk awareness leads to more robust performance and a higher probability of benchmark outperformance during the investment horizon in out-of-sample testing. The resulting strategies therefore exhibit desirable characteristics for active portfolio managers with periodic reporting requirements.

Is solar heating an appropriate choice in rural areas of northern China? Evidence from numerical simulation and life cycle assessment

Solar heating is generally regarded as a clean and low-carbon heating method, while its high initial investment hinders its promotion in economically underdeveloped areas. With the implementation of the clean heating policy and the proposal of the carbon neutralization target, rural bulk coal heating in northern China is restricted. The Chinese government proposes to widely adopt solar heating to meet the heating demands of rural residents. In this research, the application of solar assisted heat pump systems in Beijing, Tianjin, Hebei and its surrounding areas in China is numerically simulated. A new evaluation method under the same initial investment constraint is proposed to verify its benefits throughout the entire life cycle. The results indicate that although solar thermal heating has the lowest environmental impact and carbon emissions among various heating methods, it is not the best solution to rural clean heating. The reason is that equal investment in other projects can bring much more benefits, such as roof solar photovoltaic. In contrast to the air source heat pump and photovoltaic panel scheme with the same initial investment, solar heating has obvious negative environmental impact, 53.3 % higher economic cost, 35.9 tons more carbon emissions, and 105.9 % higher roof area occupation. The sensitivity analysis of solar fraction, geographical coordinates, and energy price also supports the above findings. The recommendation is proposed to promote air source heat pumps or solar photovoltaic, rather than solar thermal collectors, so as to reduce the cost of rural clean heating and carbon emission reduction.

Economic policy uncertainty: A new firm‐level measurement and impact on enterprise innovation strategies

AbstractThis paper investigates the impact of the perception of economic policy uncertainty (PEPU) on enterprise innovation strategies. Using a text‐based method and a sample of Chinese listed companies from 2007 to 2021, we find that PEPU negatively affects substantive and strategic innovations, and its negative impact on strategic innovations is more noticeable than that on substantive innovations. Mechanism analysis shows that PEPU affects enterprise innovation strategies through innovation investment and financing constraints. Moreover, enterprises with a high shareholding ratio of executives and private enterprises are more likely to suffer the negative impact of PEPU. Finally, analyst attention and internal control quality may weaken the inhibitory impact of PEPU on enterprise innovation strategies.

Interconnectedness between Supply Chain Resilience, Industry 4.0, and Investment

Background: As industry and society move towards the second decade of the deluge of advanced technologies of the Industry 4.0 (I4.0) age, introduced circa 2012, it is evident that the global economy continues to grapple with a plethora of ever-intensifying disruptions and vulnerabilities that inflict unexpected and uncontrolled damages on multiple organizational processes. These circumstances demand significant paradigm shifts, placing supply chain resilience (SCR) in the foreground of boardrooms and agendas of executive meetings. Method: This paper presents a bibliometric analysis of selected articles that examine the intricate interplay of I4.0 and SCR under investment constraints. Employing a funnel approach, this study delves deeper into about a hundred papers that were initially selected from a pool of approximately four thousand publications on SCR. The study thoroughly analyzes the interconnection between SCR, I4.0, and investment (INV) while classifying these articles in a structured manner, based on industry type and focus. Results: The primary aim of the paper is to identify trends, gaps, and potential opportunities for future research on the SCR-I4.0-INV interplays. Conclusions: The findings reveal that industries are converging towards the implementation of digital technologies as a strategic move to tackle unexpected, unplanned, and undesired situations. This research illuminates the needs for organizations to prioritize supply chain resilience in the face of disruptions and vulnerabilities while highlighting the potential of digital technologies to enhance their resilience, therefore ensuring sustainable growth.

Flood Insurance, Building Codes, and Public Adaptation: Implications for Airport Investment and Financial Constraints

This paper investigates the impact of flood management policies on airport investment and the resulting financial constraints. Specifically, it examines the effects of flood insurance, building codes, and public adaptation investment on the investment decisions of 100 United States airports located in flood-prone areas. The paper estimated the financial loss from extreme precipitations and flooding using novel data from the United States Federal Emergency Management Agency, and a differences-in-differences framework leveraging the introduction of the 2012 Biggert–Waters reform of the National Flood Insurance Program. The findings reveal that while flood insurance costs negatively influence overall airport investment, they do not significantly affect investment–cash sensitivity. On the other hand, the introduction of stricter building codes and public adaptation investment leads to increased cash usage for investment purposes, particularly among airports exposed to extreme precipitation and flood risks. Furthermore, the analysis suggests that the observed increase in financial constraints resulting from stricter building codes and public adaptation investment is likely driven by the asymmetry of information rather than the materiality of flood risk. In other words, public investment in flood risk reduction appears to signal to investors that the airport is exposed to flood risk, potentially leading to increased financial constraints. This finding highlights the importance of considering information asymmetry when assessing the impact of flood management policies on financial constraints. Understanding the underlying drivers of these effects is crucial for supporting resilient infrastructure development and informing effective decision-making in flood-prone areas.

