Quantitative Selection of Long-Short Hedge Funds

We develop a quantitative model to select hedge funds in the long-short equity sector. The selection strategy is verified on a survivorship-bias-free hedge fund database, from January 1990 to September 2002. We focus on the hedge funds acting exclusively in the U.S. market. We identify Fama-French factors and GSCI as the risk factors. Based on the evidence that many hedge funds do not exhibit persistent performance, we believe that persistent alpha is not generated based on publicly available information and opportunistic changes of exposure with respect to the risk factors. Instead we expect moderate exposure funds to be those who establish investment decisions based on special information or proprietary research. A hedge fund selection strategy is introduced and checked with out-of-sample data. A simulation of hedge funds from 1927 to 2002 is conducted. The funds selected according to our strategy demonstrate superior performance persistently.Hedge Fund; Long Short Strategy; Fama-French; Commodity; Performance Persistence; Skewness; Selection

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains a challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. We deploy PinSage at Pinterest and train it on 7.5 billion examples on a graph with 3 billion nodes representing pins and boards, and 18 billion edges. According to offline metrics, user studies and A/B tests, PinSage generates higher-quality recommendations than comparable deep learning and graph-based alternatives. To our knowledge, this is the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures.Comment: KDD 201

Coexistence Designs of Radar and Communication Systems in a Multi-path Scenario

The focus of this study is on the spectrum sharing between multiple-input multiple-output (MIMO) communications and co-located MIMO radar systems in multi-path environments. The major challenge is to suppress the mutual interference between the two systems while combining the useful multi-path components received at each system. We tackle this challenge by jointly designing the communication precoder, radar transmit waveform and receive filter. Specifically, the signal-to-interference-plus-noise ratio (SINR) at the radar receiver is maximized subject to constraints on the radar waveform, communication rate and transmit power. The multi-path propagation complicates the expressions of the radar SINR and communication rate, leading to a non-convex problem. To solve it, a sub-optimal algorithm based on the alternating maximization is used to optimize the precoder, radar transmit waveform and receive filter iteratively. Simulation results are provided to demonstrate the effectiveness of the proposed design

Testing of high current transformer by non-uniform equivalent magnetomotive force method

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