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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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DTSTART:20230312T080000
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DTSTART:20231105T070000
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DTSTART;TZID=America/Chicago:20230911T120000
DTEND;TZID=America/Chicago:20230911T130000
DTSTAMP:20260410T135421
CREATED:20230815T203638Z
LAST-MODIFIED:20230828T142103Z
UID:1157-1694433600-1694437200@www.math.ttu.edu
SUMMARY:Seminar Cancelled - Deep Reinforcement Learning for ESG financial portfolio management
DESCRIPTION:Speaker: Prof. Eduardo C. Garrido Merchán\, Faculty of Economic and Business Sciences (ICADE)\, Comillas Universidad Pontifica \nAbstract: This paper investigates the application of Deep Reinforcement Learning (DRL) for Environment\, Social\, and Governance (ESG) financial portfolio management\, with a specific focus on the potential benefits of ESG score-based market regulation. We leveraged an Advantage Actor-Critic (A2C) agent and conducted our experiments using environments encoded within the OpenAI Gym\, adapted from the FinRL platform. The study includes a comparative analysis of DRL agent performance under standard Dow Jones Industrial Average (DJIA) market conditions and a scenario where returns are regulated in line with company ESG scores. In the ESG-regulated market\, grants were proportionally allotted to portfolios based on their returns and ESG scores\, while taxes were assigned to portfolios below the mean ESG score of the index. The results intriguingly reveal that the DRL agent within the ESG-regulated market outperforms the standard DJIA market setup. Furthermore\, we considered the inclusion of ESG variables in the agent state space\, and compared this with scenarios where such data were excluded. This comparison adds to the understanding of the role of ESG factors in portfolio management decision-making. We also analyze the behaviour of the DRL agent in IBEX 35 and NASDAQ-100 indexes. Both the A2C and Proximal Policy Optimization (PPO) algorithms were applied to these additional markets\, providing a broader perspective on the generalization of our findings. This work contributes to the evolving field of ESG investing\, suggesting that market regulation based on ESG scoring can potentially improve DRL-based portfolio management\, with significant implications for sustainable investing strategies.
URL:https://www.math.ttu.edu/mathematicalfinance/event/deep-reinforcement-learning-for-esg-financial-portfolio-management/
LOCATION:via Zoom
CATEGORIES:Fall 2023,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/08/merchan.jpg
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DTSTART;TZID=America/Chicago:20230922T140000
DTEND;TZID=America/Chicago:20230922T150000
DTSTAMP:20260410T135421
CREATED:20230807T163516Z
LAST-MODIFIED:20230807T171515Z
UID:1129-1695391200-1695394800@www.math.ttu.edu
SUMMARY:ESG integration strategy for stocks portfolios based on a resampling methodology with a multivariate normal distribution
DESCRIPTION:Speaker: Prof. Antonio Francisco de Almeida da Silva Jr.\, Department of Business Administration\, Universidade Federal de Bahia\, Salvador Brazil \nabstract: The aim of the work is to present a framework for ESG integration and to analyze the consequences of considering environmental\, social and governance (ESG) factors in the optimization of investment portfolios. We use a multivariate normal distribution of returns and we generate portfolios by an optimization process combined with a Monte Carlo simulation. After applying an ESG filtering strategy to portfolios\, we show that the ex-ante costs (optimization process) of the ESG integration strategy may be very low compared to other approaches. The methodology presented in this paper avoid the complexity of modifying the utility function to include a new objective.
URL:https://www.math.ttu.edu/mathematicalfinance/event/esg-integration-strategy-for-stocks-portfolios-based-on-a-resampling-methodology-with-a-multivariate-normal-distribution/
LOCATION:via Zoom
CATEGORIES:Fall 2023,Seminars
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DTSTART;TZID=America/Chicago:20230929T140000
DTEND;TZID=America/Chicago:20230929T150000
DTSTAMP:20260410T135421
CREATED:20230809T161037Z
LAST-MODIFIED:20230809T161332Z
UID:1136-1695996000-1695999600@www.math.ttu.edu
SUMMARY:Folly and Fantasy in Finance
DESCRIPTION:Speaker: Prof. Dilip Madan\, Robert H. Smith School of Business\, University of Maryland \nAbstract: Strategies for selecting hedging measures that both respect certain market values of cash flows and yet maintain control on their distance from physical measures are advocated\, proposed and implemented. The hedging criterion is the maximization of a conservative valuation of the hedged position. Such values are modeled as nonlinear expectations based on measure distortions. Measure selections and conservative value maximizing hedges are illustrated for options on SPY and nine sector ETFs. \nSpeaker Bio: Dilip Madan is Emeritus Professor of Finance at the Robert H. Smith School of Business. He specializes in Mathematical Finance. Currently he serves as a consultant to Morgan Stanley\, Meru Capital and Caspian Capital. He has also consulted with Citigroup\, Bloomberg\, the FDIC and Wachovia Securities. He is a founding member and Past President of the Bachelier Finance Society. He received the 2006 von Humboldt award in applied mathematics\, was the 2007 Risk Magazine Quant of the year\, received the 2008 Medal for Science from the University of Bologna and held the 2010 Eurandom Chair. He is managing editor of Mathematical Finance\, co-editor of the Review of Derivatives Research\, associate editor of the Journal of Credit Risk and Quantitative Finance. His work is dedicated to improving the quality of financial valuation models\, enhancing the performance of investment strategies\, and advancing the efficiency of risk allocation in modern economies. Recent major contributions have appeared in Mathematical Finance\, Finance and Stochastics\, Quantitative Finance\, the Journal of Computational Finance\, The International Journal of Theoretical and Applied Finance\, The Journal of Risk\, The Journal of Credit Risk among other journals.
URL:https://www.math.ttu.edu/mathematicalfinance/event/folly-and-fantasy-in-finance/
LOCATION:via Zoom
CATEGORIES:Fall 2023,Seminars
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/08/Madan.jpg
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