Financial Decision Support Research Articles

Who can win with a cost and profitability system?

AbstractWhat kinds of companies have successful cost and profitability systems? The authors surveyed companies from all over the world to find out. The results show that significant investments in time and money are required, but companies that succeed see significant benefits from their cost systems and share some common traits. They have a culture that encourages employees not only to understand their costs and profits, but also to act on the information. These companies often use activity‐based costing and use their systems for operational, financial, and strategic decision support. They can also determine cost and profitability at all appropriate levels—for activities, products, customers, and departments. © 2010 Wiley Periodicals, Inc.

A New Fuzzy DSS/ES for Stock Portfolio Selection using Technical and Fundamental Approaches in Parallel

A Decision Support System/Expert System for stock portfolio selection presented where at first step, both technical and fundamental data used to estimate technical and fundamental return and risk (1 phase); Then, the estimated values are aggregated with the investor preferences (2 phase) to produce convenient stock portfolio. In the 1 phase, there are two expert systems, each of which is responsible for technical or fundamental estimation. In the technical expert system, for each stock, twenty seven candidates are identified and with using rough sets-based clustering method (RC) the effective variables have been selected. Next, for each stock two fuzzy rulebases are developed with fuzzy C-Mean method and Takai-SugenoKang (TSK) approach; one for return estimation and the other for risk. Thereafter, the parameters of the rule-bases are tuned with backpropagation method. In parallel, for fundamental expert systems, fuzzy rule-bases have been identified in the form of “IF-THEN” rules through brainstorming with the stock market experts and the input data have been derived from financial statements; as a result two fuzzy rule-bases have been generated for all the stocks, one for return and the other for risk. In the 2 phase, user preferences represented by four criteria and are obtained by questionnaire. Using an expert system, four estimated values of return and risk have been aggregated with the respective values of user preference. At last, a fuzzy rule base having four rules, treats these values and produce a ranking score for each stock which will lead to a satisfactory portfolio for the user. The stocks of six manufacturing companies and the period of 2003-2006 selected for data gathering. Keywords—Stock Portfolio Selection, Fuzzy Rule-Base Expert Systems, Financial Decision Support Systems, Technical Analysis, Fundamental Analysis.

NPV-based decision support in multi-objective design using evolutionary algorithms

Optimum design problems are frequently formulated using a single excellence criterion (minimum mass or similar) with evolutionary algorithms engaged as decision-support tools. Alternatively, multi-objective formulations are used with a posteriori decision-making amongst the Pareto candidate solutions. The former typically introduces excessive simplification in the decision space and subjectivity, the latter leads to extensive numerical effort and postpones the compromise decision-making. In both cases, engineering excellence metrics such as minimum mass can be misleading in terms of performance of the respective design in the given operational environment. This paper presents an alternative approach to conceptual design where a compound objective function based on the Net Present Value (NPV) and Internal Rate of Return (IRR) aggregate performance metrics is developed. This formulation models the integral value delivered by the candidate designs over their respective life-cycles by applying value-based NPV discounting to all objectives. It can be incorporated as an a priori compromise and consequently viewed as a weighted sum of individual objectives corresponding to their economically faithful representation over the entire operational life-time of the designs. The multi-objective design optimization is consequently expanded from purely engineering terms to coupled engineering–financial decision support.

Financial decision support for marketers in the Norwegian fishing and furniture industries

Purpose – The purpose of this paper is to address financial decision support for marketers and provide suggestions for improvement potentials.Design/methodology/approach – The context is the Norwegian furniture and fishing industries. The samples consisted of 118 respondents, 69 from the fishing industry and 49 from the furniture industry, with an average response rate of 33 per cent. Respondents reported on six groups of marketing costs, gave an overall evaluation of their existing and potential management accounting systems and of the systems' existing and potential decision support in nine marketing decision areas.Findings – Marketing costs represented 8.9 per cent of total revenues in the fishing industry and 16.2 per cent in the furniture industry. The difference can be attributed to items that resulted in revenue reductions and promotional costs. Both industries showed significant potential for improvements in management accounting systems. Priorities regarding the nine decision support areas differ...

