Yong Qu

Northeastern University

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Helin Fu

Central South University

Cheng-Da Xie

Central South University

Ying Yuan

Yueyang Hospital

Peng Chang

China State Construction Engineering (China)

Guiqian Cao

Central South University

Cooperative Institutions

Central South University

Nanjing Zhongyi Architectural Design Institute (China)

Yueyang Hospital

Shanghai University of Traditional Chinese Medicine

Minzu University of China

Universidad del Noreste

University of Electronic Science and Technology of China

Northeastern University

Hebei GEO University

Guangdong Hydropower Planning & Design Institute

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Predicting Equity Premium: A New Momentum Indicator Selection Strategy With Machine Learning

Journal of Forecasting (2024)

Yong Qu Ying Yuan

ABSTRACT We propose a new momentum‐determined indicator‐switching (N‐MDIS) strategy, harnessing the power of machine learning to enhance the accuracy of equity premium prediction. Specifically, we re‐examine the regime‐dependent feature of univariate predictive regression relative to the benchmark. Furthermore, we investigate the prediction mechanism of the momentum‐determined indicator‐switching (MDIS) strategy and validate the significance of market regime information for the MDIS. Our findings demonstrate an overwhelmingly superior ex‐post forecasting performance compared with the MDIS. More notably, our empirical results substantiate that machine learning greatly aids in momentum indicator selection. The results show that the N‐MDIS with machine learning generates more accurate ex‐ante equity premium forecasts than both MDIS strategy and N‐MDIS strategy with logistic regression, yielding statistically and economically significant results. Moreover, our new approach exhibits robust forecasting performance across a series of robustness tests.

Equity

Equity premium puzzle

Momentum (technical analysis)

10.1002/for.3200

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Research on Vault Settlement during Three-Step Tunnel Construction Process Based on Sandstone Rheological Experiment

Materials (2024)

Peng Chang Yong Qu Helin Fu Cheng-Da Xie Guiqian Cao

Tunnel stability is influenced by the rheological properties of the surrounding rock. This study, based on the Ganshen high-speed railway tunnel project, examines the rheological characteristics of siltstone and sandstone through laboratory tests and theoretical analysis. Rheological curves and parameters are derived, revealing the time-dependent deformation mechanisms of the surrounding rocks. A numerical simulation model is created using these parameters to analyze deformation and stress characteristics based on different rock levels and inverted arch closure distances. Results indicate that sandstone follows the Cvisc model, with the Maxwell elastic modulus increasing under higher loads while the viscous coefficient decreases. The vault displacement is mainly affected by the surrounding rock strength; lower strength leads to greater displacement, which also increases with the closure distance of the inverted arch. These findings are crucial for determining the optimal closure distance of inverted arches in sandstone conditions.

Closure (psychology)

Settlement (finance)

10.3390/ma17184619

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