{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T18:37:56Z","timestamp":1776105476086,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Financial risk, defined as the chance to deviate from return expectations, is most commonly measured with volatility. Due to its value for investment decision making, volatility prediction is probably among the most important tasks in finance\nand risk management. Although evidence exists that enriching purely financial models with natural language information can improve predictions of volatility, this task is still comparably underexplored. We introduce PRoFET, the first neural model for volatility prediction jointly exploiting both semantic language representations and a comprehensive set of financial features. As language data, we use transcripts from quarterly recurring events, so-called \"earnings calls\"; in these calls, the performance of publicly traded companies is summarized and prognosticated by their management. We show that our proposed architecture, which models verbal context with an attention mechanism, significantly outperforms the previous state-of-the-art and other strong baselines. Finally, we visualize this attention mechanism on the token-level, thus aiding interpretability and providing a use case of PRoFET as a tool for investment decision support.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/724","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"5211-5217","source":"Crossref","is-referenced-by-count":8,"title":["PRoFET: Predicting the Risk of Firms from Event Transcripts"],"prefix":"10.24963","author":[{"given":"Christoph Kilian","family":"Theil","sequence":"first","affiliation":[{"name":"Data and Web Science Group, University of Mannheim, Germany"}]},{"given":"Samuel","family":"Broscheit","sequence":"additional","affiliation":[{"name":"Data and Web Science Group, University of Mannheim, Germany"}]},{"given":"Heiner","family":"Stuckenschmidt","sequence":"additional","affiliation":[{"name":"Data and Web Science Group, University of Mannheim, Germany"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:51:22Z","timestamp":1564300282000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/724"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/724","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}