Portfolio Planning Research Articles

A Coevolutionary Estimation of Distribution Algorithm for Group Insurance Portfolio

With the rapid development of the insurance industry, more diverse insurance products are produced for consumers. Insurance portfolio problems have received increasing attention. While most studies focus on insurance portfolio problem for a single insured, insurance portfolio problems for a specific group of insured are even more intricate but little attention has been paid to. In this article, we propose a group insurance portfolio model for investment allocation of several insurance policies so that the total payout of the whole group can be maximized. The statistical average value of each parameter is considered in the model to approximate the expectation payout of the group insurance portfolio problem. To solve this problem, a coevolutionary estimation of distribution algorithm (EDA) utilizing the divide-and-conquer strategy is proposed. First, as the payout of each insured under a certain portfolio plan can be calculated separately, the proposed approach decomposes the group insurance portfolio problem into several single-insured insurance portfolio problems. In this way, the dimension of the optimization problem becomes lower compared to the original problem. An adaptive EDA is proposed to optimize the portfolio plan of each insured independently. Second, the group insurance portfolio problem remains a nonseparable problem since the investment amount of each insured is limited by the total investable amount of the whole group. A particle swarm optimization algorithm is adopted to cooperate with the EDA to optimize the proportion of allocation to each insured. The proposed algorithm is verified on various scenarios. The experimental results validate that the proposed approach is effective for the group insurance portfolio problem.

Solving Periodic Investment Portfolio Selection Problems by a Data-Assisted Multiobjective Evolutionary Approach

Classic portfolio selection problems mainly focus on high-risk financial markets with tradeoffs between returns and risk. However, more risk-averse investors pursue long-term portfolio planning with the objectives of maximizing final returns and maximizing flexibility. This article addresses a new type of the portfolio problem, called periodic investment portfolio selection problems (PIPSPs), in which investors periodically allocate resources to financial products with different periods. A multiobjective model for PIPSPs is first presented. With a mechanism for utilizing the data generated during the implementation of multiobjective evolutionary algorithms (MOEAs), a data-assisted MOEA (DA-MOEA) is proposed to solve PIPSPs. The main idea of a DA-MOEA is to combine a MOEA with a data-assisted process that consists of three components: 1) feature construction; 2) data fusion model development; and 3) obtained information utilization. To solve the addressed PIPSPs, two versions of DA-MOEAs with baselines of nondominated sorting and decomposition-based mechanisms are implemented, namely, the data-assisted NSGA-II (DA-NSGA-II) and data-assisted MOEA/D (DA-MOEA/D). In the developed DA-MOEAs for PIPSPs, a feature construction process and a data fusion model are well designed for mining data with different formats. To validate the algorithms, two sets of test instances are generated. The experimental results demonstrate the efficacy of the data-assisted process. Furthermore, the effects of the algorithm components, such as the data source sizes, information types, and information utilization strategies, are investigated.

Portfolio Planning: a Comprehensive Approach to Limited Resources and Project Prioritization

The main issue for portfolio managers is to set a realistic schedule that ensures that the resources needed for operations are readily available on deadline dates, which is the classic goal of the Resource-Constrained Multi-Project scheduling problem (RCMPSP). This article presents an algorithm that provides solution to RCMPSP, where each project has a different priority for the organization. The key feature of this algorithm is that it includes project prioritization during the planning process. Before planning, the portfolio manager determines which projects are relevant for the company. With this in mind, the algorithm uses market mechanisms to determine which activities should be prioritized in the planning process. Based on this, the algorithm creates an artificial market and enables to plan those projects that are of higher priority. Thus, out of a number of possible schedules corresponding to resource constraints (i.e., the solution provided by classic approaches to RCMPSP), the approach considered in the article allows obtaining an optimal portfolio schedule. This schedule, in addition to having the shortest possible duration of the portfolio, allows planning activities in accordance with priorities of the portfolio manager and, consequently, priorities of the company.

