Strategic Investment Planning Research Articles

Invest in Idea: Crowd Funding with Predicting Startup Success

Abstract: Crowd funding enables individuals or businesses to raise funds for their projects, ventures, or caused by tapping into a large pool of contributors. It democratizes access to capital, allowing creators to bypass traditional financial institutions and engage directly with their community or target audience. This method fosters innovation, empowers grassroots movements, and facilitates the realization of diverse ideas that might otherwise struggle to secure funding through conventional channels. Utilizing a rich dataset encompassing funding details, milestones, relationships, and geographical data of various startups, we embark on an in- depth analysis involving data preprocessing, feature engineering, and exploratory data analysis to uncover key success determinants. Employing advanced classifiers like LGBM, XG Boost and gradient Boosting, our model undergoes rigorous training and evaluation, with LGBM emerging as the top performer, achieving an accuracy of 90.48%. The analysis underscores the critical role of funding, and investor presence in forecasting startup success. This research not only equips stakeholders with a powerful predictive tool but also highlights significant features influencing startup outcomes, offering a novel perspective on strategic investment planning. Additionally, we developed an interactive frontend platform to complement our predictive model. Utilizing AngularJS, the platform serves as a gateway for startups to register their ventures, providing realtime access to predictive insights. Through intuitive forms, startups enter essential information such as geographical location, funding received, and sector of operation. Upon submission, the data is processed by our backend, which evaluates it against our machine learning model to predict success rates. This integration empowers startups with immediate insights and facilitates data-driven decision-making among investors, thereby fostering a more dynamic and informed startup ecosystem.

A Predictive Decision Analysis Tool for Risk Informed, Capital Investment Planning within the Department of Veterans Affairs

The Department of Veterans Affairs (VA) advances healthcare research and contributes to the Federal Response in the state of a national healthcare emergency while providing healthcare to veterans. The VA needs to maintain and improve accessible and safe healthcare infrastructure using risk-informed decision models. The VA relies on the Strategic Capital Investment Planning (SCIP) process to allocate resources but lacks predictive modeling. The Strategic Analysis and Risk Tool (START) creates a user-friendly interface to display environmental and veteran migration risk data, leveraging Power BI and Python. Our research presents a georeferenced risk assessment model that provides insights to regional and facility decision makers about these risks. This risk score helps SCIP decision makers allocate limited resources among VA facilities.

The Effects of Net Profit Margin, Debt Ratio, Total Assets Turnover, and Current Ratio on the Stock Prices of IDX 30 Companies within 2018-2022

This research delves into the intricate relationship between financial metrics and stock prices within the Indonesian market context. Analyzing data from IDX 30 companies over the period 2018-2022, the study focuses on net profit margin, debt ratio, total assets turnover, and current ratio as key variables. The findings reveal that net profit margin exerts a positive significant influence on stock prices, highlighting the allure of profitable companies to investors. Conversely, the current ratio exhibits a negative impact, indicating the nuanced interplay between liquidity and stock valuation. Notably, total assets turnover does not significantly influence stock prices, emphasizing the multifaceted nature of investment decisions. Additionally, the study underscores the adverse effect of higher debt ratios on stock prices, reflecting investor concerns about financial leverage. These insights offer valuable implications for investors, analysts, and policymakers, enhancing our understanding of the Indonesian stock market dynamics and aiding in strategic investment planning.

Italy in Front of the Challenge of Infrastructure Maintenance: Existing Issues and Promising Responses

It is puzzling how the optimal maintenance of infrastructure gets so little attention in the public policy debate and in national strategic infrastructure investment plans. In the case of Italy, an information deficit, inadequate strategic planning and governance, and unpreparedness for macro changes affecting infrastructure emerge as critical root causes of neglect. Despite the increasing attention to sustainability (at national, EU and international level), it is not consistently implemented along the life cycle of infrastructure assets, which would entail adequate funding to operate and maintain existing assets. Public administration plays a critical coordination role and must tackle the excessive normative complexity and consequent lengthiness of the authorization processes. Despite some unmet challenges, several ongoing interventions show Italy’s commitment to address some of the issues. Notably, the creation of an integrated repository tracking all the country’s infrastructure projects, or the national guidelines recently issued to rank and prioritize bridge safety interventions.

