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    题名: 結合因子選股與交易策略之股票績效回測模型—應用於陸股市場;A Stock Performance Backtesting Model Combining Factor Stock Selection and Trading Strategies for China Stock Market
    作者: 吳昱嬋;Wu, Yu-Chan
    贡献者: 資訊管理學系
    关键词: 量化交易;因子投資;技術策略;基本面分析
    日期: 2023-07-13
    上传时间: 2024-09-19 16:45:45 (UTC+8)
    出版者: 國立中央大學
    摘要: 現今投資者會利用許多工具與策略進行投資,其中量化交易是近年成為趨勢的方式之一。許多研究基於已驗證的理論選擇對報酬率有影響的有效因子,以不同的因子組合發展出了各種多因子模型,之後也有學者開始對因子選股加入策略實驗其對績效之影響。
    中國股市是一個較不成熟的市場,投資者結構以散戶為主,散戶的短期投機行為導致股價波動性高、投資風險也較高,然而中國人口眾多、擁有巨大市場潛力、經濟正在快速增長,且中國與台灣地理位置相近,有許多貿易與投資往來,因此本研究欲利用量化交易的方式找出陸股市場投資之方向。
    對此本研究延續王崇驊(2022)所建置之模型,新增中國股市回測所需物件,整合在同一系統,使其能對不同股票市場進行因子選股及回測,並加入順勢布林通道為基礎之移動停利策略,實驗因子選股結合不同交易策略對績效帶來的影響。
    ;Investors today utilize various tools and strategies for investment, among which quantitative trading has emerged as one of the trends in recent years. Many studies have developed diverse multi-factor models based on validated theories that identify effective factors influencing returns. Subsequently, scholars have started to experiment with incorporating factor selection strategies to examine their impact on performance.
    The Chinese stock market is a developing market, with retail investors comprising the majority. The short-term speculative behavior of retail investors leads to high stock price volatility and increased investment risk. However, China′s large population, enormous market potential, rapid economic growth, and geographical proximity to Taiwan foster significant trade and investment activities. Therefore, this study aims to utilize quantitative trading to identify investment opportunities in the Chinese stock market.
    In this regard, this study builds upon the model developed by Wang et al. (2022) and incorporates the necessary components for backtesting in the Chinese stock market. These additions are integrated into the same system, allowing for factor selection and backtesting across different stock markets. Furthermore, we implement a strategy that incorporates trailing stops in a trend-following Bollinger Bands strategy. This study aims to experiment with the combination of factor selection and different trading strategies to assess their performance.
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