Portfolio Value Research Articles

Fee structure and equity fund manager’s optimal locking in profits strategy

We study the effects of fee structures on fund managers’ strategies for locking in profits. Utilizing the optimal stopping time method, we identify two critical portfolio value thresholds that signal when a manager will choose to lock in profits. Fee components such as management fees, self-investment ratios, and high-water marks significantly influence these decisions. Specifically, higher management fees are associated with increased risk aversion, leading to a narrower continuation region, indicating a preference for lower risk. Conversely, performance fees encourage greater risk-taking. We use the S&P 500 Index and NASDAQ Composite index as representatives of managers’ portfolios and apply our model to illustrate how managers adjust their profit-locking strategies in response to their desired rewards.

Predictive analytics in customer behavior: Anticipating trends and preferences

In order to effectively manage their customers, businesses need to thoroughly analyze the costs and advantages associated with various alternative expenditures and investments and determine the most effective way to allocate resources to marketing and sales activities over time. Those in charge of making decisions will reap the benefits of decision support models that estimate the value of the customer portfolio and tie expenses to customers' purchasing behavior. In the current work, various machine learning algorithms such as Decision Tree (DT), Random Forest (RT), Logistic Regression (LR), Support Vector Machines (SVM), and gradient boosting are used to predict customer behavior. The evaluation criteria considered in the work include precision, recall, F1-Score, and ROC-AUC. The accuracy values obtained for DT, RT, LR, SVM, and gradient boosting are 0.787, 0.806, 0.826, 0.826, and 0.823, respectively. The results emphasize RT and LR's good performance, while the values of 0.620, 1, 0.766, and 0.878 for the precision, recall, F1-score, and ROC-AUC score outperform the rest. The novelty of this work lies in employing a comprehensive set of machine learning algorithms to predict customer behavior, with a particular emphasis on the superior performance of RF and LR models, as demonstrated by their high precision, recall, F1-score, and ROC-AUC values.

Towards a comprehensive framework for risk assessment of organizational development project portfolios

The benefits of risk management in the context of project portfolios have been widely recognized in the literature. However, approaches that assess the risk of organizational development project portfolios from the perspective of how the portfolio delivers value to the parent organization remain largely unexplored. To address this gap, our research takes a constructivist approach and an organizational perspective on project portfolios. We conducted twenty-eight semi-structured interviews and used thematic analysis to identify and relate four themes of a comprehensive project portfolio risk assessment (PPRA) framework: "project portfolio as the organizational unit for PPRA"; "organizational capabilities as portfolio outcomes in which PPR can be assessed"; "project portfolio levels as sources of risk factors in PPRA"; and "balance between project portfolio attributes complexity". Within the framework of organizational development project portfolios, this study contributes to our understanding of PPRA by providing two propositions: (1) The capabilities to be generated by the project portfolio can be used as the portfolio primary results on which PPRA can assess the risk of the project portfolio, establishing the impact of PPR on the project portfolio value delivering to the parent organization, and (2) The risk factors that impact the project portfolio expected results can be represented into PPRA as ‘output-related’ risk factors and ‘outcome-related’ risk factors.

Multispecies Portfolios of U.S. Marine Fisheries: Ecosystem‐Based Fisheries Management Reduces Economic Risk

Fisheries management has focused on single stocks, not directly accounting for species interactions, and usually only considering economic factors in post hoc analysis. This approach has been successfully applied for many species over many years, but may also inadvertently result in greater risks being incurred. We demonstrate a portfolio optimization approach to inform a broader set of fishery concerns as a way to emphasize species interactions and economic considerations in resource management decision making. The approach can use readily available data on landings and revenue to generate easily digestible indicators of risk, namely the risk gap (i.e., the difference between actual and optimal portfolio values). Herein, we calculate portfolio efficiency frontiers that minimize risk for target revenue outcomes and resulting risk gaps for commercial fisheries using the top 25 landed‐value species in six U.S. fisheries regions. Most regions exhibited a risk gap on the order of US$20–50 million, collectively on average over $250 million. Risk gaps can be used as ecosystem‐level indicators to inform managers of the unnecessary risk being assumed for a given level of revenue for a portfolio of fisheries stocks, which can move us towards operational ecosystem‐based fisheries management.

