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We find that imposing economic constraint on stock return forecasts based on the Interquartile Range of equity premium can significantly strengthen predictive performance. Specifically, we construct a judgment mechanism that truncates the outliers in forecasts of stock return. We prove that our constraint approach can realize more accurate predictive information relative to the unconstraint approach from the perspective of statistics and economics. In addition, the new constraint approach can effectively defeat CT constraint and CDA strategy. The three mixed models we proposed can further enhance the accuracy of prediction, especially the mixed model combined with our constraint approach. Finally, utilizing our new constraint approach can help investors obtain considerable economic gains. With the application of extension and robustness analysis, our results are robust.
Zhifeng Dai; XiaoMing Chang. Predicting Stock Return with Economic Constraint: Can Interquartile Range Truncate the Outliers? Mathematical Problems in Engineering 2021, 2021, 1 -12.
AMA StyleZhifeng Dai, XiaoMing Chang. Predicting Stock Return with Economic Constraint: Can Interquartile Range Truncate the Outliers? Mathematical Problems in Engineering. 2021; 2021 ():1-12.
Chicago/Turabian StyleZhifeng Dai; XiaoMing Chang. 2021. "Predicting Stock Return with Economic Constraint: Can Interquartile Range Truncate the Outliers?" Mathematical Problems in Engineering 2021, no. : 1-12.
Using long-term government bond yield (LTY), corporate bond yields spread (DFY) and Treasury bill rate (TBL) as the proxies, we find bond yield can effectively predict WTI and Brent spot prices. In-sample analysis indicates that bond yield variables have substantial explanatory power on oil returns, and there are significant Granger causality relationships from LTY and DFY to oil returns. In out-of-sample forecast, bond yield variables defeat historical average benchmark as well as the competing predictors from both statistical and economic perspectives. Moreover, the predictive abilities of bond yield variables can be tremendously enhanced with multivariate prediction methods. We prove that the prediction power of bond yield variables partially stems from their abilities on capturing oil market sentiment. Our findings survive a series of robustness checks.
Zhifeng Dai; Jie Kang. Bond yield and crude oil prices predictability. Energy Economics 2021, 97, 105205 .
AMA StyleZhifeng Dai, Jie Kang. Bond yield and crude oil prices predictability. Energy Economics. 2021; 97 ():105205.
Chicago/Turabian StyleZhifeng Dai; Jie Kang. 2021. "Bond yield and crude oil prices predictability." Energy Economics 97, no. : 105205.
The poor out‐of‐sample performance of mean–variance portfolio model is mainly caused by estimation errors in the covariance matrix and the mean return, especially the mean return vector. Meanwhile, in financial practice, what most investors actually like is to hold a few stocks in their portfolio. The goal of this paper is to propose some new efficient mean–variance portfolio selection models by considering the following aspects: (i) use the L1‐regularization in objective function to obtain sparse portfolio; (ii) use the shrinkage method of Ledoit and Wolf, Journal of Economics Financial, 2003, 10, 603–621 to estimate the covariance matrix; (iii) use the robust optimization method to mitigate the estimation errors of the expected return. Finally, empirical analysis demonstrates that the proposed strategies have better out‐of‐sample performance.
Zhifeng Dai; Jie Kang. Some new efficient mean–variance portfolio selection models. International Journal of Finance & Economics 2021, 1 .
AMA StyleZhifeng Dai, Jie Kang. Some new efficient mean–variance portfolio selection models. International Journal of Finance & Economics. 2021; ():1.
Chicago/Turabian StyleZhifeng Dai; Jie Kang. 2021. "Some new efficient mean–variance portfolio selection models." International Journal of Finance & Economics , no. : 1.
We find that combining de-noising stock returns by wavelet transform with new proposed technical indicators can significantly improve the accuracy of stock returns forecasts, in which the new technical indicators can directly reflect the trend of stock returns series. Empirical results indicate the stock returns forecasts generated by new technical indicators are statistically and economically significant both in-sample and out-of-sample prediction performance. And when multivariate information is used to predict stock returns, its predictability is also significant. In addition, it is robust for the prediction performance of new indicators using some extension and robustness analysis.
Zhifeng Dai; Huan Zhu; Jie Kang. New technical indicators and stock returns predictability. International Review of Economics & Finance 2020, 71, 127 -142.
AMA StyleZhifeng Dai, Huan Zhu, Jie Kang. New technical indicators and stock returns predictability. International Review of Economics & Finance. 2020; 71 ():127-142.
