Support Investment Decisions Research Articles

Optimisation models for project selection in asset management: an application to the water sector

AbstractA significant challenge in asset management is the selection of investment projects for infrastructures, which often relies on subjective judgement and lacks structured decision support methods. This challenge is particularly complex in water systems due to the diverse and heterogeneous nature of the components requiring investment. While the infrastructure value index (IVI) is widely used to characterise assets and support investment decisions in the water sector, its application in optimisation models for generating efficient project portfolios remains unexplored. To address this research gap, this study introduces optimisation models for generating investment portfolio plans in water systems' asset management. The proposed approach includes two mixed‐integer linear programming (MILP) models that determine optimal solutions and an evolutionary algorithm that offers sub‐optimal alternative investment selection plans to provide decision‐makers with additional choices for balancing optimal outcomes. The primary contribution of this research is the combined utilisation of MILP and evolutionary algorithms, integrating the IVI into the decision‐making process. These tools provide decision‐makers with structured methods for defining investment plans and minimising the subjective elements typically associated with such processes. To illustrate the effectiveness of the models, a case study is presented involving a pumping station of a Portuguese water company. The results demonstrate the practical application and benefits of the proposed approach in optimising investment decisions. This research contributes to advancing asset management practices by integrating quantitative optimisation techniques and leveraging the IVI, thereby enhancing the objectivity and efficiency of investment planning in water systems' asset management.

Technical Analysis, Fundamental Analysis, and Ichimoku Dynamics: A Bibliometric Analysis

This article aims to contribute to the academic knowledge in the field of scientific production regarding decision support tools for investments in the capital market, specifically focusing on fundamental analysis, technical analysis, and Ichimoku dynamics. Bibliometric analysis, following the three main laws (Bradford’s Law, Lotka’s Law, and Zipf’s Law), was employed to evaluate scientific production, identify publication patterns, and uncover gaps and collaboration networks over the last thirty years. To achieve these objectives, 1710 relevant academic publications on the topic were analyzed and retrieved from the Web of Science (WOS) database, pertaining to the last 30 years, between 1990 and 22 May 2023. The significance of this article lies in the contributions of the findings, which advance scientific knowledge by identifying gaps in the knowledge and research, particularly in the limited literature on Ichimoku; our review reveals a growing trend of research in this area. Another notable conclusion is the emergence of new research topics and areas of interest, as well as the identification of collaboration networks among authors, institutions, and countries. Moreover, the article provides valuable insights for financial professionals and investors who are interested in applying these methodologies as methods for price forecasting. The highlighted results support investment decision making, trading strategies, and portfolio management.

Investment Decision Support System Using Credibility Analysis With Fuzzy Interest Rate

Today, with a fluctuating course of the economy, it is inevitable that the interest method used by people for investment will also fluctuate. There may be serious inconsistency between the current interest rate and the interest rate at the time of the investment. Therefore, in order to eliminate these inconsistent situation, fuzzy set theory is used and the case where the interest rate parameter is fuzzy variable is examined. In this way, it is provided that the uncertain interest rate is close to the real-life interest rate.

Novel Harmonic Distortion Prediction Methods for Meshed Transmission Grids with Large Amount of Underground Cables

The tremendous and fast green transition in Denmark has initiated the large-scale grid-integration of renewable energy sources, electrification of energy consumption, and establishment of PtX and Energy Islands, setting goals for transmission grid development—such as the establishment of new connections—and for grid reconstruction—such as the extensive substitution of overhead lines (OHLs) with underground cables (UGCs). The share of UGCs in the Danish transmission grid is increasing. Presence of UGC has resulted in that resonances of the harmonic impedance characteristics of the transmission grid are brought within the harmonic order range coinciding with the harmonic emission sources and causing systemwide increase of the harmonic voltage distortion in the 400 kV transmission grid. The transformation of the 400 kV transmission grid has given rise to the need to predict harmonic voltage distortion using simulation models to secure an adequate power quality and support investment decisions and harmonic mitigation for the grid stage, which has not yet been established and which differs from the present grid. This paper presents the experiences of Energinet, the Transmission System Operator (TSO) in Denmark, with harmonic distortion in the Danish transmission grid due to the establishment of 400 kV UGCs, and the development of measurement-validated methods for harmonic distortion simulation and prediction. The paper also presents ongoing developments within, and research addressing, the prediction of harmonic distortion in meshed grids; for example, it explores where and how an analytical approach can replace observational studies with many numerical simulations. The methods shall make it possible to predict whether, where in the transmission grid, and for which harmonic orders connections that have not yet been commissioned may cause the violation of the planning levels, and which mitigations are necessary for bringing the harmonic distortion below the planning levels with respect to a given margin.

