Divide and Contrast: A Text-Based Method for Firm Market Risk Prediction

Published Online:https://doi.org/10.1287/ijoc.2023.0195

References

  • Beltagy I, Peters ME, Cohan A (2020) Longformer: The long-document transformer. Preprint, submitted April 10, https://arxiv.org/abs/2004.05150.Google Scholar
  • Bernard VL, Thomas JK (1989) Post-earnings-announcement drift: Delayed price response or risk premium? J. Accounting Res. 27:1–36.CrossrefGoogle Scholar
  • Black F, Scholes M (2019) The pricing of options and corporate liabilities. World Scientific Reference on Contingent Claims Analysis in Corporate Finance: Volume 1: Foundations of CCA and Equity Valuation (World Scientific, Singapore), 3–21.CrossrefGoogle Scholar
  • Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J. Machine Learn. Res. 3:993–1022.Google Scholar
  • Brooks C, Persand G (2003) Volatility forecasting for risk management. J. Forecasting 22(1):1–22.CrossrefGoogle Scholar
  • Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, et al. (2020) Language models are few-shot learners. Adv. Neural Inform. Processing Systems, vol. 33 (MIT Press, Cambridge, MA), 1877–1901.Google Scholar
  • Brownlees CT, Gallo GM (2010) Comparison of volatility measures: A risk management perspective. J. Financial Econometrics 8(1):29–56.CrossrefGoogle Scholar
  • Bushee BJ, Matsumoto DA, Miller GS (2003) Open versus closed conference calls: The determinants and effects of broadening access to disclosure. J. Accounting Econom. 34(1–3):149–180.CrossrefGoogle Scholar
  • Cazier RA, Pfeiffer RJ (2016) Why are 10-k filings so long? Accounting Horizons 30(1):1–21.CrossrefGoogle Scholar
  • Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. Internat. Conf. Machine Learn. (PMLR, New York), 1597–1607.Google Scholar
  • Cheng D, Yang F, Wang X, Zhang Y, Zhang L (2020) Knowledge graph-based event embedding framework for financial quantitative investments. Special Interest Group Inform. Retrieval (ACM, New York), 2221–2230.CrossrefGoogle Scholar
  • Dessaint O, Foucault T, Frésard L (2024) Does alternative data improve financial forecasting? The horizon effect. J. Finance 79(3):2237–2287.CrossrefGoogle Scholar
  • Devlin J, Chang MW, Lee K, Toutanova K (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. NAACL-HLT (Association for Computational Linguistics, Pennsylvania), 4171–4186.Google Scholar
  • Doran JS, Peterson DR, Price SM (2012) Earnings conference call content and stock price: The case of REITs. J. Real Estate Finance Econom. 45(2):402–434.CrossrefGoogle Scholar
  • Dumas B, Kurshev A, Uppal R (2009) Equilibrium portfolio strategies in the presence of sentiment risk and excess volatility. J. Finance 64(2):579–629.CrossrefGoogle Scholar
  • Frankel R, Jennings JN, Lee JA (2017) Using natural language processing to assess text usefulness to readers: The case of conference calls and earnings prediction. Preprint, submitted February 14, https://dx.doi.org/10.2139/ssrn.3095754.Google Scholar
  • Frankel R, Johnson M, Skinner DJ (1999) An empirical examination of conference calls as a voluntary disclosure medium. J. Accounting Res. 37(1):133–150.CrossrefGoogle Scholar
  • Galke L, Scherp A (2022) Bag-of-words vs. graph vs. sequence in text classification: Questioning the necessity of text-graphs and the surprising strength of a wide MLP. 60th Annual Meeting Assoc. Comput. Linguistics (Association for Computational Linguistics, Pennsylvania), 4038–4051.Google Scholar
  • Giorgi JM, Nitski O, Wang B, Bader GD (2021) DeCLUTR: Deep contrastive learning for unsupervised textual representations. ACL/IJCNLP (Association for Computational Linguistics, Pennsylvania), 879–895.Google Scholar
