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              <text>Employing Deep Learning in Intraday Stock Trading</text>
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              <text>Deep Learning; Long Short-Term Memory (LSTM); Machine Learning; Recurrent Neural Networks(RNN); Stock Market Forecasting</text>
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              <text>Accurate stock price prediction is a significant benefit to the Stock investors. The future Stock value of any company is determined by Stock market prediction. A successful prediction of the stock's future price could result in a significant profit; Hence investors prefer a precise Stock price prediction. Although there are many different approaches to helps in forecasting stock prices, this paper will briefly look into the deep learning models and compare LSTM model and its variants. The key intention of this study is to propose a model that is best suitable and can be implemented to forecasting trend of stock prices. This paper focuses on binary classification problem, predicting the next-minute price movement of SPDR SP 500 index The testing experiments performed on the SPDR SP 500 index reveals that the variants of LSTM models, Slim LSTM1, slim LSTM2, and Slim LSTM3 with less parameters, provide better performance when compared to the Standard LSTM Model.  2020 IEEE.</text>
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              <text>Taroon G.; Tomar A.; Manjunath C.; Balamurugan M.; Ghosh B.; Krishna A.V.N.</text>
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              <text>Proceedings - 2020 5th International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2020, pp. 209-214.</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICRCICN50933.2020.9296174" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICRCICN50933.2020.9296174&lt;/a&gt;
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              <text>ISBN: 978-172818818-8</text>
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              <text>Taroon G., CHRIST (Deemed to Be University), Computer Science and Engineering, School of Engineering and Technology, Bengaluru, India; Tomar A., CHRIST (Deemed to Be University), Computer Science and Engineering, School of Engineering and Technology, Bengaluru, India; Manjunath C., CHRIST (Deemed to Be University), Computer Science and Engineering, School of Engineering and Technology, Bengaluru, India; Balamurugan M., CHRIST (Deemed to Be University), Computer Science and Engineering, School of Engineering and Technology, Bengaluru, India; Ghosh B., Institute of Management CHRIST, Deemed to Be University, School of Business Management, Bengaluru, India; Krishna A.V.N., CHRIST (Deemed to Be University), Computer Science and Engineering, School of Engineering and Technology, Bengaluru, India</text>
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