Multi-agent System Approach to React to Sudden Environmental Changes

2011 11th International Conference on Intelligent Systems Design and Applications

2011

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To use human factors together with financial ones in portfolio management task we analyze lengthy series of successes and losses of numerous automated high frequency trading systems that buy and sell assets. We found that in spite of sparse, bimodal non-Gaussian time series, modern Markowitz solutions can be applied to weigh up contributions of diverse trading strategies. Training history should be rather short in situations where technological, social, financial, economic and political situations are changing swiftly. The Markowitz portfolio coefficients finding algorithm can be improved by careful application of the regularization and matrix structurization methods.

Section: B Sample Size Issues In Hft Portfolio Creationmentioning

confidence: 99%

High frequency trading portfolio optimisation: Integration of financial and human factors

2011 11th International Conference on Intelligent Systems Design and Applications

2011

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“…Very important among the problems that need novel solutions is the increase in the accuracy of small sample based estimates of the different portfolios efficiency in static and changing environments. In the future, instead of bit static best agent selection procedure, adaptive evolvable multi-agent system, similar to that considered in [17], could be developed.…”

Section: Discussionmentioning

confidence: 98%

“…The problem of determination of the proper training set size cannot be avoided in the portfolio management as well. In situations where a character of the time series is changing in time, genetic algorithms [14] or evolvable multi-agent systems [17] can be used.…”

Section: Introductionmentioning

confidence: 99%

Three decision making levels in portfolio management

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

2012

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To improve portfolio management process we suggest using profit histories of automated trading strategies instead of actual assets. Such history can be generated by simulating hundreds of automated trading strategies (robots). We developed three-level decision making system aimed to find the portfolio weights. At the first level, virtual robots trade the assets, at the second level we create virtual profit fusion agents that calculate weighted sums of the profit series created by the first level robots. At the third level, we rank the fusion agents, select a set of the best ones and construct the final portfolio. Experiments with real financial 2004-2011 years data confirm usefulness of the novel approach.

“…Therefore, the lengthy time series are often unsuitable for precise portfolio calculation. One needs to employ shorter training histories (Raudys, Zliobaite 2005;Raudys, Mitasiunas 2007;Raudys 2013). In our analysis of diverse trading strategies we face data where in ¾ of days the trading robots refuse to operate.…”

Section: History Length Selectionmentioning

confidence: 99%

Sustainable Economy Inspired Large-Scale Feed-Forward Portfolio Construction

Technological and Economic Development of Economy

Pabarskaite

2014

To understand large-scale portfolio construction tasks we analyse sustainable economy problems by splitting up large tasks into smaller ones and offer an evolutional feed-forward system-based approach. The theoretical justification for our solution is based on multivariate statistical analysis of multidimensional investment tasks, particularly on relations between data size, algorithm complexity and portfolio efficacy. To reduce the dimensionality/sample size problem, a larger task is broken down into smaller parts by means of item similarity – clustering. Similar problems are given to smaller groups to solve. Groups, however, vary in many aspects. Pseudo randomly-formed groups compose a large number of modules of feed-forward decision-making systems. The evolution mechanism forms collections of the best modules for each single short time period. Final solutions are carried forward to the global scale where a collection of the best modules is chosen using a multiclass cost-sensitive perceptron. Collected modules are combined in a final solution in an equally weighted approach (1/N Portfolio). The efficacy of the novel decision-making approach was demonstrated through a financial portfolio optimization problem, which yielded adequate amounts of real world data. For portfolio construction, we used 11,730 simulated trading robot performances. The dataset covered the period from 2003 to 2012 when environmental changes were frequent and largely unpredictable. Walk-forward and out-of-sample experiments show that an approach based on sustainable economy principles outperforms benchmark methods and that shorter agent training history demonstrates better results in periods of a changing environment.