Journal articles: 'Financial Decision Support'

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Kundisch, Dennis, and Jochen Dzoienziol. "Decision Support for Financial Planning." Journal of Decision Systems 17, no. 2 (January 2008): 175–209. http://dx.doi.org/10.3166/jds.17.175-209.

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Zopounidis, Constantin, Dimitrios Niklis, and Michalis Doumpos. "“Financial decision support”: feature issue editorial." EURO Journal on Decision Processes 6, no. 1-2 (March 9, 2018): 61–62. http://dx.doi.org/10.1007/s40070-018-0080-9.

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McDonald, H. Stephen, Chris Stori, and Greg Dennis. "FINANCIAL DECISION SUPPORT FOR PROJECT FINANCING AND REVENUE PROGRAM DEVELOPMENT." Proceedings of the Water Environment Federation 2003, no. 11 (January 1, 2003): 28–29. http://dx.doi.org/10.2175/193864703784756075.

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Palma-dos-Reis, António, and Fatemeh “Mariam” Zahedi. "Designing personalized intelligent financial decision support systems." Decision Support Systems 26, no. 1 (July 1999): 31–47. http://dx.doi.org/10.1016/s0167-9236(99)00027-5.

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Holland, Michael M., and J. Louis Heck. "PROPHET: A FINANCIAL MANAGEMENT DECISION SUPPORT SYSTEM." Financial Review 20, no. 3 (August 1985): 60. http://dx.doi.org/10.1111/j.1540-6288.1985.tb00238.x.

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Dimitras, A., and C. Siriopoulos. "Modelling and decision support in financial markets." Operational Research 6, no. 2 (May 2006): 83–84. http://dx.doi.org/10.1007/bf02941225.

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Shumilin, P. E., and V. A. Eremenko. "ACCOUNTING MODEL OF DECISION SUPPORT SYSTEM." Scientific Review: Theory and Practice 10, no. 4 (May 4, 2020): 612–19. http://dx.doi.org/10.35679/2226-0226-2020-10-4-612-619.

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The digital development of the economy opens up new horizons for accounting. On the one hand, dissolution of accounting in corporate management systems takes place, on the other hand, the accounting functions for managing economic information remain relevant. This article uses the accounting modeling method. We offer a five-blocks accounting model of the decision support system. The model is formed by such blocks as the interface for collecting primary data on company transactions in the context of the formation of financial, managerial, strategic accounting accounts, ETL (extract, transform, loading) of processes for combining credentials from various sources within the framework of a structured work plan of accounts; predicted accounting iterations, having a synergistic, reorganization, reorganization, immunization, hedging and other areas; express audit of the management decision, which consists in assessing the impact of the management decision on the effectiveness of the company, which includes such elements as tax and legal expertise; SWOT analysis; reporting visualization tools that allow you to generate different types of reporting: financial, managerial, statistical, not just in tabular form, but using digital visualization methods; accounting and analytical indicators of managerial decisions, which can be described as a system of indicators reflecting the financial and economic situation of the enterprise under the influence of managerial decisions; the state of its financial stability, profitability, solvency, liquidity; the size of the property of the founders. The introduction and use of this model will allow generating relevant accounting information based on the needs of management, supporting the adoption of management decisions at a scientifically sound level that meets the criteria of business efficiency and protect the interests of owners.

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Tsai, Chih-Fong. "Financial decision support using neural networks and support vector machines." Expert Systems 25, no. 4 (September 2008): 380–93. http://dx.doi.org/10.1111/j.1468-0394.2008.00449.x.

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Tynjala, Teemu. "Validation in Supply Chain Decision Support Systems." International Journal of Information Systems and Supply Chain Management 5, no. 2 (April 2012): 39–58. http://dx.doi.org/10.4018/jisscm.2012040103.

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Decision Support Systems are used currently to support various high impact decisions in industry. This study explores the subcategory of DSSs related to Supply Chain Management. More precisely we explore the problem of integrating companies’ strategic supply chain decision support systems to financial reporting systems for the purpose of validation. The study is done through literature survey, and inductive case studies. The author finds that integration is challenging because systems are typically disjoint and capture data at different levels of granularity. The author conceptualizes the present problem and proposes a generic solution framework.

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Ruzakova, O. "DECISION SUPPORT SYSTEM IN THE TASKS OF FINANCIAL ANALYSIS." Agrosvit, no. 5 (March 19, 2019): 67. http://dx.doi.org/10.32702/2306-6792.2019.5.67.

