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Journal articles on the topic 'Investment decision support'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Gulati, Anil, and Mahmoud M. Yasin. "Decision Support in Commodities Investment." Industrial Management & Data Systems 94, no. 1 (February 1994): 23–27. http://dx.doi.org/10.1108/02635579410053370.

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2

Kekytė, Ieva, and Viktorija Stasytytė. "Comparative Analysis of Investment Decision Models." Mokslas - Lietuvos ateitis 9, no. 2 (June 2, 2017): 197–208. http://dx.doi.org/10.3846/mla.2017.1023.

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Rapid development of financial markets resulted new challenges for both investors and investment issues. This increased demand for innovative, modern investment and portfolio management decisions adequate for market conditions. Financial market receives special attention, creating new models, includes financial risk management and investment decision support systems.Researchers recognize the need to deal with financial problems using models consistent with the reality and based on sophisticated quantitative analysis technique. Thus, role mathematical modeling in finance becomes important. This article deals with various investments decision-making models, which include forecasting, optimization, stochatic processes, artificial intelligence, etc., and become useful tools for investment decisions.

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Astafiev, S. A., and Guo Yib. "REAL ESTATE INVESTMENT DECISION SUPPORT SYSTEM." Journal «Izvestiya vuzov. Investitsiyi. Stroyitelstvo. Nedvizhimost» 8, no. 1 (2018): 23–27. http://dx.doi.org/10.21285/2227-2917-2018-1-23-27.

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4

Müller, Marcel Philipp, Sebastian Stöckl, Steffen Zimmermann, and Bernd Heinrich. "Decision Support for IT Investment Projects." Business & Information Systems Engineering 58, no. 6 (March 8, 2016): 381–96. http://dx.doi.org/10.1007/s12599-016-0423-7.

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Ahmad Zaidi, Atikah Zulaikha, and Nor Suziwana Hj Tahir. "Factors That Influence Investment Decision Making Among Potential Individual Investors in Malaysia." ADVANCES IN BUSINESS RESEARCH INTERNATIONAL JOURNAL 5, no. 1 (June 30, 2019): 9. http://dx.doi.org/10.24191/abrij.v5i1.9969.

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Individual investments behaviour is concerned with choices about purchases of small amounts of securities for his or her own account. Decision tools often support investment decisions. It is assumed that information structure and the factors in the market systematically influence individuals’ investment decisions as well as market outcomes. Decision tools often support investment decisions. It is assumed that information structure and the factors in the market systematically influence individuals’ investment decisions as well as market outcomes. Investor market behaviour derives from psychological principles of decision making to explain why people buy or sell stocks. These factors will focus upon how investors interpret and act on information to make investment decisions. The purpose of the study was to identify the factors that influence investment decision making among potential individual investors in Malaysia. Three behavioural factors might influence investment decision making which are accounting-information, firm-image coincidence and personal-financial-needs. A set of questionnaire was distributed to 384 potential investors in Malaysia specifically in housing area of Klang Valley as population of this study. Based on the findings, it showed that there is positive relationship between accounting-information, firm-image-coincidence and personal-financial-needs in investment decision making. Hence, between these three behavioural factors, accounting-information, firm-image coincidence and personal-financial-needs, the main influential factor is accounting-information. This study also proposed a future research for investment decision making and give implications to the potential investors, community, organization, policy makers and investment practitioners.

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Efendi, Tino Feri, and Andriani Putri Wihartati. "Decision Support System for Share Investment Using The Capital Assetpricing Method (CAPM)." International Journal of Computer and Information System (IJCIS) 2, no. 1 (February 28, 2021): 18–22. http://dx.doi.org/10.29040/ijcis.v2i1.25.

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Investment is the placement of a number of funds at this time with the hope of obtaining benefits in the future. Stocks are one of the most popular investments. The current millennial generation is interested in investing in stocks because the capital required is not too large. However, in choosing a good stock for investment, the ability to read financial ratios is required. Errors in reading financial ratios will cause stock investment not to go as expected. To help with this, a system capable of supporting decisions is needed. There are several methods that can be used to produce a decision support system. In this study, the authors use the Capital Asset Pricing Model (CAPM) method in designing a decision support system in stock investment selection.The method in this research is through observation, interview, and literature study. The system design is made using Contex Diagram, HIPO, DAD, and database design. The system is made in a program with the PHP programming language. The process of determining the selection of stock investments using the Capital Asset Pricing Model (CAPM) method can simplify the determination process. Then with the method. can make it easier to determine the selection of stock investment.The final result in the stock investment selection process is a report that states investment (Ri> E) or not investment (Ri <E).