Dynamic Relationship between the Return of Gold, Crude Oil, and the Stock Exchange of Thailand Based on a Vector Autoregressive Model

This research aims to investigate the dynamic relationship between the return of gold (RGOLD), crude oil (RCO), and the stock exchange of Thailand (RSET) using the Vector Autoregressive model: VAR(p) to analyze the secondary monthly data from January 2002 to October 2022. The paper reports three key findings. Firstly, the Granger causality test reveals two direct relationships: one between RCO and RSET, and the other between RSET, RCO, and RGOLD. Moreover, the researcher estimated the relationship between these variables by using the VAR(1) model. Secondly, the impulse response function (IRF) is applied by Granger causality results. When a variable is shocked by an impulse in the system, the RCO to RSET response increases by 0.21 percent, the RSET to RGOLD response increases by 0.10 percent, and the RCO to RGOLD response increases by 0.04 percent, before decreasing and approaching equilibrium. Finally, variance decomposition shows that the greatest portion of total variation in RGOLD, RCO, and RSET can be explained by itself in the long run. Moreover, the variation of RSET can be affected by the variation of RCO while the variation of RGOLD can be affected by the variation of RCO and RSET. Thus, investors, securities analysts, or fund managers can use the study’s results for strategic investment planning.

INVESTMENT STRATEGY OF THE ENTERPRISE AND PECULIARITIES OF ITS FORMATION IN MODERN CONDITIONS

Taking into account the current crisis conditions of economic activity, the formation issues of investment activity perspective directions of the enterprise and, in particular, formation of investment strategy are especially urgent. Strategic investment planning is an effective tool for solving this problem. At the present stage, implementation of investment strategy at enterprises is limited mainly by real investments. But with the development of the global investment market, the potential of enterprises to increase their efficiency through expansion of investment activity is growing considerably. The investment strategy is the basic plan of the enterprise’s activities in the field of its investment activity, which defines the priorities of its directions and forms, the nature of investment resources formation and the sequence of stages of realization of long-term investment goals that ensure effective development of the enterprise. The process of formation of investment strategy was considered at LLC ASTARTA-Kyiv, one of the largest vertically integrated agro-industrial holdings of Ukraine. The activities of which have a significant impact on the social development of the population and the environment. That is why, in order to support sustainable environment and community welfare, the company applies and constantly improves sustainable agricultural practices aimed at mitigating climate change, protecting the environment and investing in social protection of people in regions where its own assets are concentrated. It has been proved that in determining the economic efficiency of measures from investments of Astarta-Kyiv LLC it is necessary to take into account the effect in all forms of development: Increase of profit of farms, decrease of production costs, improvement of quality of environment, decrease of consumption of natural resources, increase of living standards of population, decrease of disease, improvement of working conditions and rest of employees. It is justified that the main directions of improvement of investment activity of Astarta-Kyiv LLC are the perfect investment strategy of the enterprise. Due to which the company receives high stable profit from direct and financial investments, the risk of investment investments is reduced, which contributes to modernization and improvement of investment activity of the enterprise

A hybrid Delphi-AHP multi-criteria analysis of Moving Block and Virtual Coupling railway signalling

The railway industry needs to investigate overall impacts of next generation signalling systems such as Moving Block (MB) and Virtual Coupling (VC) to identify development strategies to face the forecasted railway demand growth. To this aim an innovative multi-criteria analysis (MCA) framework is introduced to analyse and compare VC and MB in terms of relevant criteria including quantitative (e.g. costs, capacity, stability, energy) and qualitative ones (e.g. safety, regulatory approval). We use a hybrid Delphi-Analytic Hierarchic Process (AHP) technique to objectively select, combine and weight the different criteria to more reliable MCA outcomes. The analysis has been performed for different rail market segments including high-speed, mainline, regional, urban and freight corridors. The results show that there is a highly different technological maturity level between MB and VC given the larger number of vital issues not yet solved for VC. The MCA also indicates that VC could outperform MB for all market segments if it reaches a comparable maturity and safety level. The provided analysis can effectively support the railway industry in strategic investment planning of VC.