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Tsang, Edward, Paul Yung, and Jin Li. "EDDIE-Automation, a decision support tool for financial forecasting." Decision Support Systems 37, no. 4 (September 2004): 559–65. http://dx.doi.org/10.1016/s0167-9236(03)00087-3.

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Klose, Steven L., and Joe L. Outlaw. "Financial and Risk Management Assistance: Decision Support for Agriculture." Journal of Agricultural and Applied Economics 37, no. 2 (August 2005): 415–23. http://dx.doi.org/10.1017/s107407080000688x.

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The Financial and Risk Management (FARM) Assistance program created by Texas Cooperative Extension is a strategic analysis service offered to farmers and ranchers in Texas. The program serves as an example of large-scale, focused programming by extension agencies, as well as the implementation of technical stochastic simulation methods for use on the farm.

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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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Mcmahon, Richard G. P. "Expert Systems and Financial Decision Support in Small Businesses." International Small Business Journal: Researching Entrepreneurship 8, no. 2 (January 1990): 23–33. http://dx.doi.org/10.1177/026624269000800202.

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Kozlov, V. V., T. V. Tomashevska, and N. I. Kuznetsov. "Using of Optimization Models in Financial Decision Support Systems." Statistics of Ukraine 88, no. 1 (May 1, 2020): 75–83. http://dx.doi.org/10.31767/su.1(88)2020.01.09.

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The article discusses the use of optimization models in financial decision support systems (DSS). The architecture of the DSS is considered. It is determined by the nature of the interaction of its main components: the user interface, database and data warehouse, documents and rules, models and analytical tools, communications infrastructure and networks, as well as elements of these parts. The architecture of the DSS for solving problems of financial management is determined by the nature of interaction of its main components. Conceptual and functional models are presented. The functional model of the DSS reflects the structure of control actions on decision-makers, persons involved in the implementation of corrective actions necessary for effective financial performance. According to the functional model, a block diagram of the DSS is proposed. The block diagram of decision support consists of three main subsystems and provides modular-block construction. The proposed system is characterized by an open architecture and can be easily modified for functional expansion or for connecting and using external databases. The DSS should provide a common operating environment for modeling objects in a dynamically changing economic situation. The operating environment contains basic calculation algorithms, and allows the user to create own algorithms for calculating cash flows and used indicators. Thus, the key element of the integrated decision support system for managing the financial condition of the enterprise is the models laid down in the basis of the system. The architecture of the DSS based on algorithms is considered. A DSS of this type contains a set of algorithms for solving a selected class of problems. Of the factors that influence the choice of a specific architecture of the DSS in financial management, we can highlight (i) the need for further development of the system, (ii) its adaptation, and (iii) the application of the evolutionary approach to the development of the DSS. The components of the system are: data input component, resource allocation component, strategy selection component, output component. The resource allocation component contains the following sub-components: determination of dependence coefficients, solution of optimization problem, solution of equation. The strategy selection component includes the sub-components for calculating the values of integral design characteristics, for calculating the values of groups of design characteristics, and for the pairwise comparison of strategies. The information model of the component “The distribution of financial resources of the enterprise” is presented. The DSS performs data processing and checks for critical decision characteristics. The formalized structure of the algorithm for using the proposed models of distribution of financial resources, types of model integration are considered.

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Serrano-Silva, Yosimar Oswaldo, Yenny Villuendas-Rey, and Cornelio Yáñez-Márquez. "Automatic feature weighting for improving financial Decision Support Systems." Decision Support Systems 107 (March 2018): 78–87. http://dx.doi.org/10.1016/j.dss.2018.01.005.

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Musinszki, Zoltán, and Gábor Béla Süveges. "STRATEGIC DECISION-MAKING SUPPORTED BY TRADITIONAL FINANCIAL INDICATORS." Oradea Journal of Business and Economics 4, no. 1 (March 2019): 29–37. http://dx.doi.org/10.47535/1991ojbe057.