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Del Giudice, Vincenzo, Pierfrancesco De Paola, Torrieri Francesca, Peter J. Nijkamp, and Aviad Shapira. "Real Estate Investment Choices and Decision Support Systems." Sustainability 11, no. 11 (June 2, 2019): 3110. http://dx.doi.org/10.3390/su11113110.

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The evaluation of real estate assets is currently one of the main focal points addressed by territorial marketing strategies, with the view of developing high-performing or competitive cities. Given the complexity of the driving forces that determine the behavior of actors in a real estate market, it is necessary to identify a priori the factors that determine the competitive capacity of a city, to attract investments. The decision support system allows taking into account the key factors that determine the “attractiveness” of real estate investments in competitive urban contexts. This study proposes an integrated complex evaluation model that is able to map out and encapsulate the multidimensional spectrum of factors that shape the attractiveness of alternative real estate options. The conceptual–methodological approach is illustrated by an application of the model to a real-world case study of investment choice in the residential sector of Naples.

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8

Heikkilä, Tuomo. "A decision support system to evaluate the business impacts of machine-to-machine system." Benchmarking: An International Journal 22, no. 2 (March 2, 2015): 201–21. http://dx.doi.org/10.1108/bij-11-2012-0080.

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Purpose – The tightening competition and performance pressure in companies often leave no time or space for the assessment of business impacts of different investments and projects. In addition, in many cases the assessment may be challenging and there is no experience available to undertake it. Despite that companies often commit to different projects and investments without careful planning and vision of the costs it may cause. The purpose of this paper is to create a decision support system in order to facilitate and increase the assessment of business impacts of different investments concerning to machine-to-machine (M2M) systems. Design/methodology/approach – The created decision support system is composed of cost-benefit analysis including several investment decision methods. In order to deepen the understanding on it, the system was applied to two cases from the M2M business. Findings – During the study it was found that different financial metrics might give contradictory results when deciding whether to undertake an investment. In addition, a significant finding was how much some variables may have significance to the eligibility of an investment than others. The study also gave understanding how long payback time can be and how risky the investments might be in different M2M applications. Originality/value – The study describes the created decision support system and it is applied to two different M2M applications. The system provides a comprehensive combination of different financial metrics, which will help any manager make decisions whether an investment is eligible or not.

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Arif, Farrukh, Mehmet Emre Bayraktar, and Arindam G. Chowdhury. "Decision Support Framework for Infrastructure Maintenance Investment Decision Making." Journal of Management in Engineering 32, no. 1 (January 2016): 04015030. http://dx.doi.org/10.1061/(asce)me.1943-5479.0000372.

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Matthews, Joe, Keith Newman, Amy Green, Lee Fawcett, Neil Thorpe, and Karsten Kremer. "A decision support toolkit to inform road safety investment decisions." Proceedings of the Institution of Civil Engineers - Municipal Engineer 172, no. 1 (March 2019): 53–67. http://dx.doi.org/10.1680/jmuen.16.00057.

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Jain, Bharat A., and Barin N. Nag. "A decision-support model for investment decisions in new ventures." European Journal of Operational Research 90, no. 3 (May 1996): 473–86. http://dx.doi.org/10.1016/0377-2217(95)00154-9.

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12

Kosenko, Viktor. "DECISION SUPPORT SYSTEM IN PLANNING INVESTMENT PROJECTS." Innovative Technologies and Scientific Solutions for Industries, no. 4 (6) (December 17, 2018): 113–19. http://dx.doi.org/10.30837/2522-9818.2018.6.113.

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13

Glavić, Draženko, Miloš N. Mladenović, and Marina Milenković. "Decision Support Framework for Cycling Investment Prioritization." Journal of Advanced Transportation 2019 (April 11, 2019): 1–15. http://dx.doi.org/10.1155/2019/7871426.

