產業依存與跨產業報酬預測性：機器學習方法之應用

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姓名

張佑偵(Yu-Chen Chang) 查詢紙本館藏

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經濟學系

論文名稱

產業依存與跨產業報酬預測性：機器學習方法之應用

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摘要(中)

本文以台灣股票市場的產業加權指數報酬作為預測標的，建構產業輪動投資組合，探討Lasso迴歸是否真的有助於投資決策，通過實證結果發現，樣本內結果顯示前一期產業報酬對於個別產業報酬有一定的預測能力，台灣各產業間存在連動性，且個別產業能預測其他產業以及被其他產業預測的預測能力不一。紡織纖維類、金融保險業、資訊服務業及建材營造類常被Lasso選擇預測其他產業的超額報酬率。此外，本文基於OLS post-Lasso方法去建構產業輪動投資組合，將預測出來的報酬率分五等分位，做多（做空）預測超額報酬率較好（較差）的產業加權指數，發現OLS post-Lasso投資組合的表現尚可，其年化平均超額報酬率和夏普比率均高於使用OLS來建構的投資組合，同時，OLS post-Lasso的投資組合在景氣較不好期間有較好的投資表現。

摘要(英)

This paper uses the industry-weighted index returns of Taiwan stock market as the target of forecast, constructs an industry-rotation portfolio, and explores whether Lasso regression is really helpful for investment decision-making. The in-sample results show that some industries can predict the excess return of other industries. The textile fiber, finance and insurance industry, information service industry, and building construction industry are often selected by Lasso to predict the excess return of other industries. In addition, this paper constructs an industry-rotation portfolio based on OLS post-Lasso, buys (sells) industry-weighted indexes that predicts highest (lowest) excess returns , and finds that the portfolio based on OLS post-Lasso has not bad performance, and its annual average excess return and sharpe ratio are both higher than the portfolio based on OLS. At the same time, the portfolio based on OLS post-Lasso has better investment performance during periods of bad economic conditions.

關鍵字(中)

★ Lasso
★ OLS post-Lasso
★ 預測迴歸模型

關鍵字(英)

★ Lasso
★ OLS post-Lasso
★ Predictive regression

論文目次

目錄
第一章緒論 ............................................... 1
第二章文獻回顧 ............................................... 4
2.1 產業預測股價之文獻探討 ............................................... 4
2.2 迴歸模型正規化之文獻回顧 ............................................... 5
第三章研究方法 ............................................... 8
3.1 模型架構 ............................................... 8
3.2 資料來源與樣本選擇 ............................................... 10
第四章實證結果 ............................................... 12
4.1 全樣本敘述統計 ............................................... 12
4.2 樣本內迴歸結果 ............................................... 16
4.3 樣本外迴歸結果 ............................................... 23
4.4 景氣循環下的投資組合表現 ............................................... 24
4.5 基於樣本迴歸結果探討Lasso表現 ............................................... 26
第五章結論與建議 ............................................... 28
5.1 研究結果 ............................................... 28
5.2 研究限制與建議 ............................................... 28
參考文獻 ............................................... 30
附錄 ............................................... 34

參考文獻

英文文獻
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中文文獻
郝沛毅、歐仁彬、黃天受、林振穎、吳建生（2018）, “透過新聞文章預測股價漲跌趨勢－結合情緒分析、主題模型與模糊支持向量機,” 資訊管理學報, 25, 363-395。
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黃台心、鍾銘泰、楊淳如（2015）, “運用向量誤差修正模型探討台灣各產業與股市大盤間資訊傳遞速度,” 管理與系統, 22, 1-31。

指導教授

徐之強(Chih-Chiang Hsu)

審核日期

2021-6-30

推文