return predictability
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2022 ◽  
pp. 1-25
Author(s):  
Christos Argyropoulos ◽  
Ekaterini Panopoulou ◽  
Nikolaos Voukelatos ◽  
Teng Zheng

2022 ◽  
Author(s):  
Zhongjin Lu ◽  
Steven G. Malliaris ◽  
Zhongling Qin

2021 ◽  
Vol 14 (12) ◽  
pp. 620
Author(s):  
Jungah Yoon ◽  
Xinfeng Ruan ◽  
Jin E. Zhang

In this paper, we study the skewness risk and its return predictability in the energy market. Skewness risk is often used to measure the possibility of market crash. We study both physical skewness (market skewness and cross-sectional average realized skewness) estimated from underlying stock returns and risk-neutral skewness evaluated from the options market. We find a significant positive relationship between one-month-ahead market return and average realized skewness in the energy market. This unique feature should be noted by investors and carefully considered by energy policymakers.


2021 ◽  
Author(s):  
Chulwoo Han

This paper documents the bimodality of momentum stocks: both high- and low-momentum stocks have nontrivial probabilities for both high and low returns. The bimodality makes the momentum strategy fundamentally risky and can cause a large loss. To alleviate the bimodality and improve return predictability, this paper develops a novel cross-sectional prediction model via machine learning. By reclassifying stocks based on their predicted financial performance, the model significantly outperforms off-the-shelf machine learning models. Tested on the U.S. market, a value-weighted long-short portfolio earns a monthly alpha of 2.4% (t-statistic = 6.63) when regressed against the Fama–French five factors plus the momentum and short-term reversal factors. This paper was accepted by Kay Giesecke, finance.


2021 ◽  
Author(s):  
Francisco Gomes ◽  
Alexander Michaelides ◽  
Yuxin Zhang

We propose target date funds modified to exploit stock return predictability driven by the variance risk premium. The portfolio rule of these tactical target date funds (TTDFs) is extremely simplified relative to the optimal one, making it easy to implement and to communicate to investors. We show that saving for retirement in TTDFs generates economically large welfare gains, even after we introduce turnover restrictions and transaction costs, and after taking into account parameter uncertainty. This predictability also appears to be uncorrelated with individual household risk, suggesting that households are in a prime position to exploit it. This paper was accepted by Tomasz Piskorski, finance.


2021 ◽  
pp. 102128
Author(s):  
Alok Kumar ◽  
Zicheng Lei ◽  
Chendi Zhang

Author(s):  
Cathy Yi-Hsuan Chen ◽  
Matthias R. Fengler ◽  
Wolfgang Karl Härdle ◽  
Yanchu Liu

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