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Considering the significant potential for environmental, economic, social and health benefits from cycling, transport planners around the world are considering a wide variety of strategies for its promotion. However, cycling investments still have to find their place in a coherent package among other policies. Different constraints often imply a need for prioritization in cycling project implementation. The need for prioritization list of proposed investments can be caused by different factors such as available budget, available time, and regulatory constraints. Evaluation of investments in cycling infrastructure is a field of study that still requires further development, as previous research has mostly focused on questions of what to build and where. Previously used cost-benefit methods have substantive and procedural limitations in dealing with non-commensurable effects, and dealing with multiple conflicting objectives stemming from different stakeholders. On the contrary, development of prioritization list is formulated here as a semi-structured decision problem, thus belonging to the group of multi-criteria analysis (MCA) methods. The MCA methodology implemented in this decision-support framework is based on Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). The expert-based decision-support framework includes procedures for defining list of evaluation criteria and their weights, scoring of alternatives, and sensitivity analysis. Presented decision-support framework is applied on six bicycle sections of the EuroVelo route 8 through Montenegro. Results provide a list of prioritized infrastructural investments, as well as list of criteria with weights, and sensitivity analysis. Decision-support framework is discussed in the context of further professionalizing of cycling planning, as well as short-term and long-term structuration of organization learning in the transition country context. Finally, this development opens up directions for further contextualization of decision criteria, and greater consideration of user attitudes in cycling promotion.

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Fielder, Andrew, Emmanouil Panaousis, Pasquale Malacaria, Chris Hankin, and Fabrizio Smeraldi. "Decision support approaches for cyber security investment." Decision Support Systems 86 (June 2016): 13–23. http://dx.doi.org/10.1016/j.dss.2016.02.012.

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15

Gligoric, Zoran, Cedomir Beljic, Vojin Cokorilo, and Zlatko Dragosavljevic. "Simulation model: Support to investment decision-making in the coal industry." Thermal Science 14, no. 3 (2010): 835–44. http://dx.doi.org/10.2298/tsci091030008g.

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The paper deals with the problem of using coal as an energy resource bearing in mind the requirements of the modern society. Necessary changes in the coal industry demand a certain level of investments. Making relevant investment decisions is of crucial importance for the future efficiency of the system mine-power plant. The proposed model incorporates the procedure of decision-making simulation under the conditions of uncertain parameters. Net Present Value is used as the main criterion in decision-making. Different functions of probability distribution (normal, uniform and triangular distribution) are applied for the estimation of uncertainties related to certain parameters. For others, the estimation of future conditions is based on the Monte Carlo method and the simulation of geometric Brownian motion.

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16

Falconer, Lawrence L., Charles R. Long, and James M. McGrann. "A Decision Support Aid for Beef Cattle Investment Using Expert Systems." Journal of Agricultural and Applied Economics 28, no. 1 (July 1996): 180–92. http://dx.doi.org/10.1017/s1074070800009603.

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AbstractThe beef cattle investment decision provides an excellent opportunity to increase the economic efficiency of beef cattle production. The investment questions that face beef cattle producers are of interest to beef cattle producers, educators, and financial institutions involved in lending to beef cattle producing firms. This study develops a decision support aid utilizing expert system technology to assist beef cattle producers in making well-founded investment decisions with respect to the firm's beef cattle herd.

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Ustinovichius, Leonas. "DETERMINATION OF EFFICIENCY OF INVESTMENTS IN CONSTRUCTION." International Journal of Strategic Property Management 8, no. 1 (March 31, 2004): 25–43. http://dx.doi.org/10.3846/1648715x.2004.9637505.

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Decision making is associated with ranking problems aimed to obtain a set of preference order of solutions. People can make mistakes choosing the best object for investments. Due to high cost of such mistakes, such a choice should be well founded. A major goal of paper is to develop a theoretical basis for creating a decision support system aimed to increase building construction and reconstruction investment efficiency by applying multiattribute decision making approaches and mathematical modelling. To achieve the goal, the following problems have to be solved: to analyse new models currently used in developing investment strategies in building construction and reconstruction, to make a classification of construction investment projects and to describe the stages of determining the efficiency of construction investments, to create a family of multiattribute decision methods to be used in the analysis of investment projects in building construction and reconstruction, to create multiple attribute decision support system based on the multiattribute methods developed for determining the efficiency of construction and reconstruction investment projects.