한국기업의 R&D 투자와 자금조달 제약도 분석 : 상장기업과 비상장기업 비교

한국기업의 R&D 투자와 자금조달 제약도 분석 : 상장기업과 비상장기업 비교

From scenario to roadmap: Design and evaluation of a web-based participatory watershed planning system for optimizing multistage implementation plans of management practices under stepwise investment

Planning multistage implementation plans, or roadmaps, based on the spatial distribution of a best management practice (BMP) scenario is essential for achieving watershed management goals under realistic conditions, such as stepwise investment plans that involve multiple stakeholders, including investors, economic and environmental beneficiaries. The state-of-the-art BMP roadmap optimization method can address this need for optimization but is over-specialized and complex to non-expert stakeholders. This study designed a user-friendly web-based participatory watershed planning system to assist a diverse group of stakeholders in reaching a consensus on optimal roadmaps. The participatory process of stakeholders includes iteratively proposing stepwise investment constraints, submitting roadmap optimization tasks, analyzing spatiotemporal results from multiple perspectives, and selecting preferred roadmaps. The proposed system design separates the participatory process of non-expert stakeholders from the specialized modeling process of constructing simulation-optimization tools for BMP roadmaps, which is done in advance by professional modelers and encapsulated as webservices on the server side. The webservices expose a small set of essential parameters to lower barriers to use. The interactive participatory process is presented to stakeholders through web browsers with an easy-to-use interface. The system design was evaluated by implementing an agricultural watershed planning system for soil erosion reduction and conducting a role-playing experiment involving three groups of stakeholders with different standpoints in proposing investment constraints. The experimental results show that the optimal roadmap sets exhibit progressive improvements across three-round optimizations started by different stakeholders, effectively capturing the varying perspectives of stakeholders and facilitating consensus-building among them. The idea of system design and example implementation can serve as a valuable reference for developing related user-friendly environmental decision support systems.

Incorporating DSO’s Situational Awareness in Resilience-Oriented Distribution System Planning

One of the key distinctions between extreme weather events and random events in distribution systems is the level of predictability. The distribution system operator (DSO) can anticipate the upcoming extreme weather event and its effects, which is impossible in random events. Thus, the DSO can prepare the system by taking appropriate actions to tackle the upcoming event. This characteristic provides the DSO with operational situational awareness (SA) against extreme weather events. Considering this DSO’s capability in evaluating the system resilience against extreme weather events results in a more realistic resilience estimate. This paper proposes a model for resilience-oriented distribution system planning (RODP) in which the value of the DSO’s SA is considered and modeled (namely, SA-RODP). The proposed two-stage stochastic model is formulated as a mixed-integer linear programming (MILP) problem. In the first stage, hardening sections and installing emergency distributed generations (DGs), battery energy storage systems (BESSs), and automatic switches are modeled. The second stage contains the DSO’s decisions corresponding to operation scenarios. These decisions are made in two successive operation periods, namely, the preventive operation period (before the event to prepare the system) and the emergency operation period (during and after the event to mitigate the effects of the event). The DSO’s decisions in the preventive operation period are made according to each scenario, reflecting the DSO’s SA. The objective function of SA-RODP comprises the investment costs of the first stage and the expected operation cost of the second stage. The model constraints include the investment constraints and power flow and operational requirements constraints in each scenario. The model’s effectiveness is validated by several numerical simulations on the IEEE 33-bus test system. SA-RODP model simultaneously reduces the required investment cost and boosts the system resilience compared to conventional RODP frameworks in which the DSO’s SA is not considered. Further, this model increases the DSO’s capability to confront extreme weather events.

Roadmap to digital supply chain resilience under investment constraints

ABSTRACT In a world bombarded with unprecedented challenges, it is paramount for organizations to embrace resilience in all their processes, albeit supply chain management. In the wake of ever-intensifying race for digital transformation, it is crucial for organizations to follow structured roadmaps towards building digital supply chain resilience (DSCR) through striking a balance between supply chain resilience (SCR) and supply chain vulnerabilities (SCV), in a manner that does not erode profits, neither expose the organization to increasing vulnerabilities. The paper addresses the evolution of SCR, the SCR and SCV drivers, and recommends investment in particular Industry 4.0 (I4.0) enablers. The outcomes of this paper are (a) a discussion of the complementary relationship between SCR and SCV drivers; (b) a thorough understanding of the possible contributions of I4.0 technologies towards DSCR; and (c) a roadmap for future research into the integration of SCR drivers and SCV constrained by the resilience fitness space (RFS).