The portfolio planning, implementing, and governing process: An inductive approach

Portfolio management has long been viewed as a major source of competitive differentiation in technology-intensive organizations with substantial R&D investment. Yet, little work has addressed how portfolio management unfolds. Furthermore, the extant work has largely conceptualized the portfolio management process in a deductive manner, offering limited propositions for understanding the inner workings of portfolio management in real-world settings. Drawing on an inductive, qualitative study of nine cases in the electrical machinery, medical device, and pharmaceutical sectors, our work opens the black box of portfolio planning, implementing, and controlling process in practice. We find that organizations manage portfolios through five interdependent phases (ecosystem surveillance, portfolio strategy development, business case management, portfolio decision-making, and new product/service management), with four distinct actors (corporate, top management, portfolio management, and portfolio management governance functions) being actively engaged in different phases. Our study also established three main levers that shape the portfolio management process in practice (governance, integration, and information). Based on these findings, implications for portfolio management studies and practices are discussed.

Maximum Entropy Bi-Objective Model and its Evolutionary Algorithm for Portfolio Optimization

Diversification of investment is a well-established practice for reducing the total risk of investing. Portfolio optimization is an effective way for investors to disperse investment risk and increase portfolio return. Under the assumption of no short selling, a bi-objective minimizing portfolio optimization model, in which the first objective is a semi-absolute deviation mean function used to measure the portfolio risk, and the second objective is a maximum entropy smooth function used to measure the portfolio return, is given in this paper. Also, a maximum entropy multi-objective evolutionary algorithm is designed to solve the bi-objective portfolio optimization model. In order to obtain a sufficient number of uniformly distributed portfolio Pareto optimal solutions located on the true Pareto frontier and fully exploit the useful asset combination modes which can lead the search process toward the frontier direction quickly in the objective space, a subspace multi-parent uniform crossover operator and a subspace decomposition mutation operator are given. Furthermore, a normalization method to deal with the tight constraint and the convergence analysis of the proposed algorithm are also discussed. Finally, the performance of the proposed algorithm is verified by five benchmark investment optimization problems. The performance evaluations and results analyses illustrate that the proposed algorithm is capable of identifying good Pareto solutions and maintaining adequate diversity of the evolution population. Also, the proposed algorithm can obtain faster and better convergence to the true portfolio Pareto frontier compared with the three state-of-the-art multi-objective evolutionary algorithms. The result can also provide optimal portfolio plan and investment strategy for investors to allocate and manage asset effectively.

Methodology for Managing Disruptive Innovation by Value-Oriented Portfolio Planning

Disruptive innovations (DI) have the potential to fundamentally change markets and their power relations: Specifically, established companies are confronted with the threat of being forced out of the market by DI. At the same time, companies also have the opportunity to control the market’s development by developing DI themselves. This raises the question of how a proactive management of DI can be systematized. Here, the approach of innovation portfolio management (IPM) provides support by identifying, evaluating, and selecting a company’s most promising product ideas. The management of DI is challenging due to their characteristics, such as diversity, high uncertainty, especially with regard to customer acceptance, difficult comparability with existing products, potential for cannibalization, and substitution of existing products. For this reason, new approaches to managing DI are required in literature and practice. This paper presents a novel methodology to support—especially established—companies in proactively generating DI via existing processes in their IPM. For this purpose, the methodology supports the early identification of the product idea’s disruptive potential so that these can be handled appropriately in the further course of the process, such as the evaluation of all product ideas in the company during IPM. Thus, the risk can be minimized that promising DI are not rejected due to unsuitable procedures, but are brought to market maturity. The methodology contributes to the literature by showing how DI can be pursued embedded in existing corporate structures and also in competition with other product ideas in the company—in existing approaches, DI is primarily considered singularly and detached from the existing corporate context. The methodology is validated by the example of the digital camera, which disrupted the photo industry, as well as through interviews in a practical context.

Impact of fast-track regulatory designations on strategic commercialization decisions for autologous cell therapies.