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18

Chuah, Lay Lian, Wai Ching Poon, and Balachandher Krishnan Guru. "Uncertainty and Private Investment Decision in Malaysia." Modern Applied Science 12, no. 9 (August 14, 2018): 71. http://dx.doi.org/10.5539/mas.v12n9p71.

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This study examines the effects of uncertainty on irreversible aggregate investment using data from 1971-2010. Results provide evidence to support the argument that macroeconomic uncertainties are important in the forward-looking investment decision-making process. This study concludes that the demand and lagged demand uncertainties have a relatively stronger effect on investments compared with other macro uncertainties. The structure of the economy, the depth of the financial system, and the promotion of trade openness reduce the negative impact of uncertainties on investment. The study also finds the elasticity of the user cost of capital to be less than unity, indicating limited scope for governments to influence investment through tax incentives.

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19

Poh, K. L. "An Intelligent Decision Support System for Investment Analysis." Knowledge and Information Systems 2, no. 3 (August 2000): 340–58. http://dx.doi.org/10.1007/pl00011646.

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20

Liu, Yan, Ting-Hua Yi, and Cui-Qin Wang. "Investment Decision Support for Engineering Projects Based on Risk Correlation Analysis." Mathematical Problems in Engineering 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/242187.

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Investment decisions are usually made on the basis of the subjective judgments of experts subjected to the information gap during the preliminary stages of a project. As a consequence, a series of errors in risk prediction and/or decision-making will be generated leading to out of control investment and project failure. In this paper, the variable fuzzy set theory and intelligent algorithms integrated with case-based reasoning are presented. The proposed algorithm manages the numerous fuzzy concepts and variable factors of a project and also sets up the decision-making process in accordance with past cases and experiences. Furthermore, it decreases the calculation difficulty and reduces the decision-making reaction time. Three types of risk correlations combined with different characteristics of engineering projects are summarized, and each of these correlations is expounded at the project investment decision-making stage. Quantitative and qualitative change theories of variable fuzzy sets are also addressed for investment risk warning. The approach presented in this paper enables the risk analysis in a simple and intuitive manner and realizes the integration of objective and subjective risk assessments within the decision-makers' risk expectation.

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21

Trippi, Robert R. "Decision Support and Expert Systems for Real Estate Investment Decisions: A Review." Interfaces 20, no. 5 (October 1990): 50–60. http://dx.doi.org/10.1287/inte.20.5.50.

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22

Bai, Sarah, and Yijun Zhao. "Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches." Systems 9, no. 3 (July 22, 2021): 55. http://dx.doi.org/10.3390/systems9030055.

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This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard based on quantitative and qualitative characteristics of the companies. Collaborating with the management team of Rose Street Capital (RSC), we explore the most influential factors of their balanced scorecard using their retrospective investment decisions of successful and failed startup companies. Our study employs six standard machine learning models and their counterparts with an additional feature selection technique. Our findings suggest that “planning strategy” and “team management” are the two most determinant factors in the firm’s investment decisions, implying that qualitative factors could be more important to startup evaluation. Furthermore, we analyzed which machine learning models were most accurate in predicting the firm’s investment decisions. Our experimental results demonstrate that the best machine learning models achieve an overall accuracy of 78% in making the correct investment decisions, with an average of 87% and 69% in predicting the decision of companies the firm would and would not have invested in, respectively. Our study provides convincing evidence that qualitative criteria could be more influential in investment decisions and machine learning models can be adapted to help provide which values may be more important to consider for a venture capital firm.

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Potryvaieva, Natalia, Inha Pelypkanych, and Oleksandra Potryvaieva. "Effective Investment Decision for Fixed Assets Management." Modern Economics 23, no. 1 (October 27, 2020): 174–79. http://dx.doi.org/10.31521/modecon.v23(2020)-28.

