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Ziying Li

Researcher at University of Chicago

Publications -  18
Citations -  52

Ziying Li is an academic researcher from University of Chicago. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 2, co-authored 2 publications receiving 25 citations. Previous affiliations of Ziying Li include Beihang University.

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The effects of investor attention on commodity futures markets

Abstract: This study utilizes the search volume for key terms on Google as a direct and timely proxy for investor attention in order to examine how attention impacts commodity futures prices, We provide significant evidence for attention's influence on 13 commodity futures and the interaction between attention and returns, even after controlling for important macroeconomic variables. We also examine the impact of investor attention on market efficiency. Results show that rising attention, on one hand, increases information efficiency and attenuates arbitrage opportunities, whereas, on the other hand, decreases market efficiency by facilitating herd behavior.
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Investor Attention and Stock Returns: International Evidence

TL;DR: In this paper, the authors examined the asymmetric/discriminative effects of investor attention on expected stock returns among 15 markets through economic expansions and recessions and found that the predictive power of atte...
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Longitudinal Association Between Child Psychological Abuse and Neglect and Academic Achievement in Chinese Primary School Children: A Moderated Mediation Model

TL;DR: In this article , the authors investigated the relationship among child psychological abuse and neglect (CPAN), children's learning engagement, family socioeconomic status (family SES), and children's academic achievement.
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When cultural values meets professional values: a qualitative study of chinese nurses’ attitudes and experiences concerning death

TL;DR: Wang et al. as mentioned in this paper explored nurses' attitudes and coping strategies concerning death and caring for dying patients in a cultural context of death taboo, and found that nurses adopt three strategies in their practice to solve this tension: boundary-drawing to separate their personal and professional life, complying with existing cultural values at work, and constructing positive meanings for end-of-life care.
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Application of Machine Learning Algorithms to Predict Acute Kidney Injury in Elderly Orthopedic Postoperative Patients

TL;DR: By including intraoperative and preoperative risk factors, ML algorithm can predict AKI and logistic regression model performing the best and can help lead decision-making for strategies to inhibit AKI over the perioperative duration.