  • He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. Confe. Comput. Vision Pattern Recognition (Computer Vision Foundation/IEEE, Piscataway, NJ), 9729–9738.Google Scholar
  • He Y, Yang Y, Lian D, Zhang K (2025) Divide-and-contrast: A machine learning method for text-based risk prediction using earnings conference call transcripts. http://dx.doi.org/10.1287/ijoc.2023.0195.cd, https://github.com/INFORMSJoC/2023.0195.Google Scholar
  • Hoberg G, Phillips G (2016) Text-based network industries and endogenous product differentiation. J. Political Econom. 124(5):1423–1465.CrossrefGoogle Scholar
  • Hong LJ, Juneja S, Luo J (2014) Estimating sensitivities of portfolio credit risk using Monte Carlo. INFORMS J. Comput. 26(4):848–865.LinkGoogle Scholar
  • Hong W, Ji K, Liu J, Wang J, Chen J, Chu W (2021) Gilbert: Generative vision-language pre-training for image-text retrieval. Special Interest Group Inform. Retrieval (ACM, New York), 1379–1388.Google Scholar
  • Huang AH, Lehavy R, Zang AY, Zheng R (2018) Analyst information discovery and interpretation roles: A topic modeling approach. Management Sci. 64(6):2833–2855.LinkGoogle Scholar
  • Jiang G, Hong LJ, Nelson BL (2020) Online risk monitoring using offline simulation. INFORMS J. Comput. 32(2):356–375.AbstractGoogle Scholar
  • Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. Preprint, submitted December 22, https://arxiv.org/abs/1412.6980.Google Scholar
  • Kingma DP, Welling M (2013) Auto-encoding variational bayes. Preprint, submitted December 20, https://arxiv.org/abs/1312.6114.Google Scholar
  • Kitaev N, Kaiser Ł, Levskaya A (2020) Reformer: The efficient transformer. Preprint, submitted January 13, https://arxiv.org/abs/2001.04451.Google Scholar
  • Kogan S, Levin D, Routledge BR, Sagi JS, Smith NA (2009) Predicting risk from financial reports with regression. HLT-NAACL (The Association for Computational Linguistics, Pennsylvania), 272–280.Google Scholar
  • Liao J, Zhao X, Li X, Zhang L, Tang J (2021) Learning discriminative neural representations for event detection. Special Interest Group Inform. Retrieval (ACM, New York), 644–653.Google Scholar
  • Liu X, Zhang F, Hou Z, Mian L, Wang Z, Zhang J, Tang J (2021) Self-supervised learning: Generative or contrastive. IEEE Trans. Knowledge Data Engrg. 35(1):857–876.Google Scholar
  • Liu Y, Liu P (2021) SimCLS: A simple framework for contrastive learning of abstractive summarization. ACL/IJCNLP (Association for Computational Linguistics, Pennsylvania), 1065–1072.Google Scholar
  • Logeswaran L, Lee H (2018) An efficient framework for learning sentence representations. ICLR (OpenReview.net).Google Scholar
  • Loughran T, McDonald B (2016) Textual analysis in accounting and finance: A survey. J. Accounting Res. 54(4):1187–1230.CrossrefGoogle Scholar
  • Poon SH, Granger C (2005) Practical issues in forecasting volatility. Financial Anal. J. 61(1):45–56.CrossrefGoogle Scholar
  • Price SM, Doran JS, Peterson DR, Bliss BA (2012) Earnings conference calls and stock returns: The incremental informativeness of textual tone. J. Banking Finance 36(4):992–1011.CrossrefGoogle Scholar
  • Qin Y, Yang Y (2019) What you say and how you say it matters: Predicting stock volatility using verbal and vocal cues. ACL (Association for Computational Linguistics, Pennsylvania), 390–401.Google Scholar
  • Reimers N, Gurevych I (2019) Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proc. 2019 Conf. Empirical Methods Natl. Language Processing (Association for Computational Linguistics, Pennsylvania).Google Scholar