Background: Regulatory authorities around the world have introduced incentives to improve the speed-to-market of innovative therapies. Aim & methods: To better understand the capacity and portfolio planning decisions of autologous cell therapies and particularly the impact of fast-tracking designations, this paper describes a mixed-integer linear programming approach for the optimization of capacity investment and portfolio selection decisions to maximize the net present value of a candidate portfolio of therapies under different regulatory programs. Results: The illustrative example shows that fast-track designations allow a 25% earlier breakeven, 42-86% higher net present value over a 20-year horizon with earlier upfront capital and reduce the portfolio's sensitivity to uncertainties. Conclusion: Fast-track designations are effective in providing commercialization incentives, but high capital risks given the compressed timeline should be better considered.

The Morphosynthesis of Event Portfolios: Connecting Networks and the Community.

Despite the increasing employment of event portfolios by cities and regions to obtain a number of gains, there is a rather slow response from the academic community to fully understand this complex phenomenon and its potential social value. To address this asymmetry furthering the gap between scholarship and practice, the purpose of this article is to theorize the generative mechanisms that shape portfolios as social systems. Based on up-to-date theoretical development, I attempt in this paper to formulate a comprehensive theory of the integrative portfolio nature that interconnects its contextual, policy, operational, and sociocultural dimensions. I do so, by delineating the connective properties of portfolios to assemble different events and enable stakeholders to work toward the accomplishment of common portfolio goals, and by elaborating on the exigencies of portfolios for contributing to the strengthening of the host community's social fabric. This relational analysis operationalizes latest event portfolio elaborations integrated with the perspectives of community capacity-building and quality of life in order to underpin the formulation of a broader framework accounting for the intersection between strategic portfolio planning and the dynamics of stakeholder attitudes, participation in, and community engagement with portfolios. On these grounds, I suggest a new conceptual lens named “morphosynthesis” to explain the multilevel integration processes that shape event portfolios and enable the interlinking of social networks and the community through the array of events. Morphosynthesis constitutes a transdisciplinary perspective that situates the study of event portfolios as a new autotelic subdiscipline across event management, leisure, sport, culture, and tourism. It provides solid but flexible theoretical logics and heuristic means to navigate in the over-fragmented landscape of events and allied industries with the purpose to enhance their compound social value.

Design of a solar concentrator system for a green community—A dual-objective stochastic optimization model

The solar concentrator consisting of prisms is a promising solution for clean energy collection. Concentrator design and allocation play a key role for efficient solar energy collection. This study investigates a green community with many houses. A solar concentrator is installed on the roof of each house to receive sunlight. Several types of solar concentrators are delivered by a manufacturer who fabricates the solar concentrators to maximize its own profit while maintaining a level of collected energy that is acceptable to the community. A novel dual-objective model is proposed. Given uncertain energy demand, a trade-off between maximizing the manufacturer's profit and the community's energy collected is achieved when designing and allocating such a solar concentrator system. A revised genetic algorithm with chance constrained technique is proposed to seek the best resource portfolio and allocation plan for the solar concentrator system and the corresponding optimal layouts for individual solar concentrators. Pareto frontier analysis for the dual objectives is further given to highlight the proposed compromised solution. Experiments indicate that the proposed method significantly improves both energy collection efficiency and profits earned. The compromised solution is mutually accepted by the two parties. The impact of the variation of demand to profit is examined so that demand information can be quantified. This study contributes simultaneously to resolving the optimal resource portfolio and allocation of solar concentrators at a system level and to optimizing solar concentrator layout at the individual concentrator level.

