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Deciding when to buy or sell a stock is not an easy task because the marketis hard to predict, being influenced by political and economic factors. Thus,methodologies based on computational intelligence have been applied to thischallenging problem. In this work, every day the stocks are ranked by techniquefor order preference by similarity to ideal solution (TOPSIS) using technicalanalysis criteria, and the most suitable stock is selected for purchase. Evenso, it may occur that the market is not favorable to purchase on certain days,or even, the TOPSIS make an incorrect selection. To improve the selection,another method should be used. So, a hybrid model composed of empirical modedecomposition (EMD) and extreme learning machine (ELM) is proposed. The EMDdecomposes the series into several sub-series, and thus the main omponent(trend) is extracted. This component is processed by the ELM, which performsthe prediction of the next element of component. If the value predicted by theELM is greater than the last value, then the purchase of the stock isconfirmed. The method was applied in a universe of 50 stocks in the Brazilianmarket. The selection made by TOPSIS showed promising results when compared tothe random selection and the return generated by the Bovespa index.Confirmation with the EMD-ELM hybrid model was able to increase the percentageof profit tradings.

Development of a hybrid method for stock trading based on TOPSIS, EMD and ELM