Delegated Asset Management, Investment Mandates, and Capital Immobility

This paper develops a model to explain the widely used investment mandates in the institutional asset management industry based on two insights: First, giving a manager more investment flexibility weakens the link between fund performance and his effort in the designated market, and thus increases agency cost. Second, the presence of outside assets with negatively skewed returns can further increase the agency cost if the manager is incentivized to pursue outside opportunities. These effects motivate narrow mandates and tight tracking error constraints to most fund managers except those with exceptional talents. Our model sheds light on capital immobility and market segmentation that are widely observed in financial markets, and highlights important effects of negatively skewed risk on institutional incentive structures.

Dynamic Debt Runs

We develop a dynamic model of debt runs on a firm, which invests in an illiquid asset by rolling over staggered short-term debt contracts. We derive a unique threshold equilibrium, in which creditors coordinate their asynchronous rollover decisions based on the firm's publicly observable and time-varying fundamental. Fear of the firm's future rollover risk motivates each maturing creditor to run ahead of others even when the firm is still solvent. Our model provides implications on the roles played by volatility, illiquidity and debt maturity in driving debt runs, as well as on firms' capital adequacy standards and credit risk.

Rollover Risk and Credit Risk

This paper models a firm's rollover risk generated by conflict of interest between debt and equity holders. When the firm faces losses in rolling over its maturing debt, its equity holders are willing to absorb the losses only if the option value of keeping the firm alive justifies the cost of paying off the maturing debt. Our model shows that both deteriorating market liquidity and shorter debt maturity can exacerbate this externality and cause costly firm bankruptcy at higher fundamental thresholds. Our model provides implications on liquidity-spillover effects, the flight-to-quality phenomenon, and optimal debt maturity structures.

Scaling for turbulent viscosity of buoyant plumes in stratified fluids : PIV measurement with implications for submarine hydrothermal plume turbulence

© The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part I: Oceanographic Research Papers 129 (2017): 89-98, doi:10.1016/j.dsr.2017.10.006.Time-resolved particle image velocimetry (PIV) has been used to measure instantaneous twodimensional velocity vector fields of laboratory-generated turbulent buoyant plumes in linearly stratified saltwater over extended periods of time. From PIV-measured time-series flow data, characteristics of plume mean flow and turbulence have been quantified. To be specific, maximum plume penetration scaling and entrainment coefficient determined from the mean flow agree well with the theory based on the entrainment hypothesis for buoyant plumes in stratified fluids. Besides the well-known persistent entrainment along the plume stem (i.e., the ‘plumestem’ entrainment), the mean plume velocity field shows persistent entrainment along the outer edge of the plume cap (i.e., the ‘plume-cap’ entrainment), thereby confirming predictions from previous numerical simulation studies. To our knowledge, the present PIV investigation provides the first measured flow field data in the plume cap region. As to measured plume turbulence, both the turbulent kinetic energy field and the turbulence dissipation rate field attain their maximum close to the source, while the turbulent viscosity field reaches its maximum within the plume cap region; the results also show that maximum turbulent viscosity scales as νt,max = 0.030 (B/N)1/2, where B is source buoyancy flux and N is ambient buoyancy frequency. These PIV data combined with previously published numerical simulation results have implications for understanding the roles of hydrothermal plume turbulence, i.e. plume turbulence within the cap region causes the ‘plume-cap’ entrainment that plays an equally important role as the ‘plume-stem’ entrainment in supplying the final volume flux at the plume spreading level.Part of this work was financially supported by the National Natural Science Foundation of China and Natural Science Foundation of Zhejiang Province under respective Project no. 11672267 and LR16E090001 to ZH. HJ was supported by a National Science Foundation Grant NSF OCE-1038055 through the RIDGE2000 program and an internal funding from WHOI

Table Search Using a Deep Contextualized Language Model

Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks. Benefiting from multiple pretraining tasks and large scale training corpora, pretrained models can capture complex syntactic word relations. In this paper, we use the deep contextualized language model BERT for the task of ad hoc table retrieval. We investigate how to encode table content considering the table structure and input length limit of BERT. We also propose an approach that incorporates features from prior literature on table retrieval and jointly trains them with BERT. In experiments on public datasets, we show that our best approach can outperform the previous state-of-the-art method and BERT baselines with a large margin under different evaluation metrics.Comment: Accepted at SIGIR 2020 (Long

Venue topic model-enhanced joint graph modelling for citation recommendation in scholarly big data

Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author's local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue-venue interaction. To solve this problem, we propose an author topic model-enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues' capacity of exerting topic influence on other venues. The top-susceptibility captures venues' propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-The-Art methods. © 2020 ACM