Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929334965501952 |
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| author | Zhang, Haohan Hua, Fengrui Xu, Chengjin Kong, Hao Zuo, Ruiting Guo, Jian |
| author_facet | Zhang, Haohan Hua, Fengrui Xu, Chengjin Kong, Hao Zuo, Ruiting Guo, Jian |
| contents | The rapid advancement of Large Language Models (LLMs) has spurred discussions about their potential to enhance quantitative trading strategies. LLMs excel in analyzing sentiments about listed companies from financial news, providing critical insights for trading decisions. However, the performance of LLMs in this task varies substantially due to their inherent characteristics. This paper introduces a standardized experimental procedure for comprehensive evaluations. We detail the methodology using three distinct LLMs, each embodying a unique approach to performance enhancement, applied specifically to the task of sentiment factor extraction from large volumes of Chinese news summaries. Subsequently, we develop quantitative trading strategies using these sentiment factors and conduct back-tests in realistic scenarios. Our results will offer perspectives about the performances of Large Language Models applied to extracting sentiments from Chinese news texts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_14222 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements? Zhang, Haohan Hua, Fengrui Xu, Chengjin Kong, Hao Zuo, Ruiting Guo, Jian Computation and Language Artificial Intelligence Statistical Finance The rapid advancement of Large Language Models (LLMs) has spurred discussions about their potential to enhance quantitative trading strategies. LLMs excel in analyzing sentiments about listed companies from financial news, providing critical insights for trading decisions. However, the performance of LLMs in this task varies substantially due to their inherent characteristics. This paper introduces a standardized experimental procedure for comprehensive evaluations. We detail the methodology using three distinct LLMs, each embodying a unique approach to performance enhancement, applied specifically to the task of sentiment factor extraction from large volumes of Chinese news summaries. Subsequently, we develop quantitative trading strategies using these sentiment factors and conduct back-tests in realistic scenarios. Our results will offer perspectives about the performances of Large Language Models applied to extracting sentiments from Chinese news texts. |
| title | Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements? |
| topic | Computation and Language Artificial Intelligence Statistical Finance |
| url | https://arxiv.org/abs/2306.14222 |