Measuring the cost impact of hospital information systems: 1987–1994

This study measures the impact of information technology (IT) use on hospital operating costs during the late 1980s and early 1990s. Using a proprietary eight-year panel dataset (1987–1994) that catalogues application-level automation for the complete census of the 3000 U.S. hospitals with more than 100 beds, this study finds that both financial/administrative and clinical IT systems at the most thoroughly automated hospitals are associated with declining costs three and five years after adoption. At the application level, declining costs are associated with the adoption of some of the newest technologies, including systems designed for cost management, the administration of managed care contracts, and for both financial and clinical decision support. The association of cost declines with lagged IT as well as the cost patterns at the less automated hospitals both provide some evidence of learning effects.

An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring

Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented.

An entropy‐based financial decision support system (e‐FDSS) for project analysis in construction SMEs

Uncertainty contributes a major part in the accuracy of a decision‐making process while its inconsistency is always difficult to be solved by existing decision‐making tools. Entropy has been proved to be useful to evaluate the inconsistency of uncertainty among different respondents. The study demonstrates an entropy‐based financial decision support system called e‐FDSS. This integrated system provides decision support to evaluate attributes (funding options and multiple risks) available in projects. Fuzzy logic theory is included in the system to deal with the qualitative aspect of these options and risks. An adaptive genetic algorithm (AGA) is also employed to solve the decision algorithm in the system in order to provide optimal and consistent rates to these attributes. Seven simplified and parallel projects from a Hong Kong construction small and medium enterprise (SME) were assessed to evaluate the system. The result shows that the system calculates risk adjusted discount rates (RADR) of projects in an objective way. These rates discount project cash flow impartially. Inconsistency of uncertainty is also successfully evaluated by the use of the entropy method. Finally, the system identifies the favourable funding options that are managed by a scheme called SME Loan Guarantee Scheme (SGS). Based on these results, resource allocation could then be optimized and the best time to start a new project could also be identified throughout the overall project life cycle.

Towards ubiquitous information supply for individual investors: A decision support system design

This paper introduces an IT artifact called MoFiN DSS that comprises hard- and software components that provide the basis for a prototype of a financial decision support system (DSS) to support individual investors reacting to unforeseen market events. We have derived our motivation for building such a system design from behavioral finance research. Analyses of the behavior of individual investors provide evidence that this segment does react more significantly to any public news published compared to institutional investors. On the other hand, the analyses show that they react significantly slower than their institutional counterparts. Since empirical intraday event study analyses show that capital markets react promptly to new information and that excess returns decrease over a specific period of time, individual investors miss significant trading opportunities due to their current strategies of information research. We address the problem that this market segment is not able to continuously observe diverse information channels and to assess all the new information available. Our prototype decision support system continuously observes company announcements and forecasts their potential impact on the corresponding stock price. After identifying those events for which significant market reactions can be expected, wireless push-based message services provide the technical basis for prompt and location-independent information supply. Based on a novel simulation-based evaluation methodology we have developed, we demonstrate and quantify the advantages that the developed system provides to the individual investors.

The fuzzy GA-based multi-objective financial decision support model for Chinese state-owned construction firms

An ideal multi-objective financial decision support (MFDS) model for contractors should have ability of handling both the quantitative and qualitative inputs. This paper aims to develop a new MFDS model, which attempts to provide a more accurate way to support financial decision-makers in Chinese state-owned construction firms. Owing to the slow progress of passing the construction regulations, coupled with the inherited ownership problems in the Chinese booming construction industry, it is logical for Chinese project managers to make financial decisions with the consideration of the four objective functions (profit margin, risk factors, government relationship and market share), which are realistic representations of the present situation of Chinese construction industry. The proposed MFDS model is a practical method to support financial decision-making. For the model's computational performance, Module 3 (stochastic universal sampling selection, adaptive crossover and adaptive mutation of Genetic Algorithm) is more superior.

Financial decision support using neural networks and support vector machines

Abstract: Bankruptcy prediction and credit scoring are the two important problems facing financial decision support. The multilayer perceptron (MLP) network has shown its applicability to these problems and its performance is usually superior to those of other traditional statistical models. Support vector machines (SVMs) are the core machine learning techniques and have been used to compare with MLP as the benchmark. However, the performance of SVMs is not fully understood in the literature because an insufficient number of data sets is considered and different kernel functions are used to train the SVMs. In this paper, four public data sets are used. In particular, three different sizes of training and testing data in each of the four data sets are considered (i.e. 3:7, 1:1 and 7:3) in order to examine and fully understand the performance of SVMs. For SVM model construction, the linear, radial basis function and polynomial kernel functions are used to construct the SVMs. Using MLP as the benchmark, the SVM classifier only performs better in one of the four data sets. On the other hand, the prediction results of the MLP and SVM classifiers are not significantly different for the three different sizes of training and testing data.