LSTM-ReGAT: A network-centric approach for cryptocurrency price trend prediction

Predicting price trends of cryptocurrencies is a challenging task due to the highly speculative cryptocurrency market. Prior studies mainly investigate predictors such as historical trading data, macroeconomic development, and public interests in cryptocurrencies for price trend prediction while ignoring the predictive role of the relations between cryptocurrencies. In fact, the price movement of a cryptocurrency may be affected by those of other cryptocurrencies, thus, incorporating cryptocurrency interrelations can further improve the prediction performance. In this paper, we propose a novel end-to-end model for price trend prediction using long short-term memory (LSTM) and relationwise graph attention network (ReGAT), in which both individual cryptocurrency features and cryptocurrency relations are considered. Specifically, a cryptocurrency network is built by using shared features between cryptocurrencies. Further, LSTM is used to profile sequential patterns of individual cryptocurrency features. To fully extract network features, ReGAT is designed to aggregate information conveyed by different types of relations while automatically differentiating their importance. Finally, the conventional and network features are concatenated to predict the price trend. The effectiveness of LSTM-ReGAT is validated using real-world cryptocurrency market data. The trading simulations for Bitcoin and portfolios reveal that our model obtains the highest profits. Our study provides insightful implications for investment decision support in cryptocurrency market.

Software Development For Optimal Portfolio Selection Using The Markowitz Method

Investors could invest their funds in a collection of financial assets (portfolio). The optimal combination of the portfolio will optimal the return. The purpose of this study is the development of Optimal Portfolio Software to support investment decisions, using the Markowitz model. The software will support the Investment Management course in the classroom. The development process consists of Analysis, Design, and Development Stages. The output of this research is Markowitz Methods Software, that validated by expert judgment. Keywords: , Investment Management Learning, Investment Decision Making, Markowitz Methods Software, Optimal Portfolio, Portfolio.

A new stock market analysis method based on evidential reasoning and hierarchical belief rule base to support investment decision making.

Stock market analysis is helpful for investors to make reasonable decisions and maintain market stability, and it usually involves not only quantitative data but also qualitative information, so the analysis method needs to have the ability to deal with both types of information comprehensively. In addition, due to the inherent risk of stock investment, it is necessary to ensure that the analysis results can be traced and interpreted. To solve the above problems, a stock market analysis method based on evidential reasoning (ER) and hierarchical belief rule base (HBRB) is proposed in this paper. First, an evaluation model is constructed based on expert knowledge and ER to evaluate stock market sentiment. Then, a stock market decision model based on HBRB is constructed to support investment decision making, such as buying and selling stocks and holding positions. Finally, the Shanghai Stock Index from 2010 to 2019 is used as an example to verify the applicability and effectiveness of the proposed stock market analysis method for investment decision support. Experimental research demonstrates that the proposed method can help analyze the stock market comprehensively and support investors to make investment decisions effectively.

Health and economic benefits of achieving contraceptive and maternal health targets in Small Island Developing States in the Pacific and Caribbean

IntroductionReducing unmet need for modern contraception and expanding access to quality maternal health (MH) services are priorities for improving women’s health and economic empowerment. To support investment decisions, we estimated...

