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• W2769366929 abstract "In this work we present a data-driven end-to-end Deep Learning approach for time series prediction, applied to financial time series. A Deep Learning scheme is derived to predict the temporal trends of stocks and ETFs in NYSE or NASDAQ. Our approach is based on a neural network (NN) that is applied to raw financial data inputs, and is trained to predict the temporal trends of stocks and ETFs. In order to handle commission-based trading, we derive an investment strategy that utilizes the probabilistic outputs of the NN, and optimizes the average return. The proposed scheme is shown to provide statistically significant accurate predictions of financial market trends, and the investment strategy is shown to be profitable under this challenging setup. The performance compares favorably with contemporary benchmarks along two-years of back-testing." @default.
• W2769366929 created "2017-12-04" @default.
• W2769366929 creator A5031057283 @default.
• W2769366929 creator A5088980119 @default.
• W2769366929 date "2017-11-11" @default.
• W2769366929 modified "2023-09-27" @default.
• W2769366929 title "Financial Time Series Prediction Using Deep Learning" @default.
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• W2769366929 hasPublicationYear "2017" @default.
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