Is there a diversification paradox in real estate investment funds' value?

PurposeThis paper studies diversification and value in the investment portfolios of (non-listed) Real Estate Investment Funds (REIFs) exploring how the value of diversification is captured by the market and by investors (beyond reported valuations).Design/methodology/approachWe apply the Herfindahl-Hirschman Index (HHI) to study the level of concentration versus diversification in the investment portfolios of REIFs (both in terms of segment and geographical diversification). We use a dataset from INREV with data from 62 investment portfolios, with an average of 86 REIFs per portfolio for the period of 2008–2020 (to study segment diversification). We use a second dataset from INREV with data from 30 investment portfolios with an average of 79 REIFs per portfolio for the period of 2005–2020 (to study geographical diversification). We employ a cluster analysis approach to identify common features among the investment funds.FindingsWe conclude that (segment diversified) portfolios with higher degrees of leverage exhibit higher income yields, albeit diversification is captured indirectly through asset choices – more diversified portfolios tend to exhibit a stronger risk and return relationship. Also, geographical diversification creates value (more significantly by for the correct combination of countries carefully choosing what different geographies to group in the diversified portfolio).Research limitations/implicationsOne limitation of our study is that our portfolios are funds of funds, since the available data could not reach the asset detail, but we believe this does not compromise our results.Practical implicationsDiversification leads to higher risk-adjusted returns which suggests that properties may be undervalued (market value) in the framework of the Gordon Model, contrary to expectations (regarding investment value).Originality/valueInvestors capture the value of diversification differently, suggesting a gap between market value and investment value that can be explored.

Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data.

Research on the construction and application of an invention patent model based on TFAHP-FAHP algorithm

The TFAHP-FAHP algorithm model constructed in this article can preferably avoid subjective assumptions caused by human factor. The new energy vehicle power battery is a core component for improving the performance of electric vehicles, and constitutes one of the most valuable parts of the invention patent for new energy vehicles. Patent pledge financing is a brand new form with great potential. However, as a result of the imperfect system of the patent pledge financing, banks and other financial institutions are exposed to an increased risk in the process of the patent pledge financing, which seriously limits the development of the patent pledge financing for power battery. In view of the above issues, this paper uses the SPSS reliability and validity tests in order to develop a relatively complete and accurate index system. TFAHP algorithm model and fuzzy comprehensive are combined to determine the final correction coefficient. In this study, the income method is used to determine the pledge valuation value of a new energy vehicle power battery patent portfolio, and the AHP algorithm method is used to determine the weight of the value of the invention patent portfolio within the patent portfolio. As a result, in this paper, the pledge value of the patent portfolio is multiplied by a proportion of the invention patent portfolio’s value, and then multiplied by a correction coefficient to obtain the final new energy vehicle power battery invention patent portfolio pledge value. Using this reference value, technology-based energy companies can pledge financing for new energy vehicle power battery invention patent portfolios.

Currency portfolios and global foreign exchange ambiguity

This study investigates whether cross-sectional global foreign exchange (FX) ambiguity impacts currency portfolios. We observe that, in contrast to FX volatility, high FX ambiguity leads to high currency carry returns. We also reveal that FX ambiguity is weakly associated with the highest interest rate portfolio, but strongly related to the second highest interest rate portfolio. These results suggest that FX ambiguity captures elements of uncertainty that are not captured by FX volatility. In addition, FX ambiguity is not linked to returns on currency momentum and value portfolios.