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Traditional financial indicators calculated from financial statements cannot provide adequate information for the management, do not support strategic decisions. The reason for this is the methodology used in producing financial statements and the operational logic of financial accounting. Financial accounting thinks in terms of business year that is 12 months. It deals with events of the past, more accurately, with so called economic activities. Financial accounting evaluates such economic activities in terms of monetary processes, which means that the traditional financial indicators have their limits in application. Could it mean that traditional financial indicators cannot be used in the long-term strategic decision making? In our study we point out two connections between financial indicators and strategic decision making. Firstly, we focus on financial indicators used in supported strategic decision making, in strategic indicator systems. Secondly, we focus on cases in which financial indicators complement the methods of strategic decision making, add something to the tools available in strategic management.

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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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Bhayat, Imtiaz, Maurizio Manuguerra, and Clive Baldock. "A decision support model and tool to assist financial decision-making in universities." Journal of Higher Education Policy and Management 37, no. 1 (January 2, 2015): 69–82. http://dx.doi.org/10.1080/1360080x.2014.991529.

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Radojevic, Gordana, and Milija Suknovic. "Scoring models: Towards the more realistic approach." Computer Science and Information Systems 6, no. 1 (2009): 45–69. http://dx.doi.org/10.2298/csis0901045r.

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Financial decision-making is one of the most current issues of modern financial management. Financial decision-making is an area where decision support systems, knowledge-based decision support systems, and intelligent decision support systems are successfully applied. In consequence of the importance and complexity of this problem area a large number of methods of support to financial decisionmaking was developed. This paper presents the most important features of two decision support systems, a classical system and a system based on fuzzy logic. The performances of these two models are compared and the advantages achieved through the introduction of fuzzy concepts into the classical decision support systems are determined.

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Zhang, Zhe, Zhi Ye Koh, and Florence Ling. "Benchmarking contractors’ financial performance: case study of Singapore." Journal of Financial Management of Property and Construction 25, no. 2 (March 16, 2020): 183–99. http://dx.doi.org/10.1108/jfmpc-03-2019-0024.

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Purpose This study aims to develop benchmarks of the financial performance of contractors and a decision support tool for evaluation, selection and appointment of contractors. The financial benchmarks allow contractors to know where they are relative to the best-performing contractors, and they can then take steps to improve their own performance. The decision support tool helps clients to decide which contractor should be awarded the project. Design/methodology/approach Financial data between 2013 and 2015 of 44 Singapore-based contractors were acquired from a Singaporean public agency. Benchmarks for Z-score and financial ratios were developed. A decision tree for evaluating contractors was constructed. Findings This study found that between 57% and 64% of contractors stayed in the financially healthy zone from 2013 to 2015. Ratios related to financial liabilities are relatively bad compared with international standards. Research limitations/implications The limitation is that the data is obtained from a cross-sectional survey of contractors’ financial performance in Singapore over a three-year period. Regarding the finding that ratios relating to financial liabilities are weak, the implication is that contractors need to reduce their financial liabilities to achieve a good solvency profile. Contractors may use the benchmarks to check their financial performances relative to that of their competitors. To reduce financial risks, project clients may use these benchmarks to examine contractors’ financial performance. Originality/value This study provides benchmarks for contractors and clients to examine the financial performance of contractors in Singapore. A decision tree is provided to aid clients in making decisions on which contractors to appoint.

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García-Manotas, Ignacio, Eduardo Lupiani, Francisco García-Sánchez, and Rafael Valencia-García. "Populating Knowledge Based Decision Support Systems." International Journal of Decision Support System Technology 2, no. 1 (January 2010): 1–20. http://dx.doi.org/10.4018/jdsst.2010101601.

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Knowledge-based decision support systems (KBDSS) hold up business and organizational decision-making activities on the basis of the knowledge available concerning the domain under question. One of the main problems with knowledge bases is that their construction is a time-consuming task. A number of methodologies have been proposed in the context of the Semantic Web to assist in the development of ontology-based knowledge bases. In this paper, we present a technique for populating knowledge bases from semi-structured text which take advantage of the semantic underpinnings provided by ontologies. This technique has been tested and evaluated in the financial domain

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Patalay, Sandeep, and Madhusudhan Rao Bandlamudi. "Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning." Ingénierie des systèmes d information 26, no. 1 (February 28, 2021): 87–93. http://dx.doi.org/10.18280/isi.260109.