Intestinal obstruction: trends in imaging utilization and their influence in its rising hospital bill

The purpose of the study was to evaluate trends in the utilization of different imaging modalities and review how imaging utilization practices affect hospital charges for patients with intestinal obstruction. All patients discharged with a primary diagnosis of intestinal obstruction during 6 fiscal years (1999-2004) were retrospectively studied. We obtained data on patients' demographics, procedures, outcomes, imaging services utilization, and hospital and imaging charges from our institution's transition system (a clinical and financial decision support software system). The institutional review board approved this study. Surgery was performed in 26% of patients in 1999 and in 40% in 2004 (p = 0.01) with the mortality rate significantly (p < 0.01) dropping from 3.8% to 0.4%. A total of 5,292 abdominal imaging studies were obtained; 93% of those were either abdominal radiographs or abdomino-pelvic computed tomography (CT) scans. CT studies per patient increased from 0.5 in 1999 to 1 in 2004 (p < 0.01), while abdominal radiographs (mean = 2.4) did not significantly change over the entire study period (p = 0.6). Average imaging charges doubled during the study period ($1,572 to $3,012, p < 0.01). Average hospital charges increased from $18,138 in 1999 to $32,808 in 2004 (p < 0.01). The fraction of hospital charges attributed to imaging varied between 8.7% and 9.2%. CT utilization for intestinal obstruction increased from 1999 to 2004 without modality substitution. While hospital and imaging charges have significantly increased, the fraction represented by imaging has remained constant, suggesting that imaging is an unlikely cause for the increase in hospital charges.

Earnings forecasts in enterprise information systems environment

With the globalisation and integration of world financial markets, the application of enterprise information systems has become more and more popular in the financial service sector. This paper examines the analyst's decision regarding allocation of effort to the precision and timeliness of earnings forecasts with financial decision support systems. Once an analyst decides to follow a firm, the analyst must choose the level of effort to devote to generating outputs, such as earnings forecasts. This paper provides insights into this by examining three issues concerning the forecasts of the individual analyst. First, forecast accuracy and frequency are modelled simultaneously. Then, whether these two effort allocation choices are complements or substitutes is tested. Finally, how competition affects these two characteristics is examined. The accuracy and frequency are examined with simultaneous equations. Results from this analysis suggest that analysts allocate effort among these two forecast criteria in a complementary way. Finally, empirical results reveal a positive association between competition and forecast frequency by the individual analyst. However, a significant association between competition and the individual analyst's forecast accuracy is not found.

The MEFLO method's application for the feasibility assessment of energy recovery from municipal solid waste in a small integrated municipal service system

The paper presents the MEFLO ranking method and the Simple Integrated System Management (SISMan) concept for assessing the feasibility of integrated energy and Waste Management System (WMS) alternatives. The MEFLO method is based on dividing the decision-making process into five modules, including mass, energy and financial flows, legislative criteria (e.g., environmental restrictions and target values) and other decision support criteria, such as the local political guidelines. The utilisation of the MEFLO method is presented by comparing five system alternatives, including energy recovery from household waste. The calculations needed in the MEFLO assessment were performed with the SISMan spreadsheet calculation model developed at the Lappeenranta University of Technology. The MEFLO method and the SISMan concept contribute to the design and assessment of different integrated management systems. Any of the appropriate available tools can be used in the model forming and assessment processes, according to the proposed methods.

FINANCIAL DECISION SUPPORT FOR PROJECT FINANCING AND REVENUE PROGRAM DEVELOPMENT

FINANCIAL DECISION SUPPORT FOR PROJECT FINANCING AND REVENUE PROGRAM DEVELOPMENTA significant challenge facing many agencies since the expiration of the Clean Water Act (CWA) Grants Program is how to pay for the ever-increasing costs for providing wastewater service in light of more restrictive water quality objectives. The challenge is coming at a time when stakeholders and special interests are becoming more involved in the planning process. Agencies increasingly require...Author(s)H. Stephen McDonaldChris StoriGreg DennisSourceProceedings of the Water Environment FederationSubjectSession 11 - Management: Privatization, Public-Private Partnerships, Creative FinancingDocument typeConference PaperPublisherWater Environment FederationPrint publication date Jan, 2003ISSN1938-6478SICI1938-6478(20030101)2003:11L.28;1-DOI10.2175/193864703784756075Volume / Issue2003 / 11Content sourceWEFTECFirst / last page(s)28 - 29Copyright2003Word count247

