惯性因子跟踪策略的有效性：来自中国股票市场的证据

doi:10.16381/j.cnki.issn1003-207x.2019.1569

摘要/Abstract

摘要： 基于惯性/反转效应普遍存在的现象以及中国股票市场上不能直接实施惯性/反转投资策略的事实，提出能灵活抓住惯性/反转效应的惯性因子跟踪策略。通过建立和求解惯性因子跟踪策略模型，我们发现，在允许卖空的条件下，最优惯性因子跟踪组合满足两基金分离定理，两基金分别是惯性因子模仿组合和最小方差组合。选择沪深300十大行业指数作为风险资产的代表，实证考察惯性因子跟踪组合的业绩，并与等权组合和最小方差组合的业绩进行比较。结果发现：惯性因子跟踪组合可以在保持和等权组合、最小方差组合风险水平相当的情况下带来更高的收益水平和夏普比率，平均而言，惯性因子跟踪组合的年化超额收益率(8.88%)比等权组合的年化超额收益率高出3.7%，夏普比率(0.42)高出0.18；即便在考虑交易成本的情况下，惯性因子跟踪组合的净夏普比率仍然高出等权组合0.16；综合上方获利和下方风险控制两个方面看，惯性因子跟踪组合优于等权组合。惯性因子跟踪策略的有效性相对于惯性因子的计算方法和样本数据选择区间具有稳健性。

关键词: 因子跟踪；惯性；组合业绩；中国股票市场

Abstract: It is documented in the literature that the momentum and reversal effects are very prevalent in both oversea and China stock market. However, Chinese investors cannot implement the well-known momentum/reversal strategy directly to make profit due to various trading restrictions. A momentum factor tracking strategy is proposed that minimizes the variance of the difference in returns between a target portfolio and momentum factor. The momentum factor tracking model is established and solved, and the properties of the optimal portfolio are analyzed. It is found that the optimal momentum factor tracking portfolio satisfies two-fund-separation theorem, in which the two funds are momentum factor mimicking portfolio and minimum-variance portfolio determined in the traditional mean-variance model. Using ten industry indices of HS300 as risky assets in the investment universe, the performance for various momentum factor tracking portfolios is investigated and is compared with that of equally weighted portfolio (EWP) and minimum-variance portfolio (MVP). The results show that, momentum factor tracking portfolios outperform EWP and MVP, measured by expected excess return, Sharpe ratio and net Sharpe ratio. The superior performance of the momentum factor tracking portfolios are attributed to these portfolios’ ability to capture the momentum or reversal effect in stock market. To a large extent, the effectiveness of momentum factor tracking strategy is justified so that the strategy can act as a reference for asset allocation in China stock market.

Key words: factor tracking; momentum; portfolio performance; China stock market

中图分类号:

F830.91

蒋崇辉, 刘林. 惯性因子跟踪策略的有效性：来自中国股票市场的证据[J]. 中国管理科学, 2022, 30(5): 86-97.

JIANG Chong-hui, LIU Lin. The Effectiveness of Momentum Factor Tracking Strategy: Evidence from China Stock Market[J]. Chinese Journal of Management Science, 2022, 30(5): 86-97.