China's optimal development pathway of intermittent renewable power towards carbon neutrality

The carbon neutrality target proposes challenges for China's intermittent renewable power, which has experienced rapid growth in recent years. This study quantitatively analyzes the competition effects of wind power and solar photovoltaics (PV) by way of a competition model and describes their diffusion mechanisms in the energy system. Taking minimum cost as the objective, it constructs a dynamic programming model to explore the optimal development pathway of China's intermittent renewable power to 2060 under the carbon neutrality target and other typical constraints, which enriches the application of competition model in the energy sector, especially in the field of renewable energy technology diffusion. Besides, it also explores the key factors affecting the pathway by way of scenario and sensitivity analyses. This empirical study finds that solar PV outweighs wind power in the long run because the diffusion of wind power is obviously negatively affected by solar PV, whereas the inverse relationship is slightly positive. Wind power will reach its diffusion cap of 660 GW after steady growth; by comparison, solar PV will achieve its diffusion cap of 1790 GW later, but with a much higher growth rate. Investment constraint and technological level slightly affect the development pathway in the early and middle stages, but grid absorptive ability is the critical factor over the entire study period. These discussions will help governments design pathways for developing intermittent renewable energy that match carbon neutrality targets, taking into account the impact of wind power and solar PV interactions.

Analysis of Challenges Facing and Factors Influencing the Profitability of Dairy Cattle Enterprises in Southwestern Uganda

In this study, challenges experienced by dairy cattle enterprises in Southwestern Uganda and the factors influencing their profitability were respectively analyzed using exploratory factor analysis and multiple regression in STATA 15.0 statistical software. Eighteen questions relating to the challenges experienced by dairy producers in the study area were factor analyzed using principal components analysis with varimax rotation. Kaiser-Meyer-Olkin’s measure of sampling adequacy was 0.643, above the commonly recommended value of 0.6, and Bartlett’s test of sphericity was significant (ꭓ² (153) = 1670.13, P<0.001). Using both the scree plot and eigenvalues greater than 1 to determine the underlying components, the analysis yielded five factors explaining a total of 67.42% of the variance in the data. These factors were investment constraints, productivity constraints, climatic and environmental conditions, veterinary and social security services, and marketing constraints, which explained 21.32%, 13.01%, 11.97%, 11.03%, and 10.097% of the variance after rotation, respectively. The factors hypothesized to influence the profitability of dairy enterprises were; daily milk yield per lactating cow, the prevalence rate of diseases, percentage of lactating cows to those raised on the enterprise, attendance of animal production training, the unit production cost of milk, and enterprise size according to the number of animals raised. Regression analysis results of these factors revealed that the unit production cost of milk, enterprise size, and daily milk yield per lactating cow were statistically significant. The estimated model had an R-squared value of 0.92. The recommendations emphasized in this study were reducing milk production costs, rational use of production resources, adopting improved cattle breeds, improving feeding by supplementing animal diets with concentrate feeds to increase milk yield, and general improvement in dairy herd management practices, including disease control strategies.

Practical Applications of Factor Investing: From Theory to Practice

In <b><i>Factor Investing: From Theory to Practice</i></b>, from the Winter 2022 issue of <b><i>The Journal of Beta Investment Strategies</i></b>, <b>Tarun Gupta, Jay Raol,</b> and <b>Viorel Roscovan</b> (all of <b>Invesco</b>) present a simple but rigorous evidence-based framework for testing and accepting factors as valid drivers of risks and returns. They find that value, momentum, quality, and low volatility are valid factors but that the size factor is questionable. Their methodology for validating factors comprises four elements: 1) economic theory, 2) robust risk and return evidence, 3) cross-asset and cross-region validation, and 4) implementability. The authors stress that investors should consider only factors that are implementable in practice. Incorporating real-world constraints into implementability tests allows investors to identify the factors that deliver attractive risk–return trade-offs after accounting for transaction costs and investment constraints.

Analysing SME investment, financing constraints and its determinants. A stochastic frontier approach

In this paper we explore the link between Small and Medium Enterprises (SMEs) investment and their economic characteristics, together the presence of financing constraints during the post-2008 crisis recovery period. We use novel survey data for Irish SMEs between 2016 and 2018, which disaggregates investment by asset type and allows a granular assessment typically not present in the existing literature. Our approach links investment to the marginal product of capital using a stochastic frontier model to explore and measure the presence of constraints. We also test whether liquid assets, indebtedness and investment dissatisfaction impact SMEs investment constraints. We find a clear link between investment and its marginal product with elasticities of between 0.55 and 0.65; indicating that a unit per cent increase in marginal product leads to 0.55–0.65 increases in investment. The investment efficiency estimates obtained show the presence of financing constraints. We find evidence of both internal and external finance sources explaining investment efficiency of small fixed assets. Higher collateral availability contributes to improve investment efficiency for all fixed assets.