Analyzing the Relationship between Real Estate Investments and Portfolio Diversification

Real estate has long been considered an attractive investment option for individuals and institutions seeking to build wealth and diversify their portfolios. Unlike traditional investment vehicles such as stocks and bonds, real estate offers unique characteristics that can potentially enhance returns and mitigate risk. This analysis aims to explore the role of real estate investments in portfolio diversification and assess their potential impact on overall portfolio performance. Portfolio diversification is a fundamental principle in investment management, as it helps to spread risk across different asset classes and mitigate the impact of market fluctuations on a portfolio's overall value. By including assets with low or negative correlations, investors can reduce the volatility of their portfolios and potentially achieve higher risk-adjusted returns. Real estate investments, including direct property ownership, real estate investment trusts (REITs), and other real estate-related securities, have traditionally exhibited low correlations with other asset classes, such as equities and bonds. This low correlation can be attributed to the unique characteristics of real estate, including its tangible nature, the presence of rental income streams, and the potential for capital appreciation. Furthermore, real estate investments can provide a hedge against inflation, as property values and rental rates tend to increase during periods of rising prices. This feature makes real estate an attractive diversification option, particularly for investors seeking to protect their portfolios from the eroding effects of inflation. This analysis will delve into the historical performance of real estate investments, examine their risk and return characteristics, and evaluate their potential contribution to portfolio diversification. By examining empirical data and leveraging portfolio optimization techniques, we aim to provide insights into the optimal allocation of real estate investments within a diversified portfolio.

Clinical and Economic Value of a Biosimilar Portfolio to Stakeholders: An Integrative Literature Review.

While the value of individual biosimilars is evident, little is known about the value of a biosimilar portfolio beyond the cost savings between biosimilars and originators. Stakeholders may consider the value of a manufacturer's biosimilar portfolio, especially when negotiating portfolio-based contracts or other rebate programs. However, little is known about what other types of value, in addition to financial benefits, decision-makers perceive regarding a manufacturer with a biosimilar portfolio compared to those without one. The objective of this integrative literature review was to describe a conceptual framework consisting of themes that may help define the value of a biosimilar portfolio. An integrative literature review was conducted using Excerpta Medica Database (Embase) and Medical Literature Analysis and Retrieval System Online (MEDLINE). Grey literature searches of search engines, journals not indexed in Embase or MEDLINE, healthcare payers, health technology assessment bodies, value frameworks, and non-pharmaceutical industry analogs were also conducted. Eligible studies reported on the value of a biosimilar portfolio in decision-making by stakeholders. Apart from the literature, insights were gained from clinical experience and observation. No studies investigating biosimilar portfolio value were identified; however, several themes were identified that may help define the value of a biosimilar portfolio: Manufacturing; procurement, inventory, and storage; administration; education; and transaction costs. Several non-pharmaceutical industry analogs were identified: Product line length and single-supplier versus multiple-supplier procurement. Several themes were identified through other sources: Science credibility and research. Based on these themes, we developed a conceptual framework for biosimilar portfolio value. To our knowledge, this is the first study to systematically assess and create a framework for biosimilar portfolio value. The conceptual framework described here could be tested to quantify the clinical and economic value associated with a biosimilar portfolio.

Electricity market equilibria analysis on the value of demand-side flexibility portfolios’ mix and the strategic demand aggregators’ market power

Electricity market equilibrium analysis is becoming increasingly important within the today’s liberalized electricity market and regulatory context for both market participants and policy makers. The former ones want to make informed business decisions to optimize their market position, while the latter need to promote efficient equilibria that satisfy both supply and demand-side requirements and optimize social welfare. In this paper, we focus on modeling the strategic demand aggregators’ (DA) behavior via an Equilibrium Problem with Equilibrium Constraints (EPEC) formulation. We model typical demand flexibility portfolios that comprise of several types of distributed flexibility assets and renewable energy resources. We then quantify the value of demand flexibility portfolios’ mix and respective value of market power that a strategic DA may exercise with respect to each DA’s cost decrease and social welfare as a function of time for a day-ahead market use case. Simulation results show that: (i) there is an optimal equilibrium point where overall DAs’ costs decrease, while social welfare’s deteriorates negligibly, (ii) there may be a trade-off between the rate of a certain DA’s cost decrease and the rate of market power (or else % share of total demand flexibility) that this DA possesses, so the strategic DA should take this into account in order to find its optimal CAPEX/OPEX balance for its portfolio, (iii) competition among strategic DAs at the demand-side can mitigate grid congestion problems and serve as a counter-balance to the strategic behavior of the supply-side market participants.