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Introduction. Constant changes in economic and political situation create more stringent conditions for the survival of modern businesses while requiring effective management of the enterprise’s life cycle and also investing in a simple and expanded reproduction of fixed assets. An enterprise like any socio-economic organism needs constant development, growth and renewal, only these conditions ensure its competitiveness. Theoretical and methodological aspects of selection and evaluation of the investment decisions’ efficiency are constantly the focus of scientific world attention. However, it should be noted, the issues of choosing the most rational ways and stages of making an effective investment decision to manage the reproduction of fixed assets of the enterprise are not thoroughly studied, thus, they need further efforts to develop new proposals to improve their quality and prevent possible risks. Purpose. The purpose of the article is to study the factors and stages of making the appropriate investment decision to reproduce fixed assets with efficient use of resources. Results. The aspects of effective investment decision making for reproduction of fixed assets management are researched. It is proved that the attraction of sources of financing investment decisions is influenced by objective and subjective factors. The stages of making an investment decision in order to select the optimal sources of financing of the investment project are substantiated. It is found that the decision on the reproduction of the enterprise’s fixed assets should be comprehensive and aimed at both tangible and intangible components. Conclusions. Use of managerial approach in the process of reproduction of fixed assets investment lets making effective investment decision, which is implemented through some stages with appropriate methodological support. Taking into account the objective and subjective factors influencing the efficiency of the investment decision will help to avoid difficulties in managing the reproduction of fixed assets in the long run. The choice of financing source for an investment project is the most important management decision. The decision to reproduce the fixed assets of the enterprise should be comprehensive and aimed at both tangible and intangible components. The managerial decision may be considered as effective if it is directed to achieve the resource efficiency, obtained as a result of development and implementation of the investment decisions, where financial and material investments, qualified personnel, etc. can act as resources. This requires making and implementation of such decisions by qualified managers provided that the sequence of the stages would be followed, including collection of information, decision-making and ensuring effective implementation of the investment decision. Keywords: fixed assets; investment; reproduction; efficiency; management.

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24

Serrano-Cinca, Carlos, and Begoña Gutiérrez-Nieto. "A decision support system for financial and social investment." Applied Economics 45, no. 28 (October 2013): 4060–70. http://dx.doi.org/10.1080/00036846.2012.748180.

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25

Peterson, Kim. "Spatial decision support systems for real estate investment analysis." International journal of geographical information systems 7, no. 4 (July 1993): 379–92. http://dx.doi.org/10.1080/02693799308901968.

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26

Oliveira, Carlos, and Nicolas Perkowski. "Optimal investment decision under switching regimes of subsidy support." European Journal of Operational Research 285, no. 1 (August 2020): 120–32. http://dx.doi.org/10.1016/j.ejor.2019.02.019.

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27

Chen, Po-yuan, and Horng-jinh Chang. "Decision support for foreign investment strategy under hybrid uncertainty." Expert Systems with Applications 39, no. 4 (March 2012): 4582–89. http://dx.doi.org/10.1016/j.eswa.2011.10.007.

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Mukherjee, Sulagna, M. Durga Prasad, and Sudeep S. Kumar. "An investment decision dilemma." Emerald Emerging Markets Case Studies 6, no. 1 (May 2, 2016): 1–25. http://dx.doi.org/10.1108/eemcs-05-2014-0131.

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Subject area Financial Accounting and Corporate Finance. Study level/applicability Undergraduate, Post Graduate and Executive Education. Case overview T.A. Pai Management Institute (TAPMI), a leading B School in South India had established its new campus in Badagabettu village, about 5 km away from Manipal, Udupi District, Karnataka. Though the campus housed about a thousand inmates, comprising students, staff and faculty members, a proper public transport system did not develop commensurate with other facilities. The TAPMI administration was flooded with requests from various stakeholders to find a solution to this vexed problem. The Dean Administration had three options before him namely convincing the existing private bus operator to run a new bus en route TAPMI, TAPMI purchases the bus by either paying cash or availing loan from a bank or TAPMI can take a bus on lease. The predicament before Dean was to find out the most economically viable solution. Expected learning outcomes At the end of this case discussion, the participants will be able to: understand the application of breakeven analysis; prepare income statement, balance sheet, cash flow statement and forecast of cash flows; evaluate financing and investing decisions by using various techniques; discuss and debate the different alternatives available to the organization. Supplementary materials Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 1: Accounting and Finance.