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Investing in stock market requires in-depth knowledge of finance and stock market dynamics. Stock Portfolio Selection and management involve complex financial analysis and decision making policies. An Individual investor seeking to invest in stock portfolio is need of a support system which can guide him to create a portfolio of stocks based on sound financial analysis. In this paper the authors designed a Financial Decision Support System (DSS) for creating and managing a portfolio of stock which is based on Artificial Intelligence (AI) and Machine learning (ML) and combining the traditional approach of mathematical models. We believe this a unique approach to perform stock portfolio, the results of this study are quite encouraging as the stock portfolios created by the DSS are based on strong financial health indices which in turn are giving Return on Investment (ROI) in the range of more than 11% in the short term and more than 61% in the long term, therefore beating the market index by a factor of 15%. This system has the potential to help millions of Individual Investors who can make their financial decisions on stocks and may eventually contribute to a more efficient financial system.

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Zopounidis, Constantin, and Michael Doumpos. "A preference disaggregation decision support system for financial classification problems." European Journal of Operational Research 130, no. 2 (April 2001): 402–13. http://dx.doi.org/10.1016/s0377-2217(00)00044-8.

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Eom, Hyun B., Sang M. Lee, Charles A. Snyder, and F. Nelson Ford. "A Multiple Criteria Decision Support System for Global Financial Planning." Journal of Management Information Systems 4, no. 3 (December 1987): 94–113. http://dx.doi.org/10.1080/07421222.1987.11517803.

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Carlin, Bruce Ian, and David T. Robinson. "Financial Education and Timely Decision Support: Lessons from Junior Achievement." American Economic Review 102, no. 3 (May 1, 2012): 305–8. http://dx.doi.org/10.1257/aer.102.3.305.

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Using data from a finance theme park at Junior Achievement of Southern California, we explore how timely decision support is impacted by previous exposure to financial education. Some students received a 19-hour curriculum before participating, and some did not. Trained students were more frugal, paid off debt faster, and relied less on credit financing. However, trained students purchased less comprehensive health insurance, exposing themselves to greater financial risk and wealth volatility. This disparity can be explained by differences in decision support within the park. As such, it appears that education and decision support should be considered complements, not substitutes.

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Chan, Samuel W. K., and James Franklin. "A text-based decision support system for financial sequence prediction." Decision Support Systems 52, no. 1 (December 2011): 189–98. http://dx.doi.org/10.1016/j.dss.2011.07.003.

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Burstein, Frada, Julie Cowie, Arkady Zaslavsky, and Jocelyn San Pedro. "Support for real-time decision making in mobile financial applications." Information Systems and e-Business Management 6, no. 3 (March 5, 2008): 257–78. http://dx.doi.org/10.1007/s10257-008-0090-4.

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Dempster, M. A. H., and A. M. Ireland. "Object-oriented model integration in a financial decision support system." Decision Support Systems 7, no. 4 (November 1991): 329–40. http://dx.doi.org/10.1016/0167-9236(91)90062-g.

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Zopounidis, Constantin, Michalis Doumpos, and Dimitrios Niklis. "Financial decision support: an overview of developments and recent trends." EURO Journal on Decision Processes 6, no. 1-2 (February 12, 2018): 63–76. http://dx.doi.org/10.1007/s40070-018-0078-3.

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Christias, Panagiotis, Ioannis N. Daliakopoulos, Thrassyvoulos Manios, and Mariana Mocanu. "Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support." Mathematics 8, no. 5 (May 3, 2020): 717. http://dx.doi.org/10.3390/math8050717.

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This paper explores methodologies for developing intelligent automated decision systems for complex processes that contain uncertainties, thus requiring computational intelligence. Irrigation decision support systems (IDSS) promise to increase water efficiency while sustaining crop yields. Here, we explored methodologies for developing intelligent IDSS that exploit statistical, measured, and simulated data. A simple and a fuzzy multicriteria approach as well as a Decision Tree based system were analyzed. The methodologies were applied in a sample of olive tree farms of Heraklion in the island of Crete, Greece, where water resources are scarce and crop management is generally empirical. The objective is to support decision for optimal financial profit through high yield while conserving water resources through optimal irrigation schemes under various (or uncertain) intrinsic and extrinsic conditions. Crop irrigation requirements are modelled using the FAO-56 equation. The results demonstrate that the decision support based on probabilistic and fuzzy approaches point to strategies with low amounts and careful distributed water irrigation strategies. The decision tree shows that decision can be optimized by examining coexisting factors. We conclude that irrigation-based decisions can be highly assisted by methods such as decision trees given the right choice of attributes while keeping focus on the financial balance between cost and revenue.

