Employing Deep Learning in Intraday Stock Trading

Title

Employing Deep Learning in Intraday Stock Trading

Creator

Taroon G.; Tomar A.; Manjunath C.; Balamurugan M.; Ghosh B.; Krishna A.V.N.

Description

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.

Source

Proceedings - 2020 5th International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2020, pp. 209-214.

Date

2020-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Deep Learning; Long Short-Term Memory (LSTM); Machine Learning; Recurrent Neural Networks(RNN); Stock Market Forecasting

Coverage

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

Rights

Restricted Access

Relation

ISBN: 978-172818818-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICRCICN50933.2020.9296174

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