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Song, Jian-tao. "Construction of Corporate Investment Decision Support Model Based on Deep Learning." Scientific Programming 2021 (September 4, 2021): 1–7. http://dx.doi.org/10.1155/2021/2665041.

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Aiming at the problem of corporate investment decision support, this paper proposes and constructs a stock quality evaluation model based on deep learning and applies it to the stock quality evaluation of e-commerce enterprises. Firstly, LSTM neural network is used to construct the evaluation and prediction model. Secondly, the evaluation index system is constructed. Finally, the structure and parameters of the model are designed, and the prediction model is tested and evaluated through simulation experiments. The experiments prove that the model is reasonable and feasible, which can provide a reference for investors to make decisions.

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Chyan, Phie. "Decision Support System for Property Investment Selection in Makassar City." Journal of Engineering and Applied Sciences 14, no. 23 (September 25, 2019): 8705–11. http://dx.doi.org/10.36478/jeasci.2019.8705.8711.

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Zeng, Xiangxiang, Li Liu, Stephen Leung, Jiangze Du, Xun Wang, and Tao Li. "A decision support model for investment on P2P lending platform." PLOS ONE 12, no. 9 (September 6, 2017): e0184242. http://dx.doi.org/10.1371/journal.pone.0184242.

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Bhandari, Gokul, and Khaled Hassanein. "An agent-based debiasing framework for investment decision-support systems." Behaviour & Information Technology 31, no. 5 (May 2012): 495–507. http://dx.doi.org/10.1080/0144929x.2010.499477.

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Cho, Vincent. "MISMIS – A comprehensive decision support system for stock market investment." Knowledge-Based Systems 23, no. 6 (August 2010): 626–33. http://dx.doi.org/10.1016/j.knosys.2010.04.009.

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Rosenstein, Alan H. "Measuring the Benefits of Clinical Decision Support: Return on Investment." Health Care Management Review 24, no. 2 (1999): 32–43. http://dx.doi.org/10.1097/00004010-199902420-00004.

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Rosenstein, Alan H. "Measuring the Benefits of Clinical Decision Support: Return on Investment." Health Care Management Review 24, no. 2 (1999): 32–43. http://dx.doi.org/10.1097/00004010-199904000-00004.

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Metawa, Saad A. "The evaluation of lease investment opportunities: a decision‐support methodology." Facilities 13, no. 8 (August 1995): 1–7. http://dx.doi.org/10.1108/02632779510795412.

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Liao, Ziqi, and Michael Tow Cheung. "A multi-dimensional decision framework to support corporate bond investment." Journal of Decision Systems 21, no. 2 (April 2012): 161–70. http://dx.doi.org/10.1080/12460125.2012.680355.

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38

Huynh, T., and C. Lassez. "An expert decision-support system for option-based investment strategies." Computers & Mathematics with Applications 20, no. 9-10 (1990): 1–14. http://dx.doi.org/10.1016/0898-1221(90)90108-v.

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Chen, Patrick Shicheng, David C. Yen, Shu-Chiung Lin, and Chien Shan Chou. "Toward an IT investment decision support model for global enterprises." Computer Standards & Interfaces 59 (August 2018): 130–40. http://dx.doi.org/10.1016/j.csi.2018.04.001.

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40

Suh, Eui-Ho, Chang-Kyo Suh, and Nam-Chul Do. "A decision support system for investment planning on a microcomputer." Journal of Microcomputer Applications 15, no. 4 (October 1992): 297–311. http://dx.doi.org/10.1016/0745-7138(92)90011-s.

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Trippi, Robert R. "A decision support system for real estate investment portfolio management." Information & Management 16, no. 1 (January 1989): 47–54. http://dx.doi.org/10.1016/0378-7206(89)90026-8.

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42

Palkina, Elena S. "Model for making decisions on renewal the railway rolling stock of a transport organization." Transportation Systems and Technology 6, no. 3 (September 30, 2020): 76–87. http://dx.doi.org/10.17816/transsyst20206376-87.

