Computational Experiments Successfully Predict the Emergence of Autocorrelations in Ultra-High-Frequency Stock Returns
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- Jian Zhou & Gao-Feng Gu & Zhi-Qiang Jiang & Xiong Xiong & Wei Chen & Wei Zhang & Wei-Xing Zhou, 2014. "Computational experiments successfully predict the emergence of autocorrelations in ultra-high-frequency stock returns," Papers 1404.1051, arXiv.org, revised Feb 2018.
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CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Gao-Feng Gu & Xiong Xiong & Hai-Chuan Xu & Wei Zhang & Yong-Jie Zhang & Wei Chen & Wei-Xing Zhou, 2017. "An empirical behavioural order-driven model with price limit rules," Papers 1704.04354, arXiv.org.
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- Kei Katahira & Yu Chen & Gaku Hashimoto & Hiroshi Okuda, 2019. "Development of an agent-based speculation game for higher reproducibility of financial stylized facts," Papers 1902.02040, arXiv.org.
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More about this item
KeywordsComputational experiment; Order-driven model; Market efficiency; Order direction; Long memory;
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