A volatile environment and an increasing number of products along with a growing range of functions pose a challenge for companies when it comes to further development. Existing methods are no longer sufficient to cope with these challenges. In order to develop new methods, the process and challenges in the advancement of product portfolios must be understood. In this paper we conduct an interview study with ten experts to gain a better understanding of the advancement of product portfolios. Triggers, changes and actions are examined and goals and requirements for new methods are derived.

This paper aims to develop a risk assessment framework that addresses both the complexities of the risk landscape that green transition portfolios face, but is recognizable and easily understandable by stakeholders. For this purpose, we build upon the framework of NASA Technology Readiness Levels (TRLs). This study analyzes six existing readiness levels framework that are held towards uncertainty factors from the Green Transition. The TRL scale are coupled with Risk, Uncertainty, and Ignorance to score the individual level of uncertainty. The paper ends with suggestion for further studies.

This paper introduces DivFolio, a multiperiod portfolio selection and analytic software application that incorporates automated and user-determined divestment practices accommodating Environmental Social Governance (ESG) and portfolio carbon footprint considerations. This freely available portfolio analytics software tool is written in R with a GUI interface developed as an R Shiny application for ease of user experience. Users can utilize this software to dynamically assess the performance of asset selections from global equity, exchange-traded funds, exchange-traded notes, and depositary receipts markets over multiple time periods. This assessment is based on the impact of ESG investment and fossil-fuel divestment practices on portfolio behavior in terms of risk, return, stability, diversification, and climate mitigation credentials of associated investment decisions. We highlight two applications of DivFolio. The first revolves around using sector scanning to divest from a specialized portfolio featuring constituents of the FTSE 100. The second, rooted in actuarial considerations, focuses on divestment strategies informed by environmental risk assessments for mixed pension portfolios in the US and UK.

Insurers and pension funds face the challenges of historically low-interest rates and high volatility in equity markets, that have been accentuated due to the COVID-19 pandemic. Recent advances in equity portfolio management with a target volatility have been shown to deliver improved on average risk-adjusted return, after transaction costs. This paper studies these targeted volatility portfolios in applications to equity, balanced, and target-date funds with varying constraints on leverage. Conservative leverage constraints are particularly relevant to pension funds and insurance companies, with more aggressive leverage levels appropriate for alternative investments. We show substantial improvements in fund performance for differing leverage levels, and of most interest to insurers and pension funds, we show that the highest Sharpe ratios and smallest drawdowns are in targeted volatility-balanced portfolios with equity and bond allocations. Furthermore, we demonstrate the outperformance of targeted volatility portfolios during major stock market crashes, including the crash from the COVID-19 pandemic.

Companies are increasingly struggling to manage their complex product portfolios. Since they do not fully understand the complexity, intelligent solutions are required. Emerging technologies and tools offer new ways to deal with existing problems. With the help of clustering, similarities between product variants can be identified automatically, and complexity can be systematically reduced. This article aims to develop a methodological approach to identify correlations between product variants in complex product portfolios automatically by using clustering algorithms. The approach includes the systematic cleaning and transformation of product portfolio data. In addition, a guide for algorithm selection and evaluation of clustering results is presented. As the last step, the results are systematically analysed and visualised. To validate the methodical approach, it is applied to a real-world data set of a commercial vehicle manufacturer and the usefulness of the results is confirmed in an expert workshop.

Increased focus on sustainability significantly impacts product development portfolio management in organizations. This paper focuses on the significant unaccommodated uncertainty caused by the green transition for current mid- and long-term portfolio management processes. These uncertainties arise in areas such as the regulatory environment, market demands, and technical capabilities. This paper makes four contributions: First, current product portfolio management literature is mostly employing deterministic practices. Uncertainties are incompletely addressed, and current methods fail to address the deep uncertainties of the green transition. Second, building a literature-based conceptual framework of the sources of uncertainty in 6 areas: Business Models, Technology, Regulation and Legislation, Finance and Insurance, Market and Consumer, and Organizational Context. Third, preliminary empirical evidence supports the usefulness of our model for product development portfolio management. Fourth, concluding with a suggested empirical research agenda to develop a deeper understanding of challenges towards method and theory development to support the green transition on the portfolio management level.

To satisfy customer needs in the best way, companies offer them an almost infinite number of product variants. Although, an identical product was not built before, the values of its attributes must be determined during the product configuration process. This paper introduces a methodical approach to predict the values of product attributes based on customer feature configurations using machine learning. Machine learning reduces the effort compared to rule-based expert systems and is both, more accurate and faster. The approach was validated by predicting vehicle weights using industrial data.

