{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T10:04:36Z","timestamp":1763114676106,"version":"build-2065373602"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,8]]},"DOI":"10.1145\/3701716.3715235","type":"proceedings-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T16:20:01Z","timestamp":1748017201000},"page":"538-547","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["HRFT: Mining High-Frequency Risk Factor Collections End-to-End via Transformer"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7990-5580","authenticated-orcid":false,"given":"Wenyan","family":"Xu","sequence":"first","affiliation":[{"name":"School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6237-9472","authenticated-orcid":false,"given":"Rundong","family":"Wang","sequence":"additional","affiliation":[{"name":"TiMi Studio, Tencent, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7244-3458","authenticated-orcid":false,"given":"Chen","family":"Li","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9936-0000","authenticated-orcid":false,"given":"Yonghong","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1554-8429","authenticated-orcid":false,"given":"Zhonghua","family":"Lu","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.2307\/2527343"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0022109019000255"},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Becker S\u00f6ren","year":"2023","unstructured":"S\u00f6ren Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, and Niki Kilbertus. 2023. Predicting ordinary differential equations with transformers. In International Conference on Machine Learning. PMLR, 1978--2002."},{"key":"e_1_3_2_1_4_1","volume-title":"International Conference on Machine Learning. PMLR, 936--945","author":"Biggio Luca","year":"2021","unstructured":"Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, and Giambattista Parascandolo. 2021. Neural symbolic regression that scales. In International Conference on Machine Learning. PMLR, 936--945."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1997.tb03808.x"},{"key":"e_1_3_2_1_7_1","volume-title":"Linear algebra with transformers. arXiv preprint arXiv:2112.01898","author":"Charton Fran\u00e7ois","year":"2021","unstructured":"Fran\u00e7ois Charton. 2021. Linear algebra with transformers. arXiv preprint arXiv:2112.01898 (2021)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457324"},{"key":"e_1_3_2_1_9_1","volume-title":"The cross-section of expected stock returns. the Journal of Finance 47, 2","author":"Fama Eugene F","year":"1992","unstructured":"Eugene F Fama and Kenneth R French. 1992. The cross-section of expected stock returns. the Journal of Finance 47, 2 (1992), 427--465."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2020.07.002"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2464576.2482690"},{"volume-title":"Modern factor analysis","author":"Harman Harry Horace","key":"e_1_3_2_1_12_1","unstructured":"Harry Horace Harman. 1976. Modern factor analysis. University of Chicago press."},{"key":"e_1_3_2_1_13_1","first-page":"10269","article-title":"End-to-end symbolic regression with transformers","volume":"35","author":"Kamienny Pierre-Alexandre","year":"2022","unstructured":"Pierre-Alexandre Kamienny, St\u00e9phane d'Ascoli, Guillaume Lample, and Fran\u00e7ois Charton. 2022. End-to-end symbolic regression with transformers. Advances in Neural Information Processing Systems 35 (2022), 10269--10281.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_14_1","volume-title":"Marco Virgolin, Ying Jin, Michael Kommenda, and Jason H Moore.","author":"Cava William La","year":"2021","unstructured":"William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabr\u00edcio Olivetti de Fran\u00e7a, Marco Virgolin, Ying Jin, Michael Kommenda, and Jason H Moore. 2021. Contemporary symbolic regression methods and their relative performance. arXiv preprint arXiv:2107.14351 (2021)."},{"key":"e_1_3_2_1_15_1","volume-title":"Deep learning for symbolic mathematics. arXiv preprint arXiv:1912.01412","author":"Lample Guillaume","year":"2019","unstructured":"Guillaume Lample and Fran\u00e7ois Charton. 2019. Deep learning for symbolic mathematics. arXiv preprint arXiv:1912.01412 (2019)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-011-0695-2"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3490354.3494377"},{"key":"e_1_3_2_1_18_1","unstructured":"Xiao-Yang Liu Guoxuan Wang and Daochen Zha. 2023. Fingpt: Democratizing internet-scale data for financial large language models. @articlezhang2023instruct title=Instruct-fingpt: Financial sentiment analysis by instruction tuning of generalpurpose large language models author=Zhang Boyu and Yang Hongyang and Liu Xiao-Yang journal=arXiv preprint arXiv:2306.12659 year=2023 arXiv preprint arXiv:2307.10485 (2023)."},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence. 4513--4519","author":"Liu Zhuang","year":"2021","unstructured":"Zhuang Liu, Degen Huang, Kaiyu Huang, Zhuang Li, and Jun Zhao. 2021. Finbert: A pre-trained financial language representation model for financial text mining. In Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence. 4513--4519."},{"key":"e_1_3_2_1_20_1","volume-title":"NumGLUE: A suite of fundamental yet challenging mathematical reasoning tasks. arXiv preprint arXiv:2204.05660","author":"Mishra Swaroop","year":"2022","unstructured":"Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Sachdeva, Peter Clark, Chitta Baral, and Ashwin Kalyan. 2022. NumGLUE: A suite of fundamental yet challenging mathematical reasoning tasks. arXiv preprint arXiv:2204.05660 (2022)."},{"key":"e_1_3_2_1_21_1","volume-title":"Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. arXiv preprint arXiv:1912.04871","author":"Petersen Brenden K","year":"2019","unstructured":"Brenden K Petersen, Mikel Landajuela, T Nathan Mundhenk, Claudio P Santiago, Soo K Kim, and Joanne T Kim. 2019. Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. arXiv preprint arXiv:1912.04871 (2019)."},{"key":"e_1_3_2_1_22_1","first-page":"425","article-title":"A theory of Market Equilibrium under Conditions of Risk","volume":"19","author":"Prices Capital Asset","year":"1964","unstructured":"Capital Asset Prices. 1964. A theory of Market Equilibrium under Conditions of Risk. Journal of Finance 19, 3 (1964), 425--444.","journal-title":"Journal of Finance"},{"key":"e_1_3_2_1_23_1","volume-title":"BARRA's risk models. Barra Research Insights","author":"Sheikh Aamir","year":"1996","unstructured":"Aamir Sheikh. 1996. BARRA's risk models. Barra Research Insights (1996), 1--24."},{"key":"e_1_3_2_1_24_1","volume-title":"Symbolicgpt: A generative transformer model for symbolic regression. arXiv preprint arXiv:2106.14131","author":"Valipour Mojtaba","year":"2021","unstructured":"Mojtaba Valipour, Bowen You, Maysum Panju, and Ali Ghodsi. 2021. Symbolicgpt: A generative transformer model for symbolic regression. arXiv preprint arXiv:2106.14131 (2021)."},{"key":"e_1_3_2_1_25_1","volume-title":"Attention is all you need. Advances in neural information processing systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_26_1","volume-title":"Fingpt: Opensource financial large language models. arXiv preprint arXiv:2306.06031","author":"Yang Hongyang","year":"2023","unstructured":"Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang. 2023. Fingpt: Opensource financial large language models. arXiv preprint arXiv:2306.06031 (2023)."}],"event":{"name":"WWW '25: The ACM Web Conference 2025","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Sydney NSW Australia","acronym":"WWW '25"},"container-title":["Companion Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701716.3715235","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3701716.3715235","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T03:09:57Z","timestamp":1759892997000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701716.3715235"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":26,"alternative-id":["10.1145\/3701716.3715235","10.1145\/3701716"],"URL":"https:\/\/doi.org\/10.1145\/3701716.3715235","relation":{},"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"2025-05-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}