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• W2897400362 abstract "Financial time series forecasting is considered to be one of the most challenging areas in current time series analysis theory. This complexity is caused mainly by the multitude of factors that influence the series and the volatility of this factors, which means that predicting models must adapt to a wide range of circumstances. Yet, the possibility of great financial return makes it a field of great interest for many researchers. Recently, non-linear techniques have been widely used for the construction of forecasting models, due to its ability to access higher order statistics. In particular, neural networks have shown to greatly enhance classical predicting models, and over the past years Deep Learning techniques are on the rise since computational power and data availability are becoming less of a constraint. This paper aims to compare the use of deep learning methods over shallow neural networks when predicting next day closing prices of stocks traded in the Brazilian financial market. Moreover, simulations using recent historical data are performed so that the models' profitability can be assessed and compared with the well-known strategy of Buy and Hold." @default.
• W2897400362 created "2018-10-26" @default.
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• W2897400362 date "2018-07-01" @default.
• W2897400362 modified "2023-09-24" @default.
• W2897400362 title "Financial Time Series Forecasting Using Non-Linear Methods and Stacked Autoencoders" @default.
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• W2897400362 doi "https://doi.org/10.1109/ijcnn.2018.8489425" @default.
• W2897400362 hasPublicationYear "2018" @default.
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