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Zavadskas, Edmundas Kazimieras, Artūras Kaklauskas, Audrius Banaitis, and Vaidotas Trinkūnas. "SYSTEM FOR REAL TIME SUPPORT IN CONSTRUCTION MATERIALS SELECTION." International Journal of Strategic Property Management 9, no. 2 (June 30, 2005): 99–109. http://dx.doi.org/10.3846/1648715x.2005.9637531.

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The significant part of the constructions’ value consists of construction materials price. With the aim of using financial resources for the construction effectively, it is very important that well considered and reasonable decisions should be made regarding the selection of construction materials. Most of all construction on‐line systems seek to find how to make the most economic construction decisions and essentially these decisions are intended only for economic objectives. Construction alternatives that are under evaluation have to be evaluated not only from the economic position, but also take into consideration qualitative, technical, technological and other characteristics. Additionally, in seeking to facilitate the work of decision‐makers, computer technologies are used that operates according to particular models. These models are based on special mathematical methods in order to facilitate decision‐making and apply to a certain decision area. In this article, the possibilities of applying methods for popular decision‐making are analyzed regarding the selection of construction materials.

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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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Abedin, Mohammad Zoynul, Chi Guotai, Fahmida-E. Moula, A. S. M. Sohel Azad, and Mohammed Shamim Uddin Khan. "Topological applications of multilayer perceptrons and support vector machines in financial decision support systems." International Journal of Finance & Economics 24, no. 1 (October 11, 2018): 474–507. http://dx.doi.org/10.1002/ijfe.1675.

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Helmina, Monica Rahardian Ary, Imam Ghozali, Jaka Isgiyarta, and Ibnu Sutomo. "Effect of Ordo in Assessment of Financial and Non-Financial Information." Jurnal Dinamika Manajemen 11, no. 1 (March 19, 2020): 78–83. http://dx.doi.org/10.15294/jdm.v11i1.22554.

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This research focuses on investor decision making on information provided by the company. Belief-adjustment models emphasize the order of presentation of information. Order effects occur when decisions made by individuals differ after receiving evidence in a different order. In order of evidence, the characteristics of the evidence are mixed between confirmative (positive) information and unconfirmed (negative) information.The participants of this study are investors who have investment accounts. The design of the 2x4 experimental method is divided into analysis of factor 2 (presentation pattern) x 4 (information value), which aims to test that the presentation of information in step by step (SBS) will have a better impact than end of sequence (EOS). There are 8 combinations of instruments contain patterns and information values that are used as a source of stock valuation. ANOVA analysis is used for this study. The results showed that there was an effect of the pattern of information delivery in investment decision making when the SBS and EOS disclosure patterns in hypothesis 1 and hypothesis 2. The results of hypothesis 3 did not support the belief adjustment model theory.

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Pedchenko, Nataliya, Victoria Strilec, Galina M. Kolisnyk, Mariia V. Dykha, and Serhiy Frolov. "Business angels as an alternative to financial support at the early stages of small businesses’ life cycle." Investment Management and Financial Innovations 15, no. 1 (February 22, 2018): 166–79. http://dx.doi.org/10.21511/imfi.15(1).2018.15.

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In the process of small business establishment and development, it is very important to understand both the financial needs of entrepreneurs and the main obstacles and difficulties arising in the way of financing. Alternative sources of financial support, along with traditional ones, create opportunities to increase funds, but the solution to the issue of their attraction should be based on modern effective methods and decision- making technologies. The article uses the decision tree method to determine the optimal alternative to financial support of small business at the early stages of the life cycle. The results highlight the importance of alternative source of resources for small business entities, namely business angels’ means. The empirical and statistical analysis confirms that access to alternative sources of financing for small businesses in EU countries is improving, while in Ukraine, informal financing is a rather new and underdeveloped area. Based on the analysis of the advantages of using the business angels’ funds, it was concluded that they need to implement their potential in small business of Ukraine. The results show that the decision tree method is an effective tool for deciding on the prioritization of a financial alternative to the small business, and is characterized by ease of use, forecast precision and problems solution novelty.

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Turner, Michael J., and Leonard V. Coote. "Incentives and monitoring: impact on the financial and non-financial orientation of capital budgeting." Meditari Accountancy Research 26, no. 1 (April 9, 2018): 122–44. http://dx.doi.org/10.1108/medar-02-2017-0117.