Combining the multi-level perspective framework with participatory scenario development to explore the many facets of food system transitions in Germany

Transitions to more sustainable food systems are urgently needed, but they are also very complex and hard to achieve. The integration of transdisciplinary approaches into transition research can help to understand and promote sustainable food system transitions, but remains an agenda item in this research field. To fill this gap, this article describes how the multi-level perspective (MLP) can be used as a framework for explorative, participatory scenario development. On the one hand, MLP provides scholars with a framework for understanding the dynamics of transitions. On the other hand, participatory scenario development brings together academic and non-academic actors and perspectives to explore and promote possible development pathways. Based on this framework, researchers and practitioners jointly co-created four scenarios of how the landscape of five relevant European food innovations located in established food systems or niches (alternative sources of proteins, prosumer initiatives, regional value chains, school meal programs, and dietary interventions) might change by 2040 and further explored how these food innovations might evolve under the scenarios. The results provide initial insights into the role that food innovations could play in different transition pathways and also indicate the value of a diverse portfolio of food innovations to respond to changing circumstances. Furthermore, the process allowed participants to develop a shared understanding of food system dynamics and explore potential future risks and opportunities for food innovations, showing that participatory scenarios based on the MLP framework are a useful lens for exploring sustainable food system transitions.

Optimal Portfolio Using Single Index Model (SIM) For Health Sector Stocks

Investment is one of the fund management activities with the aim of obtaining future profits. In addition to profits, investors also need to consider the risks that will be faced by diversifying. Diversification is done by forming an optimal portfolio. This research aims to determine the proportion of stocks in the optimal portfolio and calculate the expected return and risk value of the optimal portfolio. The object used to form the optimal portfolio is health sector stock group for the period January 2020 - December 2022. The method used to form the optimal portfolio is Single Index Model (SIM). The results showed that there were 6 combinations of health sector stock in the optimal portfolio, such as IRRA, PRDA, SAME, SILO, MERK, and HEAL stocks of 8.94%, 9.24%, 9.34%, 11.92%, 27.15%, and 33.41% respectively with expected return of 2.68% and a risk value of 1.85%.

Oil price uncertainty and excess value of diversification

PurposeThis study examines the value implications of oil price uncertainty for investors in diversified firms using a sample of 922 USA firms from 2001 to 2019.Design/methodology/approachOur study employs a panel dataset to examine the value implications of oil price uncertainty for diversified firm investors. We consider several alternative specifications to account for unobserved factors and measurement errors that could potentially bias our results. In particular, we use alternative measures of the excess value of diversified firms and oil price uncertainty, additional control variables, fixed-effects models, the Oster test, impact threshold for confounding variable (ITCV) analysis, two-stage least square instrumental variable (2SLS-IV) analysis and the system-GMM model.FindingsWe find that the excess value of diversified firms, relative to a benchmark portfolio of single-segment firms, increases with high oil price uncertainty. The impact of oil price uncertainty is asymmetric, as corporate diversification is value-increasing for diversified firm investors only when the volatility is due to positive oil price changes and amidst supply-driven oil price shocks. The excess value increases irrespective of diversified firms’ financial constraints and oil usage. Diversified firms become conservative in their internal capital allocations with high oil price uncertainty. Such conservatism is value-increasing for diversified firm investors, as it supports higher performance in response to oil price uncertainty.Originality/valueOur study has three important implications: first, they are relevant to investors in understanding the portfolio value implications of oil price uncertainty. Second, they are helpful for firm managers while comprehending the value-relevant implications of internal capital allocations. Finally, our findings are policy relevant in the context of the future of diversified firms in developed markets.

