{"type":"ScholarlyArticle","title":"PRoFET: Predicting the Risk of Firms from Event Transcripts","authors":[{"name":"Christoph Kilian Theil","slug":"christoph-kilian-theil","given":"Christoph Kilian","family":"Theil","affiliation":"Data and Web Science Group , University of Mannheim , Germany"},{"name":"Samuel Broscheit","slug":"samuel-broscheit","given":"Samuel","family":"Broscheit","affiliation":"Data and Web Science Group , University of Mannheim , Germany"},{"name":"Heiner Stuckenschmidt","slug":"heiner-stuckenschmidt","given":"Heiner","family":"Stuckenschmidt","affiliation":"Data and Web Science Group , University of Mannheim , Germany"}],"abstract":"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\r\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.","doi":"10.24963/ijcai.2019/724","url":"https://ijcai.org/proceedings/2019/724","pdf":"https://ijcai.org/proceedings/2019/0724.pdf","markdown":"https://ijcai.org/proceedings/2019/724.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/724","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":724,"pages":{"first":5211,"last":5217},"keywords":["Multidisciplinary Topics and Applications: Finance","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[Andersen et al., 2006] Torben G. Andersen, Tim Bollerslev, Peter F. Christoffersen, and Francis X. Diebold. Volatil- ity and Correlation Forecasting. In G. Elliot, C. W. J. Granger, and A. Timmermann, editors, Handbook of Eco- nomic Forecasting, chapter 15, pages 778-878. North- Holland, Amsterdam, 2006.","title":"Volatility and Correlation Forecasting","authors":"G Torben, Tim Andersen, Peter F Bollerslev, Francis X Christoffersen, Diebold","year":"2006","venue":"Handbook of Economic Forecasting","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Anscombe, 1973] Francis J. Anscombe. Graphs in Statis- tical Analysis. The American Statistician, 27(1):17-21, 1973.","title":"Graphs in Statistical Analysis","authors":"Francis J Anscombe","year":"1973","venue":"The American Statistician","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural Machine Translation by Jointly Learning to Align and Translate. In Proceedings of ICLR, 2015.","title":"Neural Machine Translation by Jointly Learning to Align and Translate","authors":"Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio","year":"2015","venue":"Proceedings of ICLR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Baroni et al., 2014] Marco Baroni, Georgiana Dinu, and Germán Kruszewski. Don't Count, Predict! A Systematic Comparison of Context-Counting vs. Context-Predicting Semantic Vectors. In Proceedings of ACL, pages 238-247, 2014.","title":"Don't Count, Predict! A Systematic Comparison of Context-Counting vs. Context-Predicting Semantic Vectors","authors":"Marco Baroni, Georgiana Dinu, Germán Kruszewski","year":"2014","venue":"Proceedings of ACL","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Blair et al., 2001] Bevan J. Blair, Ser-Huang Poon, and Stephen J. Taylor. Forecasting S&P 100 Volatility: The In- cremental Information Content of Implied Volatilities and High-Frequency Index Returns. Journal of Econometrics, 105(1):5-26, 2001.","title":"Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities and High-Frequency Index Returns","authors":"J Bevan, Ser-Huang Blair, Stephen J Poon, Taylor","year":"2001","venue":"Journal of Econometrics","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Bojanowski et al., 2017] Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching Word Vec- tors with Subword Information. Transactions of ACL, 5:135-146, 2017.","title":"Enriching Word Vectors with Subword Information","authors":"Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov","year":"2017","venue":"Transactions of ACL","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Bollerslev, 1986] Tim Bollerslev. Generalized Autoregres- sive Conditional Heteroskedasticity. Journal of Economet- rics, 31(3):307-327, 1986.","title":"Generalized Autoregressive Conditional Heteroskedasticity","authors":"Tim Bollerslev","year":"1986","venue":"Journal of Econometrics","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Duan et al., 2018] Junwen Duan, Xiao Ding, and Ting Liu. Learning Sentence Representations over Tree Structures for Target-Dependent Classification. In Proceedings of NAACL, pages 551-560, 2018.","title":"Learning Sentence Representations over Tree Structures for Target-Dependent Classification","authors":"Duan","year":"2018","venue":"Proceedings of NAACL","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Duchi et al., 2010] John C. Duchi, Elad Hazan, and Yoram Singer. Adaptive Subgradient Methods for Online Learn- ing and Stochastic Optimization. In Proceedings of COLT, pages 257-269, 2010.","title":"Adaptive Subgradient Methods for Online Learning and Stochastic Optimization","authors":"John C Duchi, Elad Hazan, Yoram Singer","year":"2010","venue":"Proceedings of COLT","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Fama and French, 1993] Eugene F. Fama and Kenneth R. French. Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1):3-56, 1993.","title":"Common Risk Factors in the Returns on Stocks and Bonds","authors":"Eugene F Fama, Kenneth R French","year":"1993","venue":"Journal of Financial Economics","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fama and French, 1997] Eugene F. Fama and Kenneth R. French. Industry Costs of Equity. Journal of Financial Economics, 43(2):153-193, 1997.","title":"Industry Costs of Equity","authors":"Eugene F Fama, Kenneth R French","year":"1997","venue":"Journal of Financial Economics","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hansen and Lunde, 2005] Peter R. Hansen and Asger Lunde. A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)? Journal of Applied Econometrics, 20(2):873-889, 2005.","title":"A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?","authors":"R Peter, Asger Hansen, Lunde","year":"2005","venue":"Journal of Applied Econometrics","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8):1735-1780, 1997.","title":"Long Short-Term Memory","authors":"Schmidhuber Hochreiter, Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural Computation","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ioffe and Szegedy, 2015] Sergey Ioffe and Christian Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings of ICML, pages 448-456, 2015.","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","authors":"Szegedy Ioffe, Sergey Ioffe, Christian Szegedy","year":"2015","venue":"Proceedings of ICML","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kogan et al., 2009] Shimon Kogan, Dimitry Levin, Bryan R. Routledge, Jacob S. Sagi, and Noah A. Smith. Predicting Risk from Financial Reports with Regression. In Proceedings of NAACL, pages 272-280, 2009.","title":"Predicting Risk from Financial Reports with Regression","authors":"Shimon Kogan, Dimitry Levin, Bryan R Routledge, Jacob S Sagi, Noah A Smith","year":"2009","venue":"Proceedings of NAACL","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Larcker and Zakolyukina, 2012] David F. Larcker and Anastasia A. Zakolyukina. Detecting Deceptive Dis- cussions in Conference Calls. Journal of Accounting Research, 50(2):494-540, 2012.","title":"Detecting Deceptive Discussions in Conference Calls","authors":"David F Larcker, Anastasia A Zakolyukina","year":"2012","venue":"Journal of Accounting Research","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Linzen et al., 2018] Tal Linzen, Grzegorz Chrupała, and Afra Alishahi. Introduction. In Proceedings of the 2018 EMNLP Workshop BlackBoxNLP, 2018.","title":"Tal Linzen, Grzegorz Chrupała, and Afra Alishahi. Introduction","authors":"Linzen","year":"2018","venue":"Proceedings of the 2018 EMNLP Workshop BlackBoxNLP","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Loughran and McDonald, 2011] Tim Loughran and Bill McDonald. When Is a Liability Not a Liability? Tex- tual Analysis, Dictionaries, and 10-Ks. The Journal of Finance, 66(1):35-65, 2011.","title":"When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks","authors":"Mcdonald Loughran, Tim Loughran, Bill Mcdonald","year":"2011","venue":"The Journal of Finance","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Loughran and McDonald, 2016] Tim Loughran and Bill McDonald. Textual Analysis in Accounting and Finance: A Survey. Journal of Accounting Research, 54(4):1187- 1230, 2016.","title":"Textual Analysis in Accounting and Finance: A Survey","authors":"Mcdonald Loughran, Tim Loughran, Bill Mcdonald","year":"2016","venue":"Journal of Accounting Research","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Nair and Hinton, 2010] Vinod Nair and Geoffrey E. Hinton. Rectified Linear Units Improve Restricted Boltzmann Ma- chines. In Proceedings of ICML, 2010.","title":"Rectified Linear Units Improve Restricted Boltzmann Machines","authors":"Vinod Nair, Geoffrey E Hinton","year":"2010","venue":"Proceedings of ICML","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Price et al., 2012] S. McKay Price, James S. Doran, David R. Peterson, and Barbara A. Bliss. Earnings Confer- ence Calls and Stock Returns: The Incremental Informa- tiveness of Textual Tone. Journal of Banking and Finance, 36(4):992-1011, 2012.","title":"Earnings Conference Calls and Stock Returns: The Incremental Informativeness of Textual Tone","authors":"S Mckay Price, James S Doran, David R Peterson, Barbara A Bliss","year":"2012","venue":"Journal of Banking and Finance","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Rekabsaz et al., 2017] Navid Rekabsaz, Mihai Lupu, Artem Baklanov, Allan Hanbury, Alexander Duer, and Linda An- derson. Volatility Prediction Using Financial Disclosures Sentiments with Word Embedding-Based IR Models. In Proceedings of ACL, pages 1712-1721, 2017.","title":"Volatility Prediction Using Financial Disclosures Sentiments with Word Embedding-Based IR Models","authors":"Navid Rekabsaz, Mihai Lupu, Artem Baklanov, Allan Hanbury, Alexander Duer, Linda Anderson","year":"2017","venue":"Proceedings of ACL","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Tsai and Wang, 2014] Ming-Feng Tsai and Chuan-Ju Wang. Financial Keyword Expansion via Continuous Word Vector Representations. In Proceedings of EMNLP, pages 1453-1458, 2014.","title":"Financial Keyword Expansion via Continuous Word Vector Representations","authors":"Ming-Feng Tsai, Chuan-Ju Wang","year":"2014","venue":"Proceedings of EMNLP","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang and Hua, 2014] William Yang Wang and Zhenhao Hua. A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls. In Proceedings of ACL, pages 1155-1165, 2014.","title":"A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls","authors":"William Yang, Wang, Zhenhao Hua","year":"2014","venue":"Proceedings of ACL","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2013] Chuan-Ju Wang, Ming-Feng Tsai, Tse Liu, and Chin-Ting Chang. Financial Sentiment Analy- sis for Risk Prediction. In Proceedings of IJCNLP, pages 802-808, 2013.","title":"Financial Sentiment Analysis for Risk Prediction","authors":"Chuan-Ju Wang, Ming-Feng Tsai, Tse Liu, Chin-Ting Chang","year":"2013","venue":"Proceedings of IJCNLP","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Xu and Cohen, 2018] Yumo Xu and Shay B. Cohen. Stock Movement Prediction from Tweets and Historical Prices. In Proceedings of ACL, pages 1970-1979, 2018.","title":"Stock Movement Prediction from Tweets and Historical Prices","authors":"Cohen Xu, Yumo Xu, Shay B Cohen","year":"2018","venue":"Proceedings of ACL","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}