Strategic Investment Decisions for Emerging Technology Fields in the Health Care Sector Based on M&A Analysis

The existing approaches to identification of emerging technologies create a prominent opportunity for technology convergence and market growth potential. However, existing approaches either suffer from the time lag issue or have yet to explorethe assessment’s uncertainty and ambiguity. Based on a total of 14 years of mergers and acquisitions (M&A) activity data in the Health Care sector, the complex patterns between growth velocity and accelerating of M&A activities are analyzed with two quantitative indicators (Promising Index and Promising Index Sharpe Ratio) to identify emerging technological opportunities. The proposed integrative approach offers a mean to resolve the time lag issue, deal with market trend irregularity, and manage expectations of investors for emerging technology and industry. Specifically, this study aims to (i) provide a decision support system integrating M&A activity information for strategic investment planning and (ii) identify promising technologies in the Healthcare sector to manage the irregularities of market trend and investment outcome. This study is one of the first research that employs a prior data-based approach to delineate emerging technologies by analyzing the growth momentum properties of specific industry areas based on the M&A activity data.

Profiling and evaluating Chinese consumers regarding post-COVID-19 travel

ABSTRACT The tourism industry, despite its vulnerability to external shock, is acknowledged for its ‘adaptiveness’ and ‘resilience’ to external pressures. Questions arise as to whether the industry can have a rapid recovery post the COVID-19 pandemic and if so, what factors may contribute to this rebound. Based on a nation-wide survey of 3388 samples, this study seeks an understanding of Chinese consumer travel psychology and behaviour post-Covid-19. The study confirms the existence of a sizable and stable segment of mainland Chinese tourists that is likely to be the market for a possible rebound in tourism post-COVID-19. This segment is demographically heterogeneous and geographically diverse but can be differentiated by household income and wealth structure. Research findings will benefit short, medium, to long-term marketing, strategic tourism development and investment planning, aid pricing policies and allocation of limited resources.

Planning Power System Portfolio of Turkey through Analytic Hierarchy Process and Data Preprocessing Abstract

Bu çalışmada amaç, enerji konusunda dışa bağımlı bir ülke olan Türkiye’nin 2020-2039 yılları arasında ihtiyaç duyacağı elektrik enerjisini karşılayabilecek bir yatırım planı sunmaktır. Elektrik enerjisi üretimi için stratejik yatırım planı sürdürülebilir arz güvenliğinin sağlanması açısından kritik öneme sahiptir. Yatırım planının oluşturulması aşamasında çok kriterli karar verme yöntemlerinden Analitik Hiyerarşi Süreci (AHS) kullanılmıştır. Model oluşturulurken uygulanan bir dizi veri önişleme tekniği sayesinde AHS’nin öznellik yaklaşımı büyük ölçüde ortadan kaldırılarak görece daha nesnel sonuçların elde edilmesi sağlanmıştır. Çalışmaya özgünlük kazandıracak şekilde kullanılan veri önişleme teknikleri ve AHS yöntemiyle Türkiye Cumhuriyeti Enerji ve Tabii Kaynaklar Bakanlığı’nın (ETKB) stratejik hedefleri doğrultusunda bir portföy senaryosu ortaya konmuştur. Araştırma elde edilen portföy senaryosuyla ETKB’nin hedeflerini destekleyecek nitelikte bulgular ortaya koymaktadır. Buna göre, artan ihtiyacı karşılarken kullanılan enerji kaynaklarına göre elektrik üretiminde kömürün %10,6, doğal gazın %3, hidroelektrik kullanımının ise %4,3 azalacağı, rüzgârın %1,68, güneşin %4,34 ve jeotermal kullanımının da %2,98 artacağı öngörülmüştür. Sonuç olarak, yapılan planlama dışa bağımlılık oranını, fosil yakıt kullanım oranını ve emisyon değerlerini düşürürken, yenilenebilir enerji kaynaklarının kullanımını, istihdam potansiyelini ve arz güvenliğini arttırmaktadır. Bu araştırmanın sonuçları ETKB’nin 20 yıllık stratejik planında yer alan hedeflerin tutarlı olduğunu göstermesi açısından büyük önem arz etmektedir.

