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Title: Case based reasoning approach for transaction outcomes prediction on currency markets
Authors: Wang, Xiaoming
Sykora, Martin D.
Archer, Robert
Parish, David J.
Bez, Helmut E.
Issue Date: 2009
Publisher: © IEEE
Citation: WANG, X....et al., 2009. Case based reasoning approach for transaction outcomes prediction on currency markets. IN: 3rd International Workshop on Soft Computing Applications, (SOFA '09), Arad (Romania), July 29 -Aug. 1, pp. 93-98
Abstract: This paper presents a case based reasoning approach for making profit in the foreign exchange (forex) market with controlled risk using k nearest neighbour (kNN) and improving on the results with neural networks (NNs) and a combination of both. Although many professionals have proven that exchange rates can be forecast using neural networks for example, poor trading strategies and unpredictable market fluctuation can inevitably still result in substantial loss. As a result, the method proposed in this paper will focus on predicting the outcome of potential trades with fixed stop loss (ST) and take profit (TP) positions1, in terms of a win or loss. With the help of the Monte Carlo method, randomly generated trades together with different traditional technical indicators are fed into the models, resulting in a win or lose output. This is clearly a case based reasoning approach, in terms of searching similar past trade setups for selecting successful trades. There are several advantages over classical forecasting associated with such an approach, and the technique presented in this paper brings a novel perspective to problem of exchange trades predictability. The strategies implemented have not been empirically investigated with such wide a range of time granularities as is done in this paper, in any to the authors known academic literature. The profitability of this approach is back-tested at the end of this paper and highly encouraging results are reported.
Description: This is a conference paper [© IEEE]. It is also available from: http://ieeexplore.ieee.org/. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Version: Published
DOI: 10.1109/SOFA.2009.5254870
URI: https://dspace.lboro.ac.uk/2134/5885
ISBN: 9781424450541
Appears in Collections:Conference Papers (Computer Science)
Conference Papers and Contributions (Electronic, Electrical and Systems Engineering)

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