As one of the most important investing approaches, technical analysis attempts to forecast stock movement by interpreting the inner rules from historic price and volume data. To address the vital noisy nature of financial market, generic technical analysis develops technical trading indicators, as mathematical summarization of historic price and volume data, to form up the foundation for robust and profitable investment strategies. However, an observation reveals that stocks with different properties have different affinities over technical indicators, which discloses a big challenge for the indicator-oriented stock selection and investment. To address this problem, in this paper, we design a Technical Trading Indicator Optimization(TTIO) framework that manages to optimize the original technical indicator by leveraging stock-wise properties. To obtain effective representations of stock properties, we propose a Skip-gram architecture to learn stock embedding inspired by a valuable knowledge repository formed by fund manager\u2019s collective investment behaviors. Based on the learned stock representations, TTIO further learns a re-scaling network to optimize the indicator\u2019s performance. Extensive experiments on real-world stock market data demonstrate that our method can obtain the very stock representations that are invaluable for technical indicator optimization since the optimized indicators can result in strong investing signals than original ones.<\/p>\n","protected":false},"excerpt":{"rendered":"
As one of the most important investing approaches, technical analysis attempts to forecast stock movement by interpreting the inner rules from historic price and volume data. To address the vital noisy nature of financial market, generic technical analysis develops technical trading indicators, as mathematical summarization of historic price and volume data, to form up the […]<\/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":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-621180","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-8-4","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"ACM","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/11\/p894-li.pdf","id":"621186","title":"p894-li","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":621186,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/11\/p894-li.pdf"}],"msr-author-ordering":[{"type":"text","value":"Zhige Li","user_id":0,"rest_url":false},{"type":"text","value":"Derek Yang","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Li Zhao","user_id":36152,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Li Zhao"},{"type":"user_nicename","value":"Jiang Bian","user_id":38481,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jiang Bian"},{"type":"user_nicename","value":"Tao Qin","user_id":33871,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Tao Qin"},{"type":"user_nicename","value":"Tie-Yan Liu","user_id":34431,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Tie-Yan Liu"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[746515],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":746515,"post_title":"AI for Finance","post_name":"ai-for-finance","post_type":"msr-project","post_date":"2021-10-26 23:37:22","post_modified":"2022-06-15 00:00:20","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/ai-for-finance\/","post_excerpt":"Financial industry has adopted statistical analysis for different tasks for a long time and have accumulated tremendous valuable data. 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