在股票市場會將位於相同產業之公司歸於同一類當中形成類股，由於同一類股內的公司屬於同一產業所進行的商業活動大致相同，因此產業趨勢以及所受到的外部經濟影響也趨於一致，因此同一類股內的股票走勢彼此之間具備高度的關聯性。現今類股輪動被動輪動策略之研究大多著重於價格動能，然而根據過往之文獻顯示成交量動能也對股票之未來走勢習習相關，卻鮮少被納入類股輪動之討論範圍，對此本研究參考了許正諺（2021）之研究，對該研究進行延伸探討。
本研究針對許正諺（2021）之研究所提出的成交量動能因子進行跨類股正規化，並對所有動能因子進行正反項選股交叉驗證，並將上述類股選擇方式套用至美股各不同大盤加權指數所包含的公司當中以驗證各項不同挑選方式對類股輪動被動輪動策略之影響。
呈上所述，為驗證上述之各項挑選原則，本研究將建置一個類股輪動量化交易回測系統並利用此系統進行績效回測以驗證各項挑選原則之績效。實驗結果顯示不同公司範圍所試用之類股輪動被動投資策略有很大的異質性，且當採用類股輪動被動輪動投資策略時會出現大者恆大贏者通吃的現象。

In the stock market, companies in the same industry will be grouped into the same class to form Sector. Since companies in the same Sector belong to the same industry and carry out roughly same business activities, the industry trends and external economic impacts also tend to be alike, so the movements of stocks in the same Sector are highly correlated with each other. Most of the current research on the Sector rotation strategies cultivated on the price momentum. However, according to the literature, it shows that the volume momentum is also related to the future trend of the stock, but it is rarely included in the discussion of Sector rotation. This study refers to the research of 許正諺 (2021), and extends this research.
Volume momentum for strategies impacts was studied by 許正諺 (2021), and this research would focus on the cross-sector normalization of the all momentum factors which included volume momentum, and those factors were cross-checked for positive and negative stock selection. The above-mentioned Sector selection methods were applied to U.S. stocks index to verify the influence of different selection methods on the Sector rotation strategies.
To sum up, in order to verify the above selection principles, a quantitative Sector rotation backtesting system will be built and used to test the performance. The experimental results demostrate that there is great heterogeneity in the Sector rotation strategies used in different index, and when using the Sector rotation strategies, there is a phenomenon that the winner will take all on market.

Bernanke, B. S., & Kuttner, K. N. (2005). What Explains the Stock Market’s Reaction to Federal Reserve Policy? The Journal of Finance, 60(3), 1221–1257.
Bhojraj, S., Lee, C. M. C., & Oler, D. K. (2002). What’s My Line? A Comparison of Industry Classification Schemes for Capital Market Research. Journal of Accounting Research, 41(5), 745-774.
Campbell, J. Y., Grossman, S. J., & Wang, J. (1993). Trading Volume and Serial Correlation in Stock Returns. The Quarterly Journal of Economics, 108(4), 905-939.
Chebbi, K., Ammer, M. A., & Hameed, A. (2021). The COVID-19 pandemic and stock liquidity: Evidence from S&P 500. The Quarterly Review of Economics and Finance, 81, 134–142.
Conover, C. M., Jensen, G. R., Johnson, R. R., & Mercer, J. M. (2008). Sector Rotation and Monetary Conditions. The Journal of Investing, 17(1), 34-46.
Deng, M., Leippold, M., Wagner, A. F., & Wang, Q. (2022). Stock Prices and the Russia-Ukraine War: Sanctions, Energy and ESG. SSRN Electronic Journal.
Doeswijk, R. (2011). Global Tactical Sector Allocation: A QuantitativeApproach. The Journal of Portfolio Management, 38(1), 29-47.
Doeswijk, R. Q. (2008). The Optimism Cycle: Sell in May. De Economist, 156(2), 175–200.
Hrazdil, K., Trottier, K., & Zhang, R. (2013). A comparison of industry classification schemes: A large sample study. Economics Letters, 118(1), 77–80.
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.
Lee, C. M. C., & Swaminathan, B. (2000). Price Momentum and Trading Volume. The Journal of Finance, 55(5), 2017–2069.
Moskowitz, T. J., & Grinblatt, M. (1999). Do Industries Explain Momentum? The Journal of Finance, 54(4), 1249–1290.
Nuti, G., Mirghaemi, M., Treleaven, P., & Yingsaeree, C. (2011). Algorithmic Trading. Computer, 44(11), 61–69.
O’Neal, E. S. (2000). Industry Momentum and Sector Mutual Funds. Financial Analysts Journal, 56(4), 37–49.
Pardo, R. (2011). The evaluation and optimization of trading strategies. John Wiley & Sons.
Rapach, D., Strauss, J., Tu, J., & Zhou, G. (2015). Industry Interdependencies and Cross-Industry Return Predictability. SSRN Electronic Journal.
Stangl, J., Jacobsen, B., & Visaltanachoti, N. (2008). Sector Rotation over Business-Cycles. Department of Commerce, College of Business.
Tortoriello, R. (2009). Quantitative strategies for achieving alpha. McGraw Hill.
Gallant, A. R., Rossi, P. E., & Tauchen, G. (1992). Stock prices and volume. The Review of Financial Studies, 5(2), 199-242.
Stovall S. (1996). Standard & Poor’s Guide to Sector Investing. McGraw-Hill.
Murphy, J. J. (2011). Intermarket analysis: profiting from global market relationships. John Wiley & Sons.
Fama, E. F., & French, K. R. (1997). Industry costs of equity. Journal of financial economics, 43(2), 153-193.
Davey, K. J. (2014). Building winning algorithmic trading systems: A trader's journey from data mining to Monte Carlo simulation to live trading. John Wiley & Sons.
許正諺 (2021)。客製化類股輪動策略之驗證平台設計與驗證。國立中央大學資訊管理學系碩士論文。
陳正佑 (2012)。台股動量策略與反向策略投資績效之研究。國立中山大學財務管理學系博士論文。
鄭皓元 (2018)。以分散式運算分析多商品主副策略最適性之自動化平台設計與驗證。國立中央大學資訊管理學系碩士論文。