The Drivers of the Great Bull Stock Market of 2015 in China: Evidence and Policy Implications

Song, Guoxiang

doi:10.2139/ssrn.2643051

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…We conclude that their effect on the change of market statuses and ETE is weak in these periods. This conclusion is line with Song [2]. He applied multifactor models to examine the effect of macroeconomic variables and found that the bull market was not sensitive to the macroeconomic variables.…”

Section: Ete Between Sectorssupporting

confidence: 78%

“…The great majority of them were retail investors, which tended to exhibit herd behavior. Moreover, many of these novice investors engaged in leveraged trading through various channels, for example margin financing of brokerages, shadow banking, or grey-market (over-the-counter, OTC) margin lenders [2,3]. Huge amounts of borrowed money flooded into the market [3].…”

Section: Introductionmentioning

confidence: 99%

“…From late June to late August of 2015, the SSECI declined about 40% [4]. It was one of the biggest falls in global stock market history [2]. In order to stabilize the market, the Chinese government took a series of actions, including organizing state-backed financial firms collectively called the "national team" to buy stocks directly, banning short sales, stopping new initial public offerings, etc.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015

Wang

Xia

2018

Entropy

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This paper applies effective transfer entropy to research the information transfer in the Chinese stock market around its crash in 2015. According to the market states, the entire period is divided into four sub-phases: the tranquil, bull, crash, and post-crash periods. Kernel density estimation is used to calculate the effective transfer entropy. Then, the information transfer network is constructed. Nodes’ centralities and the directed maximum spanning trees of the networks are analyzed. The results show that, in the tranquil period, the information transfer is weak in the market. In the bull period, the strength and scope of the information transfer increases. The utility sector outputs a great deal of information and is the hub node for the information flow. In the crash period, the information transfer grows further. The market efficiency in this period is worse than that in the other three sub-periods. The information technology sector is the biggest information source, while the consumer staples sector receives the most information. The interactions of the sectors become more direct. In the post-crash period, information transfer declines but is still stronger than the tranquil time. The financial sector receives the largest amount of information and is the pivot node.

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Section: Ete Between Sectorssupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015

Wang

Xia

2018

Entropy

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Will Both China and the US Benefit From Chinaas Exchange Rate Reform?

Song

2018

SSRN Journal

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De los lexicones: NLP en la construcción del Lexicón de Drivers de Mercado en Español

Litterio

2019

Revista Abierta de Informática Aplicada

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En nuestra aproximación sobre el análisis e interpretación de las noticias financieras sostenemos que, además de la necesidad de un lexicón de propósitos específicos para finanzas, es fundamental contar con lexicones de “drivers de mercado”, siendo un driver de mercado, aquel factor que ejerce un efecto material sobre una actividad de otra entidad, contextualmente dependiente y que afecta al mercado financiero en un momento determinado. Desde nuestro enfoque, proponemos: en primer lugar, conceptualizar las diferentes categorías de “drivers de mercado”, en segundo lugar, explicar de manera sucinta cómo mediante las técnicas de Procesamiento de Lenguaje Natural (NLP) se realiza la construcción del lexicón de drivers, y explicitar la relevancia del lexicón de drivers en la interpretación de noticias financieras y su correlación con los movimientos del mercado.

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The Drivers of the Great Bull Stock Market of 2015 in China: Evidence and Policy Implications

Cited by 3 publications

References 8 publications

Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015

Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015

Will Both China and the US Benefit From Chinaas Exchange Rate Reform?

De los lexicones: NLP en la construcción del Lexicón de Drivers de Mercado en Español

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