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研究生:賴振億
研究生(外文):Cheng-Yi Lai
論文名稱:景氣循環之因子投資策略
論文名稱(外文):Factor investment strategy for business cycle
指導教授:葉宗穎葉宗穎引用關係林盈課林盈課引用關係
指導教授(外文):Chung-Yung YehAnchor Y. Lin
口試委員:周佩儀
口試日期:2024-07-17
學位類別:碩士
校院名稱:國立中興大學
系所名稱:財務金融學系所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:77
中文關鍵詞:景氣循環景氣轉折點美林時鐘因子投資
外文關鍵詞:Business CyclesBusiness Cycle Turning PointsMerrill Lynch Investment ClockFactor Investment
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判斷以及預測景氣循環階段對於投資者、企業以及政府機構而言都是至關重要的,因為這些信息有助於制定更為精確的投資策略和政策措施。本研究旨在探討不同景氣階段下的最佳投資策略,並評估其績效表現。首先,依據美林時鐘理論將2004年至2023年間的經濟分為不同階段,並使用Harding and Pagan、擴散指數以及結合上述兩種方式之混合法等方法判斷景氣的轉折點,接著,根據超額報酬、索提諾比率和夏普比率,將投資策略在樣本內2004年至2015年資料分為三組:最佳表現組、次佳表現組和第三組。在樣本外2016年至2023年資料中,使用Harding and Pagan認定轉折點方法之索提諾比率投資策略持有10檔標的之第二組為最佳績效之投組,通貨再膨脹、復甦、過熱、停滯性通膨四個階段的因子分別為Safety、VCF、DY、VCF,累計報酬率高達1524.49%,且其無論是下檔風險、勝率、夏普比率、索提諾比率等皆優於台灣加權指數以及不分景氣階段之單因子表現。另外,以Harding and Pagan混和擴散指數之判定法則以超額報酬投資策略績持有10檔標的之第二組為最佳績效之投組,四個景氣階段的因子依序為Safety、VCF、BETA_1year、IVOL,累計報酬率為1273.76%。
本研究通過不同的景氣轉折點認定法劃分美林時鐘的各景氣階段,研究結果顯示,透過在不同景氣階段使用該階段最適合之的因子進行投資,其績效不僅能打敗大盤甚至能超越同段時間績效最佳之單因子不分景氣階段投組。
Identifying and predicting business cycle phases is crucial for investors, businesses, and government institutions, as this information aids in formulating more precise investment strategies and policy measures. This study aims to explore the optimal investment strategies under different economic phases and evaluate their performance. First, based on the Merrill Lynch Clock theory, the economy from 2004 to 2023 is divided into different phases. The turning points of the economic cycles are determined using methods such as Harding and Pagan, the diffusion index, and a hybrid method combining the two. Then, according to excess returns, Sortino ratio, and Sharpe ratio, investment strategies from the in-sample period of 2004 to 2015 are classified into three groups: best-performing, second-best, and third-best.In the out-of-sample period from 2016 to 2023, the Sortino ratio investment strategy using the Harding and Pagan turning point method, holding 10 stocks in the second-best group, showed the best performance. The factors in the four phases of reflation, recovery, heat, and stagflation were Safety, VCF, DY, and VCF, respectively, with a cumulative return rate of 1524.49%. Moreover, it outperformed the Taiwan Weighted Index and single-factor performance regardless of the business cycle phase in terms of downside risk, win rate, Sharpe ratio, and Sortino ratio.Additionally, using the hybrid method of Harding and Pagan and the diffusion index to determine the turning points, the excess return investment strategy holding 10 stocks in the second-best group was identified as the best-performing portfolio. The factors in the four economic phases were Safety, VCF, BETA_1year, and IVOL, with a cumulative return rate of 1273.76%.

This study, through the identification of different business cycle turning points and classification of the Merrill Lynch Clock phases, shows that by employing the most suitable factors for investment during each economic phase, the performance not only surpasses the market but also exceeds the best-performing single-factor portfolios during the same period regardless of the economic phase.
