Evaluation of automated-trading strategies using an artificial market

Izumi, Kiyoshi; Toriumi, Fujio; Matsui, Hiroki

doi:10.1016/j.neucom.2008.07.020

Cited by 23 publications

(29 citation statements)

References 7 publications

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“…As a result, financial investment companies are engaged in an ongoing 'algorithmic arms race' to develop increasingly complex automated strategies for extracting profit from securities trading. It was estimated that by the end of 2010, approximately 53% of all trading in equities was carried out through automated trading [28].…”

Section: Discussionmentioning

confidence: 99%

Risk-adjusted portfolio optimisation using a parallel multi-objective evolutionary algorithm

Maguire

O’Sullivan

Moser

et al. 2012

2012 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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In this article we describe the use of a multiobjective evolutionary algorithm for portfolio optimisation based on historical data for the S&P 500. Portfolio optimisation seeks to identify manageable investments that provide a high expected return with relatively low risk. We developed a set of metrics for qualifying the risk/return characteristics of a portfolio's historical performance and combined this with an island model genetic algorithm to identify optimised portfolios. The algorithm was successful in selecting investment strategies with high returns and relatively low volatility. However, although these solutions performed well on historical data, they were not predictive of future returns, with optimised portfolios failing to perform above chance. The implications of these findings are discussed.

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Section: Discussionmentioning

confidence: 99%

Risk-adjusted portfolio optimisation using a parallel multi-objective evolutionary algorithm

Maguire

O’Sullivan

Moser

et al. 2012

2012 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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“…Some factors have been frequently used to evaluate automated trading strategies like risk and return. The market impacts are also supposed to be considered [6]. Evaluating and choosing an object requires a trade-off among various factors.…”

Section: Introductionmentioning

confidence: 99%

Exploring Evaluation Factors and Framework for the Object of Automated Trading System

Huang¹,

Cheng²,

Hou³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Automated trading system (ATS) is a computer program that combines different trading rules to find optimal trading opportunities. The objects of ATS, which are financial assets, need evaluation because that is of great significance for stakeholders and market orders. From the perspectives of dealers, agents, external environment, and objects themselves, this study explored factors in evaluating and choosing the object of ATS. Based on design science research (DSR), we presented a preliminary evaluation framework and conducted semi-structured interviews with twelve trading participants engaged in different occupations. By analyzing the data collected, we validated eight factors from literatures and found four new factors and fifty-four sub-factors. Additionally, this paper developed a relationship model of factors. The results could be used in future work to explore and validate more evaluation factors by using data mining.

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“…The interest in high frequency trading, online trading algorithms and time series analysis shows an increasing belief that new computation techniques can be helpful for decision making processes in fi nancial markets (Schmidt, Mohr et al 2010;Izumi, Toriumi et al 2009;Dempster, Lemmans 2006;Ehlers 2004;Koutroumanidis, Ioannou et al 2011). Because of raising uncertainty in fi nancial markets (Belinskaja, Galiniene 2010;Girdzijauskas, Streimikiene 2010) and growing demand for hedging businesses (Stepien 2009) trading volume from equity and currency markets increasingly move to derivatives markets (Acworth 2011).…”

Section: Introductionmentioning

confidence: 99%

Research on Futures Trend Trading Strategy Based on Short Term Chart Pattern

Masteika

Rutkauskas

2012

Journal of Business Economics and Management

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Abstract.The main task of this paper is to examine a short term trend trading strategy in futures market based on chart pattern recognition, time series and computational analysis. Specifi cations of historical data for technical analysis and equations for futures profi tability calculations together with position size measurement are also discussed in the paper. A contribution of this paper lies in a novel chart pattern related to fractal formation and chaos theory and its application to short term up-trend trading. Trading strategy was tested with historical data of the most active futures contracts. The results have given signifi cantly better and stable returns compared to the change of market benchmark (CRB index). The results of experimental research related to the size of trading portfolio and trade execution slippage are also discussed in the paper. The proposed strategy can be attractive for futures market participants and be applied as a decision support tool in technical analysis.

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Evaluation of automated-trading strategies using an artificial market

Cited by 23 publications

References 7 publications

Risk-adjusted portfolio optimisation using a parallel multi-objective evolutionary algorithm

Risk-adjusted portfolio optimisation using a parallel multi-objective evolutionary algorithm

Exploring Evaluation Factors and Framework for the Object of Automated Trading System

Research on Futures Trend Trading Strategy Based on Short Term Chart Pattern

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