On the relationship between policy uncertainty and sustainable investing

PurposeIn recent times, sustainable investment gaining much attention within the investors’ community and it is broadly driven by environmental, social and governance (ESG) factors. This study aims to examine the ESG-based sustainability index and economic policy uncertainty (EPU).Design/methodology/approachCorporate sustainability assessment procedure yields Dow Jones sustainability indexes (DJSIUS) and ESG compliant firms become a member of such indexes. To uncover the effects of policy uncertainty as follows: the study considers EPU index, equity market policy uncertainty index, economic and political events for the period 2000–2017. The authors present the study using a conditional volatility framework.FindingsThe correlation between the DJSIUS and policy uncertainty appears to be negative and statistically significant. It is apparent from the results that policy uncertainty does contain important ESG factors that explain the sustainable investment in US firms. Moreover, the stock market boom, credit crunch, Lehman collapse and fiscal crises have shown significant adverse effects on the sustainability index. More importantly, it is seen that investors’ sustainable investing considers presidential election years for portfolio planning; the uncertainty associated with the election years has also shown a negative impact on the sustainable returns.Practical implicationsFirst, sustainability is essential for the long-term stakeholders’ wealth maximization under governments’ policy uncertainty such as constrained resources, demographic and climate-change-policy, societal expectations, public-policies, regulatory structure. Second, EPU creates new opportunities and risks for sustainable firms and sustainable investing.Originality/valueThe study is novel in which the authors present the effects of uncertainty on socially responsible investing.

METHODOLOGY FOR DETERMINING THE EFFECTIVENESS OF INNOVATION ACTIVITIES IN AN INDUSTRIAL ENTERPRISE

The article substantiates the expediency of using methods of portfolio planning of innovation activities, as well as conducting a comparative assessment of the effectiveness of innovative projects considered in the formation of the innovation portfolio of the enterprise. It is determined that the system of innovation activity indicators should include not only financial indicators (ROI, share of profit from implementing innovations, etc.), but also qualitative indicators (share of ideas implemented in the company; average time elapsed from submission of a new innovative proposal to the decision to launch or abandon an innovative project, etc.). The dynamics of changes in qualitative indicators will help identify problems in the innovation management system in time and take measures before a crisis occurs. We propose the use of a balanced scorecard to assess the innovation activities of an enterprise, which includes indicators that characterize the enterprise strategy from four different and yet interrelated perspectives: finance perspective; training and personnel development; internal business processes; and customer perspective. Based on individually developed performance indicators and subsequent comparison of planned and actual data, senior managers receive information that allows them to assess the effectiveness of the implementation of strategies in four main areas. The balanced scorecard for innovation activity is a kind of monitoring tool and an effective tool for both current and long-term management of an enterprise's strategy. We have generalized and improved the methodology for assessing the economic efficiency of the organization of innovation activity in an industrial enterprise using a generalizing indicator of the efficiency of the enterprise as a whole. The proposed system of indicators of innovative activity will help the enterprise to analyse its ability to innovative work, quality of realization of this work, and will allow assessing the innovative activity of the enterprise, competitiveness of products and firm as a whole, etc.

Water Supply Portfolio Planning and Policy Evaluation under Climate Change: A Case Study of Central Taiwan

The present study examines the optimal water supply portfolio under the impact of climate change constructed by the authors previously. It incorporates feasibility planning for water supply projects, assesses a feasible water supply portfolio for central Taiwan, and uses the shadow price method to assess the rationality of the compensation policy for transferring agricultural water to ensure water supply security for the industrial sector. The study finds that Changhua and Yunlin have the highest per-unit costs of raw water, and the Nantou region has the highest carbon emission coefficient (carbon footprint) per unit of water produced. The cumulative value (2021–2031) of the water resources policy to reallocate agricultural water to achieve water supply security is about TWD 15.904–31.13 billion. The shadow price of industrial water is about TWD 40.18/cubic meter. Therefore, a compensation price for agricultural water transfer of less than TWD 40.18/cubic meter represents a rational policy.

Portfolio planning of renewable energy with energy storage technologies for different applications from electricity grid

Portfolio planning of renewable energy industry with energy storage technologies is the key to meeting the different and increasing application demands from electricity grid. However, it is a difficult and fuzzy problem because of the complex interaction between renewable energy and energy storage technologies, various applications from electricity grid, and uncertain decision environment, which has not been solved simultaneously so far. Therefore, this paper proposes a two-stage planning framework based on fuzzy multi-criteria-decision-making techniques to select the most promising renewable energy with energy storage portfolio from the aspects of energy generation, transmission and terminal application. Firstly, three types and thirteen special energy storage technology application scenarios are distinguished, and the superiorities of the typical energy storage technologies are evaluated from the generation, transmission and terminal applications via interval fuzzy number based PROMETHEE-II. On this basis, an eight-step fuzzy multi-criteria decision-making framework is adopted to study the portfolio models with six promising renewable energy and the selected energy storage technology combinations. The result shows that photovoltaic still dominate in the application of renewable energy. “Photovoltaic & CAES” in generation, “Hydropower & CAES” in generation, “Wind & CAES” in generation, “Photovoltaic & CAES, HFC” in generation, “Photovoltaic & Ni-Cd battery” in transmission, “Photovoltaic & CAES and Ni-Cd battery” in terminal application are better solutions for government or decision makers to further comprehensively plan the large-scale application of clean energy in the grid. Besides, portfolios with complementary effects are identified and analyzed separately. This research contributes to the literature.