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Purpose While investment decisions may be financial decisions, there is a growing recognition that they are also often non-financially based decisions. The purpose of this study is to report findings focused on the project selection stage of capital budgeting, which has the objectives of exploring for: the relative degree of emphasis decision makers attach to a financial and non-financial orientation in capital budgeting; and the role, if any, that two agency theory variables have on the relative degree of emphasis: a personal incentive for project go-ahead and monitoring of project outcomes through a post-audit. Design/methodology/approach Discrete choice experiments (DCEs) are used and framed in a between-subjects 2 (personal incentive) × 2 (monitoring) design. DCEs are well-suited to research questions which examine some tension between competing alternatives. For example, trade-offs involving the relative degree of emphasis decision makers attach to a financial and non-financial orientation in capital budgeting. Findings In the absence of a personal incentive and monitoring, decision makers attach a significant degree of emphasis to cash inflows and cash outflows, both financial factors, and one strategic non-financial factor being improvement in the position of the firm vis-à-vis competitors in capital budgeting. However, when decision makers receive a personal incentive from project go-ahead, they attach a lower degree of emphasis to cash outflows. Alternatively, when there is monitoring through a post-audit and a personal incentive, decision makers attach a higher degree of emphasis to cash outflows. Practical implications Decision makers attach a significant degree of emphasis to only a relatively narrow band of attributes in making a capital budgeting decision, which is true in both the absence of and in the presence of the agency conditions. There is also little support for the view that there is any higher degree of emphasis attached to a financial orientation vis-à-vis a non-financial orientation. A particularly important finding relates to the overarching goal of monitoring through a post-audit. One view is that it should foster more accurate forecasting by making forecasters aware that their efforts will be reviewed. However, the findings of this study appear to be more supportive of a view that post-audits might lead agents to become more conservative or even shy away from projects. Originality/value The study makes contributions to the growing field of research which has the objective of exploring for the relative degree of emphasis decision makers attach to a financial and non-financial orientation in capital budgeting. In particular, it extends the prior research through its investigation of the role that two agency theory variables play in the relative degree of emphasis decision makers attach to a financial and non-financial orientation: a personal incentive for project go-ahead and monitoring of project outcomes through a post-audit.

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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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Kumar, Ned, Ravindra Krovi, and Balaji Rajagopalan. "Financial decision support with hybrid genetic and neural based modeling tools." European Journal of Operational Research 103, no. 2 (December 1997): 339–49. http://dx.doi.org/10.1016/s0377-2217(97)00124-0.

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Tseng, Chiu-Che, and Piotr J. Gmytrasiewicz. "Real-time decision support and information gathering system for financial domain." Physica A: Statistical Mechanics and its Applications 363, no. 2 (May 2006): 417–36. http://dx.doi.org/10.1016/j.physa.2005.08.028.

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Wu, Chien Hsing, Shu Chen Kao, and Chuan Chun Wu. "Data-based decision support technology used in the financial service industry." International Journal of Financial Services Management 1, no. 4 (2006): 422. http://dx.doi.org/10.1504/ijfsm.2006.010121.

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Bidyuk, Petro I., Lev O. Korshevnyuk, Aleksandr P. Gozhyi, Irina O. Kalinina, Tatyana I. Prosyankina-Zharova, and Oleksandr M. Terentiev. "MODELING AND FORECASTING FINANCIAL AND ECONOMIC PROCESSES WITH DECISION SUPPORT SYSTEM." KPI Science News, no. 5-6 (October 15, 2019): 7–17. http://dx.doi.org/10.20535/kpi-sn.2019.5-6.176835.

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Ben-Assuli, Ofir. "Assessing the perception of information components in financial decision support systems." Decision Support Systems 54, no. 1 (December 2012): 795–802. http://dx.doi.org/10.1016/j.dss.2012.09.007.

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Gao, Shijia, Huaiqing Wang, Dongming Xu, and Yingfeng Wang. "An intelligent agent-assisted decision support system for family financial planning." Decision Support Systems 44, no. 1 (November 2007): 60–78. http://dx.doi.org/10.1016/j.dss.2007.03.001.

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Lin, Zhi Hong, and Ting Ting Guo. "Financial Quality Evaluation of Listed Companies Based on Factor Analysis Method." Applied Mechanics and Materials 401-403 (September 2013): 2247–51. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.2247.