The challenges of agricultural value chain assessment and governance – The example of mangoes in Burkina Faso

The integrated assessment of agricultural value chains through economic, social and environmental analyses is aimed at providing the necessary and sufficient information to guide or support investment decisions in the context of sustainable development. These assessments are based on data collection and analyses of the organisation of these value chains, as well as on their stakeholders’ strategies. Access to information, the reliability of this information, and the general context in which these value chains evolve are key dimensions. The context can be an obstacle to investment decisions and the implementation of sectoral agricultural policies. Indeed, investing requires confidence in the future, since it implies debt, and therefore risk-taking and accountability. In order to mitigate this risk, investors require information and must be able to access it. In low-income countries, difficulties in applying regulatory texts and the lack of reliable information hamper the development of sustainable agricultural value chains, as well as the processes to assess them. We show how mango value chains in Burkina Faso are concerned.

Film investment decision support: prediction model through movie merchandiseFilm investment decision support

With hundreds of millions of box office revenue, dazzling stars, and countless fans, the huge profits of the film industry seem to be more likely to cause a public sensation, but multiple data studies express that more than half of movie is unprofitable. Income diversification has become the guarantee for the development of the film industry. Besides the box office, movie merchandise has become an important part of movie revenue. Predicting movie merchandise exploitability can help investors to reduce investment risks and make better decisions. This paper adopts the empirical research method, collects data from hundreds of movies, and produces a merchandise dataset. Through data analysis, we found three core influencing factors: genres, series, and box office, then build a prediction model to support film investment. This model can quantify the exploitability of movie merchandise at the film planning stage and has an accuracy of 88.6%, which has a good generalization ability.

Design for Digitally Enabled Industrial Product-Services Systems

Planning the life cycle of industrial product-service systems (IPS2) is highly challenging due to uncertainties experienced in predicting supply (e.g. spares) and demand (e.g. availability) related factors. Whilst digitalisation offers numerous exciting avenues, industry is finding it challenging to realise the potential benefits. This paper focuses on how to design the set of digital technologies and methodologies that serve as enabling capabilities to optimise value across the life cycle. This involves offering a step by step process to compare alternative improvement opportunities (e.g. data modelling, digital twins) with the justification to support investment decisions. The systematic design methodology is tested on an aerospace component, demonstrating the added value of digitally enabled IPS2.

Combining energy dynamic simulation and multi-criteria analysis for supporting investment decisions on smart shading devices in office buildings

In the framework of the current energy transition, the building sector is experiencing a smart revolution, recognizing how the adoption of advanced control strategies and smart management systems can increase buildings energy efficiency and improve occupants’ comfort. As a key element for smart buildings, solar shading devices coupled with effective control systems need to be strategically assessed. The investment decision-making process for selecting the proper shading systems is influenced by the existence of potential conflicts from economic, environmental, social, and energy standpoints, thus asking to be supported by innovative methodological approaches able to include all these aspects in the decisional framework. To this purpose, the work aims to propose a multi-disciplinary methodological framework to support decision-makers in ranking a set of solar shading device and relative control strategy, integrating energy dynamic simulations with multi-criteria decision analysis techniques. The analysis focuses on office buildings, recognizing their huge impact in terms of energy consumptions and the need to guarantee proper indoor conditions to increase employees’ productivity and well-being. To test the applicability of the multi-step methodology for this building category, the proposed approach is applied to an office building located in the North-West of Italy, aiming to assess and compare different alternative combinations of shadings devices and management strategies. The advanced features of building energy modelling and simulation and the application of PROMETHEE II (Preference Ranking Organization METHod for Enrichment Evaluation II) method as multi-criteria analysis allows to provide recommendations to decision-makers to identify the systems to be installed, able to guarantee the best trade-off between internal comfort, operational energy, and environmental costs for the building under investigation.