The paper studies what kind of support could be applied to the management of partly configurable modular systems. The main tasks of product management, product portfolio management and product variety management are defined. In addition, a partly configurable product structure and modular system are defined. Because the limited support in the literature for managing partly configurable modular systems, the article reviews previous product development cases in which authors have been involved on lessons learnt basis, i.e., if the methods and tools used in the cases could provide support for the research objective. As a result, the existing definition of the modular system should be extended by the concepts of non-module and design decision sequence description when dealing with partly configurable modular systems. This is because engineer-to-order should be made possible in cases where it brings clear added value to the customer compared to completely pre-defined solutions that may limit the customer's interest in the offering. Tools to assess the impact of changes to the product offering are required. These should be taken into account in frameworks that are used in method and tool development.

In this paper, we use copula-GARCH models applied to daily data from March 2010 to March 2018 to test the time-varying dependence of the Liv-ex 50, a secondary market fine wine index comprised of the ten most recent vintages of the five Bordeaux First Growths, with a portfolio composed of the six main stock markets (S&P 500, CAC 40, DAX 30, FTSE 100, and Hang Seng). Our results suggest that the Liv-ex 50 underperforms the six stock indexes, but provides diversification benefits in terms of volatility, asymmetry, and extreme events. (JEL Classifications: G110, G120, Q14)

There are various strategies to control complexity and variety growth in ETO businesses. Such portfolio rationalization initiatives sometimes stall. This paper elaborates on the challenges that cause this. Challenges described in literature and challenges seen in five different industry cases are consolidated. The challenges are combined into groups and presented in the ADKAR change management model. The authors intend this list to be used for guidance In industry and expect the collection to be extended with future industry cases and challenges.

Innovation portfolio management (IPM) aims at selecting ideas with regard to their potential for innovation and measuring them considering customer and business value. The evaluation of benefits and risk is especially challenging for disruptive innovation (DI) due to their characteristics such as low comparability to existing technologies and uncertain customer reactions. This paper highlights the lack of approaches to managing DI in IPM and addresses it through a framework that expands the understanding of value-orientation in IPM, allowing for the inclusion of DI.

For a company it is necessary to know, which products can be configured using carry-over-parts or the same technology. This can become quite relevant in the context of automobile electrification, where complex mechatronic systems are used. Consisting of various mechanical components, these systems perform the required function while being actuated by electronically controlled motors. To solve this, a novel mechanism for data driven portfolio analysis based on product platforms using knowledge-based systems is proposed in this paper. Further, the mechanism is tested by applying it to an electrical motors' case study. Using this method, all possible combinations of a product platform are calculated and finally displayed in different product portfolios. Additionally, all the non-feasible motor designs are removed from the solutions portfolio using the acquired knowledge base and performing design checks. The latter are employed for penalising and eliminating from the pareto-front of the designs, which violate the thermal, mechanical and acoustic constraints. The generated product portfolio can be used further to expand the systems engineering collaboration and support decision-making.

We employ a panel data research design to examine the determinants of investment returns in the Swedish property fire insurance industry from 1903 to 1939 – a period of great economic and political uncertainty. Contrary to expectations, we find that mutual fire insurers generated systematically higher investment returns than stock fire insurers. Investment returns are inversely related to leverage but positively related to liquidity, showing that firms adopting a more precautionary investment strategy attain higher returns.

We construct portfolios of stocks and bonds that are maximally predictable with respect to a set of ex-ante observable economic variables, and show that these levels of predictability are statistically significant, even after controlling for data-snooping biases. We disaggregate the sources of predictability by using several asset groups — sector portfolios, market-capitalization portfolios, and stock/bond/utility portfolios — and find that the sources of maximal predictability shift considerably across asset classes and sectors as the return horizon changes. Using three out-of-sample measures of predictability — forecast errors, Merton's market-timing measure, and the profitability of asset-allocation strategies based on maximizing predictability — we show that the predictability of the maximally predictable portfolio is genuine and economically significant.

We examine a decision theoretic model of portfolio choice in which investors face income risk that is not directly insurable. We consider the sensitivity of savings and portfolio allocation rules to different assumptions about utility, the stochastic process for income and asset returns, and market frictions (transactions costs and short-sale constraints). Under CRRA time additive utility, habit persistence utility, and for a broad range of parameterizations, the model predicts that investors wish to borrow and invest all of their savings in stocks. This qualitative implication is robust to the introduction of significant transaction costs in the stock market, and contrasts sharply with portfolio allocation models in which there is no labor income.