PACRR: A Position-Aware Neural IR Model for Relevance Matching

In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR aiming at better modeling position-dependent interactions between a query and a document. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under multiple benchmarks.Comment: To appear in EMNLP201

WallStreetBets: An Analysis of Investment Advice Democratization

Reddit's WallStreetBets (WSB) community has come to prominence due to its role in the hype around GameStop and other meme stocks. Yet very little is known about the reliability of the investment advice disseminated on WSB. We investigate whether an anonymous, investment-focused community such as WSB can be a valuable source for investment advice and thus may constitute a way of democratizing access to financial knowledge. Our analysis reviews data spanning 28 months to assess how successful an investor relying on WSB recommendations could have been. We detect buy and sell signals and define a WSB portfolio based on the community's most popular stocks. Our evaluation shows that this portfolio has grown significantly, outperforming the S&P 500 over the reviewed time frame. We find that filtering for proactive posts yields higher returns and our review of the period before 2021 shows that the GameStop hype merely amplified previously existing characteristics