Chicago/Turabian StyleZhifeng Dai; Huan Zhu; Jie Kang. 2020. "New technical indicators and stock returns predictability." International Review of Economics & Finance 71, no. : 127-142.
The goal of our paper is to improve the accuracy of stock return forecasts by combining new technical indicators and a new two-step economic constraint forecasting model. Empirical results indicate the stock return forecasts generated by new technical indicators and new economic constraint forecasting model is statistically and economically significant both in-sample and out-of-sample prediction performance. In addition, the prediction performance of new technical indicators and new economic constraint forecasting model is robust for some extension and robustness analysis.
Zhifeng Dai; Xiaodi Dong; Jie Kang; Lianying Hong. Forecasting stock market returns: New technical indicators and two-step economic constraint method. The North American Journal of Economics and Finance 2020, 53, 101216 .
AMA StyleZhifeng Dai, Xiaodi Dong, Jie Kang, Lianying Hong. Forecasting stock market returns: New technical indicators and two-step economic constraint method. The North American Journal of Economics and Finance. 2020; 53 ():101216.
Chicago/Turabian StyleZhifeng Dai; Xiaodi Dong; Jie Kang; Lianying Hong. 2020. "Forecasting stock market returns: New technical indicators and two-step economic constraint method." The North American Journal of Economics and Finance 53, no. : 101216.
The goal of this paper is to extend the modified Hestenes-Stiefel method to solve large-scale nonlinear monotone equations. The method is presented by combining the hyperplane projection method (Solodov, M.V.; Svaiter, B.F. A globally convergent inexact Newton method for systems of monotone equations, in: M. Fukushima, L. Qi (Eds.)Reformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods, Kluwer Academic Publishers. 1998, 355-369) and the modified Hestenes-Stiefel method in Dai and Wen (Dai, Z.; Wen, F. Global convergence of a modified Hestenes-Stiefel nonlinear conjugate gradient method with Armijo line search. Numer Algor. 2012, 59, 79-93). In addition, we propose a new line search for the derivative-free method. Global convergence of the proposed method is established if the system of nonlinear equations are Lipschitz continuous and monotone. Preliminary numerical results are given to test the effectiveness of the proposed method.
Zhifeng Dai; Huan Zhu. A Modified Hestenes-Stiefel-Type Derivative-Free Method for Large-Scale Nonlinear Monotone Equations. Mathematics 2020, 8, 168 .
AMA StyleZhifeng Dai, Huan Zhu. A Modified Hestenes-Stiefel-Type Derivative-Free Method for Large-Scale Nonlinear Monotone Equations. Mathematics. 2020; 8 (2):168.
Chicago/Turabian StyleZhifeng Dai; Huan Zhu. 2020. "A Modified Hestenes-Stiefel-Type Derivative-Free Method for Large-Scale Nonlinear Monotone Equations." Mathematics 8, no. 2: 168.
We find that mixing existing forecasting models can significantly improve prediction performance of stock returns. Empirical results suggest that the stock return forecasting by three proposed mixed models are more significant both in statistical and economic terms than the corresponding models in Campbell and Thompson (2008), Wang et al. (2018) and Zhang et al. (2019). This improvement of predictability is also remarkable when we employ the multivariate information to predict stock return. The prediction performance of mixed models is robust to a series of robustness test. Particularly, the three proposed mixed models obtain superior out-of-sample forecasting performance of stock return for business cycles, rolling window predictions and different out-of-sample periods.
Zhifeng Dai; Huan Zhu. Stock return predictability from a mixed model perspective. Pacific-Basin Finance Journal 2020, 60, 101267 .
AMA StyleZhifeng Dai, Huan Zhu. Stock return predictability from a mixed model perspective. Pacific-Basin Finance Journal. 2020; 60 ():101267.
Chicago/Turabian StyleZhifeng Dai; Huan Zhu. 2020. "Stock return predictability from a mixed model perspective." Pacific-Basin Finance Journal 60, no. : 101267.
Forecasting stock market returns has great significance to asset allocation, risk management, and asset pricing, but stock return prediction is notoriously difficult. In this paper, we combine the sum-of-the-parts (SOP) method and three kinds of economic constraint methods: non-negative economic constraint strategy, momentum of return prediction strategy, and three-sigma strategy to improve prediction performance of stock returns, in which the price-earnings ratio growth rate (gm) is predicted by economic constraint methods. Empirical results suggest that the stock return forecasts by proposed models are both statistically and economically significant. The predictions of proposed models are robust to various robustness tests.
