Using 4128 single jumps detected from high frequency data of 220 individual stocks in SZ300 P index, this paper investigates the liquidity dynamics around price jumps in Chinese market.Some interesting empirical resul...Using 4128 single jumps detected from high frequency data of 220 individual stocks in SZ300 P index, this paper investigates the liquidity dynamics around price jumps in Chinese market.Some interesting empirical results are obtained and the corresponding explanations are given. The frequency of positive jumps is quite higher than that of negative jumps. The trading volumes and average trade sizes are all in a high level around positive jumps. The relatively low liquidities around negative jumps show that negative jumps may be generated and enlarged by poor liquidity provision.The price reversal after price jumps is significant, and price reversal lasts longer after positive jumps.Moreover, the size and direction of jumps are significantly correlated with the returns and trades in the post-jump trading time. These findings are believed to be associated with the high proportion of retail investors and their herding behavior for price trend chasing.展开更多
Big Personal Data is growing explosively. Consequently, an increasing number of internet users are drowning in a sea of data. Big Personal Data has enormous commercial value; it is a new kind of data asset. An urgent ...Big Personal Data is growing explosively. Consequently, an increasing number of internet users are drowning in a sea of data. Big Personal Data has enormous commercial value; it is a new kind of data asset. An urgent problem has thus arisen in the data market： How to price Big Personal Data fairly and reasonably. This paper proposes a pricing model for Big Personal Data based on tuple granularity, with the help of comparative analysis of existing data pricing models and strategies. This model is put forward to implement positive rating and reverse pricing for Big Personal Data by investigating data attributes that affect data value, and analyzing how the value of data tuples varies with information entropy, weight value, data reference index, cost, and other factors. The model can be adjusted dynamically according to these parameters. With increases in data scale, reductions in its cost,and improvements in its quality, Big Personal Data users can thereby obtain greater benefits.展开更多
基金supported by the National Natural Science Foundation under Grant Nos.71431008,71532013,71501170Zhejiang Provincial National Science Foundation under Grant No.LQ16G010001the fund provided by Zhejiang Provincial Key Research Base for Humanities and Social Science Research(Applied Economics in Zhejiang Gongshang University)
文摘Using 4128 single jumps detected from high frequency data of 220 individual stocks in SZ300 P index, this paper investigates the liquidity dynamics around price jumps in Chinese market.Some interesting empirical results are obtained and the corresponding explanations are given. The frequency of positive jumps is quite higher than that of negative jumps. The trading volumes and average trade sizes are all in a high level around positive jumps. The relatively low liquidities around negative jumps show that negative jumps may be generated and enlarged by poor liquidity provision.The price reversal after price jumps is significant, and price reversal lasts longer after positive jumps.Moreover, the size and direction of jumps are significantly correlated with the returns and trades in the post-jump trading time. These findings are believed to be associated with the high proportion of retail investors and their herding behavior for price trend chasing.
基金supported in part by the National Natural Science Foundation of China (Nos. 61332001, 61272104, and 61472050)the Science and Technology Planning Project of Sichuan Province (Nos. 2014JY0257, 2015GZ0103, and 2014-HM01-00326SF)
文摘Big Personal Data is growing explosively. Consequently, an increasing number of internet users are drowning in a sea of data. Big Personal Data has enormous commercial value; it is a new kind of data asset. An urgent problem has thus arisen in the data market： How to price Big Personal Data fairly and reasonably. This paper proposes a pricing model for Big Personal Data based on tuple granularity, with the help of comparative analysis of existing data pricing models and strategies. This model is put forward to implement positive rating and reverse pricing for Big Personal Data by investigating data attributes that affect data value, and analyzing how the value of data tuples varies with information entropy, weight value, data reference index, cost, and other factors. The model can be adjusted dynamically according to these parameters. With increases in data scale, reductions in its cost,and improvements in its quality, Big Personal Data users can thereby obtain greater benefits.