Moderasi Hubungan Financial Literacy dengan Keputusan Investasi terhadap Peran Herding Behavior bagi Investor Pemula

The role of herding behavior (HD) is very much needed in moderation to support Investment Decisions (IC) for novice investors. This study aims to measure the size of Financial Literacy (FL) in making the right decisions. The data processed in this study are 158 novice investor respondents who invest in stock-type securities in Indonesia. The sampling method was carried out randomly (probability sampling) which was collected through the Google form. The data analysis method used is to answer the truth of the hypothesis using the help of the Structural Equation Model (SEM). The results of hypothesis testing found that FL had a positive and significant effect on KI and HD. The magnitude of the influence value is less than 0.05. The same test model for HD also has a positive effect on KI with a value smaller than 0.05. The test value of this effect on HD is not a direct effect, with a coefficient value of -0.039 which is smaller than 0.05. The conclusion of this study is that HD does not moderate the relationship between FL and KI for novice investors who invest in stock-type securities in Indonesia. So that this research can be used as a reference in making decisions for novice investors.

PP48 A Micro-Costing Study For Circulating Tumor DNA testing In Oncology

IntroductionCirculating tumor DNA (ctDNA) is a promising new biomarker with multiple potential applications in cancer care. As part of the “ctDNA on the way to implementation in the Netherlands (COIN)” project, an early, comprehensive Health Technology Assessment (HTA) is ongoing. Information about the costs of ctDNA testing is essential for implementation. Estimating the total cost associated with ctDNA-testing is challenging due to variation in the workflow, wide range in purchase and operational costs of the platforms, and the highly dynamic field. As a first step in the HTA, the aim of this study was to develop a flexible micro-costing framework and open-access model for consistent cost calculation of ctDNA-testing.MethodsFirst, the complete diagnostic workflow of ctDNA-testing was mapped based on expert discussions. This step-wise workflow was used as the foundation of the framework. Second, the activity-based costing method was used and included costs for personnel, materials, equipment, overhead, housing, and test failures. Third, the framework was validated by experts and by applying the cost calculation model to six case studies.ResultsThe diagnostic workflow was mapped from blood sample collection to reporting the diagnostic findings. The framework was developed from a Dutch perspective and takes into account the testing volume. The total cost per sample for the case studies with different workflows and testing volumes ranged from EUR 168 to EUR 7,638.ConclusionsThe developed micro-costing framework can be used to calculate the costs for ctDNA-testing for different workflows. The results from the case studies show the wide range of costs for ctDNA-testing and that the costs are determined by the choice of platform, setting, and testing volume. The open access model allows users to adapt and specify parameters in the diagnostic workflow matching their setting and can be used to support investment decisions and future cost-effectiveness studies.

Chemical Networks: A Methodology to Rapidly Assess the Environmental Impact of Chemical Processes

As the chemicals industry transitions towards a net zero future, rapid assessment of the sustainability metrics of different process results will be essential to support investment decisions in innovation and deployment. Life cycle analysis (LCA) offers the gold standard for process assessment, but LCA can take weeks or months to complete, with incomplete databases and inflexibility in comparing different chemical pathways. In this study, we demonstrate an alternative and complementary methodology. By simplifying the metrics used to describe chemical processes, each process may be linked to another by its feedstocks and products. This generates a network of the chemical industry, which may be investigated using graph theory principles. A case study of the plastics industry is provided, using publicly available information to quantitatively compare with a more formalised and detailed LCA approach. This methodology proves useful for quickly estimating the carbon intensity and water footprint of thousands of routes. Further development, such as including Scope 3 emissions and additional industrial data, may further improve the methodology.

Financial and environmental efficiency of CDM projects: Analysis and classification for investment decisions

AbstractThe main purpose of this paper is to analyze the efficiency of Clean Development Mechanism (CDM) projects implemented worldwide and understand their main characteristics, seeking to support investment decisions and expand the scientific understanding of the financial and environmental impact of these projects. To achieve this goal, a sample of 2352 CDM projects was selected from a United Nations Framework Convention on Climate Change (UNFCCC) database and analyzed first with a two‐stage data envelopment analysis (DEA) model that allowed the evaluation of the financial return and environmental efficiency of these projects. DEA results provide an efficiency ranking that was then analyzed with a classification tree (CHAID algorithm), revealing some main characteristics of the projects with higher efficiencies, like their sizes, locations, and type of CDM. CDMs are projects that demand a significant quantity of resources and effort to produce the expected outcomes, so it is crucial for public managers and investors to know the project profiles that generate the best financial and environmental results. In this sense, this study presents a completely original methodology for this kind of analysis and reveals important insights for these agents and researchers in this field.