Zhifeng Dai; Huiting Zhou. Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method. Sustainability 2020, 12, 541 .
AMA StyleZhifeng Dai, Huiting Zhou. Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method. Sustainability. 2020; 12 (2):541.
Chicago/Turabian StyleZhifeng Dai; Huiting Zhou. 2020. "Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method." Sustainability 12, no. 2: 541.
Zhifeng Dai; Huiting Zhou; Xiaodi Dong. Forecasting stock market volatility: the role of gold and exchange rate. AIMS Mathematics 2020, 5, 5094 -5105.
AMA StyleZhifeng Dai, Huiting Zhou, Xiaodi Dong. Forecasting stock market volatility: the role of gold and exchange rate. AIMS Mathematics. 2020; 5 (5):5094-5105.
Chicago/Turabian StyleZhifeng Dai; Huiting Zhou; Xiaodi Dong. 2020. "Forecasting stock market volatility: the role of gold and exchange rate." AIMS Mathematics 5, no. 5: 5094-5105.
Zhifeng Dai; Huan Zhu; Fenghua Wen. Two nonparametric approaches to mean absolute deviation portfolio selection model. Journal of Industrial & Management Optimization 2020, 16, 2283 -2303.
AMA StyleZhifeng Dai, Huan Zhu, Fenghua Wen. Two nonparametric approaches to mean absolute deviation portfolio selection model. Journal of Industrial & Management Optimization. 2020; 16 (5):2283-2303.
Chicago/Turabian StyleZhifeng Dai; Huan Zhu; Fenghua Wen. 2020. "Two nonparametric approaches to mean absolute deviation portfolio selection model." Journal of Industrial & Management Optimization 16, no. 5: 2283-2303.
In this article, we combine the sum-of-the-parts (SOP) method with Ensemble Empirical Mode Decomposition (EEMD) to forecast stock market returns. We obtain very significant stock return predictability both in statistical and economic terms. Interestingly, the strongest performance is achieved by the extended SOPEEMD method to forecast stock market returns when the price-earnings multiple growth is forecasted using the dividend yield as predictor (Roos2of 21.25%) with monthly data and the book-to-market ratio as predictor achieves Roos2 of 20.05% with monthly data. The highest monthly CER gains for the extended SOPEEMD method are for book-to-market ratio reach 14.11%. Furthermore, the evidence based on robust check supports the feasibility of our forecasting strategy.
Zhifeng Dai; Huan Zhu. Forecasting stock market returns by combining sum-of-the-parts and ensemble empirical mode decomposition. Applied Economics 2019, 52, 2309 -2323.
AMA StyleZhifeng Dai, Huan Zhu. Forecasting stock market returns by combining sum-of-the-parts and ensemble empirical mode decomposition. Applied Economics. 2019; 52 (21):2309-2323.
Chicago/Turabian StyleZhifeng Dai; Huan Zhu. 2019. "Forecasting stock market returns by combining sum-of-the-parts and ensemble empirical mode decomposition." Applied Economics 52, no. 21: 2309-2323.
The purpose of this paper is to analyze whether the fluctuations of RMB/USD exchange rate can predict the Chinese industry return volatilities during post-financial crisis period. Our in-sample results show there is significant Granger causality from RMB/USD exchange rate fluctuations to China’s industry return volatilities. The out-of-sample results also indicate the RMB/USD exchange rate fluctuations extracts significantly useful information from the predictors. Further analysis about the energy industry shows that simple linear regression is sufficient for capturing predictive relationships between RMB/USD exchange rate fluctuations and energy industry volatility.
Zhifeng Dai; Huan Zhu; Xiaodi Dong. Forecasting Chinese industry return volatilities with RMB/USD exchange rate. Physica A: Statistical Mechanics and its Applications 2019, 539, 122994 .
AMA StyleZhifeng Dai, Huan Zhu, Xiaodi Dong. Forecasting Chinese industry return volatilities with RMB/USD exchange rate. Physica A: Statistical Mechanics and its Applications. 2019; 539 ():122994.
Chicago/Turabian StyleZhifeng Dai; Huan Zhu; Xiaodi Dong. 2019. "Forecasting Chinese industry return volatilities with RMB/USD exchange rate." Physica A: Statistical Mechanics and its Applications 539, no. : 122994.