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Background: At present, there is an urgent problem of renovation of rolling stock characterized by a high degree in Russia. The leading position in the country's transport system belongs to railway transport. In the context of declining demand for transportation investments in railcars, which represent a significant amount of capital investment, require reasonable management decisions. Aim: is to work out a decision-making model for renewal the transport organization's railway rolling stock. Methods: of technical, economic, investment and financial analysis, decision tree, graphical modeling, system approach. Results: The basic components of the decision-making model are determined. The key indicators of railway rolling stock renewal are defined, reflecting the criteria for making managerial decisions in the field of operational, investment and financial activities. A graphical model is proposed that interprets the decision support system for purchasing new railcars. Conclusion: Using the proposed model of decision-making in the field of renovation of rolling stock allows to transfer this process to a qualitatively new level, based on the results of an objective assessment of the current and forecast state of the management object according to various alternative scenarios and based on the selection of the rational decision by comparing the expected results for each of the considered alternatives and analysis degree of their compliance with the determined goals due to its versatility and complexity.

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van Haaster, F., and T. E. Kleynhans. "DECISION TREE MODELLING TO SUPPORT INVESTMENT DECISIONS ON FLOWER PRODUCTION FOR THE NORTHERN HEMISPHERE." Agrekon 41, no. 1 (March 2002): 75–96. http://dx.doi.org/10.1080/03031853.2002.9523587.

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Chan, F. T. S., H. K. Chan, and M. H. Chan. "An integrated fuzzy decision support system for multicriterion decision-making problems." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 217, no. 1 (January 1, 2003): 11–27. http://dx.doi.org/10.1243/095440503762502251.

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In order to compete effectively in today's rapidly changing world economy, decision making should be made fast and accurate, especially when many tangible and intangible factors are considered together. Making decisions to implement advanced manufacturing technologies (AMTs) is a good example. However, not all AMT implementations are successful because most of the conventional investment justification criteria are built on tangible factors, such as cost reduction, only. Intangiabl long-term benefits such as improvement in quality, increase in flexibility, and fast delivery are always disregarded. The objective of this paper is to present an integrated approach, which incorporates different approaches (e.g. strategic, economic and analytic evaluations) for assessing the benefits among several alternative proposals, such as implementing AMT, by a fuzzy multicriterion decision-making method.

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Andrew, Don. "An Index to Measure the Integrity of Investment Companies Investing Responsibility." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 5, no. 5 (2020): 36–51. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.55.3004.

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Stakeholders realise the value and impact of Responsible Investment (RI) upon making informed decisions about investments. Due to this, more organisations are pressured to report on RI performances and put positive and/or negative strategies in place to address ESG issues and to implement ESG policies into the primary strategy of their operations. There are many governments and organisations globally which support sustainable investment and as one such administration, South Africa has legislated to manage RI issues (www.gov.za). Recognition is given to the both CRISA and PRI as well as taking the integrated environmental, social and governance (ESG) considerations into the investment decision making process into consideration when assisting in identifying, managing and mitigating potential ESG risks to achieve sustainable long-term investment outcomes.

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Gleißner, Werner, and Cay Oertel. "Conceptual framework for real estate transactions." Journal of Property Investment & Finance 38, no. 3 (March 21, 2020): 245–62. http://dx.doi.org/10.1108/jpif-01-2020-0014.

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PurposeThe purpose of this paper is the development for a conceptual framework with regard to the risk management of real estate positions as foundation for transaction decisions. In this context, the current market environment and legal obligations are the main drivers for market participants to improve their risk management practices. Based on this environment, a practical but science backed model is outlined.Design/methodology/approachThe paper uses a conceptual approach based on the existing literature to develop a practical decision support system. In addition, the current risk management best practices are outlined to illustrate the corporate and methodological foundation for the decision support system.FindingsThe conceptual model development reveals a clear necessity for the supplementation of price to value measures. Additional measures are derived from theoretic considerations based on Monte Carlo Simulation approaches to the risk management of property investments. These additional risk metrics support investors in order make risk-appropriate decisions.Practical implicationsThe resulting decision support system can be applied to the risk management of transaction decisions. Here, the model can be applied in any investment decision to support portfolio management considerations from a comprehensive risk management perspective. Investors can implement the system as part of their transaction procedure.Originality/valueThe existing body of literature mainly focuses on macroeconomic ratios in the context of decision support. In contrast, the present paper reveals a corporate decision support system, which is supposed to foster decisions of market agents especially with regard to potential price and value divergences and tightening legal obligations.