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Financial index of listed companies is the key to understanding and evaluating the company's financial quality. This article introduces the method of factor analysis in comprehensive evaluation of financial quality, uses SPSS software for a comprehensive evaluation of electrical machinery and equipment manufacturing industry 10 listed companies financial quality, in order to provide support for investors and managers to make investment decisions and management decision.

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Ivanov, Mikhail N. "INFORMATION ANALYSIS DECISION SUPPORT SYSTEM FOR STRATEGIC PLANNING OF REGIONAL AND URBAN DEVELOPMENT." SOFT MEASUREMENTS AND COMPUTING 1, no. 11 (2020): 67–76. http://dx.doi.org/10.36871/2618-9976.2020.11.006.

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This article discusses the ways to improve the quality of the educational process with using virtual simulators. The main trends in the use of simulators in education are presented. The practical experience of the Financial University under the Government of the Russian Federation on the development of the Open Online Academy online.fa.ru is presented. Specific examples of the developed virtual simulators «Financial Fund Management», «Russian as Foreign» and «Financial Director» for online courses of the Open Online Academy of the Financial University are considered. The description of the structure of a virtual simulator is given on the example of a simulator «Blockchain and Cryptocurrencies»

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Bărbuță-Mișu, Nicoleta, Mara Madaleno, and Vasile Ilie. "Analysis of Risk Factors Affecting Firms’ Financial Performance—Support for Managerial Decision-Making." Sustainability 11, no. 18 (September 4, 2019): 4838. http://dx.doi.org/10.3390/su11184838.

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This paper aims to investigate how financial variables and exogenous crises influence firms’ financial performance, and how these factors may help managers in decision-making to increase their firm’s wealth. The dynamic interactions among variables were studied by applying a panel vector autoregressive model using annual data for a sample of non-financial firms from European countries. Results indicate that liquidity, leverage and productivity positively affect firm performance, while solvency and asset turnover are positive and statistically significant only in the case of return on equity. Labour productivity induces that firms tend to display larger efforts to keep financial performance in face of a crisis, considering that the crisis reveals a negative statistical impact over return on assets.

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Tuslaela, Tuslaela. "THE SCHOLARSHIP AWARDING DECISION SUPPORT SYSTEM USES THE TOPSIS METHOD." Jurnal Riset Informatika 2, no. 4 (September 25, 2020): 201–6. http://dx.doi.org/10.34288/jri.v2i4.154.

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Education has an important role as an effort to educate the nation. Education is the right of everyone, but not everyone can continue higher education for various reasons. One of them is limited financial and financial resources. TOPSIS is a multi-criteria dynamic strategy or elective decision which is an elective that has the littlest good ways from the positive ideal arrangement and the largest distance from the negative ideal solution from a geometric point of view using the Euclidean distance. This study aims to determine the criteria for scholarship recipients that can be implemented in a campus, school, or other social institution that provides funds for scholarship assistance. The research results are expected to serve as a reference for the decision making process. The results showed a preference value of 0.66 which is based on academic achievement

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Parvez, Sahar. "EFFECT OF EMOTIONAL INTELLIGENCE AND FINANCIAL LITERACY ON INVESTMENT DECISION MAKING WITH A MEDIATING ROLE OF RISK PERCEPTION." Jinnah Business Review 02, no. 02 (July 1, 2014): 12–20. http://dx.doi.org/10.53369/fxpp3681.

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This research paper examines the impact of emotional intelligence and financial literacy on investment decision with a mediating role of risk perception. The data is collected by using questionnaire, from a sample of 152 investors, from stock exchange and banks. The results support that to make adequate investment decisions, investors should be financially literate and have control on their emotions. However, risk perception of investors does not mediate this relationship.

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Friães dos Santos, Joana Patrícia, Amélia Maria Pires, and Paula Odete Fernandes. "The importance to financial information in the decision-making process in company’s family structure." Contaduría y Administración 63, no. 2 (May 9, 2018): 38. http://dx.doi.org/10.22201/fca.24488410e.2018.1727.

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<p class="Pa10"> In Portugal most enterprises have a family pattern and an important role. Its importance is recognized and justifies the option to develop this research, which aims to determine whether these enterprises assign importance to financial information in the decision-making process. The work allowed gathering enough evidence to conclude on the importance of financial statements for the decision-making process, in particular as regards the use of the balance sheet and income statement. It was also concluded that financial information is primarily used to assess the financial impact, support the current management, investment decisions and comply with tax obligations.</p>

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Journal articles on the topic 'Financial Decision Support'

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