Design Innovation Methodology Handbook – Embedding Design in Organizations

The Design Innovation Methodology handbook represents a contribution to our design journey. This handbook was developed by a number of contributors from the United States and Singapore. Through a co-creation effort and common interests to innovate together, the intent is to make a difference for all persons in our communities and society. Readers are provided with a meaningful and practical guide, reference booklet, and living document in which to engage Design Innovation at the apex of Design Thinking and Systems Thinking, and beyond. We sincerely hope that this guide will inspire and embolden all readers and partners to push the the boundaries of human-centered systems innovation across one's entire portfolio and strategic plan. In doing so, the future will be bright, and we will have an impact beyond anything we can imagine or foresee. We wish you the very best as you embrace your personal Design Innovation journey. To Design Innovation, and Beyond!!

How to manage disruptive innovation - a conceptual methodology for value-oriented portfolio planning

Markets and industries are currently characterized by high dynamics, leading to diverse and volatile customer requirements. This complex market environment fosters the appearance of innovations with high novelty and uncertainty. As a result, the probability of disruptive innovations (DI) increases, which have the potential to change markets in the long-term and represent an existential threat especially to established companies. Against this background, the question arises if companies can proactively create DI. For this reason, innovation portfolio management (IPM) aims to identify the most promising new product ideas. However, the identification of DI is challenging because of their characteristics such as low comparability with existing products and uncertain customer reactions. For this reason, the literature highlights that no approaches exist that integrate and consider DI in IPM.This paper introduces a practical concept methodology to implement DI in IPM that can be placed in the early phase of product development, focusing on the potential evaluation of diverse product ideas. The concept provides a five-step approach, which can be roughly divided into the anticipation of the product idea’s disruptive potential and the holistic evaluation of the product ideas. For the application of the concept, suitable methods are proposed and developed, whereby the specifics of DI are consistently considered.

Exchange Rate Forecasting Based on Deep Learning and NSGA-II Models.

Today, the global exchange market has been the world's largest trading market, whose volume could reach nearly 5.345 trillion US dollars, attracting a large number of investors. Based on the perspective of investors and investment institutions, this paper combines theory with practice and creatively puts forward an innovative model of double objective optimization measurement of exchange forecast analysis portfolio. To be more specific, this paper proposes two algorithms to predict the volatility of exchange, which are deep learning and NSGA-II-based dual-objective measurement optimization algorithms for the exchange investment portfolio. Compared with typical traditional exchange rate prediction algorithms, the deep learning model has more accurate results and the NSGA-II-based model further optimizes the selection of investment portfolios and finally gives investors a more reasonable investment portfolio plan. In summary, the proposal of this article can effectively help investors make better investments and decision-making in the exchange market.