Recently, by imposing the regularization term to objective function or additional norm constraint to portfolio weights, a number of alternative portfolio strategies have been proposed to improve the empirical performance of the minimum-variance portfolio. In this paper, we firstly examine the relation between the weight norm-constrained method and the objective function regularization method in minimum-variance problems by analyzing the Karush–Kuhn–Tucker conditions of their Lagrangian functions. We give the range of parameters for the two models and the corresponding relationship of parameters. Given the range and manner of parameter selection, it will help researchers and practitioners better understand and apply the relevant portfolio models. We apply these models to construct optimal portfolios and test the proposed propositions by employing real market data.
Zhifeng Dai. A Closer Look at the Minimum-Variance Portfolio Optimization Model. Mathematical Problems in Engineering 2019, 2019, 1 -8.
AMA StyleZhifeng Dai. A Closer Look at the Minimum-Variance Portfolio Optimization Model. Mathematical Problems in Engineering. 2019; 2019 ():1-8.
Chicago/Turabian StyleZhifeng Dai. 2019. "A Closer Look at the Minimum-Variance Portfolio Optimization Model." Mathematical Problems in Engineering 2019, no. : 1-8.
In this paper, an adaptive three-term conjugate gradient method is proposed for solving unconstrained problems, which generates sufficient descent directions at each iteration. Different from the existent methods, a dynamical adjustment between Hestenes–Stiefel and Dai–Liao conjugacy conditions in our proposed method is developed. Under mild condition, we show that the proposed method converges globally. Numerical experimentation with the new method indicates that it efficiently solves the test problems and therefore is promising.
Xiao-Liang Dong; Zhi-Feng Dai; Reza Ghanbari; Xiang-Li Li. An Adaptive Three-Term Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition. Journal of the Operations Research Society of China 2019, 9, 411 -425.
AMA StyleXiao-Liang Dong, Zhi-Feng Dai, Reza Ghanbari, Xiang-Li Li. An Adaptive Three-Term Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition. Journal of the Operations Research Society of China. 2019; 9 (2):411-425.
Chicago/Turabian StyleXiao-Liang Dong; Zhi-Feng Dai; Reza Ghanbari; Xiang-Li Li. 2019. "An Adaptive Three-Term Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition." Journal of the Operations Research Society of China 9, no. 2: 411-425.
Mean–variance portfolios have been criticized because of unsatisfying out-of-sample performance and the presence of extreme and unstable asset weights. The bad performance is caused by estimation errors in inputs parameters, that is the covariance matrix and the expected return vector, especially the expected return vector. This topic has attracted wide attention. In this paper, we aim to find better portfolio optimization model to reduce the undesired impact of parameter uncertainty and estimation errors of mean–variance portfolio model. Firstly, we introduce a sparse mean–variance portfolio model, and give some insight about sparsity. Secondly, we propose two sparse and robust portfolio models by using objective function regularization and robust optimization. Finally, three empirical studies are proposed with real market data.
Zhifeng Dai; Fei Wang. Sparse and robust mean–variance portfolio optimization problems. Physica A: Statistical Mechanics and its Applications 2019, 523, 1371 -1378.
AMA StyleZhifeng Dai, Fei Wang. Sparse and robust mean–variance portfolio optimization problems. Physica A: Statistical Mechanics and its Applications. 2019; 523 ():1371-1378.
Chicago/Turabian StyleZhifeng Dai; Fei Wang. 2019. "Sparse and robust mean–variance portfolio optimization problems." Physica A: Statistical Mechanics and its Applications 523, no. : 1371-1378.
Parameter uncertainty and estimation errors often cause the presence of unstable asset weights and the poor performance of portfolio model. In addition, in the real world, most investors prefer to to choose a small number of stocks to invest. In this paper, we propose some improved sparse and stable portfolio models by combining the shrinkage method and objective function L1 regularization method. An ‘optimal’ shrinkage constant is obtained by minimizes the expected distance between the shrinkage estimator and the true covariance matrix. Moreover, we investigate the combination of the constant correlation and objective function L1 regularization method. Empirical studies show that the proposed strategies have better out-of-sample performance than many other strategies for tested datasets.
Zhifeng Dai; Fenghua Wen. Some improved sparse and stable portfolio optimization problems. Finance Research Letters 2018, 27, 46 -52.
AMA StyleZhifeng Dai, Fenghua Wen. Some improved sparse and stable portfolio optimization problems. Finance Research Letters. 2018; 27 ():46-52.
Chicago/Turabian StyleZhifeng Dai; Fenghua Wen. 2018. "Some improved sparse and stable portfolio optimization problems." Finance Research Letters 27, no. : 46-52.
