High Frequency Trading Volatility, Market Microstructure Noise and Institutional Investors

As stocks being traded more intensely daily, many recent studies show that the efficiency and accuracy of some measures developed from low frequency data may not be well guaranteed. It is worth investigating the relationship between institutional investor's behavior and stock return volatility in this new circumstance. We estimate stock return volatility with the two-scales realized volatility (TSRV) estimator which corrects the effect from market microstructure noise on stock prices. Using all transactions of S\&P 500 constituent stocks over the most recent 10 years, we find a positive association between the levels of institutional ownership and daily stock return volatility. This association is not constant over time. Institutional investors are more conservative while the market is more volatile, and they become more aggressive when market calms down. We also analyze quarterly changes in stock return volatility and institutional ownership ratio, and we find evidence that past increase in institutional ownership changes will positively impact the changes in volatility for the future, and past increases in volatility changes will decrease the institutional ownership in the future. But there is no causality relationship between institutional ownership and market microstructure noise. Most institutional investors act similarly except banks. We also find evidence for institutional herding and that stocks with higher institutional ownership ratios attract more high turnover traders, which could be the possible channels through which institutional investors affect stock return volatility

DataSheet1_Identification and functional analysis of ovarian lncRNAs during different egg laying periods in Taihe Black-Bone Chickens.ZIP

Introduction: Long non-coding RNA (lncRNA) refers to a category of non-coding RNA molecules exceeding 200 nucleotides in length, which exerts a regulatory role in the context of ovarian development. There is a paucity of research examining the involvement of lncRNA in the regulation of ovary development in Taihe Black-Bone Chickens. In order to further investigate the egg laying regulation mechanisms of Taihe Black-Bone Chickens at different periods, transcriptome analysis was conducted on the ovarian tissues at different laying periods.Methods: This study randomly selected ovarian tissues from 12 chickens for RNA-seq. Four chickens were selected for each period, including the early laying period (102 days, Pre), the peak laying period (203 days, Peak), and the late laying period (394 days, Late). Based on our previous study of mRNA expression profiles in the same ovarian tissue, we identified three differentially expressed lncRNAs (DE lncRNAs) at different periods and searched for their cis- and trans-target genes to draw an lncRNA-mRNA network.Results and discussion: In three groups of ovarian tissues, we identified 136 DE lncRNAs, with 8 showing specific expression during the early laying period, 10 showing specific expression during the peak laying period, and 4 showing specific expression during the late laying period. The lncRNA-mRNA network revealed 16 pairs of lncRNA-target genes associated with 7 DE lncRNAs, and these 14 target genes were involved in the regulation of reproductive traits. Furthermore, these reproductive-related target genes were primarily associated with signaling pathways related to follicle and ovary development in Taihe Black-Bone Chickens, including cytokine-cytokine receptor interaction, TGF-beta signaling pathway, tyrosine metabolism, ECM-receptor interaction, focal adhesion, neuroactive ligand-receptor interaction, and cell adhesion molecules (CAMs). This study offers valuable insights for a comprehensive understanding of the influence of lncRNAs on poultry reproductive traits.</p

Overall survival and disease-free survival of the entire group.

<p>Overall survival and disease-free survival of the entire group.</p

Details of patients(N = 24).

<p>Details of patients(N = 24).</p

Patient characteristics(N = 24).

<p>Patient characteristics(N = 24).</p

Univariate analysis of survival.

<p>Univariate analysis of survival.</p

Kaplan-Meier overall survival (A p = 0.032) and disease-free survival (B p = 0.045) stratified by histology.

<p>Kaplan-Meier overall survival (A p = 0.032) and disease-free survival (B p = 0.045) stratified by histology.</p