本研究旨在透過分析經過篩選後的 9 項經濟與市場變數，探討台灣加權股價指數的領先指標，提升投資人決策的精準性與有效性。
本研究應用單根檢定、交叉相關性分析、Granger 因果檢定、共整合檢定及 Bry-Boschan 法，探討變數與大盤之間的短期及長期關係、領先期數，以及分析波峰、波谷的領先或滯後期數，探討各變數的峰谷是否主要領先於大盤，以此評估其作為領先指標的有效性。
發現台積電股票日收盤價在多項分析中展現強大的領先能力，為短期市場趨勢的重要指標。那斯達克與費城半導體指數之峰谷也顯示與台灣加權股價指數之峰谷有高度的領先相關性。最後透過回測分析，發現以台積電股票日收盤價、費城半導體指數及那斯達克指數作為指標的交易策略均顯著優於大盤指數表現。

This study aims to analyze nine selected economic and market variables to explore the leading indicators of the Taiwan Weighted Stock Index, enhancing the precision and effectiveness of investors' decision-making.
The study applies unit root tests, cross-correlation analysis,Granger causality tests, cointegration tests, and the Bry-Boschan method to investigate the short-term and long-term relationships between variables and the index, examine leading periods, and analyze the leading or lagging periods of peaks and troughs. It evaluates whether the peaks and troughs of these variables predominantly lead the index, thereby assessing their effectiveness as leading indicators.
The findings reveal that the daily closing price of TSMC stock demonstrates strong leading capabilities in multiple analyses, making it an important indicator of short-term market trends. The peaks and troughs of the NASDAQ and the Philadelphia Semiconductor Index also show a high degree of leading correlation with the peaks and troughs of the Taiwan Weighted Stock Index. Lastly, through backtesting analysis, trading strategies using the daily closing price of TSMC stock, the Philadelphia Semiconductor Index, and the NASDAQ as indicators significantly outperformed the performance of the Taiwan Weighted Stock Index.

目錄
致謝詞	I
摘要	II
Abstract	III
目錄	V
圖目錄	VI
表目錄	VII
第一章 緒論	1
第一節 前言	1
第二節 研究主題與目的	2
第三節 論文架構	3
第二章 文獻回顧	4
第一節 台灣加權股價指數及景氣循環領先性指標研究	4
第二節 各國大盤及景氣領先性指標研究	5
第三節 定義大盤波峰及谷底之研究	5
第三章 資料來源與研究方法	6
第一節 資料來源與變數說明	6
第二節 資料分析與實證方法	14
第四章 實證結果分析及說明	21
第一節 分析結果概述	21
第二節 回測分析實證	31
第五章 結論	41
參考文獻	43
附錄	49

圖目錄
【圖1-1】台灣加權指數日線圖(2020-2024.9.2)	2

表目錄
【表3-1】總體經濟變數與指標介紹表	8
【表3-2】Dickey-Fuller 檢定之臨界值	15
【表3-3】跡檢定之臨界值	18
【表3-4】最大特性根檢定之臨界值	19
【表4-1】台灣加權股價指數及指標變數單根檢定量表	21
【表4-2】台灣股價加權指數與指標變數交叉相關性分析表	23
【表4-3】台灣加權股價指數與指標變數因果檢定結果	24
【表4-4】台灣加權股價指數與指標變數共整合檢定結果	27
【表4-5】台灣加權股價指數峰谷與指標變數峰谷領先、滯後或同步次數	29
【表4-6】指標變數領先台灣加權股價指數平均日數	30
【表4-7】以CNN恐懼與貪婪指數為指標交易之績效	32
【表4-8】以三大法人買進金額(億)為指標交易之績效	33
【表4-9】以外銷訂單動向指數（以家數計）為指標交易之績效	34
【表4-10】以半導體設備進口值（新台幣百萬元）為指標交易之績效	35
【表4-11】以台積電股票日收盤價為指標交易之績效	36
【表4-12】以那斯達克指數為指標交易之績效	37
【表4-13】以費城半導體指數為指標交易之績效	38
【表4-14】以新增訂單除以客戶存貨為指標交易之績效	39