参考文献

［1］ De Bondt W F M, Thaler R. Does the stock market overreact?［J］. Journal of Finance, 1985, 40(3): 793-805.
［2］ Jegadeesh N, Titman S. Returns to buying winners and selling losers: Implications for stock market efficiency［J］. Journal of Finance, 1993, 48(1): 65-91.
［3］潘莉, 徐建国. A股个股回报率的惯性与反转［J］. 金融研究, 2011(1): 149-166.Pan Li, Xu Jianguo. Momentum and reversal of A-share individual stock return［J］. Journal of Financial Research, 2011(1): 149-166.
［4］权小锋, 洪涛, 吴世农. 选择性关注，鸵鸟效应与市场异象［J］. 金融研究, 2012(3): 109-123.Quan Xiaofeng, Hong Tao, Wu Shinong. Selective attention, ostrich effect and market anomalies［J］. Journal of Financial Research, 2012, (3): 109-123.
［5］鲁臻, 邹恒甫. 中国股市的惯性与反转效应研究［J］. 经济研究, 2007(9): 145-155.Lu Zhen, Zou Hengfu. Momentum and reversal in China stock market［J］. Economic Research Journal, 2007(9): 145-155.
［6］ Chou P H, Ko K C, Yang N T. Asset growth, style investment, and momentum［J］. Journal of Banking and Finance, 2019, 98: 108-124.
［7］ Hurn S, Pavlov V. Momentum in Australian Stock Returns［J］. Australian Journal of Management, 2013, 28(2): 141-155.
［8］ Carhart M M. On the persistence in mutual fund performance［J］. Journal of Finance, 1997, 52(1): 57-82.
［9］ Fama E F, French K R. Common risk factors in the returns on stocks and bonds［J］. Journal of Financial Economics, 1993, 33(1)： 3-56.
［10］张舰, 李学峰, 王建虎. 我国券商与证券投资基金的惯性反转策略比较研究——基于交易策略弹性指数［J］. 财贸研究, 2010, (4): 90-97.Zhang Jian, Li Xuefeng, Wang Jianhu. A comparative research of momentum and contrarian trading strategy between security firms and security investment funds in China in terms of trading strategy elasticity index［J］. Finance and Trade Research, 2010, (4): 90-97.
［11］沈可挺, 刘煜辉. 中国股市中惯性与反向投资策略的获利模式［J］. 管理科学学报, 2006, 9(6):43-52.Shen Keting, Liu Yuhui. Investment performance of momentum strategies and contrarian strategies in China stock market［J］. Journal of Management Sciences in China, 2006, 9(6): 43-52.
［12］鲁万波, 黄光麟, Kris Boudt.股市涨跌预测与量化投资策略:基于时变矩成分分析［J］.中国管理科学,2020,28(2):1-12.Lu Wanbo, Huang Guanglin, Kris Boudt. Stock market rise-fall forcast and quantitative investment strategy: Based on time varying MCA［J］. Chinese Journal of Management Science, 2020, 28(2):1-12.
［13］ Yang Xuebing, Zhang Huilan. Extreme absolute strength of stocks and performance of momentum strategies［J］ Journal of Financial Markets, 2019, 44: 71-90.
［14］ Yang Yurun, Goncu A, Pantelous A A. Momentum and reversal strategies in Chinese commodity future markets［J］. International Review of Financial Analysis, 2018, 60: 177-196.
［15］ Best M J, Grauer R R. Positively weighted minimum variance portfolios and the structure of asset expected returns［J］. Journal of Financial and Quantitative Analysis, 1992, 27(4): 513-537.
［16］ Merton R C. On estimating the expected return on the market: An exploratory investigation［J］. Journal of Financial Economics, 1980, 8(4): 323-361.
［17］李俭富. 指数投资组合加权机制选择研究:市值加权、等权还是基本面价值加权［J］. 中国管理科学, 2014, 22 (S1):375-381.Li Jianfu. The study of weighting methods choice for index portfolio: market capitalization weighting, equal weighting or fundamental value weighting［J］. Chinese Journal of Management Science, 2014, 22(S1): 375-381.
［18］杨瑞成, 邢伟泽. CRMW风险缓释效用目标跟踪的债券投资组合优化策略研究［J/OL］. 中国管理科学, 2020, DOI:10.16381/j.cnki.issn1003-207x.2019.1726Yang Ruicheng, Xing Weize. Bond portfolio optimization strategy with target tracking of CRMW default risk mitigation utility［J/OL］. Chinese Journal of Management Science, 2020, DOI: 10.16381/j.cnki.issn1003-207x.2019.1726
［19］马景义, 单璐琪, 方彤.一种增强型指数追踪模型设计及应用［J］.数量经济技术经济研究, 2017, 34(5):107-121.Ma Jingyi, Shan Luqi, Fang Tong. A new enhanced index tracking model and its application［J］. The Journal of Quantitative & Technical Economics, 2017, 34(5):107-121.
［20］ Lamont O A. Economic tracking portfolios［J］. Journal of Econometrics, 2001, 105:161-184.
［21］ Cooper I, Priestley R. Real investment and risk dynamics［J］. Journal of Financial Economics, 2011, 101:182-205.
［22］ Jiang Chonghui, Du Jiangze, An Yunbi. Combining the minimum-variance and equally-weighted portfolios: Can portfolio performance be improved?［J］. Economic Modelling, 2019, 80: 260-274.
［23］ DeMiguel V, Garlappi L, Uppal R. Optimal versus nave diversification: How inefficient is the 1/N portfolio strategy?［J］. Review of Financial Studies, 2009, 22(5): 1915-1953.
［24］ Jagannathan R, Ma T. Risk reduction in large portfolios: Why imposing the wrong constraints helps［J］. Journal of Finance, 2003, 58(4): 1651-1684.
［25］ Qian E. On the holy grail of “upside participation and downside protection”［J］. Journal of Portfolio Management, 2015, 41(2): 11-22.

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