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| Title: | 利用大型語言模型分析公司重大訊息並生成當沖交易策略
Analyzing Material Information with Large Language Models for Generating Day Trading Signals |
| Authors: | 柯昱均
Ke, Yu-Chun |
| Contributors: | 黃泓智
Huang, Hong-Chih
柯昱均
Ke, Yu-Chun |
| Keywords: | 大型語言模型
生成式人工智慧
當沖交易策略
重大訊息分析
股價趨勢預測
事件研究法
機器學習
集成學習
Large language models
Generative AI
Day trading strategies
Material information analysis
Stock price prediction
Event study methodology
Machine learning
Ensemble learning |
| Date: | 2024 |
| Issue Date: | 2025-02-04 16:03:53 (UTC+8) |
| Abstract: | 本研究探討生成式人工智慧（Generative AI）中的大型語言模型（LLMs）在金融市場的應用，特別是利用其分析上市公司重大訊息並生成當沖交易策略。雖然LLMs在多領域已有顯著進展，金融應用尚屬初步探索階段。本研究旨在驗證ChatGPT是否具備分析重大訊息以推斷股價趨勢的能力，並評估其交易策略效果。資料範圍聚焦於臺灣50指數成分股的重大訊息及新聞資料，並利用事件研究法進行異常報酬檢定。此外，研究結合技術指標與機器學習模型，期望透過多種訊號的整合，提升交易策略的準確性與表現。
研究結果顯示，大型語言模型在做空和多空策略中表現出顯著優勢，能有效捕捉市場中的負面訊息，尤其在市場情緒不穩定或趨勢反轉時具有較高的預測靈敏度。然而，做多策略的表現相對不佳，主要因為正面訊息的來源多樣且可能存在資訊洩漏，使得依賴重大訊息的做多策略風險較高。對於漲跌二分類任務，模型表現較為穩定，能清晰區分市場趨勢；但在漲跌三分類任務中，模型準確度有所下降，因為細緻的分類使得邊界情況下的預測更加困難。在集成學習方面，投票法雖能減少單一模型偏差帶來的風險，但也使的模型變的較為平庸。相對地，堆疊法通過結合多種機器學習模型的判斷，有效改善了做多策略的表現，並在做空及多空策略中展現優異效果。綜合來看，大型語言模型在多空策略及漲跌二分類任務中展現出應用潛力，並顯示結合歷史股價資訊的機器學習模型能夠有效提升交易決策品質。
This study explores the application of large language models (LLMs) in generative artificial intelligence (Generative AI) for financial markets, focusing on analyzing major corporate announcements and generating day trading strategies. Although LLMs have advanced significantly in various fields, their financial applications remain in the early stages. The research assesses whether ChatGPT can forecast stock price trends from material information and evaluates the effectiveness of these trading strategies. The study examines announcements and news related to Taiwan 50 Index constituents, applying event study methodology to detect abnormal returns. It also integrates technical indicators and machine learning models to enhance strategy accuracy.
The results show that LLMs excel in short-selling and mixed strategies, effectively capturing negative market signals, especially during periods of instability or trend reversals. Long strategies perform less well due to varied sources of positive information and potential information leakage, increasing risk. The model is consistent in binary classification but less accurate in ternary classification due to increased complexity. While voting methods in ensemble learning reduce bias, they yield mediocre results. Stacking methods, combining multiple machine learning models, improve long strategy performance and excel in short-selling and mixed strategies. Overall, LLMs show potential in mixed strategies and binary classification, and integrating historical stock price information with machine learning models effectively enhances trading decision quality. |
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| Description: | 碩士
國立政治大學
風險管理與保險學系
111358030 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111358030 |
| Data Type: | thesis |
| Appears in Collections: | [風險管理與保險學系] 學位論文
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