
Portfolio selection based on a new constructed Sharpe index using high frequency data
WANG Yan, CHEN Min, ZHAO Zi-long
Systems Engineering - Theory & Practice ›› 2015, Vol. 35 ›› Issue (1) : 17-25.
Portfolio selection based on a new constructed Sharpe index using high frequency data
Both stock selection and optimal portfolio choice play crucial roles in setting portfolio. This paper proposed a new method for stock selection based on constructing a Sharpe index using intra-day high frequency data. Several portfolio strategies are considered too. An out-of-sample empirical analysis with respect to a sample of A shares listed on Shanghai stock exchange shows that the new stock selection method often offers higher risk-adjusted returns and lower levels of risk. Moreover, combined with optimal portfolio strategy, the new method can get considerable excess returns.
stock selection / high frequency data / Sharpe index / portfolio strategy / out-of-sample / excess return {{custom_keyword}} /
[1] Markowitz H M. Portfolio selection[J]. Journal of Finance, 1952, 7: 77-91.
[2] Michaud R O. The Markowitz optimization enigma: Is optimized optimal?[J]. Financial Analysts Journal, 1989, 45: 31-42.
[3] Best M J, Grauer R R. On the sensitivity of mean-variance-efficient portfolios to changes in asset means: Some analytical and computational results[J]. Review of Financial Studies, 1991, 4: 315-342.
[4] Chopra V K, Ziemba W T. The effect of errors in means, variance and covariances on optimal portfolio choice[J]. The Journal of Portfolio Management, 1993, 19: 6-11.
[5] Kan R, Zhou G. Optimal portfolio choice with parameter uncertainty[J]. Journal of Financial and Quantitative Analysis, 2007, 42: 621-656.
[6] Ledoit O, Wolf M. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection[J]. Journal of Empirical Finance, 2003, 10: 603-621.
[7] Ledoit O, Wolf M. A well-conditioned estimator for large-dimensional covariance matrices[J]. Journal of Multivariate Analysis, 2004, 88: 365-411.
[8] Ledoit O, Wolf M. Honey, I shrunk the sample covariance matrix: Problems in mean-variance optimization[J]. Journal of Portfolio Management, 2004, 30: 110-119.
[9] Tu J, Zhou G. Markowitz meets Talmud: A combination of sophisticated and naive diversication strategies[R]. Working Paper, 2009.
[10] Fan J, Fan Y, Lü J. Large dimensional covariance matrix estimation via a factor model[J]. Journal of Econometrics, 2008, 147: 186-197.
[11] Kourtis A, Dotsis G, Markellos R N. Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix[J]. Journal of Banking and Finance, 2012, 36: 2522-2531.
[12] Jagannathan R, Ma T. Risk reduction in large portfolios: Why imposing the wrong constraints helps[J]. Journal of Finance, 2003, 58: 1651-1684.
[13] Fan J, Zhang J, Yu K. Asset allocation and risk assessment with gross exposure constrainsts for vast portfolios[R]. Working Paper, 2009.
[14] Brodie J, Daubechies I, De Mol C, et al. Sparse and stable Markowitz portfolios[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106: 12267-12272.
[15] Efron B, Hastie T, Johnstone I, et al. Least angle regression (with discussions)[J]. The Annals of Statistics, 2004, 32: 409-499.
[16] Levin A. Stock selection via nonlinear nulti-factor models[J]. Advances in Neural Information Processing Systems, 1996: 966-972.
[17] Fan A, Palaniswami M. Stock selection using support vector machines[C]//Neural Networks, 2001, Proceedings, IJCNN'01, 2001, 3: 1793-1798.
[18] Van der Hart J, Slagter E, Van Dijk D. Stock selection strategies in emerging markets[J]. Journal of Empirical Finance, 2003, 10(1-2): 105-132.
[19] Rouwenhorst K G. International momentum strategies[J]. Journal of Finance, 1998, 53: 267-284.
[20] Griffin J M, Ji X, Martin J S. Momentum investing and business cycle risk: Evidence from pole to pole[J]. Journal of Finance, 2003, 58: 2515-2547.
[21] Rachev S, Jasic T, Stoyanov S, et al. Momentum strategies based on reward-risk stock selection criteria[J]. Journal of Banking and Finance, 2007, 31(8): 2325-2346.
[22] Lucas A, Van Dijk R, Kloek T. Stock selection, style rotation, and risk[J]. Journal of Empirical Finance, 2002, 9: 1-34.
[23] Subrahmanyam A. The cross-section of expected stock returns: What have we learnt from the past twenty-five years of research?[J]. European Financial Management, 2010, 16(1): 27-42.
[24] Daniel K, Moskowitz T. Momentum crashes[R]. Working Paper, Columbia University, 2011.
[25] Daniel K, Jagannathan R, Kim S. Tail risk in momentum strategy returns[R]. Working Paper, Columbia University and Northwestern University, 2012.
[26] Andersen T, Bollerslev T, Diebold F X, et al. The distribution of realized stock return volatility[J]. Journal of Financial Economics, 2001, 61: 43-76.
[27] Tibshirani R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society, Series B, 1996, 58(1): 267-288.
[28] Rockafellar R T, Uryasev S. Optimization of conditional Value-at-Risk[J]. Journal of Risk, 2000, 2: 21-41.
[29] DeMiguel V, Garlappi L, Uppal R. Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?[J]. Review of Financial Studies, 2009, 22: 1915-1953.
[30] Zhang L, Mykland P A, Ait-Sahalia Y. A tale of two time scales: Determining integrated volatility with noisy high-frequency data[J]. Journal of the American Statistical Association, 2005, 100(472): 1394-1411.
[31] Bandi F M, Russell J R. Separating microstructure noise from volatility[J]. Journal of Financial Economics, 2006, 79(3): 655-692.
[32] 徐正国, 张世英. 调整“已实现”波动率与GARCH及SV模型对波动的预测能力的比较研究[J]. 系统工程, 2004, 22(8): 60-63.Xu Zhengguo, Zhang Shiying. The comparative research on volatility prediction ability of adjusted realized volatility, GARCH model and SV model[J]. Systems Engineering, 2004, 22(8): 60-63.
[33] 郭名嫒, 张世英. 赋权己实现波动及其长记忆、最优抽样频率选择[J]. 系统工程学报, 2006, 21(6): 568-573.Guo Mingyuan, Zhang Shiying. Weighted realized volatility and its long memory and optimal frequency[J]. Journal of Systems Engineering, 2006, 21(6): 568-573.
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