第一章 緒論 1
第一節 研究背景及動機 1
第二節 研究目的 1
第三節 研究架構 2
第二章 文獻回顧 3
第一節 景氣循環文獻回顧 3
第二節 因子文獻回顧 4
第三章 資料來源及研究方法 6
第一節 資料來源與研究期間 6
第二節 資料處理 8
第三節 美林時鐘景氣階段判定 9
第四節 消費者物價指數年增率及產出缺口 10
第五節 總經變數篩選 11
第六節 轉折點之判定 13
第七節 因子篩選及投組建構 15
第八節 績效衡量 17
第四章 資料分析與實證結果 18
第一節 景氣轉折點判定 18
第二節 有效因子篩選 23
第三節 因子投資組合 24
第四節 景氣階段之因子投資 26
第五節 樣本外績效表現 28
第五章 結論與建議 31
第一節 結論 31
第二節 研究限制與建議 31
參考文獻 32
附錄 73
第一節 因子定義 73
第二節 總經變數文獻來源 76
K, M. G., Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. Journal of the Royal Statistical Society, 109(3), 298.
Chen, N., Roll, R., & Ross, S. A. (1986).Economic forces and the stock market. The Journal of Business, 59(3), 383-403.
McQueen, G., & Roley, V. V. (1993). Stock prices, news, and business conditions.The Review of Financial Studies, 6(3), 683–707.
Brocato, J., & Steed, S. (1998). Optimal asset allocation over the business cycle. The Financial Review, 33(3), 129–148.
Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects.
Journal of financial markets, 5(1), 31-56.
Van Vliet, P., & Blitz, D. (2011). Dynamic strategic asset allocation: Risk and return across the business cycle. Journal of Asset Management, 12(5), 360–375.
Bry, G., & Boschan, C. (1971). Cyclical analysis of time series: selected procedures and computer programs.
Harding, D., & Pagan, A. (2002). Dissecting the cycle: a methodological investigation. Journal of Monetary Economics, 49(2), 365–381.
Hickman, B. G. (1959). Diffusion, acceleration, and business cycles. The American Economic Review, 49(4), 535-565.
Stock, J. H., & Watson, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167–1179.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587-615.
Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica: Journal of the econometric society, 768-783.
Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. The Journal of Finance, 47(2), 427-465.
Datar, V. T., Naik, N. Y., & Radcliffe, R. (1998). Liquidity and stock returns: An alternative test. Journal of Financial Markets, 1(2), 203–219.
Næs, R., Skjeltorp, J. A., & Ødegaard, B. A. (2011). Stock market liquidity and the business cycle. The Journal of Finance, 66(1), 139–176.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65–91.
Chordia, T., & Shivakumar, L. (2002). Momentum, business cycle, and time‐varying expected returns. The Journal of Finance, 57(2), 985–1019.
Novy-Marx, R. (2013). The other side of value: The gross profitability premium. Journal of Financial Economics, 108(1), 1–28.
Issah, M., & Antwi, S. (2017). Role of macroeconomic variables on firms’ performance: Evidence from the UK. Cogent Economics & Finance, 5(1), 1405581.
Banerjee, A., & Marcellino, M. (2006). Are there any reliable leading indicators for US inflation and GDP growth? International Journal of Forecasting, 22(1), 137–151.
Rich, R. W., & Steindel, C. (2007). A comparison of measures of core inflation. Economic Policy Review, 13(3).
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, 58(1), 267–288.
黃朝熙(2007),「台灣通貨膨脹預測」,中央銀行季刊,第二十九卷第一期,頁 5- 29。
黃裕烈(2016)。精進景氣循環認定之計量方法。委託研究計畫。
許育銘(2022)。因子投資在景氣循環下之應用。國立中興大學財務金融學系碩士論文。
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