預測臺灣股市風險溢酬分位數 - 以偏分量迴歸整合高維資訊

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姓名

霜皓帆(Hao-Fan Shuang) 查詢紙本館藏

畢業系所

財務金融學系

論文名稱

預測臺灣股市風險溢酬分位數 - 以偏分量迴歸整合高維資訊
(Predicting Quantiles of Taiwan Equity Risk Premium - Aggregating Information via Partial Quantile Regression)

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摘要(中)

本文以臺灣加權股價指數之風險溢酬第 5(崩盤)、20(大跌)、50(平盤)、80(大漲)、 95 (過熱) 之條件分量為預測目標。樣本為 2000 年 1 月至 2021 年 12 月的月頻率資料。本文收集包括總體經濟指標、基本價值變數、交易活動指標、價格趨勢、貨幣及信用指標共 34 個預測變數，採用包括主成份分析、偏最小平方迴歸法，以及偏分量迴歸共計 3 種降維模型萃取有用資訊估計共同因子，進而預測風險溢酬之條件分量。透過全樣本與樣本外遞迴式預測實證分析中比較各模型的優劣，我們發現主成份分析法之預測績效最差;偏最小平方迴歸法則受制於其共同因子無法因應因各目標分量的需求，只在預測崩盤、大跌具有預測能力;偏分量迴歸則得益於其共同因子會隨目標分量改變而不同，使其能夠穩定地預測 5 個不同分量且具有明顯優勢。此外，透過檢驗偏分量迴歸之共同因子變數組成成份，我們發現價格趨勢變數在各個條件分量下都具有預測能力;基本價值變數在預測平盤時較具優勢;交易活動變數在預測崩盤與過熱時較能體現其重要性;總體經濟指標適用於大漲及過熱;貨幣及信用變數則是以平盤以下較具有預測力。最後，本文嘗試以偏分量迴歸之預測值建構交易策略，在考慮了交易成本、參考夏普比率、確定等價收益率 (CER) 等績效衡量指標下，我們所建構的交易策略明顯優於買進並持有 (Buy and Hold)，可見預測條件分量具有相當的經濟價值。

摘要(英)

We conduct a predictive performance analysis of the 5 conditional quantiles of the TAIEX equity risk premium using extracted information from high dimensional data from different aspects of risk premium: price trend, fundamental, trading activity, macroeconomic, monetary variables. We pick the 5th, 20th, 50th, 80th, 95th quantiles to represent the 5 market states of the risk premium as “collapse”, “plummet”, “stable”, “soar”, “boom”, respectively. We perform a comparative analysis among dimension reduction models, including Principal Component Analysis (PCA), Partial Least Squared (PLS), Partial Quantile Regression (PQR). The empirical results show that (1) PCA performs the worst in forecasting; (2) As the PLS factors fail to characterize the spectrum of each target quantile, there is an obvious "asymmetric" phenomenon in that PLS predicts well only in the “collapse” and “plummet” states; (3) PQR can predict 5 different quantiles consistently and have obvious advantages over the other two methods. Finally, in order to confirm the economic benefits of predicting conditional quantiles, we construct a trading strategy using the predicted values from the PQR. Taking into account transaction costs, and performance measures such as the Sharpe ratio, and the certainty equivalent rate of return, the constructed trading strategy delivers significantly economic values and outperforms the commonly used Buy and Hold strategy.

關鍵字(中)

★ 臺灣股價指數
★ 機器學習
★ 降維
★ 分位數
★ 投資策略
★ 樣本外預測

關鍵字(英)

★ Return premium predictability
★ Machine learning
★ Dimension reduction
★ Quantiles
★ Asset allocation
★ Out-of-sample forecasting

論文目次

目錄
摘要 V
ABSTRACT VI
致謝 VIII
目錄 IX
圖目錄 XI
表目錄 XI
第一章緒論 1
第二章資料建構與來源 4
第三章研究方法建構 10
3-1 分量迴歸 10
3-2 模型建構 11
3-2-1 因子估計 12
3-2-2分量預測迴歸 13
3-3 績效衡量 14
3-4 變數重要性衡量方法 14
第四章實證結果 15
4-1 全樣本分析 15
4-1-1 單一變數 15
4-1-2 因子模型 - 單一類別 18
4-1-3 因子模型 - 全變數 20
4-1-4 變數重要性 22
4-2 樣本外預測 24
4-2-1 單一變數 24
4-2-2因子模型 - 單一類別 26
4-2-3 因子模型 - 全變數 27
4-2-4 變數重要性 - 樣本外 29
第五章模型之經濟價值 31
第六章結論 35
中文參考文獻 37
英文參考文獻 37