Measuring the Cost Impact of Hospital Information Systems: 1987-1994

This study measures the impact of information technology (IT) use on hospital operating costs during the late 1980&amp;amp;#x27;s and early 1990&amp;amp;#x27;s. Using a proprietary eight-year panel dataset (1987-1994) that catalogues application-level automation for the complete census of the 3,000 U.S. hospitals with more than 100 beds, this study finds that both financial/administrative and clinical IT systems at the most thoroughly automated hospitals are associated with declining costs three and five years after adoption. At the application level, declining costs are associated with the adoption of some of the newest technologies, including systems designed for cost management, the administration of managed care contracts, and for both financial and clinical decision support. The association of cost declines with lagged IT as well as the cost patterns at the less automated hospitals both provide evidence of learning effects.

Designing personalized intelligent financial decision support systems

The variety of investment methods and the complexity of investment decisions have increased steadily in the last two decades. This growth has created a need for comprehensive and expandable financial decision support systems (DSS) that embody major approaches toward investment decisions. The question is whether such systems should take into account the investor's unique requirements and personal characteristics. The answer to this question is critical to the development of personal intelligent financial agents. In this paper, we present the design of such a system, the creation of a prototype, and its use in an exploratory investigation of the impact of investors' individual characteristics on their use of models in making investment decisions. More specifically, we report on how the gender of investors, and their attitudes towards risk, relate to their choice of investment models, and provide evidence for the possibility of personalizing DSS.

Feeding a Financial Decision Support System with Textual Information

Many expert systems need a lot of data. For a long time this point has appeared to be a bottleneck for the growth of Artificial Intelligence applications. A major way to provide an expert system with knowledge is to enter it by hand. With the maturity of Natural Language Processing (NLP), a new way has been opened with automatic Information Extraction (IE) from text. This paper briefly presents a financial decision support system, named SAPE, connected with an IE system. This application is used by Caisse des depots et consignations (CDC) in order to anticipate takeover bids on the European stock markets. It provides ways to manage the highly complex and moving network of European shareholdings. SAPE is available on CDC group"s intranet and is used by fund managers as part of their everyday work. This paper also describes how our NLP system, Exoseme, uses the economic newswire from the Agence France-Presse (French press agency) in order to extract information on shareholdings and how this information is managed by the user to provide SAPE database with the large amount of information needed for its computations. After months of use, IE has appeared to be a powerful concrete solution. Moreover, if the economic value of takeover bids has lead us to pay particular attention to shareholdings, this approach can be extended to other events. In fact, with IE, new possibilities for portfolio decision support systems are coming. This paper presents the improvements we plan, and discusses those, though tempting, that are still out of reach due to the lack of adaptive tools.

Financial decision support with hybrid genetic and neural based modeling tools

This paper presents a comparative investigation of hybrid genetic classifiers vis-a-vis neural classifiers and statistical models in the financial domain. It is hypothesized that the proposed hybrid genetic classifier will perform better than the statistical counterpart. We provide a brief overview of the hybrid genetic classifier and discuss the design issues when applied to developing classification models for financial decision support. Further, the models are tested on a liquidation-merger problem. Results are consistent with the hypothesized premise. The proposed genetic classifiers outperform the statistical model. Implications of the comparison and issues for future research are addressed.

An Integrated Dynamic Financial Analysis and Decision Support System for a Property Catastrophe Reinsurer

AbstractThis paper describes the dynamic financial analysis model currently being used by a property catastrophe reinsurer to manage its business. The model is-an integral part of the day-to-day operations at the Company, and is used as a decision making tool in the underwriting, investment, and capital management processes. The paper begins by describing the framework that the Company uses for risk management. This includes a classification of the risks facing the Company, which is used to define and prioritize their implementation in the model. Also included is a description of the conceptual approach the Company takes to evaluate the tradeoff between risk and return. The paper then goes on to describe the structure and operation of the dynamic financial analysis model and provides examples of its use at the Company, along with illustrative examples of the various types of output it produces.

084039 (E50, M54) An integrated dynamic financial analysis and decision support system for a property catastrophe reinsurer : Lowe S.P. and Stanard J.N.,Casualty Actuarial Society Forum, 1996, pp.89–118

084039 (E50, M54) An integrated dynamic financial analysis and decision support system for a property catastrophe reinsurer : Lowe S.P. and Stanard J.N.,Casualty Actuarial Society Forum, 1996, pp.89–118