Determining the Effect of Return on Equity (ROE) on Price EarningsRatio (PER) and Price to Book Value (PBV) in LQ45 companies,Indonesia

This research was conducted to determine the effect of Return on Equity (ROE) on Price Earnings Ratio (PER) and Price to Book Value (PBV) in LQ45 companies listed on the Indonesia Stock Exchange (BEI). The data studied were financial reports published by 37 companies for four periods, namely 2015 to 2018. The data processing program uses the Eviews 9 program. The contribution of this article is to support investment decisions. The processing results show that ROE has a positive and significant effect on PER. and ROE has a positive and significant effect on PBV. Both are shown by the results of data processing in a positive correlation coefficient and beta coefficient. This correlation shows that in investors making investment decisions, ROE is used to make investment decisions. Then ROE will have an impact on company value. The higher the ROE, the higher the firm value will be. Furthermore, other factors influence PER and PBV, namely dividend playout ratio, return on assets, profit margin, inflation, interest rates, economic conditions, and company competition, both similar and different.

New Members Selection for the Expansion of Energy Communities

Energy communities are key enablers for end-users to actively participate in the energy transition in a more consumer-centric context. This paper focuses on the expansion of existing energy communities that may need to select new members among a pool of candidates. Selection is based on heuristic methods for better explainability and to promote a transparent selection process from end-users’ perspectives. The proposed methodology is further verified with an accurate optimization-based energy management strategy. The member selection is performed in an iterative process where the best potential candidate is added as a new member of the energy community before running the same procedure over successive iterations. Simulations were performed for a complete month with a real community of six houses and nine potential candidates. The proposed rule-based method achieves similar ranks among candidates for two investigated metrics and return the same results as the more accurate optimization. Furthermore, the results show a hint on how to identify the best location (i.e., member) to install new assets that can contribute best to the energy community since it can boost the value brought by the candidates to the community. In that sense, the proposed method also serves as an investment decision support tool as well as a selection strategy for inhabitants of an energy community.

Appraising the availability of biomass residues in India and their bioenergy potential

Biomass produced from agriculture at present provides most energy services in developing nations. In India, enormous quantities of biomass are produced for conversion into valuable energy. Bioenergy production from agricultural leftovers, animal manure, and municipal waste has the potential to meet of the rising need sustainable energy. It is a practical and sustainable option since the energy produced from the above mentioned sources can minimise the use of fossil fuels, reduce greenhouse gas emissions, and alleviate the effects of climate change. In addition, it can boost marginal and small farmers in terms of income and job opportunities. Evaluating agricultural leftovers, animal manure, and municipal waste as bioenergy resources can provide a method of tapping renewable energy opportunities. It is possible to minimise constraints for using agricultural leftovers, animal manure, and municipal waste, support investment decisions, and maximise the utilisation of biomass resources available. This study is intended to establish the amount of energy demand in India that can be met by using crop residues, animal manure, logging residues, and municipal waste. The annual energy potential of these biomass waste was quantified and assessed in the study. It has been determined that the technical bioenergy potential of these biomass resources is 1.29 × 103 PJ in 2.31 × 104 Mm3 of biogas and 7.79 × 102 PJ in 3.49 × 104 Ml of cellulosic ethanol. However, the country must overcome techno-economic barriers to handle the projects likely to be initiated soon.

3rd Annual International Pharmacoeconomic Forum Highlights.

3rd Annual International Pharmacoeconomic Forum Highlights.