  • Sawhney R, Agarwal S, Thakkar M, Wadhwa A, Shah RR (2021) Hyperbolic online time stream modeling. Special Interest Group Inform. Retrieval (ACM, New York), 1682–1686.Google Scholar
  • Tay Y, Bahri D, Metzler D, Juan D, Zhao Z, Zheng C (2020a) Synthesizer: Rethinking self-attention in transformer models. Preprint, submitted May 2, https://arxiv.org/abs/2005.00743.Google Scholar
  • Tay Y, Dehghani M, Abnar S, Shen Y, Bahri D, Pham P, Rao J, Yang L, Ruder S, Metzler D (2020b) Long range arena: A benchmark for efficient transformers. Preprint, submitted November 8, https://arxiv.org/abs/2011.04006.Google Scholar
  • Theil CK, Broscheit S, Stuckenschmidt H (2019) Profet: Predicting the risk of firms from event transcripts. Internat. Joint Conf. Artificial Intelligence, 5211–5217.Google Scholar
  • Tong C, Peng H, Bai X, Dai Q, Zhang R, Li Y, Xu H, Gu X (2021) Learning discriminative text representation for streaming social event detection. IEEE Trans. Knowledge Data Engrg. 35(12):12295–12309.CrossrefGoogle Scholar
  • Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, Rozière B, et al. (2023) LLaMA: Open and efficient foundation language models. Preprint, submitted February 27, https://arxiv.org/abs/2302.13971.Google Scholar
  • Van den Oord A, Li Y, Vinyals O (2018) Representation learning with contrastive predictive coding. Preprint, submitted July 10, https://arxiv.org/abs/1807.03748.Google Scholar
  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv. Neural Inform. Processing Systems, vol. 30 (MIT Press, Cambridge, MA).Google Scholar
  • Wang WY, Hua Z (2014) A semiparametric Gaussian copula regression model for predicting financial risks from earnings calls. ACL (The Association for Computer Linguistics, Pennsylvania), 1155–1165.Google Scholar
  • Yan Y, Li R, Wang S, Zhang F, Wu W, Xu W (2021) ConSERT: A contrastive framework for self-supervised sentence representation transfer. ACL/IJCNLP (Association for Computational Linguistics, Pennsylvania), 5065–5075.Google Scholar
  • Yang Y, Zhang K, Fan Y (2022) Analyzing firm reports for volatility prediction: A knowledge-driven text-embedding approach. INFORMS J. Comput. 34(1):522–540.LinkGoogle Scholar
  • Yang Y, Qin Y, Fan Y, Zhang Z (2023) Unlocking the power of voice for financial risk prediction: A theory-driven deep learning design approach. MIS Quart. 47(1):63–96.CrossrefGoogle Scholar
  • Ye Z, Qin Y, Xu W (2020) Financial risk prediction with multi-round Q&A attention network. Internat. Joint Conf. Artificial Intelligence, 4576–4582.Google Scholar
  • Zaheer M, Guruganesh G, Dubey KA, Ainslie J, Alberti C, Ontanon S, Pham P, Ravula A, Wang Q, Yang L, et al. (2020) Big bird: Transformers for longer sequences. Adv. in Neural Inform. Processing Systems, vol. 33 (MIT Press, Cambridge, MA), 17283–17297.Google Scholar
  • Zhang Y, He R, Liu Z, Lim KH, Bing L (2020a) An unsupervised sentence embedding method by mutual information maximization. EMNLP (Association for Computational Linguistics, Pennsylvania), 1601–1610.Google Scholar
  • Zhang Y, Zhao P, Li B, Wu Q, Huang J, Tan M (2020b) Cost-sensitive portfolio selection via deep reinforcement learning. IEEE Trans. Knowledge Data Engrg. 34(1):236–248.Google Scholar
  • Zhao X, Fang X, He J, Huang L (2022) Exploiting expert knowledge for assigning firms to industries: A novel deep learning method. Preprint, submitted September 11, https://arxiv.org/abs/2209.05943.Google Scholar
  • Zhou K, Wang H, Zhao WX, Zhu Y, Wang S, Zhang F, Wang Z, Wen JR (2020) S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. Proc. 29th ACM Internat. Conf. Inform. Knowledge Management (ACM, New York), 1893–1902.Google Scholar
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