解構與預測產業報酬率-多元構面降維之觀點;Factorizing for Equity Return Predictability

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Title:	解構與預測產業報酬率-多元構面降維之觀點;Factorizing for Equity Return Predictability
Authors:	宋明澤;Song, Ming-Ze
Contributors:	財務金融學系
Keywords:	股票報酬率預測;解構;選股策略;偏分量迴歸;分量迴歸;equity return predictability;factorizing;stock selection strategy;partial quantile regression;quantile regression
Date:	2023-07-24
Issue Date:	2024-09-19 16:14:24 (UTC+8)
Publisher:	國立中央大學
Abstract:	本文以美國各產業上市公司之投資組合報酬率分位數 (10%, 50%, 90%) 為預測標的，樣本期間為 1990 年 1 月至 2021 年 12 月的月頻率資料，受 Feltham-Ohlson Model 啟發，蒐集代表公司基本面的財務、經營、技術、供應、企業社會責任類別，以及代表其他未來資訊的情緒和總體經濟等類別共 414 個變數，再藉由降維方法萃取類別因子，並輸入分量迴歸模型進行預測。首先，在十個產業中，報酬率的中位數難以預測，這與效率市場假說一致。而消費品耐久財、製造業、能源業以及科技業在極端分配 (10%, 90%) 皆具可預測性。第二，在不同時期下，組成產業報酬率的結構隨時間不斷轉變，與 Lucas Critique 一致，因此，在不同的經濟結構與環境下，使用什麼變數與模型預測報酬率變得十分重要。第三，本研究也將各類別因子解構出重要變數，提供予投資人與金融機構研究員在進行股票評價時明確的研究方向。最後，本文創建投資策略，屬監督式學習的偏分量迴歸方法 (PQR) 在選股策略中能明顯區分出風險比率較高與較低的個股，科技業之多空避險策略在 2000 年至 2021 年創造 400% 的累積報酬率，超出買入並持有科技股市值加權指數近 200%。相比平均數，極端分配觀察到了更多報酬率的重要資訊，更能掌握報酬率的全貌，面對報酬率的變化，能發展出更多元的策略，更加靈活的分析與調整資產配置。;This paper focuses on the quantiles (10%, 50%, 90%) of the equity return of listed companies in the United States. The sample period covers monthly data from January 1990 to December 2021. Inspired by the Feltham-Ohlson Model, this study collects a total of 414 variables with categories which include financial, operating, technical, supply, ESG, sentiment, and macroeconomics. Input the factors after extracting through dimension reduction into quantile regression model for prediction. Firstly, among the ten industries, it is difficult to predict the median return, which is consistent with the Market Efficiency Hypothesis. However, the consumer durables, manufacturing, energy, and technology industries exhibit predictability in extreme distribution (10%, 90%). Secondly, the structure of equity return undergoes continuous transformation over time, which is consistent with the Lucas Critique. Therefore, it becomes crucial to determine which variables and models to use for return rate prediction under different economic structures. Thirdly, this study decomposes important variables from each category factor, providing clear research directions for investors and financial researchers in stock valuation. Lastly, this paper creates stock selection strategy using a supervised learning partial quantile regression (PQR) method. The strategy effectively distinguishes individual stocks with higher and lower risk ratios. The long-short hedge strategy in the technology industry achieved a cumulative return of 400% from 2000 to 2021, outperforms the buy-and-hold market value-weighted technology stock index by nearly 200%. Compared to the mean, extreme distributions capture more important information and provides a comprehensive understanding of equity return. In the face of changing equity return, it enables more diverse strategies and more flexible adjustment of asset allocation.
Appears in Collections:	[Graduate Institute of Finance] Electronic Thesis & Dissertation

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