Industrial Energy Retrofit Planning Using Automated Retrofit Targeting

There is increasing pressure for industrial sites to minimise energy consumption. A long-term energy reduction strategy plays a critical role in realising energy savings. A key exchange mechanism of energy for sites is the heat exchanger network, which can be revamped and retrofitted to lift process energy efficiency. Retrofit analysis of heat exchanger networks commonly looks for a modified design that could provide significant energy savings for a comparatively modest investment but rarely does the analysis consider how retrofit projects can be sequenced as part of an energy retrofit plan while staying with capital investment constraints. The challenge in developing a long-term plan is the identification of projects and their ideal order out of the numerous possible combinations. This paper aims at developing an energy retrofit planning tool by performing a multi-stage retrofit analysis based on the automated retrofit targeting algorithm used to search for retrofit designs. The key output of the energy retrofit planning analysis is several sets of retrofit plans that can be further analysed for case-specific practicality (e.g., space, distance, and fluid compatibility) and developed into an energy retrofit plan, forming a part of a company’s overall energy strategy. The method is demonstrated using two industrial case studies with complex features and room for multiple sets of retrofit modifications. For the petrochemical complex case study, the study concludes that a 4-stage retrofit plan could be undertaken under a strategic investment plan that in total requires 11 modifications to the system, which generates 1.47 million EUR in annual profit with a payback of 1.75 years.

Managing expectations with emotional accountability: making City Hospitals accountable during the COVID-19 pandemic in Turkey

PurposeThis paper explores the role of the COVID-19 pandemic in the financial and parliamentary accountability mechanisms of public-private partnership (PPP) “City Hospitals” in Turkey. Diverse and changing accountability mechanisms are explored regarding budgetary, affordability and emotions during the COVID-19 pandemic.Design/methodology/approachThis is a case study of City Hospitals in Turkey. Empirical data are collected and analyzed qualitatively from publicly available government and related sources, Turkish National Audit reports (Sayistay), strategic healthcare investment plans, relevant laws, decrees and NGO reports and news articles.FindingsExisting accountability mechanisms for arranging and/or delivering value-for-money (VfM) in Turkish PPP hospitals are weak. This provided policy makers with more flexibility to manage expectations of its citizens in dealing with COVID-19 pandemic. Political decision makers, through PPPs, created political capital for themselves by engaging in emotional accountability at the expense of better financial and parliamentary accountability.Originality/valueThis article contributes to the literature by articulating how roles of accountability change in crisis and introduces the concept of emotional accountability during a period of heavy infrastructure investments in City Hospitals in Turkey.

Research and Development Efficiency in Public and Private Sectors: An Empirical Analysis of EU Countries by Using DEA Methodology

Both the fourth industrial revolution (Industry 4.0) and its embedded technology diffusion exponentially progress and grow in terms of technical change and socioeconomic impact. The aim of this study was the evaluation of research and development efficiency in the public and private sectors in EU countries. The Data Envelopment Analysis (DEA) methodology, within which the slack-based model was applied, was used to achieve this aim. The Malmquist index (MI) was used to calculate changes in research and development efficiency during 2010/2013 and 2014/2017. The results present a decrease in total Research and Development (R&D) productivity in public and private sectors for an average of EU countries (28). However, Spain, Slovenia, and Portugal (in the public sector), and Ireland and Romania (in the private sector) revealed an increase of a total R&D productivity during 2010/2013 and 2014/2017 that was primarily influenced by an increase of technical efficiency (catch-up effect). Similarly, the results confirm the differences in R&D efficiency in private and public sectors in the European countries. The study’s results also provide a valuable platform for creators of national strategic and innovative investment and educational plans, and creators of relevant policies and create a platform for national and international benchmarking indicators.