參考文獻

中文參考文獻
徐婉容 (2020). 認定與預測臺灣股市大跌. 中央銀行季刊, 42(2) ,37-72 莊家彰, & 管中閔. (2005). 台灣與美國股市價量關係的分量迴歸分析. 經
濟論文, 33(4), 379-404.
英文參考文獻
Aikman, D., Haldane, A. G., & Nelson, B. D. (2015). Curbing the credit cycle. Economic Journal, 125(585), 1072-1109.
Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets, 5(1), 31-56.
Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross‐section of volatility and expected returns. Journal of Finance, 61(1), 259-299.
Baker, M., & Wurgler, J. (2000). The equity share in new issues and aggregate stock returns. Journal of Finance, 55(5), 2219-2257.
Balduzzi, P., & Lynch, A. W. (1999). Transaction costs and predictability: Some utility cost calculations. Journal of Financial Economics, 52(1), 47-78.
Bali, T. G., Cakici, N., & Whitelaw, R. F. (2011). Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of Financial Economics, 99(2), 427-446.
Barbee Jr, W. C., Mukherji, S., & Raines, G. A. (1996). Do sales–price and debt–equity explain stock returns better than book–market and firm size? Financial Analysts Journal, 52(2), 56-60.
Baur, D. G., Dimpfl, T., & Jung, R. C. (2012). Stock return autocorrelations
49
revisited: A quantile regression approach. Journal of Empirical Finance,
19(2), 254-265.
Brunnermeier, M., Farhi, E., Koijen, R. S., Krishnamurthy, A., Ludvigson, S. C.,
Lustig, H., Nagel, S., & Piazzesi, M. (2021). Perspectives on the Future of
Asset Pricing. Review of Financial Studies, 34(4), 2126-2160.
Campbell, J. Y. (1987). Stock returns and the term structure. Journal of Financial
Economics, 18(2), 373-399.
Campbell, J. Y., & Shiller, R. J. (1988). The dividend-price ratio and
expectations of future dividends and discount factors. Review of Financial
Studies, 1(3), 195-228.
Campbell, J. Y., & Vuolteenaho, T. (2004). Inflation illusion and stock prices.
American Economic Review, 94(2), 19-23.
Chen, S.-S. (2012). Consumer confidence and stock returns over market
fluctuations. Quantitative Finance, 12(10), 1585-1597.
Chordia, T., Subrahmanyam, A., & Anshuman, V. R. (2001). Trading activity
and expected stock returns. Journal of financial Economics, 59(1), 3-32. De Bondt, W. F., & Thaler, R. (1985). Does the stock market overreact? Journal
of Finance, 40(3), 793-805.
Fama, E. F., & French, K. R. (1989). Business conditions and expected returns
on stocks and bonds. Journal of Financial Economics, 25(1), 23-49. Flannery, M. J., & Protopapadakis, A. A. (2002). Macroeconomic factors do influence aggregate stock returns. Review of Financial Studies, 15(3),
751-782.
Gettleman, E., & Marks, J. M. (2006). Acceleration strategies. SSRN Electronic
Journal.
Giglio, S., Kelly, B., & Pruitt, S. (2016). Systemic risk and the macroeconomy:
50

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. The
Review of Financial Studies, 30(12), 4389-4436.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine
learning. The Review of Financial Studies, 33(5), 2223-2273.
Gu, S., Kelly, B., & Xiu, D. (2021). Autoencoder asset pricing models. Journal
of Econometrics, 222(1), 429-450.
Jegadeesh, N. (1990). Evidence of predictable behavior of security returns.
Journal of finance, 45(3), 881-898.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling
losers: Implications for stock market efficiency. Journal of Finance, 48(1),
65-91.
Jurdi, D. J. (2022). Predicting the Australian equity risk premium. Pacific-Basin
Finance Journal, 71, 101683.
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.
Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: A
unified model of risk and return. Journal of Financial Economics, 134(3),
501-524.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica:
Journal of the Econometric Society, 33-50.
Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic
Perspectives, 15(4), 143-156.
51

Lamont, O. (1998). Earnings and expected returns. Journal of Finance, 53(5), 1563-1587.
Lewellen, J. (2004). Predicting returns with financial ratios. Journal of Financial Economics, 74(2), 209-235.
Light, N., Maslov, D., & Rytchkov, O. (2017). Aggregation of information about the cross section of stock returns: A latent variable approach. Review of Financial Studies, 30(4), 1339-1381.
Mele, A. (2007). Asymmetric stock market volatility and the cyclical behavior of expected returns. Journal of Financial Economics, 86(2), 446-478.
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.
Nurazi, R., & Usman, B. (2016). Bank stock returns in responding the contribution of fundamental and macroeconomic effects. Journal of Economics and Policy, 9(1), 134-149.
Rosenberg, B., Reid, K., & Lanstein, R. (1985). Persuasive evidence of market inefficiency. Journal of Portfolio Management, 11(3), 9-16.
Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147- 162.
Thomas, J. K., & Zhang, H. (2002). Inventory changes and future returns. Review of Accounting Studies, 7(2), 163-187.
Welch, I., & Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. Review of Financial Studies, 21(4), 1455-1508.

指導教授

葉錦徽

審核日期

2022-9-12

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