有別於以往論文，大多為預測單一新興市場國家股票報酬，也僅僅探討當地總經因子與其股票報酬的關係；本研究目的，在於探討美國、歐洲及中國的總體經濟與整體新興市場股市的連動性，筆者預期前者的興衰將連帶影響後者表現，此外，亦提出總體經濟趨勢的概念，期望達到類似於動能投資策略的效果。故本研究將美國、歐洲及中國的經濟變數、金融變數，轉為趨勢變數後，利用簡單迴歸尋找與新興市場股市具有共同趨勢性的變數，再採用逐步迴歸形成預測模型，代入最新總體經濟變數數值後，最終會得到買進或賣出的指標，利用此預測方法，投資在MSCI新興市場指數，基本模型交易績效顯示，預測未來1個月，勝率最高達57.62%、年化報酬最高達9.53%；預測未來3個月勝率最高達67.74%、年化報酬最高達11.72%。

Forecasting stock price of a single emerging market is well documented in previous papers, but most of them only discuss the relationship between local economic factors and their stock returns. Different from previous papers, the goal of this paper is to investigate the influence of macroeconomic factors in United States, Europe and China on the emerging markets index. Also, author expects that economic cycles is closely related to stock price indices. Similar to momentum investment, we construct trend variables. First, this paper transforms economic and financial variables into trend variables. Next, through simple regression, we can find variables which move together with stock markets. Finally, apply stepwise regression model to forecast MSCI emerging markets index, then we invest in the index based on buy and sell signals. Our empirical results are as following. Forecasting the next month, the odds are about 57.62%, and the annualized return is about 9.53%. Forecasting the next two months, the odds are about 67.74%, and the annualized return is about 11.72%.

論文審定書 i
摘要 ii
Abstract iii
目錄 iv
圖次 v
表次 vi
第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究流程 2
第二章 文獻探討 3
第三章 研究方法 5
第一節 趨勢變數定義說明 5
第二節 研究樣本、資料來源、變數敘述 7
第三節 實證研究模型 15
第四章 實證結果 17
第一節 候選變數 17
第二節 敘述統計及相關係數 21
第三節 簡單迴歸-檢驗趨勢變數顯著性及篩選最適動能期數 26
第四節 簡單迴歸-入選變數比較說明 33
第五節 簡單迴歸係數分析 36
第六節 模型績效呈現 41
第五章 結論與建議 45
第一節 結論 45
第二節 未來研究方向 46
參考文獻 48

范辛亭. (2012). 基于择时功效的股市宏观多因素预测模型. 長江證券研究報告.
Wong, W. K., Penm, J., Terrell, R. D., & Lim, K. (2004). The Relationship Between Stock Markets Of Major Developed Countries And Asian Emerging Markets. Journal of Applied Mathematics and Decision Sciences, 8(4), pp. 201-208.
Abugri, B. A. (2008). Empirical relationship between macroeconomic volatility and stock returns: Evidence from Latin American markets. International Review of Financial Analysis, 17(2), pp. 396-410.
Bailey, W., & Chung, P. Y. (1995). Exchange Rate Fluctuations, Political Risk, and Stock Returns: Some Evidence from an Emerging Market. Journal of Financial and Quantitative Analysis, 30(4), pp. 541-561.
Basher , S. A., & Sadorsky, P. (2006). Oil price risk and emerging stock markets. Global Finance Journal, 17(2), pp. 224-251.
Bekaert, G., & Harvey, C. R. (1995). Time‐Varying World Market Integration. The Journal of Finance, 50(2).
Bilson, C. M., Brailsford, T. J., & Hooper, V. C. (2002). The explanatory power of political risk in emerging markets. International Review of Financial Analysis, 11(1), pp. 1-27.


Bruner, R. F., Li, W., Kritzman , M., Myrgren, S., & Page, S. (2008). Market integration in developed and emerging markets:Evidence from the CAPM. Emerging Markets Review, 9(2), pp. 89-103.
Cakici, N., Fabozzi, F. J., & Tan, S. (2013). Size, value, and momentum in emerging market stock returns. Emerging Markets Review, 16, pp. 46-65.
Chang, P.-C., & Liu, C.-H. (2008). A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications, 34(1), pp. 135-144.
Esfahanipour, A., & Aghamiri, W. (2010). Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis. Expert Systems with Applications, 37(7), pp. 4742-4748.
Fama, E. F. (1981). Stock Returns, Real Activity, Inflation, and Money. The American Economic Review, 71(4), pp. 545-565.
Gay, Jr., R. D. (2008). Effect Of Macroeconomic Variables On Stock Market Returns For Four Emerging Economics:Brazil,Russia,India And China. International Business & Economics Research Journal, 7(3).
Hadavandi, E., Shavandi, H., & Ghanbari, A. (2010). Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge-Based Systems, 23(8), pp. 800-808.


Hooker, M. A. (2004). Macroeconomic factors and emerging market equity returns:a Bayesian model selection approach. Emerging Markets Review, 5(4), pp. 379-387.
Izumi, K., & Ueda, K. (1999). Analysis Of Dealers' Processing Financial News Based on Artificial Market Approach. Journal of Computational Intelligence in Finance.
Koutoulas, G., & Kryzanowski, L. (1996). Macrofactor Conditional Volatilities, Time‐Varying Risk Premia and Stock Return Behavior. The Financial Review, 31(1), pp. 169-195.
Quah , J. T., & Sriganesh, M. (2008). Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications, 35(4), pp. 1721-1732.