Exploring the Dynamics between M&A Activities and Industry-Level Performance

This study investigates the correlation between mergers and acquisitions (M&As) activities and industry-level performance. While extensive research on M&As has focused on financial performance at the firm-level around the merger announcement, not much focus has been given to the relationship between M&A activities and financial performance at the industry level. Using global data from the S&P (Standard & Poor’s) Capital IQ platform database, this study examines the significance of relationships of 12 industry-level financial values with M&A frequency and transaction value across 11 industry sectors throughout 2009–2018. The results show that M&A activities play a key role in identifying industries with lots of potential and that strategic investment planning can be drawn from both industry and time lag perspectives. This study bridges the gap by exploring the complexity of M&A performance across various firms and industries, and supports forward-looking investment processes by delineating emerging industries with expected positive returns.

Formation of the electric power infrastructure system of the region as a priority task of strategic planning of socio-economic development of territories

The article considers the organization of strategic planning for the development of infrastructure systems in the economic space of the region. The subject of the study is the processes of strategic planning of infrastructure systems in the region. The purpose of the article is to study the features of strategic planning for the development of electric power infrastructure systems in the economic space of the region. The theoretical and methodological basis of the study was the theory of spatial development, strategic planning, infrastructure systems and methods for assessing their impact on the socio-economic development of territories. The properties of infrastructure systems as an object of strategic planning are determined. From the position of the institutional approach, the features of the formation of the strategy for the development of infrastructure systems in the region, the multilevel nature of the planning subjects are revealed. The authors analyzed the organization of strategic planning of spatial infrastructure systems in the management of regional development on the example of the electric power industry. The institutional environment for the development of electric power infrastructure in the Russian Federation is considered on the basis of an analysis of the timing and frequency of preparation of strategic documents, consistency of development goals. Strategic risks of not implementing strategic planning documents have been identified. On the example of the Sverdlovsk region, the balance of strategic investment and socio-economic planning for the development of territories and electric power infrastructure is analyzed. The actual tasks of strategic planning for the development of infrastructure systems are identified, which consist in ensuring connectivity, synchronicity of territorial development plans with infrastructure development, consistency of planning processes, implementation of regional and corporate sector development strategies.

OPTIMIZING PREDICTIVE ACCURACY WITH GRADIENT BOOSTED TREES IN FINANCIAL FORECASTING

Financial forecasting is indispensable for various financial sectors' strategic decision-making, risk management, and investment planning. Traditional statistical methods, though valuable, frequently struggle to capture the intricate, non-linear patterns embedded within financial data. In response to these limitations, Gradient Boosted Trees (GBTs) have emerged as a formidable ensemble learning technique renowned for enhancing predictive accuracy. This comprehensive review paper delves into applying GBTs in financial forecasting, scrutinizing their methodological underpinnings, distinct advantages, and comparative performance against other machine learning approaches. GBTs, with their real-world applications in financial forecasting, operate by sequentially combining multiple weak learners, typically decision trees, to iteratively refine predictions by focusing on residual errors. This iterative approach enables GBTs to effectively model complex relationships and interactions in financial datasets, outperforming traditional models like ARIMA and linear regression in many scenarios. Moreover, the paper addresses critical implementation challenges associated with GBTs, such as hyperparameter tuning and computational complexity, which are pivotal for achieving optimal performance. The review identifies promising avenues for future research, including integrating GBTs with deep learning techniques and advancements in real-time forecasting capabilities. By elucidating these aspects, this paper aims to provide insights that enhance the application and efficacy of GBTs in financial forecasting contexts.

Innovation and investment monitoring as a tool of information support for activisation of the innovation and investment activity of an enterprise

