Leveraging Multiview Data Through Discrete and Regularized Deep Learning for Dynamic Financial Risk Prediction

References

• Abbasi A, Parsons J, Pant G, Sheng ORL, Sarker S (2024) Pathways for design research on artificial intelligence. Inform. Systems Res. 35(2):441–459.Link, Google Scholar
• Allaj E, Sanfelici S (2023) Early warning systems for identifying financial instability. Internat. J. Forecasting 39(4):1777–1803.Crossref, Google Scholar
• Ban GY, El Karoui N, Lim AE (2018) Machine learning and portfolio optimization. Management Sci. 64(3):1136–1154.Link, Google Scholar
• Blumenstock G, Lessmann S, Seow HV (2022) Deep learning for survival and competing risk modelling. J. Oper. Res. Soc. 73(1):26–38.Crossref, Google Scholar
• Borchert P, Coussement K, De Caigny A, De Weerdt J (2023) Extending business failure prediction models with textual website content using deep learning. Eur. J. Oper. Res. 306(1):348–357.Crossref, Google Scholar
• Brady TF, Tenenbaum JB (2013) A probabilistic model of visual working memory: Incorporating higher order regularities into working memory capacity estimates. Psych. Rev. 120(1):85–109.Crossref, Google Scholar
• Bybee L, Kelly B, Su Y (2023) Narrative asset pricing: Interpretable systematic risk factors from news text. Rev. Financial Stud. 36(12):4759–4787.Crossref, Google Scholar
• Chen H, Hwang BH (2022) Listening in on investors’ thoughts and conversations. J. Financial Econom. 145(2):426–444.Crossref, Google Scholar
• Chen H, De P, Hu Y, Hwang BH (2014) Wisdom of crowds: The value of stock opinions transmitted through social media. Rev. Financial Stud. 27(5):1367–1403.Crossref, Google Scholar
• Chen G, Huang L, Xiao S, Zhang C, Zhao H (2024) Attending to customer attention: A novel deep learning method for leveraging multimodal online reviews to enhance sales prediction. Inform. Systems Res. 35(2):829–849.Link, Google Scholar
• Clarke J, Chen H, Du D, Hu YJ (2021) Fake news, investor attention, and market reaction. Inform. Systems Res. 32(1):35–52.Link, Google Scholar
• Das A, Kong W, Sen R, Zhou Y (2024) A decoder-only foundation model for time-series forecasting. ICML’24 Proc. 41st Internat. Conf. Machine Learn. (JMLR), 10148–10167.Google Scholar
• Dessaint O, Foucault T, Fresard L (2024) Does alternative data improve financial forecasting? The horizon effect. J. Finance 79(3):2237–2287.Crossref, Google Scholar
• Do K, Tran T (2020) Theory and evaluation metrics for learning disentangled representations. Proc. Eighth Internat. Conf. Learn. Representations, 1–30.Google Scholar
• Dong W, Liao S, Zhang Z (2018) Leveraging financial social media data for corporate fraud Detection. J. Management Inform. Systems 35(2):461–487.Crossref, Google Scholar
• Gorishniy Y, Rubachev I, Babenko A (2022) On embeddings for numerical features in tabular deep learning. Adv. Neural Inform. Processing Systems 35:24991–25004.Google Scholar
• Gregor S, Hevner AR (2013) Positioning and presenting design science research for maximum impact. MIS Quart. 37(2):337–355.Crossref, Google Scholar
• Hendershott T, Zhang X, Zhao JL, Zheng Z (2021) FinTech as a game changer: Overview of research frontiers. Inform. Systems Res. 32(1):1–17.Link, Google Scholar
• Jiang C, Zhou Y, Chen B (2023) Mining semantic features in patent text for financial distress prediction. Tech. Forecasting Soc. Change 190:122450.Crossref, Google Scholar
• Johnson NF (1970) The role of chunking and organization in the process of recall. Bower GH, ed. Psychology of Learning and Motivation, vol. 4 (Academic Press, New York), 171–247.Google Scholar
• Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y (2018) DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1):24.Crossref, Google Scholar
• Kim B, Wattenberg M, Gilmer J, Cai C, Wexler J, Viegas F (2018) Interpretability beyond feature attribution: Quantitative testing with concept activation vectors. Proc. 35th Internat. Conf. Machine Learn., PMLR, vol. 80, 2668–2677.Google Scholar
• Korangi K, Mues C, Bravo C (2023) A transformer-based model for default prediction in mid-cap corporate markets. Eur. J. Oper. Res. 308(1):306–320.Crossref, Google Scholar
• Lee C, Zame W, Yoon J, Van Der Schaar M (2018) DeepHit: A deep learning approach to survival analysis with competing risks. Proc. AAAI Conf. Artificial Intelligence 32(1):2314–2321.Google Scholar
• Lev B, Gu F (2016) The End of Accounting and the Path Forward for Investors and Managers (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
• Liang PP, Lyu Y, Fan X, Wu Z, Cheng Y, Wu J, Chen L et al. (2021) MultiBench: Multiscale benchmarks for multimodal representation learning. Adv. Neural Inform. Processing Systems 2021(DB1):1–20.Google Scholar
• Lim B, Arık SÖ, Loeff N, Pfister T (2021) Temporal fusion transformers for interpretable multi-horizon time series forecasting. Internat. J. Forecasting 37(4):1748–1764.Crossref, Google Scholar
• Lin M, Sias RW, Wei Z (2023) Experts vs. nonexperts in online crowdfunding markets. MIS Quart. 47(1):97–125.Crossref, Google Scholar