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D’Angelo, Letizia, Noel Finnerty, Federico Seri, Alessandro Piccinini, Ronan Coffey, Carlos Tighe, PJ Mealy, Marc Mellotte, and Marcus Keane. "A Global Energy Management System to Support Sustainable Decision Making." Proceedings 20, no. 1 (August 6, 2019): 22. http://dx.doi.org/10.3390/proceedings2019020022.

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Global energy consumption has risen enormously over the past century due to population growth and increasing energy use per person. Industrial production consumes a significant portion of global energy resources. Thus, industrial sector’s investment in energy efficiency is critical to a sustainable future. For most global enterprises the consumption of energy and natural resources represents a major overhead and developing sustainable energy policies can represent a significant competitive advantage due to the growing price of energy and volatility of supply. This symbiotic relationship can lead to the mutual benefits of increasing industrial efficiency whilst allowing the transition to a sustainable renewables-based energy future and needs to be significantly harnessed. This paper describes a decision support framework to help industrial organisations make positive investment decisions on energy performance improvement projects.

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Koyama, Hiroki, Narihiko Yoshida, and Kakuro Amasaka. "The A-MPM Decision-Making Model For Film Project Investment." International Business & Economics Research Journal (IBER) 11, no. 3 (February 15, 2012): 323. http://dx.doi.org/10.19030/iber.v11i3.6865.

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This paper looks at the financing of commercial film projects in Japan. A scientific approach is used to quantify the factors that film producers and investors use to make investment decisions regarding film projectsa process that was previously unarticulated. The result of this research is the creation of the A-MPM (Amasakalabs Movie Projects Performance Model), a shared decision-making model for film project investment that aims to promote quality investment decisions and support partnerships between film producers and investors during the subsequent process of filmmaking.

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Ortiz-Teran, Elena, Ibai Diez, and Joaquin Lopez-Pascual. "An ALE Meta-Analysis on Investment Decision-Making." Brain Sciences 11, no. 3 (March 21, 2021): 399. http://dx.doi.org/10.3390/brainsci11030399.

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It is claimed that investment decision-making should rely on rational analyses based on facts and not emotions. However, trying to make money out of market forecasts can trigger all types of emotional responses. As the question on how investors decide remains controversial, we carried out an activation likelihood estimation (ALE) meta-analysis using functional magnetic resonance imaging (fMRI) studies that have reported whole-brain analyses on subjects performing an investment task. We identified the ventral striatum, anterior insula, amygdala and anterior cingulate cortex as being involved in this decision-making process. These regions are limbic-related structures which respond to reward, risk and emotional conflict. Our findings support the notion that investment choices are emotional decisions that take into account market information, individual preferences and beliefs.

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Nikkels, Melle J., Joseph H. A. Guillaume, Peat Leith, Neville J. Mendham, Pieter R. van Oel, Petra J. G. J. Hellegers, and Holger Meinke. "Participatory Crossover Analysis to Support Discussions about Investments in Irrigation Water Sources." Water 11, no. 7 (June 26, 2019): 1318. http://dx.doi.org/10.3390/w11071318.

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Regional long-term water management plans depend increasingly on investments by local water users such as farmers. However, local circumstances and individual situations vary and investment decisions are made under uncertainty. Water users may therefore perceive the costs and benefits very differently, leading to non-uniform investment decisions. This variation can be explored using crossover points. A crossover point represents conditions in which a decision maker assigns equal preference to competing alternatives. This paper presents, applies, and evaluates a framework extending the use of the concept of crossover points to a participatory process in a group setting. We applied the framework in a case study in the Coal River Valley of Tasmania, Australia. Here, farmers can choose from multiple water sources. In this case, the focus on crossover points encouraged participants to engage in candid discussions exploring the personal lines of reasoning underlying their preferences. Participants learned from others’ inputs, and group discussions elicited information and insights considered valuable for both the participants and for outsiders on the factors that influence preferences. We conclude that the approach has a high potential to facilitate learning in groups and to support planning.

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