政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152473

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152473

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题名:	模糊性與資產定價：台灣加權指數的實證研究與機器學習應用 Ambiguity and asset pricing: Empirical study and machine learning applications using the Taiwan Weighted Stock Index
作者:	林晉毅 Lin, Chin-I
贡献者:	廖四郎 Liao, Szu-Lang 林晉毅 Lin, Chin-I
关键词:	模糊性 Knightian uncertainty Ambiguity 神經網路 LSTM 台灣加權指數 Knightian uncertainty Ambiguity Neural network LSTM Taiwan Weighted Stock Index
日期:	2024
上传时间:	2024-08-05 12:18:55 (UTC+8)
摘要:	傳統資產定價模型主要考慮風險，而忽略了機率本身的不確定性，即模糊性。本研究採用台灣加權指數作為研究對象，參考了Brenner and Izhakian (2018)提出的實證方法來測量台灣市場中的模糊性程度，並從台灣的市場數據中評估投資者對模糊性的態度。實證結果顯示，模糊性在股票市場中具有價格，且當預期的有利報酬機率較高時，投資者對模糊性表現出厭惡態度。此外，本研究嘗試將模糊性作為一種新指標，並將其引入神經網路與長短期記憶(LSTM)機器學習模型，以觀察其對股票價格預測的影響。結果顯示，加入模糊性後，機器學習模型的預測準確度有提升的趨勢。 Traditional asset pricing models primarily focus on risk, often neglecting the uncertainty inherent in probabilities, known as ambiguity. This research examines the Taiwan Weighted Index, using the empirical method by Brenner and Izhakian (2018) to quantify the level of ambiguity in the Taiwanese market. By analyzing market data from Taiwan, the study evaluates investor attitudes towards ambiguity. The findings indicate that ambiguity is indeed reflected in stock market prices, and investors show aversion to ambiguity when the likelihood of favorable returns is high. Furthermore, this study explores incorporating ambiguity as a new indicator into neural network and long short-term memory (LSTM) machine learning models to assess its impact on stock price predictions. The results indicate an improvement in prediction accuracy for machine learning models with the inclusion of ambiguity.
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描述:	碩士國立政治大學金融學系 111352034
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0111352034
数据类型:	thesis
显示于类别:	[金融學系] 學位論文

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