Three Essays in Institutional Trading and Corporate Finance

Thesis advisor: Thomas ChemmanurMy dissertation is comprised of three chapters. In this first chapter, I study the effect of social connections on mutual fund investors' information production and accuracy of their signals. While connected investors have access to information in their social network (information diffusion effect), social connections also reduce their incentives to acquire costly information, since they can free ride on connected peers ("free riding on friends" effect). I find this negative "free riding on friends" effect of social connections dominates information diffusion effect in the mutual fund industry, using fund managers' connections built upon their prior career experiences. First, I find that connected funds are more likely to hold the same stocks and to trade in the same direction, relative to unconnected funds. Second, I find that funds with lower network centrality earn higher alphas, even after controlling for other fund and manager characteristics. A one-standard-deviation increase in eigenvector centrality predicts a decrease of 29-37 basis points in annualized fund alphas. Third, when I define a stock-level variable PMC (Peripheral minus Central) as the difference in average portfolio weights between peripheral funds and central funds, I find that stocks with higher PMC have significantly higher abnormal stock returns. A one-standard-deviation increase in PMC predicts an increase of 1.48%-1.52% in the next quarter risk-adjusted returns (annualized). Finally, I find that PMC predicts firms' future earnings surprises. In the second chapter, co-authored with Thomas Chemmanur, Yingzhen Li, and Jie Xie, we propose a "noisy signaling" hypotheses of open market share repurchase (OMSR) programs, where the equity market equilibrium that prevails after OMSR program announcements is a partial pooling rather than a fully separating equilibrium. We argue that two complementary mechanisms, namely, actual share repurchases by firms and information production by institutions, serve to reduce the residual equity market information asymmetry facing firms subsequent to OMSR program announcements. We test the implications of this noisy signaling hypothesis using transaction-level data on trading by institutions and by a subsample of identified hedge funds, and find strong support for the above hypothesis. In the third chapter, co-authored with Thomas Chemmanur, and Jiekun Huang, we analyze how the geographical locations of institutions affect their investments in IPOs and various characteristics of the IPOs that they invest in. We argue that institutions geographically close to each other may free-ride on each other's information when evaluating IPOs, resulting in IPOs dominated by geographically clustered institutions reflecting less accurate information signals compared to those dominated by geographically dispersed institutions. We find that the equity holdings of institutions in IPOs are influenced more by the investments made by neighboring institutions. We show that an increase in the geographical dispersion of the institutions investing in an IPO is associated with higher IPO price revisions, higher firm valuations at offering and secondary market, larger IPO initial returns, greater long-run post-IPO stock returns lower information asymmetry facing an IPO firm in the equity market. Finally, the predictive power of institutional trading post-IPO for subsequent long-run stock returns and earnings surprises for the first fiscal-year end after the IPO is greater for geographically isolated institutions compared to those that are geographically clustered.Thesis (PhD) — Boston College, 2017.Submitted to: Boston College. Carroll School of Management.Discipline: Finance

Session-based Recommendation with Graph Neural Networks

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graph-structured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.Comment: 9 pages, 4 figures, accepted by AAAI Conference on Artificial Intelligence (AAAI-19

Interleukin-10-819 promoter polymorphism in association with gastric cancer risk

<p>Abstract</p> <p>Background</p> <p>Potential functional allele T/C single nucleotide polymorphism (SNP) of Interleukin 10 (IL-10) promoter -819 (rs1800871) has been implicated in gastric cancer risk. We aimed to explore the role of T/C SNP of IL-10 -819 in the susceptibility to gastric cancer through a systematic review and meta-analysis.</p> <p>Methods</p> <p>Each initially included article was scored for quality appraisal. Desirable data were extracted and registered into databases. 11 studies were ultimately eligible for the meta-analysis of IL-10 -819 T/C SNP. We adopted the most probably appropriate genetic model (recessive model). Potential sources of heterogeneity were sought out via subgroup and sensitivity analyses, and publication biases were estimated.</p> <p>Results</p> <p>IL-10 -819 TT genotype is associated with the overall reduced gastric cancer risk among Asians and even apparently observed among high quality subgroup Asians. IL-10-819 TT genotype is not statistically associated with the overall reduced gastric cancer susceptibility in persons with <it>H. pylori </it>infection compared with controls without <it>H. pylori </it>infection. IL-10 -819 TT genotype is reversely associated with diffuse-subtype risk but not in intestinal-subtype risk. IL-10 -819 TT genotype is not reversely associated with non-cardia or cardia subtype gastric cancer susceptibility.</p> <p>Conclusions</p> <p>IL-10 -819 TT genotype seems to be more protective from gastric cancer in Asians. Whether IL-10 -819 TT genotype may be protective from gastric cancer susceptibility in persons infected with <it>H. pylori </it>or in diffuse-subtype cancer needs further exploring in the future well-designed high quality studies among different ethnicity populations. Direct sequencing should be more used in the future.</p

Eagle-YOLO : An Eagle-Inspired YOLO for Object Detection in Unmanned Aerial Vehicles Scenarios

Funding Information: This research was funded by National Natural Science Foundation of China OF FUNDER grant number 41471333, 61304199. This research was funded by Fujian Provincial Department of Science and Technology OF FUNDER grant number 2021Y4019, 2020D002, 2020L3014, 2019I0019. This research was funded by Fujian University of Technology OF FUNDER grant number KF-J21012. This research was funded by Shenzhen Science and Technology Innovation Program OF FUNDER grant number JCYJ20220530160408019. This research was funded by Basic and Applied Basic Research Foundation of Guangdong Province OF FUNDER grant number 2023A1515011915. This research was funded by the Key Research and Development Project of Hunan Province of China OF FUNDER grant number 2022GK2020. This research was funded by Hunan Natural Science Foundation of China OF FUNDER grant number 2022JJ30171. Publisher Copyright: © 2023 by the authors.Peer reviewedPublisher PD