An accelerated three-term conjugate gradient method is proposed, in which the search direction can satisfy the sufficient descent condition as well as extended Dai–Liao conjugacy condition. Different from the existent methods, a dynamical compensation strategy in our proposed method is considered, that is Li–Fushikuma-type quasi-Newton equation is satisfied as much as possible, otherwise, to some extent, the singular values of iteration matrix of search directions will adaptively clustered, which substantially benefits acceleration the convergence or reduction in the condition number of iteration matrix. Global convergence is established under mild conditions for general objective functions. We also report some numerical results to show its efficiency.
Xiaoliang Dong; Deren Han; Zhifeng Dai; Lixiang Li; Jianguang Zhu. An Accelerated Three-Term Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition. Journal of Optimization Theory and Applications 2018, 179, 944 -961.
AMA StyleXiaoliang Dong, Deren Han, Zhifeng Dai, Lixiang Li, Jianguang Zhu. An Accelerated Three-Term Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition. Journal of Optimization Theory and Applications. 2018; 179 (3):944-961.
Chicago/Turabian StyleXiaoliang Dong; Deren Han; Zhifeng Dai; Lixiang Li; Jianguang Zhu. 2018. "An Accelerated Three-Term Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition." Journal of Optimization Theory and Applications 179, no. 3: 944-961.
Highway pavement as an important component of transport infrastructure has significant impacts on economy, society, and environment. The management of highway pavement has been traditionally focused on economy. In this study, the impacts of management decisions are examined in three dimensions, including life-cycle cost (LCC), energy consumption, and greenhouse gas (GHG) emissions. Quantitative models to predict the three dimensions are developed from mechanistic-empirical pavement analysis results. Two decision variables, pavement thickness and threshold roughness level for pavement resurfacing, are found to be significant in affecting the three dimensions. These two variables are subsequently used as decision variables in multiobjective optimization. The ranges of decisions that result in minimum LCC, energy consumption, and GHG emissions are identified through multiobjective optimization. Although the analysis is illustrated in the context of pavement design and management in Hong Kong, the analysis techniques and procedures can be easily applied in other regions.
Dan Chong; Yuhong Wang; Zhifeng Dai; Xiaojun Chen; Dawei Wang; Markus Oeser. Multiobjective optimization of asphalt pavement design and maintenance decisions based on sustainability principles and mechanistic-empirical pavement analysis. International Journal of Sustainable Transportation 2018, 12, 461 -472.
AMA StyleDan Chong, Yuhong Wang, Zhifeng Dai, Xiaojun Chen, Dawei Wang, Markus Oeser. Multiobjective optimization of asphalt pavement design and maintenance decisions based on sustainability principles and mechanistic-empirical pavement analysis. International Journal of Sustainable Transportation. 2018; 12 (6):461-472.
Chicago/Turabian StyleDan Chong; Yuhong Wang; Zhifeng Dai; Xiaojun Chen; Dawei Wang; Markus Oeser. 2018. "Multiobjective optimization of asphalt pavement design and maintenance decisions based on sustainability principles and mechanistic-empirical pavement analysis." International Journal of Sustainable Transportation 12, no. 6: 461-472.
Zhifeng Dai; Fenghua Wen. A generalized approach to sparse and stable portfolio optimization problem. Journal of Industrial & Management Optimization 2018, 14, 1651 -1666.
AMA StyleZhifeng Dai, Fenghua Wen. A generalized approach to sparse and stable portfolio optimization problem. Journal of Industrial & Management Optimization. 2018; 14 (4):1651-1666.
Chicago/Turabian StyleZhifeng Dai; Fenghua Wen. 2018. "A generalized approach to sparse and stable portfolio optimization problem." Journal of Industrial & Management Optimization 14, no. 4: 1651-1666.
In this note, we aim at improving the proof of Theorem 2.1, 2.2, and Theorem 4.2 in Andrei (J Optim Theory Appl 141:249–264, 2009).
Zhifeng Dai. Comments on Hybrid Conjugate Gradient Algorithm for Unconstrained Optimization. Journal of Optimization Theory and Applications 2017, 175, 286 -291.
AMA StyleZhifeng Dai. Comments on Hybrid Conjugate Gradient Algorithm for Unconstrained Optimization. Journal of Optimization Theory and Applications. 2017; 175 (1):286-291.
Chicago/Turabian StyleZhifeng Dai. 2017. "Comments on Hybrid Conjugate Gradient Algorithm for Unconstrained Optimization." Journal of Optimization Theory and Applications 175, no. 1: 286-291.