Hierarchical fuzzy case based reasoning with multi-criteria decision making for financial applications
18 Dec 2007-pp 226-234
TL;DR: This paper presents a framework for using a case-based reasoning system for stock analysis in financial market using a hierarchical structure for case representation and incorporates a multi-criteria decision-making algorithm which furnishes the most suitable solution with respect to the current market scenario.
Abstract: This paper presents a framework for using a case-based reasoning system for stock analysis in financial market. The unique aspect of this paper is the use of a hierarchical structure for case representation. The system further incorporates a multi-criteria decision-making algorithm which furnishes the most suitable solution with respect to the current market scenario. Two important aspects of financial market are addressed in this paper: stock evaluation and investment planning. CBR and multi-criteria when used in conjunction offer an effective tool for evaluating goodness of a particular stock based on certain factors. The system also suggests a suitable investment plan based on the current assets of a particular investor. Stock evaluation maps to a flat case structure, but investment planning offers a scenario more suited for structuring the case into successive detailed layers of information related to different facets. This naturally leads to a hierarchical case structure.
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TL;DR: The PROMETHEE Methods are particularly appropriate to treat multicriteria problems of the following type: as mentioned in this paper, for which A is a finite set of possible alternatives and fj(x), j = 1, 2,…,k a set of k evaluation criteria.
Abstract: The PROMETHEE Methods are particularly appropriate to treat multicriteria problems of the following type:
$$Max\,\left\{ {{f_1}(x),{f_2}(x),...,{f_j}(x),...,{f_k}(x)|x \in A} \right\}$$
(1.1)
for which A is a finite set of possible alternatives and fj(x), j = 1, 2,…,k a set of k evaluation criteria.
9 citations
01 Jan 2014
TL;DR: The assessment should be used for ranking solutions gained from a case-based reasoning system and to reveal contributions of criteria values to the overall assessment.
Abstract: Decision support systems can recommend strategies for disaster management, which can be further discussed by decision-makers. To provide rationales for the recommendations, the strategies need to be assessed according to relevant criteria. If several strategies are available, the criteria can be used for ranking the strategies. This paper addresses the issue concerning the choice of suitable criteria from several perspectives. The assessment integrates concepts on robustness, experience with regard to the implementation of a strategy, quantifiable ratios which can be deduced from simulations, and system-specific parameters. Objectives are to facilitate transparency with respect to the assessments, to provide a basis for discussions concerning the strategies, and to preserve adaptability and flexibility to account for the variability of disasters and users’ preferences. The assessment should be used for ranking solutions gained from a case-based reasoning system and to reveal contributions of criteria values to the overall assessment.
4 citations
TL;DR: In this article, the authors investigated the possibility of financial time series behaviour forecasting through artificial intelligence concepts and tools (artificial neural networks, fuzzy logic, neuro-fuzzy systems).
Abstract: This research aims at characterizing and modelling the investors’ behaviours present on the Romanian capital market, by analyzing the behaviours proposed by the efficient markets theory and investigating the possibility of financial time series behaviour forecasting through artificial intelligence concepts and tools (artificial neural networks, fuzzy logic, neuro-fuzzy systems) . The analysis of various forecasting strategies has been conducted using data sets on a daily basis, on a time horizon of nine years, for a total of 22 companies listed on BSE and for the BET and BET-C exchange indexes; the research is differentiating the pre-crisis period and the crisis period.
2 citations
Cites background from "Hierarchical fuzzy case based reaso..."
...Numerous studies exploit hybrid neuro-fuzzy systems, obtaining encouraging results and proposing various architectures (Gupta and Rao, 1994; Brown and Harris, 1994; Pedrycz, 1995; Buckley and Hayashi, 1995; Dash et al., 1995; Lie and Sharaf, 1995; Studer and Masulli, 1997; Padmakumari et al., 1999; Mitra and Hayashi, 2000; Kulkarni, 2001; Lee et al., 2002; Craiger et al., 2003; Kim et al., 2004; Dušan, 2004; Srinivasa et al., 2006; Sushmita and Chaudhury, 2007; Radeerom et al., 2012)....
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...…Hayashi, 1995; Dash et al., 1995; Lie and Sharaf, 1995; Studer and Masulli, 1997; Padmakumari et al., 1999; Mitra and Hayashi, 2000; Kulkarni, 2001; Lee et al., 2002; Craiger et al., 2003; Kim et al., 2004; Dušan, 2004; Srinivasa et al., 2006; Sushmita and Chaudhury, 2007; Radeerom et al., 2012)....
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01 Jan 2010
TL;DR: It is proposed to cluster any data set by optimizing with respect to a cluster score function, assigning a positive real worth to each data subset and thereby dealt with in pseudo-Boolean form, at the multilinear extension (MLE) allows to evaluate fuzzy data subsets or clusters as well.
Abstract: This paper proposes to cluster any finite data set by optimizing with respect to a cluster score function,assigning a positive real worth to each data subset and thereby dealt with in pseudo-Boolean form, sothat the multilinear extension (MLE) allows to evaluate fuzzy data subsets or clusters as well. A fuzzyclustering being a collection of fuzzy clusters over which every data point has to distribute a unitary mem-bership mass, the objective function (to be maximized) is global worth, obtained through summation overconstituents fuzzy clusters of their own worth as given by the score function MLE. Optimization thenproceeds by means of pseudo-Boolean techniques, leading to a local-search algorithm. Also, any fuzzyclustering is shown to admit some hard one (or partition of the data set) that does at least as good, andconcavity of the objective function is interpreted in terms of the underlying clustering problem. Key words: pseudo-Boolean function, optimization, clustering, fuzzy clustering, algorithm.
