解構與預測產業報酬率-多元構面降維之觀點

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姓名

宋明澤(Ming-Ze Song) 查詢紙本館藏

畢業系所

財務金融學系

論文名稱

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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至系統瀏覽論文 (2025-7-13以後開放)

摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 股票報酬率預測
★ 解構
★ 選股策略
★ 偏分量迴歸
★ 分量迴歸

關鍵字(英)

★ equity return predictability
★ factorizing
★ stock selection strategy
★ partial quantile regression
★ quantile regression

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
一、緒論 1
二、資料 5
2.1 資料來源 5
2.2 資料清理 5
2.3 創建產業投組資料 5
2.4 區分變數類別 6
三、研究方法 10
3.1 第一階段-降維方法 11
3.1.1 主成分分析PCA 11
3.1.2 偏最小平方迴歸 PLS 11
3.1.3 偏分量迴歸 PQR 11
3.2 第二階段-遞迴式迴歸方法 12
3.3 模型預測能力衡量方法 13
3.4 類別及變數重要性衡量方法 13
四、實證結果 15
4.1 樣本外預測結果 15
4.1.1 消費品非耐久財 16
4.1.2 消費品耐久財 17
4.1.3 製造業 18
4.1.4 能源業 19
4.1.5 科技業 20
4.1.6 通訊業 21
4.1.7 零售業 21
4.1.8 醫藥業 22
4.1.9 水電業 22
4.1.10 其他產業 23
4.1.11 小結 23
4.2 重要類別因子 24
4.2.1 消費品耐久財 24
4.2.2 製造業 26
4.2.3 能源業 29
4.2.4 科技業 31
4.2.5 小結 34
4.3 重要變數 35
4.3.1 消費品耐久財 35
4.3.2 製造業 37
4.3.3 能源業 40
4.3.4 科技業 42
五、模型之經濟價值 - 投資策略應用 45
5.1 科技業擇時策略 45
5.2 科技業選股策略 47
六、結論 50
附錄 52
參考文獻 63

參考文獻

Chen, A. Y., & Zimmermann, T. (2021). Open source cross-sectional asset pricing. Critical Finance Review, forthcoming.
Daniel, K., Hirshleifer, D., & Sun, L. (2020). Short-and long-horizon behavioral factors. Review of Financial Studies, 33(4), 1673-1736.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417.
Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. Journal of Finance, 47(2), 427-465.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Fama, E. F., & French, K. R. (1995). Size and book‐to‐market factors in earnings and returns. Journal of Finance, 50(1), 131-155.
Fama, E. F., & French, K. R. (1997). Industry costs of equity. Journal of Financial Economics,
43(2), 153-193.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.
Feltham, G. A., & Ohlson, J. A. (1995). Valuation and clean surplus accounting for operating and financial activities. Contemporary Accounting Research, 11(2), 689-731.
Frankel, R. M., & Lee, C. M. (1996). Accounting diversity and international valuation (No. 96). New York Stock Exchange.
Giglio, S., Kelly, B., & Pruitt, S. (2016). Systemic risk and the macroeconomy: An empirical evaluation. Journal of Financial Economics, 119(3), 457-471.
Green, J., Hand, J. R., & Zhang, X. F. (2017). The characteristics that provide independent information about average US monthly stock returns. Review of Financial Studies, 30(12), 4389-4436.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223-2273.
Harvey, C. R., Liu, Y., & Zhu, H. (2016). … and the cross-section of expected returns. Review of Financial Studies, 29(1), 5-68.
Hou, K., Mo, H., Xue, C., & Zhang, L. (2019). Which factors? Review of Finance, 23(1), 1-35.
Hou, K., Xue, C., & Zhang, L. (2015). Digesting anomalies: An investment approach. Review of Financial Studies, 28(3), 650-705.
Hou, K., Xue, C., & Zhang, L. (2020). Replicating anomalies. Review of Financial Studies, 33(5), 2019-2133.
Kelly, B., & Pruitt, S. (2013). Market expectations in the cross‐section of present values. Journal of Finance, 68(5), 1721-1756.
Kelly, B., & Pruitt, S. (2015). The three-pass regression filter: A new approach to forecasting using many predictors. Journal of Econometrics, 186(2), 294-316.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica: 33-50.
Kozak, S., Nagel, S., & Santosh, S. (2020). Shrinking the cross-section. Journal of Financial Economics, 135(2), 271-292.
Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. Journal of Finance, 20(4), 587-615.
Liu, J., & Ohlson, J. A. (2000). The Feltham-Ohlson (1995) model: empirical implications. Journal of Accounting, Auditing & Finance, 15(3), 321-331.
Lo, K., & Lys, T. (2000). The Ohlson model: contribution to valuation theory, limitations, and empirical applications. Journal of Accounting, Auditing & Finance, 15(3), 337-367.
Lucas Jr, R. E. (1976). Econometric policy evaluation: A critique. In Carnegie-Rochester Conference Series on Public Policy (Vol. 1, pp. 19-46). North-Holland.
Lundholm, R. J. (1995). A tutorial on the Ohlson and Feltham/Ohlson models: answers to some frequently asked questions. Contemporary Accounting Research, 11(2), 749-761.
McCracken, M. W., & Ng, S. (2016). FRED-MD: A monthly database for macroeconomic research. Journal of Business & Economic Statistics, 34(4), 574-589.
Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica: 768-783.
Neely, C. J., Rapach, D. E., Tu, J., & Zhou, G. (2014). Forecasting the equity risk premium: the role of technical indicators. Management Science, 60(7), 1772-1791.
Rapach, D., & Zhou, G. (2013). Forecasting stock returns. In Handbook of Economic Forecasting (Vol. 2, pp. 328-383). Elsevier.
Ross, S. A. (2013). The arbitrage theory of capital asset pricing. In Handbook of the Fundamentals of Financial Decision Making: Part I (pp. 11-30).
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
Stambaugh, R. F., & Yuan, Y. (2017). Mispricing factors. Review of Financial Studies, 30(4), 1270-1315.
Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147-162.
Wegelin, J. A. (2000). A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. University of Washington, Tech. Rep.
Zaremba, A., Umutlu, M., & Maydybura, A. (2020). Where have the profits gone? Market efficiency and the disappearing equity anomalies in country and industry returns. Journal of Banking & Finance, 121, 105966.

指導教授

葉錦徽(Jin-Huei Yeh)

審核日期

2023-7-24

推文