Introduction. Information support for innovation and investment activity is an independent tool of its activisation and is a process of continuous purposeful selection of relevant integrated indicators, which are necessary for the coordination of planning, control, production and collecting information on all areas of innovation and investment activities. The main methodological tool of information support for innovative and investment activity should be a monitoring method, which is a component of the controlling system and allows a manager to control the implementation of innovation and investment strategy on the basis of the indicators monitoring.
 Aim and tasks. The aim of this work is to provide methodological basis for organization of innovation and investment monitoring system at the enterprise as a tool of information support for activisation of its innovation and investment activity.
 Research results. Innovation and investment monitoring should be considered as a system of monitoring, evaluation and forecasting of key changes in innovation and investment activity of an economic entity. The methodical basis of the innovation and investment controlling are the indicators of production and financial accounting, integrated into a single database based on the modern information and communication technologies. Innovation and investment monitoring includes three components: monitoring of the external environment of the investment projects, monitoring of the general results of the strategic investment plan, monitoring of the specific actions performance (monitoring of efforts and individual results).
 The article considers the management support system building, which constitutes of subsystems for personal information, analysts, operational, middle and senior management level.
 Conclusions. Information support for innovation and investment activities is an independent direction of its activation and is a process of continuous targeted selection of relevant integrated indicators. Monitoring method is considered as the main methodological tool of information support for the activation of innovation and investment activity. In order to build information and investment monitoring system it is proposed to use the suggested conceptual approach to the formation of innovation and investment controlling, system of indicators of innovation and investment monitoring, monitoring of legislative support of innovation and investment activities, the appropriate information systems in the enterprise.

A Modified Boltzmann Machine for Solving Distribution System Expansion Planning in Malaysia

<p>This paper proposes an effective technique to solve Distribution System Expansion Planning (DSEP) problem by using the artificial neural network. The proposed technique will be formulated by using mean-variance analysis (MVA) approach in the form of mixed-integer quadratic programming problem. It consists of two layers neural network which combine Hopfield network and Boltzmann machine (BM) in upper and lower layer respectively named as Modified BM. The originality of the proposed technique is it will delete the unit of the second layer, which is not selected in the first layer in its execution. Then, the second layer is restructured using the selected units. Due to this feature, the proposed technique will improve time consuming and accuracy of solution. Referring to the case study demonstrated in this paper, the significance outputs obtained are the improvement in computational time and accuracy of solution provided. As the solution provided various of options, the proposed technique will help decision makers in solving DSEP problem. As a result, the performance of strategic investment planning in DSEP certainly enhanced.</p>

Location-allocation model for external beam radiotherapy as an example of an evidence-based management tool implemented in healthcare sector in Poland

Background and purposeExternal beam radiotherapy (EBRT) is one of three key treatment modalities of cancer patients. Its utilisation and outcomes depend on a plethora of variables, one of which is the distance a patient must travel to undergo the treatment. The relation between distance and utilisation is clearly visible in Poland. At the same time no strategic investment plan is observed. This work proposes a method of resolving these two issues. Materials and methodsWe propose a mixed-integer linear programming model that aims to optimise the distribution of linear accelerators among selected locations in such a way that a patient’s journey to the nearest EBRT is as short as possible. The optimisation is done with observance of international guidelines concerning EBRT capacity. With the use of proposed theoretical framework, we develop a national, strategic plan for linear accelerator investments. ResultsAccording to model assumptions decentralisation of EBRT, together with new equipment purchases, is required to ensure optimal access to EBRT. ConclusionsThe results were incorporated into Healthcare Needs Maps for Poland. The plan based on the results of this study, implemented by 2025, should deal with the most pressing concerns of Polish EBRT.

Integration of RAMS in LCC analysis for linear transport infrastructures. A case study for railways.

Life-cycle cost (LCC) analysis is an economic technique used to assess the total costs associated with the lifetime of a system in order to support decision making in long term strategic planning. For complex systems, such as railway and road infrastructures, the cost of maintenance plays an important role in the LCC analysis. Costs associated with maintenance interventions can be more reliably estimated by integrating the probabilistic nature of the failures associated to these interventions in the LCC models. Reliability, Maintainability, Availability and Safety (RAMS) parameters describe the maintenance needs of an asset in a quantitative way by using probabilistic information extracted from registered maintenance activities. Therefore, the integration of RAMS in the LCC analysis allows obtaining reliable predictions of system maintenance costs and the dependencies of these costs with specific cost drivers through sensitivity analyses. This paper presents an innovative approach for a combined RAMS & LCC methodology for railway and road transport infrastructures being developed under the on-going H2020 project INFRALERT. Such RAMS & LCC analysis provides relevant probabilistic information to be used for condition and risk-based planning of maintenance activities as well as for decision support in long term strategic investment planning.