王昭銘 (2009)。VIX 波動率指數與亞洲股市之動態關聯性研究。碩士論文，南華大學，財務管理研究所。


王政勳 (2010)。領先指標對台灣景氣趨勢預測能力的探討：非線性因果關係檢定的應用。碩士論文，國立中央大學，經濟學系。


王悅瑄 (2014)。股市循環與產業類股投資策略之分析。碩士論文，朝陽科技大學，財務金融系。


李承璟 (2003)。台股趨勢與景氣指標之分析。碩士論文，國立臺灣大學，經濟學系。


施柏榮 (2011)。台灣指數、NASDAQ 指數與費半指數相互間的影響。碩士論文，嶺東科技大學，財務金融系。


侯育旻 (2020)。以市場投資人常用指標預測台股大盤指數。碩士論文，輔仁大學，經濟學系。


劉曜竹 (2004)。領先指標對台灣景氣趨勢預測能力的探討: 非線性因果關係檢定的應用。碩士論文，東海大學，經濟學系。


劉照群 (2008)。美國費城半導體股市、台灣股市、台積電股價的關聯性之實證研究。碩士論文，國立台北大學，統計學系。


許光華、李見發、嚴宗銘、吳采真 (2010)。價值股與成長股的投資策略分析－考量股市循環之實證。商管科技季刊, 11(4), 421–456。


張愷凌 (2009)。景氣循環、總體經濟變數與台灣股價指數的關係性研究。碩士論文，國立交通大學，管理學院碩士在職專班財務金融組。


張簡士煌 (2005)。台灣股市與國際股市關聯性之研究。碩士論文，朝陽科技大學，企業管理系。


黃聿銘 (2008)。台灣領先指標對景氣循環預測能力的探討。碩士論文，國立中央大學，經濟學系。


黃鉦展 (2018)。那斯達克與費城半導體指數對台灣電子股指數之影響：ARDL 模型之應用。碩士論文，國立高雄科技大學，企業管理系碩士在職專班。


楊士漢 (2008)。台灣股市與美國那斯達克的動態相關性分析。碩士論文，國立交通大學，企業管理碩士學程。


洪嘉陽 (2019)。台灣加權股價指數領先指標研究。碩士論文，國立臺灣大學，經濟學系。


嚴浩瑋 (2023)。通貨膨脹對股市之影響。碩士論文，淡江大學，財務金融學系碩士班。


Al-Rjoub, S. A. M., & Azzam, H. (2012). Financial crises, stock returns and volatility in an emerging stock market: The case of Jordan. Journal of Economic Studies, 39(2), 178–211.


Bordo, M. D., & Wheelock, D. C. (2006). When do stock market booms occur? The macroeconomic and policy environments of 20th-century booms. Federal Reserve Bank of St. Louis Working Paper Series.


Bry, G., & Boschan, C. (1971). Cyclical analysis of time series: Selected procedures and computer programs. National Bureau of Economic Research.


Chauvet, M., and Potter, S. (2000). Coincident and leading indicators of the stock market. Journal of Empirical Finance, 7, 87–111. 


Chen, S. L., Huang, S. C., & Lin, Y. M. (2007). Using multivariate stochastic volatility models to investigate the interactions among NASDAQ and major Asian stock indices. Applied Economics Letters, 14(2), 127–133.


Chen, S. S. (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators. Journal of Banking & Finance, 33, 211–223.

Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072.


Enders, W., & Chung, P. (2004). Solution manual for Walter Enders’ Applied Econometric Time Series (2nd edition). University of Alabama.


Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.


Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1008.


Estrella, A., and Mishkin, F. S. (1998). Predicting U.S. recessions: Financial variables as leading indicators. The Review of Economics and Statistics, 80(1), 45–61.


Farrell, H., & O’Connor, F. (2024). The CNN Fear and Greed Index as a predictor of US equity index returns. SSRN Electronic Journal.


Ferli, O., & Husodo, Z. (2012). Dynamic conditional correlation analysis in Asia Pacific and Latin America equity market: Interdependence and contagion. SSRN Electronic Journal.


Gómez Martínez, R., Medrano García, M. L., & Prado Román, C. (2023). CNN Fear and Greed Index as trend signal in global financial markets. SSRN Electronic Journal.


Gonzalez, L., Hoang, P., Powell, J. G., & Shi, J. (2006). Defining and dating bull and bear markets: Two centuries of evidence. Multinational Finance Journal, 10(1/2), 81–116.


Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.


Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111–120.


Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254


Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration – with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210.


Johnson, J. (2023). Can the US Fear and Greed Index influence stock trading in the Asia Pacific region now that the dominance of the US is declining? SSRN Electronic Journal.


Mandacı, P. E., & Alsu, E. (2020). Is Purchasing Managers’ Index (PMI) a leading indicator for stock, bond, and foreign exchange markets in Turkey? İşletme Fakültesi Dergisi, 21(1), 219-233.


Nonejad, N. (2017). Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why? Journal of Empirical Finance, 42, 131–154.


Osterwald-Lenum, M. (1992). A note with quantiles of the asymptotic distribution of the maximum likelihood cointegration rank test statistics. Oxford Bulletin of Economics and Statistics, 54(3), 461–472.


Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23–46.


Singh, T., Mehta, S., & Varsha, M. S. (2011). Macroeconomic factors and stock returns: Evidence from Taiwan. Journal of Economics and International Finance, 2(4), 217–227.


Stylios, C. D., & Kreinovich, V. (2018). Dow theory’s peak-and-trough analysis justified. In M. Ceberio & V. Kreinovich (Eds.), Constraint Programming and Decision Making: Theory and Applications (pp. 123–133). Springer.


Sun, W. T., Chiao, H. T., Chang, Y. S., & Yuan, S. M. (2019). Forecasting monthly average of Taiwan stock exchange index. Communications in Computer and Information Science, 1013, 302–309.


Tsai, K. T., Lih, J. S., & Ko, J. Y. (2012). The overnight effect on the Taiwan stock market. Physica A: Statistical Mechanics and its Applications, 391(23), 6497–6505.


Yang, L. T. (2020). The correlation between economic indicators and Taiwan stock market: A case study of leading and lagging indicators. Financial Studies, 24(4), 41–59.