Design and Analysis of an Intelligent Decision Support System for Trading and its Application to Electricity Trading

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dc.contributor.advisorRuhe, Guenther
dc.contributor.advisorDenzinger, Jörg
dc.contributor.authorMaurice, Sebastian Augustine
dc.date.accessioned2013-09-23T22:25:56Z
dc.date.available2013-11-12T08:00:17Z
dc.date.issued2013-09-23
dc.date.submitted2013en
dc.description.abstractThe financial trading market is a highly complex and dynamic system, which is the limiting factor preventing any model from accurately predicting its movements. Because of this limiting factor, trading in a market can be risky for individuals and institutions that could experience financial losses and this can impact the overall economy. It remains an on-going research challenge to find approaches to minimize the risk in trading. The focus of this PhD research is on a multi-agent based simulation approach to provide decision support to traders to help minimize the risk from trading. We address four problems in the existing research on decision support systems for trading: 1) lack of a modeling framework, 2) lack of direction on modeling personas, 3) lack of direction on how to provide decision support to traders, and 4) lack of analysis on quality of forecasts. The main contributions of the research is the design, analysis, development and validation of a new decision support paradigm for trading called T-Evolve*. We have also developed a new intelligent decision support technology called TRAMAS. The paradigm together with TRAMAS supports traders by allowing them to simulate different market models composed of agents with different personas and forecast beliefs. Exploring and analysing the simulation results provides guidance to traders on potential market outcomes; this is then used to develop a trading plan for tomorrow’s market. The core element of TRAMAS is the incorporation of actors with different personas and forecasts beliefs, instantiated by agents. The advantage of our approach is twofold. First, different personas of participants exist in every market, thus incorporating personas is a natural representation of the real market. Second, forecast beliefs are a natural belief of participants because forecasts about the future market play a critical role in how a market may develop tomorrow. Two industrial-oriented case studies with empirical evidence support our approach. The validation of our approach by nine industry experts confirms, within the context of this research, that our approach has merit and can be useful in a real-world setting. TRAMAS also predicts with 77% accuracy the direction of future markets for six different days.en_US
dc.identifier.citationMaurice, S. A. (2013). Design and Analysis of an Intelligent Decision Support System for Trading and its Application to Electricity Trading (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25562en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/25562
dc.identifier.urihttp://hdl.handle.net/11023/1011
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectFinance
dc.subjectIndustrial
dc.subjectFinance
dc.subjectElectronics and Electrical
dc.subjectIndustrial
dc.subjectIndustrial
dc.subject.classificationDecision Support Systemen_US
dc.subject.classificationMulti-Agent Systemen_US
dc.subject.classificationFinancial Tradingen_US
dc.subject.classificationIntelligent Decision Support Systemen_US
dc.subject.classificationForecasten_US
dc.titleDesign and Analysis of an Intelligent Decision Support System for Trading and its Application to Electricity Trading
dc.typedoctoral thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
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