• Lu T, Zhang Y, Li B (2023) Profit vs. equality? The case of financial risk assessment and a new perspective on alternative data. MIS Quart. 47(4):1517–1556.Crossref, Google Scholar
• Lu T, Zhang L, Zhang X, Zhao Z (2025) Beyond risk: A measure of distribution uncertainty. Inform. Systems Res. 36(2):944–961.Link, Google Scholar
• Mai F, Tian S, Lee C, Ma L (2019) Deep learning models for bankruptcy prediction using textual disclosures. Eur. J. Oper. Res. 274(2):743–758.Crossref, Google Scholar
• Rhee SG, Wu F (2012) Anything wrong with breaking a buck? An empirical evaluation of NASDAQ’s $1 minimum bid price maintenance criterion. J. Financial Markets 15(2):258–285.Crossref, Google Scholar
• Van Den Oord A, Vinyals O, Kavukcuoglu K (2017) Neural discrete representation learning. Adv. Neural Inform. Processing Systems 30:6306–6315.Google Scholar
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. Adv. Neural Inform. Processing Systems 30:5998–6008.Google Scholar
• Wang G, Zhang F (2021) A sequence-to-sequence model with attention and monotonicity loss for tool wear monitoring and prediction. IEEE Trans. Instrumentation Measurement 70:3525611.Crossref, Google Scholar
• Wang Z, Jiang C, Zhao H (2022) Know where to invest: Platform risk evaluation in online lending. Inform. Systems Res. 33(3):765–783.Link, Google Scholar
• Wang Z, Jiang C, Zhao H (2023a) Depicting risk profile over time: A novel multiperiod loan default prediction approach. MIS Quart. 47(4):1455–1486.Crossref, Google Scholar
• Wang L, Li Y, Chignell M (2021a) Combining ranking and point-wise losses for training deep survival analysis models. 2021 IEEE Internat. Conf. Data Mining (IEEE, Piscataway, NJ), 689–698.Google Scholar
• Wang P, Li Y, Reddy CK (2019) Machine learning for survival analysis: A survey. ACM Comput. Surveys 51(6):1–36.Crossref, Google Scholar
• Wang G, Ma J, Chen G (2023b) Attentive statement fraud detection: Distinguishing multimodal financial data with fine-grained attention. Decision Support Systems 167:113913.Crossref, Google Scholar
• Wang Z, Jiang C, Zhao H, Ding Y (2020) Mining semantic soft factors for credit risk evaluation in peer-to-peer lending. J. Management Inform. Systems 37(1):282–308.Crossref, Google Scholar
• Wang J, Jiang C, Zhou L, Wang Z (2024a) Assessing financial distress of SMEs through event propagation: An adaptive interpretable graph contrastive learning model. Decision Support Systems 180:114195.Crossref, Google Scholar
• Wang X, Chen H, Tang SA, Wu Z, Zhu W (2024b) Disentangled representation learning. IEEE Trans. Pattern Anal. Machine Intelligence 46(12):9677–9696.Crossref, Google Scholar
• Wang G, Chen G, Zhao H, Zhang F, Yang S, Lu T (2021b) Leveraging multisource heterogeneous data for financial risk prediction: A novel hybrid-strategy-based self-adaptive method. MIS Quart. 45(4):1949–1998.Crossref, Google Scholar
• Wang Z, Zhang H, Wang J, Jiang C, He H, Ding Y (2025) Forecasting time to risk based on multi-party data: An explainable privacy-preserving decentralized survival analysis method. Inform. Processing Management 62(1):103881.Crossref, Google Scholar
• Wiegrebe S, Kopper P, Sonabend R, Bischl B, Bender A (2024) Deep learning for survival analysis: A review. Artificial Intelligence Rev. 57(3):65.Crossref, Google Scholar
• Wu D, Ma X, Olson DL (2022) Financial distress prediction using integrated Z-score and multilayer perceptron neural networks. Decision Support Systems 159:113814.Crossref, Google Scholar
• Wu C, Jiang C, Wang Z, Ding Y (2024) Predicting financial distress using current reports: A novel deep learning method based on user-response-guided attention. Decision Support Systems 179:114176.Crossref, Google Scholar
• Xu Y, Chen Y, Sun L, Chen Y (2025) Corporate credit scoring method based on unlabeled data and multi-source data. Decision Support Systems 198:114543.Crossref, Google Scholar
• Xu JJ, Chen D, Chau M, Li L, Zheng H (2022) Peer-to-peer loan fraud detection: Constructing features from transaction data. MIS Quart. 46(3):1777–1792.Crossref, Google 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.Crossref, Google Scholar
• Yin C, Jiang C, Jain HK, Wang Z (2020) Evaluating the credit risk of SMEs using legal judgments. Decision Support Systems 136:113364.Crossref, Google Scholar
• Yu Y, Du Z, Meng L, Li J, Hu J (2024) Adaptive online continual multi-view learning. Inform. Fusion 103:102020.Crossref, Google Scholar
• Zafari K, Biggemann S, Garry T (2023) Development of business-to-business relationships in turbulent environments. Indust. Marketing Management 111:1–18.Crossref, Google Scholar
• Zhang Y, Sugiyama M (2024) Enriching disentanglement: From logical definitions to quantitative metrics. Adv. Neural Inform. Processing Systems 37:71900–71961.Google Scholar
• Zhao S, Xu K, Wang Z, Liang C, Lu W, Chen B (2022) Financial distress prediction by combining sentiment tone features. Econom. Model. 106:105709.Crossref, Google Scholar
• Zhu Y, Liu X, Sheng ORL (2025) Post-earnings-announcement drift prediction: Leveraging postevent investor responses with multitask learning. Inform. Systems Res. 36(4):2191–2212.Link, Google Scholar