摘要(英) |
Many scholars utilize multi-factor stock selection models in an attempt to identify factors that can generate profit for investors in a single market. They also incorporate trading strategies to improve performance. However, few studies compare whether there are differences in factors and strategy selection between different markets on the same stock selection mode. The question arises whether factors that work in one market can be applied to different markets, and the impact and differences in performance of the same factors across different markets.
In this study, a backtesting system is developed using Python to simultaneously backtest the Taiwan and US stock markets. Fourteen single-factor and four dual-factor models are statistically tested to verify their stock selection effectiveness in the Taiwan and US markets. Additionally, five strategies including buy and hold, inverse Bollinger Bands, bargaining hunting, inverse high-low channels, and Bollinger Bands are applied to examine whether the utilization of these strategies can enhance profitability or reduce risk. The study aims to compare whether there are differences in factor selection and strategy implementation between the Taiwan and US stock markets. |
參考文獻 |
Bollinger, J. (2001). Bollinger on Bollinger Bands. McGraw-Hill Education.
Carhart, M. M. (1997). On persistence in mutual fund performance. The journal of finance, 52(1), 57-82.
Davey, K. J. (2014). Building Winning Algorithmic Trading Systems : A Trader’s Journey from Data Mining to Monte Carlo Simulation to Live Trading. Wiley.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of financial economics, 33(1), 3-56.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of financial economics, 116(1), 1-22.
FMP. (2022). Financial Modeling Prep. https://site.financialmodelingprep.com/
Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American statistical Association, 47(260), 583-621.
MSCI. (2022). Market classification. https://www.msci.com/our-solutions/indexes/market-classification
Pardo, R. (2008). The Evaluation and Optimization of Trading Strategies. Wiley.
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611.
TEJ. (2022). 台灣經濟新報資料庫. https://www.tej.com.tw/
Tortoriello, R. (2008). Quantitative Strategies for Achieving Alpha: The Standard and Poor′s Approach to Testing Your Investment Choices. McGraw-Hill Finance & Investing.
Zar, J. H. (2010). Biostatistical Analysis. Pearson.
丁鵬 (2012)。量化投資:策略與技術。 電子工業出版社。
王崇驊 (2022)。結合因子選股與系統交易的股票操作系統之分析設計與實作 – 以美股市場為例。 國立中央大學資訊管理學系研究所碩士論文,未出版,桃園市。
卓育辰 (2021)。結合因子分析與程式交易應用於台股之自動化回測與驗證平台。 國立中央大學資訊管理學系研究所碩士論文,未出版,桃園市。
林柏年 (2022)。以量化交易驗證類股輪動策略之挑選原則與績效評估— 以美股為例。 國立中央大學資訊管理學系研究所碩士論文,未出版,桃園市。
栗原伸一, & 丸山敦史 (2019)。統計學圖鑑。 楓葉社文化。
張林忠 (2014)。分析師關鍵報告 2 :張林忠教你程式交易。 寰宇。 |