{"id":746515,"date":"2021-10-26T23:37:22","date_gmt":"2021-10-27T06:37:22","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=746515"},"modified":"2022-06-15T00:00:20","modified_gmt":"2022-06-15T07:00:20","slug":"ai-for-finance","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/ai-for-finance\/","title":{"rendered":"AI for Finance"},"content":{"rendered":"
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AI for Finance<\/h1>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n

Financial industry has adopted statistical analysis for different tasks for a long time and have accumulated tremendous valuable data. These conditions leave a big potential of AI technologies to empower financial industry.<\/p>\n\n\n\n

In particular, we start with the intelligent quant investment as our first exploration area. Now we also expand our research on RegTech like anti-money laundry.<\/p>\n\n\n\n

We mainly focus on several typical challenges \/ research directions in applying AI techniques into Machine learning. 1) How to mine patterns in heterogeneous, noisy and correlated data? 2) How to deal with the data\/concept drifting? 3) How to measure and control the risk in a data-driven way? 4) How to model the real-world feedback of a decision and how to coordinate multiple correlated sequential decisions?<\/p>\n\n\n\n

We build an opensource AI-oriented quant investment platform Qlib to accelerate the research exploration and algorithm landing.<\/p>\n\n\n\n

By solving these common challenges in applying AI technologies in financial industry, we have capabilities to empower companies and customers in financial industry by building Azure services specific for financial industry.<\/p>\n\n\n\n\n\n

<\/p>\n\n\n","protected":false},"excerpt":{"rendered":"

Financial industry has adopted statistical analysis for different tasks for a long time and have accumulated tremendous valuable data. These conditions leave a big potential of AI technologies to empower financial industry. In particular, we start with the intelligent quant investment as our first exploration area. Now we also expand our research on RegTech like […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-746515","msr-project","type-msr-project","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[760456,764677,815635,815641,815647,815653,621171,815659,621180,815665,719443,725305,756841],"related-downloads":[745627],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[{"id":0,"name":"Publications","content":"

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  1. Lin, Hengxu, et al. \"Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport<\/a>.\"\u00a0Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining<\/em>. 2021.<\/li>\r\n \t
  2. Wu, Xueqing, et al. \"Temporally Correlated Task Scheduling for Sequence Learning.\"\u00a0<\/a>International Conference on Machine Learning<\/em>. PMLR, 2021.<\/li>\r\n \t
  3. Fang, Yuchen, et al. \"Universal Trading for Order Execution with Oracle Policy Distillation<\/a>.\"\u00a0arXiv preprint arXiv:2103.10860<\/em>\u00a0(2021).<\/li>\r\n \t
  4. Xu, Wentao, et al. \"REST: Relational Event-driven Stock Trend Forecasting<\/a>.\"\u00a0Proceedings of the Web Conference 2021<\/em>. 2021.<\/li>\r\n \t
  5. Yang, Xiao, et al. \"Qlib: An AI-oriented Quantitative Investment Platform<\/a>.\"\u00a0arXiv preprint arXiv:2009.11189<\/em>\u00a0(2020).<\/li>\r\n \t
  6. Yang, Xiao, et al. \"A divide-and-conquer framework for attention-based combination of multiple investment strategies<\/a>.\"\u00a02019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)<\/em>. IEEE, 2019.<\/li>\r\n \t
  7. Wang, Lewen, et al. \"Conservative or Aggressive? Confidence-Aware Dynamic Portfolio Construction<\/a>.\"\u00a02019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)<\/em>. IEEE, 2019.<\/li>\r\n \t
  8. Chen, Chi, et al. \"Investment behaviors can tell what inside: Exploring stock intrinsic properties for stock trend prediction<\/a>.\"\u00a0Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining<\/em>. 2019.<\/li>\r\n \t
  9. Li, Zhige, et al. \"Individualized indicator for all: Stock-wise technical indicator optimization with stock embedding<\/a>.\"\u00a0Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining<\/em>. 2019.<\/li>\r\n \t
  10. Ding, Yi, et al. \"Investor-imitator: A framework for trading knowledge extraction<\/a>.\"\u00a0Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining<\/em>. 2018.<\/li>\r\n \t
  11. Hu, Ziniu, et al. \"Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction<\/a>.\"\u00a0Proceedings of the eleventh ACM international conference on web search and data mining<\/em>. 2018.<\/li>\r\n \t
  12. Lin, Hengxu, et al. \"Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation<\/a>.\"\u00a0arXiv preprint arXiv:2107.05201<\/em>\u00a0(2021).<\/li>\r\n \t
  13. Tang, Hongshun, et al. \"ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting<\/a>.\"\u00a0arXiv preprint arXiv:2012.06289<\/em>\u00a0(2020).<\/li>\r\n \t
  14. Chen, Chi, et al. \"Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction<\/a>.\"\u00a0arXiv preprint arXiv:2002.06878<\/em>\u00a0(2020).<\/li>\r\n<\/ol>"}],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Weiqing Liu","user_id":39300,"people_section":"Section name 0","alias":"weiqiliu"}],"msr_research_lab":[199560],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/746515"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":6,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/746515\/revisions"}],"predecessor-version":[{"id":852567,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/746515\/revisions\/852567"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=746515"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=746515"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=746515"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=746515"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=746515"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}