1 citations
Additional excerpts
...Giovanni Rossi...
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29 Oct 2013
TL;DR: In this paper, a statistical process control and artificial intelligence model was used to improve the quality of investing in the highly volatile Egyptian stock exchange by combining the concepts of Statistical Process Control and Artificial Intelligence.
Abstract: This paper attempts to improve the quality of investing in the highly volatile Egyptian Stock Exchange by combining the concepts of statistical process control and artificial intelligence. Control charts were used to construct a statistically controlled stock market prediction model to support the decision of stock investors. The suggested model is mainly based on the concepts of Case-based Reasoning which is an artificial intelligent methodology that imitates the human problem-solving and reasoning behavior. Hit rate was applied as a performance measure of the quality of prediction for the suggested model. Results of predicting 900 next day stock predictions during January 2012 had a mean absolute prediction error of 2.096 LE and a hit ratio of 67%. After using the quality controlled process, the mean absolute prediction error was reduced to 1.92 L.E. and the hit ratio increased to 72%.
References
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TL;DR: The main features of real-world problems for which the outranking approach is appropriate and the concept of outranking relations are described and the definition of such out ranking relations is given for the main ELECTRE methods.
Abstract: In the first part of this paper, we describe the main features of real-world problems for which the outranking approach is appropriate and we present the concept of outranking relations. The second part is devoted to basic ideas and concepts used for building outranking relations. The definition of such outranking relations is given for the main ELECTRE methods in Part 3. The final part of the paper is devoted to some practical considerations.
1,751 citations
"Hierarchical fuzzy case based reaso..." refers background in this paper
...ELECTRE [9], [10] and PRMETHEE [11] are some of the ranking function that could be used to outrank one similar case over the other based on certain parameters....
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TL;DR: An overview of the current research and development directions in knowledge and data engineering is provided, with respect to programmability and representation, design tradeoffs, algorithms and control, and emerging technologies.
Abstract: The authors provide an overview of the current research and development directions in knowledge and data engineering. They classify research problems and approaches in this area and discuss future trends. Research on knowledge and data engineering is examined with respect to programmability and representation, design tradeoffs, algorithms and control, and emerging technologies. Future challenges are considered with respect to software and hardware architecture and system design. The paper serves as an introduction to this first issue of a new quarter. >
708 citations
TL;DR: The PROMETHEE Methods are particularly appropriate to treat multicriteria problems of the following type:==================¯¯¯¯¯¯¯¯¯¯676======676============672======676676======672¯¯676¯¯672======671======676¯¯671======672¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯676¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯677======676¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯671¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯672¯¯671¯¯676¯¯¯¯¯¯¯¯¯¯672¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯676¯¯676』676======671¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯676¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯676¯¯¯¯¯¯672』672======672』676¯¯682======676』672¯¯672¯¯¯¯¯¯¯¯¯¯671』676¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Abstract: The PROMETHEE Methods are particularly appropriate to treat multicriteria problems of the following type:
$$Max\,\left\{ {{f_1}(x),{f_2}(x),...,{f_j}(x),...,{f_k}(x)|x \in A} \right\}$$
(1.1)
for which A is a finite set of possible alternatives and fj(x), j = 1, 2,…,k a set of k evaluation criteria.
93 citations
TL;DR: The technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction, is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design.
Abstract: Case based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, so most CBR systems dealing with complex problem solving tasks have to use multiple cases. The paper describes and evaluates the technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design.
82 citations
"Hierarchical fuzzy case based reaso..." refers background in this paper
...Although, work like [5] and [6] presented the concept of a hierarchical CBR, but the concept proposed involve reusing of multiple cases at various levels of abstraction....
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TL;DR: A new learning technique which extracts new case vectors using Dynamic Adaptive Ensemble CBR (DAE CBR), which originates from finding combinations of parameter and updating and applying an optimal CBR model to application or domain area is proposed.
Abstract: This paper proposes a new learning technique which extracts new case vectors using Dynamic Adaptive Ensemble CBR (DAE CBR). The main idea of DAE CBR originates from finding combinations of parameter and updating and applying an optimal CBR model to application or domain area. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index.
67 citations
"Hierarchical fuzzy case based reaso..." refers background in this paper
...[2] proposed the daily financial condition indicator (DFCI) monitoring financial market built on CBR, [3], in their work proposed a new learning A....
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...Although, work like [5] and [6] presented the concept of a hierarchical CBR, but the concept proposed involve reusing of multiple cases at various levels of abstraction....
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...: PReMI 2007, LNCS 4815, pp. 226–234, 2007. c© Springer-Verlag Berlin Heidelberg 2007 technique which extracts new case vectors using Dynamic Adaptive Ensemble CBR, which again deals with the prediction of the overall stock market....
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...[2] proposed the daily financial condition indicator (DFCI) monitoring financial market built on CBR, [3], in their work proposed a new learning A. Ghosh, R.K. De, and S.K. Pal (Eds.)...
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