Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization

Singh, Pritpal; Borah, Bhogeswar

doi:10.1016/j.ijar.2013.09.014

Cited by 89 publications

(49 citation statements)

References 43 publications

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“…Political considerations impact the level of confidence in a nation's government, the climate of stability and level of certainty [25].…”

Section: ) Balance Of Paymentsmentioning

confidence: 99%

“…H. C. Aladag, U. Yolco and E. Egrioglu [6] present an evaluation of the portfolio optimisation strategies by three agents: rational agent, interference agent, and technical analysis agent. P. Singh and B. Borah [7] apply a multi-agent system where the agents' intelligence is based on fuzzy expert system. O. Badawy and A. Almotwaly [8] developed the neural networks and neuro-fuzzy computing for taking into account the geometrical patterns of the financial data.…”

Section: Introductionmentioning

confidence: 99%

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Fundamental analysis in the multi-agent trading system

Korczak¹,

Hernes²,

Bac³

2016

Annals of Computer Science and Information Systems

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Abstract-The paper presents issues related to developing methods for fundamental analysis used to expand capabilities of multi-agent trading system, to better predict the financial market. The fundamental analysis indicators can be used as confirmation of decisions generated by other strategies of the system. The first part of the article discusses briefly the fundamental analysis issues in relation to the online trading on FOREX market. The statistical analysis of correlations of the different time series indicators and algorithms of fundamental analysis agents are examined. The final part discusses the results of the performance evaluation of selected investment strategies, including fundamental-based agents. I. INTRODUCTIONn trading support systems, the advices might be computed by one or many algorithms, or by one or many software agents using one or many information sources. An overview of of the agents operating on financial markets has already been given by B. LeBaron [1]. Currently, most trading systems are based on one or only a few algorithms. For example, the solutions described by L. Mendes, P. Godinho and J. Dias [2] use genetic algorithms to perform analysis on the basis of historical quotations. The system described by J.R. Thompson, J.R. Wilson and E. P. Fitts [3] is based on the multifractal time series. In the system described in [4,5] technical analysis indicators are used. There are many solutions based on multi-agent approach. H. C. Aladag, U. Yolco and E. Egrioglu [6] present an evaluation of the portfolio optimisation strategies by three agents: rational agent, interference agent, and technical analysis agent. P. Singh and B. Borah [7] apply a multi-agent system where the agents' intelligence is based on fuzzy expert system. O. Badawy and A. Almotwaly [8] developed the neural networks and neuro-fuzzy computing for taking into account the geometrical patterns of the financial data. M. Aloud, E.P.K. Tsang and R. Olsen [9] introduce an agent-based model for simple prediction of financial markets, where each agent predicts the development of selected subsets of the assets pairs in real time by separately examining the similarities between ask and bid assets histories. P. Kaltwasser [10] describes an agent that uses multiple behavioral techniques to make bidding decisions in the face of market uncertainty. J. Glattfelder, A. Dupuis and R. Olsen [11] develop a system that supports multiple strategies, but they use only movingaverage crossover strategies in the current stage of development. The paper by R. Barbosa and O. Belo [12] describes a multi-agent system that consists of a set of trading models such as an ensemble of classifiers, regression models, case-based reasoning, and an expert system. F. H. Westerhoff [13] presents a system where two groups of agents applying the methods of fundamental and technical analysis try to shape market dynamics.Summing up, more often the trading advice is provide by multiple software agents that use mainly technical analysis. However, many papers relate...

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“…Political considerations impact the level of confidence in a nation's government, the climate of stability and level of certainty [25].…”

Section: ) Balance Of Paymentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fundamental analysis in the multi-agent trading system

Korczak¹,

Hernes²,

Bac³

2016

Annals of Computer Science and Information Systems

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“…Fuzzy logic is used, for example, in multi-agent systems for information extraction [34], energy management [26] or robotics [18]. Fuzzy logic was also used for trading on FOREX, for example, in Expert Advisor [31] or technical analysis system [9] or fuzzy time series forecasting [1], [8], [36]. However, in these systems, the probability of decisions is ranged to [0..1].…”

Section: Fuzzy Logic As Agents' Knowledge Representationmentioning

confidence: 99%

Fuzzy logic in the multi-agent financial decision support system

Korczak

Hernes

Bac

2015

Annals of Computer Science and Information Systems

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Abstract-The article presents the application of a fuzzy logic in building the trading agents of the a-Trader system. The system supports investment decisions on the FOREX market. The first part of the article contains a discussion related to the use of fuzzy logic as an agents' knowledge representation. Next, the algorithms of the selected fuzzy logic buy-sell decision agents are presented. In the last part of the article the agent performance is evaluated on real FOREX data.

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“…The proposed prediction models use, for example, genetic algorithms [1], fundamental and technical analysis [2,3,4,5], neural networks and neuro-fuzzy computing [6], behavioral techniques [7]. There are also many multi-agent approach based solutions [8,9,10,11,12]. The trend is that in most cases multiple software agents that use different methods and techniques help provide trading advice.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Financial Time Series Forecasting in A-Trader System

Korczak¹,

Hemes²

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-The paper presents aspects related to developing methods for financial time series forecasting using deep learning in relation to multi-agent stock trading system, called A-Trader. On the basis of this model, an investment strategies in A-Trader system can be build. The first part of the paper briefly discusses a problem of financial time series on FOREX market. Classical neural networks and deep learning models are outlined, their performances are analyzed. The final part presents deployment and evaluation of a deep learning model implemented using H20 library as an agent of A-Trader system.

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Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization

Cited by 89 publications

References 43 publications

Fundamental analysis in the multi-agent trading system

Fundamental analysis in the multi-agent trading system

Fuzzy logic in the multi-agent financial decision support system

Deep Learning for Financial Time Series Forecasting in A-Trader System

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