Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)

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Use of NLP-Powered Sentiment Analysis in Trading Strategy

Authors
Zanyang (Neil) Chen1, *
1UWC Changshu China, 88 W. Kunchenghu Road, Changshu, Jiangsu, China
*Corresponding author. Email: neilchenhenri@gmail.com
Corresponding Author
Zanyang (Neil) Chen
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-198-2_13How to use a DOI?
Keywords
sentiment analysis; trading strategy; execution; rule-based dictionary; insight weight portfolio construction method; backtest
Abstract

The research paper focused on sentiment analysis powered by natural language processing and its use in trading strategy. Previous sentiment analysis based trading solutions are usually proprietary and produce market reports on a daily or weekly basis. We, therefore, aimed to address the lack of transparency and execution efficiency in these solutions. By taking advantage of the algorithmic trading platform QuantConnect and referring to its documentation, we built our very own algorithm directly executable for live trading. The algorithm adopted a rule-based dictionary approach to analyze the market sentiment towards different stocks and the insight weight method to construct the portfolio based on the sentiment analysis results. The algorithm was backtested using the historical data and optimized for higher performance. The final testing results showed that the sentiment analysis powered trading strategy is able to achieve a similar performance to the benchmark, which is the S&P500 index, both in periods of bull markets and bear markets, with the potential of outperforming under favorable market conditions.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-198-2_13
ISSN
2589-4900
DOI
10.2991/978-94-6463-198-2_13How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Zanyang (Neil) Chen
PY  - 2023
DA  - 2023/08/10
TI  - Use of NLP-Powered Sentiment Analysis in Trading Strategy
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 109
EP  - 115
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_13
DO  - 10.2991/978-94-6463-198-2_13
ID  - Chen2023
ER  -