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X-WR-CALNAME:Mathematical Finance
X-ORIGINAL-URL:https://www.math.ttu.edu/mathematicalfinance
X-WR-CALDESC:Events for Mathematical Finance
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TZID:America/Chicago
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DTSTART:20220313T080000
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DTSTART:20221106T070000
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DTSTART;TZID=America/Chicago:20220902T140000
DTEND;TZID=America/Chicago:20220902T150000
DTSTAMP:20260411T002632
CREATED:20210803T150608Z
LAST-MODIFIED:20230109T143801Z
UID:813-1662127200-1662130800@www.math.ttu.edu
SUMMARY:Artificial intelligence with uncertainty quantification can plug gaps in climate science and inform multi-sector resilience
DESCRIPTION:Speaker: Prof. Auroop Ganguly\, Civil & Environmental Engineering\, Northeastern University \nAbstract: Global climate and earth system models (ESMs)\, which numerically solve partial differential equations with high performance simulations\, continue to have knowledge gaps and exhibit intrinsic variability for stakeholder relevant variables and resolutions. Data-driven sciences integrated with process understanding\, especially the physics or biogeochemistry that may not be fully captured within the simulations\, are critical to improve model parameterizations\, develop a comprehensive characterization of variability and uncertainty\, and extract scientific insights from archived model simulations. Furthermore\, data-driven discrete event simulations have been proposed to incorporate societal dimensions such as management of watersheds in the land component of earth system models. The first part of this presentation will rely on our work at the Sustainability and Data Sciences Laboratory (SDS Lab) and the extant literature to elucidate the role of Artificial Intelligence (AI) and high performance computing (HPC)\, along with falsifiability and Uncertainty Quantification (UQ)\, in three areas\, specifically\, post-processing ESM simulations with knowledge-guided AI for extracting stakeholder and policy relevant insights\, embedding AI within ESM for improving processes and\narameterizations\, and incorporating human and societal dimensions within ESMs. The second part of the presentation will focus on Machine Learning (ML)\, even touching upon the “unreasonable effectiveness” of Deep Learning\, based downscaling in climate with a particular focus on UQ along with evaluation and falsifiability\, such that ESM simulations at lower resolutions can be credibly translated to information across local to regional scales to enable stakeholder decisions and policy. The presentation will conclude with a short discussion on making climate science actionable by relying not just on governmental or intergovernmental action but also through innovations in the private sector via large corporations and sustainable startups.
URL:https://www.math.ttu.edu/mathematicalfinance/event/spring-2022-test-1-event/
LOCATION:via Zoom
CATEGORIES:Colloquia,Fall 2022
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/ganguly.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20220909T120000
DTEND;TZID=America/Chicago:20220909T130000
DTSTAMP:20260411T002632
CREATED:20210803T150342Z
LAST-MODIFIED:20230109T143847Z
UID:811-1662724800-1662728400@www.math.ttu.edu
SUMMARY:Environmental\, Social\, Governance scores and the missing pillar: Why does missing information matter?
DESCRIPTION:Speaker: Dr. Oezge Sahin\, Mathematical Statistics\, Technical University of Munich \nAbstract: Environmental\, Social\, and Governance (ESG) scores measure companies’ performanceconcerning sustainability and societal impact and are organized on three pillars:  Environmental (E)\, Social (S)\, and Governance (G).\nThese complementary non-financial ESG scores should provide information about the ESG performance and risks of different companies. However\, the extent of not yet published ESG information makes the reliability of ESG scores questionable. To explicitly denote the not yet published information on ESG category scores\, a new pillar\, the so-called Missing (M) pillar\, is formulated. Environmental\, Social\, Governance\, and Missing (ESGM) scores are introduced to consider the potential release of new information in the future. Furthermore\, an optimization scheme is proposed to compute ESGM scores\, linking them to the companies’ riskiness. By relying on the data provided by Refinitiv\, we show that the ESGM scores strengthen the companies’ risk relationship. These new scores could benefit investors and practitioners as ESG exclusion strategies using only ESG scores might exclude assets with a low score solely because of their missing information and not necessarily because of a low ESG merit. \n 
URL:https://www.math.ttu.edu/mathematicalfinance/event/fall-2021-test-1-event/
LOCATION:via Zoom
CATEGORIES:Colloquia,Fall 2022
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/sahin.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20220916T140000
DTEND;TZID=America/Chicago:20220916T150000
DTSTAMP:20260411T002632
CREATED:20210802T152236Z
LAST-MODIFIED:20230109T145123Z
UID:805-1663336800-1663340400@www.math.ttu.edu
SUMMARY:ESGBERT: Language model to help with classification tasks related to companies ESG practice
DESCRIPTION:Speaker: Srishti Mehra\, School of Information\, UC Berkeley \nAbstract: Environmental\, Social\, and Governance (ESG) are non-financial factors that are garnering attention from investors as they increasingly look to apply these as part of their analysis to identify material risks and growth opportunities. Some of this attention is also driven by clients who\, now more aware than ever\, are demanding for their\nmoney to be managed and invested responsibly. As the interest in ESG grows\, so does the need for investors to have access to consumable ESG information. Since most of it is in text form in reports\, disclosures\, press releases\, and 10-Q filings\, we see a need for sophisticated natural language processing (NLP) techniques for classification tasks for ESG text. We hypothesize that an ESG domain specific pre-trained model will help with such and study building of the same in this paper. We explored doing this by fine-tuning BERT’s pre-trained weights using ESG specific text and then further fine-tuning the model for a classification task. We were able to achieve accuracy better than the original BERT and   baseline models in environment-specific classification tasks. \n  \n 
URL:https://www.math.ttu.edu/mathematicalfinance/event/test-event/
LOCATION:via Zoom
CATEGORIES:Colloquia,Fall 2022
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/mehra.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20220923T130000
DTEND;TZID=America/Chicago:20220923T140000
DTSTAMP:20260411T002632
CREATED:20210615T185501Z
LAST-MODIFIED:20230109T144119Z
UID:463-1663938000-1663941600@www.math.ttu.edu
SUMMARY:The China trade shock and the ESG performances of US firms
DESCRIPTION:Speaker: Prof. Hui Xu\, Accounting and Finance\, Lancaster University \nAbstract: How does import competition from China affect engagement on ESG initiatives by US corporates? On the one hand\, reduced profitability due to import competition and lagging ESG performance of Chinese exporters can disincentivize US firms to put more resources to ESG initiatives. On the other hand\, the shift from labor-intensive production to capital/technology-intensive production along with offshoring may improve the US company’s ESG performance. Moreover\, US companies have incentives to actively pursue more ESG engagement to differentiate from Chinese imports. Exploiting a trade policy in which US congress granted China the Permanent Normal Trade Relations and the resulting change in expected tariff rates on Chinese imports\, we find that greater import competition from China leads to an increase in the US company’s ESG performance. The improvement primarily stems from “doing more positives” and from more involvement on environmental initiatives. Indirect and direct evidence shows that the improvement is not driven by the change in production process or offshoring\, but is consistent with product differentiation. Our results suggest that the trade shock from China has significant impact on the US company’s ESG performance.
URL:https://www.math.ttu.edu/mathematicalfinance/event/frechet-hoeffding-bounds-mass-transportation-and-worst-case-dependence-structure/
LOCATION:via Zoom
CATEGORIES:Colloquia,Fall 2022
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/xu.png
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