Robust Prediction Intervals for Valuation of Large Portfolios of Variable Annuities: A Comparative Study of Five Models

Robust Prediction Intervals for Valuation of Large Portfolios of Variable Annuities: A Comparative Study of Five Models

Patent Value Evaluation of Communication Enterprises Based on Real Option Method

Patents are an important asset for a company, especially for telecommunications companies. Evaluating the value of patent portfolios and analyzing the benefits brought by patents is of great significance to the development of communication enterprises with multiple patents. In this paper, the gray GM(1,1) model, random forest algorithm and Monte Carlo simulation are introduced into the B-S option pricing model of the real option method. The gray GM(1,1) model has unique utility for the analysis and modeling of short time series and few statistical data, and has the characteristics of high prediction accuracy and good effect. The random forest algorithm has great advantages in the calculation of revenue sharing rate, and Monte Carlo simulation can optimize volatility parameters. Therefore, the B-S model is used to analyze the 5G patent data of ZTE Co., Ltd. with access technology functions, which has certain operability and reference.

PORTFOLIO MODELS FOR OPTIMIZING DRAWDOWN DURATION

The drawdown duration, which measures the time elapsed since the portfolio obtained its maximum value, is an important criterion in active portfolio management for institutional investors. Although several optimization models exist for controlling portfolio drawdown magnitude (i.e. the percentage drop in portfolio value from its latest peak value), developing similar models for the drawdown duration has received minimal attention in the literature. Therefore, this paper develops a family of models for optimizing average, maximum and tail drawdown duration formulated as mixed-integer linear programming (MILP) problems, allowing the utilization of powerful solvers to identify optimal asset portfolios. We apply the developed models to real data on historical returns to compare their performance against traditional and drawdown-based portfolio selection models. The results indicate that the developed models lead to decrease in drawdown duration levels both in in-sample and out-of-sample tests. The constructed efficient frontiers also show a clear trade-off between minimizing drawdown duration and maximizing expected returns.

The value premium in the Vietnamese equity market

In recent decades, the Efficient Market Hypothesis has been the subject of debate among professionals and academics. In this hypothesis, the value premium is a key aspect that challenges market efficiency. The main objective of this study is to comprehensively investigate the value versus growth anomaly in the Vietnamese market between 2013 and 2023. Based on the empirical data, value portfolios have yielded a greater average return than growth portfolios in the Vietnamese stock market during this period. Although their levels of market risk (measured by beta) are nearly the same, the added-risk level of value portfolios is substantially higher than growth portfolios. Therefore, the value premium in Vietnam is compensated for bearing a higher risk level, consistent with the risk-based explanation.

Scenario selection with LASSO regression for the valuation of variable annuity portfolios

Variable annuities (VAs) are increasingly becoming popular insurance products in many developed countries which provide guaranteed forms of income depending on the performance of the equity market. Insurance companies often hold large VA portfolios and the associated valuation of such portfolios for hedging purposes is a very time-consuming task. There have been several studies focusing on inventing techniques aimed at reducing the computational time including the selection of representative VA contracts and the use of a metamodel to estimate the values of all contracts in the portfolio. In addition to the selection of representative contracts, this paper proposes using LASSO regression to select a set of representative scenarios, which in turn allows for the set of representative contracts to expand without significant increase in computational load. The proposed approach leads to a remarkable improvement in the computational efficiency and accuracy of the metamodel.

Univariate and Multivariate Volatility Models for Portfolio Value at Risk

Univariate and Multivariate Volatility Models for Portfolio Value at Risk