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    题名: 運用Google Trends情緒萃取建構人工智慧量化交易策略：以台灣加權指數期貨為例
    Devising Quantitative Trading Strategies with Artificial-Intelligence using Google Trends Sentiment Extraction：The Case of TAIEX Futures
    作者: 王德諭
    Wang, De-Yu
    贡献者: 江彌修
    Chiang, Mi-Hsiu
    王德諭
    Wang, De-Yu
    关键词: Google Trends
    機器學習
    隨機森林
    市場情緒萃取
    台灣加權指數期貨
    下方風險
    Google Trends
    Machine learning
    Random forest
    Market sentiment extraction
    TAIEX futures
    Down-side risk
    日期: 2021
    上传时间: 2021-07-01 18:09:32 (UTC+8)
    摘要: 基於Google Trends的投資人情緒萃取，本文提供一具情緒表徵學習能力的集成預測框架。以隨機森林模型建構台灣加權指數期貨量化交易策略為例，本文探究輔以情緒萃取的分類器特徵生成之於模型預測能力及其量化交易策略之影響。本文的研究發現，輔以市場負面情緒(FEARS指數)以及股市關注度(Company_SVI)特徵生成，能有效提高隨機森林模型之陰性預測能力，其量化交易策略於測試區間之累積損益與風險比率皆勝出於大盤。特別地，我們發現2020年新冠疫情之後，輔以情緒特徵生成之模型預測能力及交易策略績效都能夠有效提升，在獲得與大盤相同績效的同時，承受虧損的幅度以及時間皆呈現大幅縮減。另外，當允許市場情緒萃取作近一步正負面之區分，本文發現陰性預測率雖能更有效提升，然而對下方風險的趨避能力下降，從而減損其量化交易策略之績效。
    By extracting public investor sentiment from Google Trends, this thesis provides an ensemble prediction framework that allows for sentiment representation-learning. Based on random forest models, TAIEX futures trading strategies are devised to examine the impacts of the added sentiment dimension on the random forest models’ predictive abilities and the trading strategies’ risk-reward performances. Our numerical findings show that, sentiment assisted representation-learning, when attributed by FEARS and Company_SVI indices, can effectively improve the downside predictive ability of random forest models, resulting in higher cumulative returns and better risk-return profiles relative to simple buy-and-holds. Further evidence suggests that, adopting sentiment assisted representation learning, especially during the post-pandemic era (after 2020), helps to maintain a comparable risk-return profile relative to that of a buy-and-hold while at the same time significantly reduces the extent of losses and the time endured for losses. In addition, upon further categorizing market sentiment as positive or negative, the random forest models’ downside predictive power is found to increase while the strategies’ downside-risk-aversive ability seems to decrease, leading to an overall detrimental effect on trading performance.
    參考文獻: 一、中文部分
    林哲鵬, 李春安, & 葉智丞. (2012). 投資人情緒與價格動能之關聯性. 管理與系統, 19(4), 729-759.
    鄭仁杰, & 江彌修. (2019). 漫步於隨機森林-輔以多數決學習的台股指數期貨交易策略. 經濟論文, 47(3), 395-448.


    二、英文部分
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    描述: 碩士
    國立政治大學
    金融學系
    108352030
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0108352030
    数据类型: thesis
    DOI: 10.6814/NCCU202100594
    显示于类别:[金融學系] 學位論文

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