Browsing by Author "Koochakzadeh, Negar"

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    A Heuristic Stock Portfolio Optimization Approach Based on Data Mining Techniques
    (2013-03-11) Koochakzadeh, Negar; Alhajj, Reda
    Portfolio optimization is the process of making investment decisions on holding a set of financial assets to meet various criteria. A variety of investment assets around the world make this multi-faceted decision problem very complicated. Econometric and statistical models as well as machine learning and data mining techniques have been used by many researchers and analysts to propose heuristic solutions for portfolio optimization. However, a literature review shows that the existing models are still not practical as they do not always perform better than even the naïve strategy of investing in all available assets in the market. The methodology proposed in this thesis is an alternative heuristic solution to help investors make stock investment decisions through a semi-automated process. The proposed solution is based on the fact that the investment decision cannot be fully automated because investors’ preferences that are the key factors in making investment decision, vary among different people. For this purpose, a semi-automated framework called SMPOpt (Stock Market Portfolio Optimizer) has been designed and implemented. In the proposed framework, the goal is to learn from the historical fundamental analysis of companies to discover the optimum portfolio by considering investors’ preferences. The Portfolio optimization problem is formulated and broken down into steps to be able to apply data mining techniques such as Clustering and Ranking, and Social Network Analysis. Some of these techniques are customized based on the temporal behaviour of financial datasets. For instance, the ranking algorithm based on Support Vector Machine (SVMRank) is modified and a new algorithm called Time- Series SVMRank is proposed. A comprehensive experimental study has been conducted using the real stock exchange market datasets from the past recent decades to evaluate the proposed portfolio optimization solution. The obtained results confirmed the strength of the proposed methodology.
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    ItemOpen Access
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    A Tester-Assisted Methodology for Test Redundancy Detection
    (2010-01-17) Koochakzadeh, Negar; Garousi, Vahid
    Test redundancy detection reduces test maintenance costs and also ensures the integrity of test suites. One of the most widely used approaches for this purpose is based on coverage information. In a recent work, we have shown that although this information can be useful in detecting redundant tests, it may suffer from large number of false-positive errors, that is, a test case being identified as redundant while it is really not. In this paper, we propose a semiautomated methodology to derive a reduced test suite from a given test suite, while keeping the fault detection effectiveness unchanged. To evaluate the methodology, we apply the mutation analysis technique to measure the fault detection effectiveness of the reduced test suite of a real Java project. The results confirm that the proposed manual interactive inspection process leads to a reduced test suite with the same fault detection ability as the original test suite.
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    ItemOpen Access
    A Tester-Assisted Methodology for Test Redundancy Detection
    (Hindawi Publishing Corporation, 2010) Koochakzadeh, Negar; Garousi, Vahid