Design and Analysis of an Intelligent Decision Support System for Trading and its Application to Electricity Trading
Date
2013-09-23
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Abstract
The 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.
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Finance, Industrial, Finance, Electronics and Electrical, Industrial, Industrial
Citation
Maurice, 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/25562