Natural Hazards Reconnaissance With the NHERI RAPID Facility

In 2016 NSF funded an interdisciplinary multi-institutions team to develop and operate the Natural Hazards Reconnaissance Facility (known as the “RAPID”) as part of the Natural Hazards Engineering Research Infrastructure (NHERI) program. To enable support of state of the art natural hazard reconnaissance research, the RAPID facility’s instrumentation portfolio and operational plan were developed over the following two years. Input was gathered from the natural hazards engineering community, the facility’s leadership, the National Science Foundation, and an interdisciplinary, external steering committee of scientists with decades of reconnaissance-based research experience in natural hazards and disasters. Beginning operations in September 2018, the RAPID now provides instrumentation, software, training, and support services to investigators performing field-based research before, during, and after natural hazards. Since beginning operations, RAPID has supported the data collection efforts of more than 40 missions. Applications have ranged across disciplines and hazards. They have also included data gathering in large-scale experimental facilities. These activities have produced an unprecedented amount of high-quality field data sets that are archived on the DesignSafe cyberinfrastructure platform. This paper describes the development of the RAPID facility, the equipment portfolio, and services provided by RAPID, including a custom-built reconnaissance software platform called RApp, and the opportunities RAPID provides for user training. Additionally, overviews of three example deployments of the RAPID equipment and data collection services are described; descriptions include the field data collection processes, the resulting high-quality data sets, and the impact of the data and resulting research on natural hazard resilience.

Optimal sub-contractor selection and allocation in a multiple construction project: Project portfolio planning in practice

Subcontracting is known as a global concern in construction industry. Appropriate selection of sub-contractors (SCs) significantly affects scheduling. However, review of the recent literature suggests that the issues of selecting SC and scheduling construction projects are investigated separately. Moreover, general contractors (GCs) usually conduct a number of projects simultaneously, while the literature demonstrates that subcontracting is often studied when the GC is conducting only one project. In order to fill these gaps, the current research study is conducted to investigate the subjects of subcontracting and scheduling construction projects when more than one project must be done at the same time. To this aim, a mixed-integer linear programming model is developed to minimize the cost of the project in two different conditions. In the first condition, the deadline can be postponed by paying a penalty. In contrast, in the second condition, the deadline cannot be postponed under no circumstances. Then, the proposed model is solved by applying the general algebraic modelling system. The findings demonstrate that the proposed model is reliable to provide an optimized schedule when several projects must be conducted at the same time.

Emission implications of electric vehicles in Japan considering energy structure transition and penetration uncertainty

Vehicle electrification represents a promising solution to address the challenges of energy security and sustainable mobility. The environmental performance of electric vehicles is heavily dependent on the energy structure which is undergoing a profound transition. Moreover, the deployment of electric vehicles is critically affected by policies. In this context, this study investigated the impact of energy structure transition and penetration uncertainty on emissions of electric vehicles in Japan from 2000 to 2030. The results suggested that the greenhouse gas and reactive nitrogen were mitigated for both energy sources of electricity mix transition and ammonia energy carrier compared with the baseline. Trade-off between the electricity mix transition and ammonia energy carrier scenarios was observed. Specifically, the electricity mix transition results demonstrated a more significant emission reduction of reactive nitrogen, while an ammonia energy carrier led to a more significant emission reduction of greenhouse gas. Finally, key policy implications were summarized by providing insights into energy portfolio planning and electric vehicle deployment from the perspective of climate change mitigation and sustainable reactive nitrogen management.

How MCDM method minimizes the gap between portfolio planning and portfolio implementation.

This paper presents a comparison between portfolio planning and portfolio implementation in Tanzania power sector and suggests a Multi-Criteria Decision Making (MCDM) method which can be considered as a balanced approach in minimizing discrepancy between portfolio planning and implementation. The research is chiefly developed based on Analytic Hierarchy Process (AHP) approach and the subsequent data collection and data analysis were tailored to suit this approach. An extensive literature review ranging from various scholar papers to corporate research reports is undertaken to establish the most relevant project appraisal criteria in Tanzania power industry. Total of 15 experts took part in the survey in which the survey findings were integrated into the literature review toward structuring the proposed MCDM framework. The analysis of findings suggest the following insights; (1) inadequate cross-stakeholders engagement at portfolio planning stage observed in Tanzania power sector which is largely due to the confined and quantitative portfolio planning method; (2) MCDM should be used as a complementary method toward integrating non-tangible/qualitative variables into other quantitative criteria at portfolio planning stage, which can mitigate the extent of deviation between portfolio planning and implementation; (3) It is crucial to employ MCMD as a decision support tool toward prioritizing major infrastructure projects at portfolio planning stage to ensure the consistency between portfolio planning and implementation.