{"title":"Rescheduling with New Orders Under Bounded Disruption","authors":"Stefan Lendl, Ulrich Pferschy, Elena Rener","doi":"10.1287/ijoc.2023.0038","DOIUrl":"https://doi.org/10.1287/ijoc.2023.0038","url":null,"abstract":"INFORMS Journal on Computing, Ahead of Print. <br/>","PeriodicalId":13620,"journal":{"name":"Informs Journal on Computing","volume":"70 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140173002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Forecasting market volatility, especially high-volatility incidents, is a critical issue in financial market research and practice. Business news as an important source of market information is often exploited by artificial intelligence–based volatility forecasting models. Computationally, deep learning architectures, such as recurrent neural networks, on extremely long input sequences remain infeasible because of time complexity and memory limitations. Meanwhile, understanding the inner workings of deep neural networks is challenging because of the largely black box nature of large neural networks. In this work, we address the first challenge by proposing a long- and short-term memory retrieval (LASER) architecture with flexible memory and horizon configurations to forecast market volatility. Then, we tackle the interpretability issue by devising a BEAM algorithm that leverages a large pretrained language model (GPT-2). It generates human-readable narratives verbalizing the evidence leading to the model prediction. Experiments on a Wall Street Journal news data set demonstrate the superior performance of our proposed LASER-BEAM pipeline in predicting high-volatility market scenarios and generating high-quality narratives compared with existing methods in the literature.
History: Accepted by Ram Ramesh, Area Editor for Date Science & Machine Learning.
Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0055) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0055). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/.
{"title":"Let the Laser Beam Connect the Dots: Forecasting and Narrating Stock Market Volatility","authors":"Zhu (Drew) Zhang, Jie Yuan, Amulya Gupta","doi":"10.1287/ijoc.2022.0055","DOIUrl":"https://doi.org/10.1287/ijoc.2022.0055","url":null,"abstract":"<p>Forecasting market volatility, especially high-volatility incidents, is a critical issue in financial market research and practice. Business news as an important source of market information is often exploited by artificial intelligence–based volatility forecasting models. Computationally, deep learning architectures, such as recurrent neural networks, on extremely long input sequences remain infeasible because of time complexity and memory limitations. Meanwhile, understanding the inner workings of deep neural networks is challenging because of the largely black box nature of large neural networks. In this work, we address the first challenge by proposing a long- and short-term memory retrieval (LASER) architecture with flexible memory and horizon configurations to forecast market volatility. Then, we tackle the interpretability issue by devising a BEAM algorithm that leverages a large pretrained language model (GPT-2). It generates human-readable narratives verbalizing the evidence leading to the model prediction. Experiments on a <i>Wall Street Journal</i> news data set demonstrate the superior performance of our proposed LASER-BEAM pipeline in predicting high-volatility market scenarios and generating high-quality narratives compared with existing methods in the literature.</p><p><b>History:</b> Accepted by Ram Ramesh, Area Editor for Date Science & Machine Learning.</p><p><b>Supplemental Material:</b> The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0055) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0055). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/.</p>","PeriodicalId":13620,"journal":{"name":"Informs Journal on Computing","volume":"119 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140170922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chenbo Shi, Mohsen Emadikhiav, Leonardo Lozano, David Bergman
INFORMS Journal on Computing, Ahead of Print.
INFORMS 计算期刊》，印刷版。
{"title":"Constraint Learning to Define Trust Regions in Optimization over Pre-Trained Predictive Models","authors":"Chenbo Shi, Mohsen Emadikhiav, Leonardo Lozano, David Bergman","doi":"10.1287/ijoc.2022.0312","DOIUrl":"https://doi.org/10.1287/ijoc.2022.0312","url":null,"abstract":"INFORMS Journal on Computing, Ahead of Print. <br/>","PeriodicalId":13620,"journal":{"name":"Informs Journal on Computing","volume":"25 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140150648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Convergence Rates of Zeroth Order Gradient Descent for Łojasiewicz Functions","authors":"Tianyu Wang, Yasong Feng","doi":"10.1287/ijoc.2023.0247","DOIUrl":"https://doi.org/10.1287/ijoc.2023.0247","url":null,"abstract":"INFORMS Journal on Computing, Ahead of Print. <br/>","PeriodicalId":13620,"journal":{"name":"Informs Journal on Computing","volume":"82 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140128781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A FAST Method for Nested Estimation","authors":"Guo Liang, Kun Zhang, Jun Luo","doi":"10.1287/ijoc.2023.0118","DOIUrl":"https://doi.org/10.1287/ijoc.2023.0118","url":null,"abstract":"INFORMS Journal on Computing, Ahead of Print. <br/>","PeriodicalId":13620,"journal":{"name":"Informs Journal on Computing","volume":"43 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140054971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Regret Minimization and Separation in Multi-Bidder, Multi-Item Auctions","authors":"Çağıl Koçyiğit, Daniel Kuhn, Napat Rujeerapaiboon","doi":"10.1287/ijoc.2022.0275","DOIUrl":"https://doi.org/10.1287/ijoc.2022.0275","url":null,"abstract":"INFORMS Journal on Computing, Ahead of Print. <br/>","PeriodicalId":13620,"journal":{"name":"Informs Journal on Computing","volume":"128 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140033899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exact Solution of the Single-Picker Routing Problem with Scattered Storage","authors":"Katrin Heßler, Stefan Irnich","doi":"10.1287/ijoc.2023.0075","DOIUrl":"https://doi.org/10.1287/ijoc.2023.0075","url":null,"abstract":"INFORMS Journal on Computing, Ahead of Print. <br/>","PeriodicalId":13620,"journal":{"name":"Informs Journal on Computing","volume":"25 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140006333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Decision Diagram-Based Branch-and-Bound with Caching for Dominance and Suboptimality Detection","authors":"Vianney Coppé, Xavier Gillard, Pierre Schaus","doi":"10.1287/ijoc.2022.0340","DOIUrl":"https://doi.org/10.1287/ijoc.2022.0340","url":null,"abstract":"INFORMS Journal on Computing, Ahead of Print. <br/>","PeriodicalId":13620,"journal":{"name":"Informs Journal on Computing","volume":"53 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140006330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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