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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:20221007T120000
DTEND;TZID=America/Chicago:20221007T130000
DTSTAMP:20260411T144504
CREATED:20210615T190305Z
LAST-MODIFIED:20230109T144150Z
UID:467-1665144000-1665147600@www.math.ttu.edu
SUMMARY:Cross-sectional explanatory power of ESG features
DESCRIPTION:Speaker: Prof. Damien Challet\, Mathematics & Computer Science\, University of Paris-Saclay \nAbstract: We systematically investigate the links between price returns and ESG features. We propose a cross-validation scheme with random company-wise validation to mitigate the relative initial lack of quantity and quality of ESG data\, which allows us to use most of the latest and best data to both train and validate our models. Boosted trees successfully explain a single bit of annual price returns not accounted for in the traditional market factor. We check with benchmark features that ESG features do contain significantly more information than basic fundamental features alone. The most relevant sub-ESG feature encodes controversies. Finally\, we find opposite effects of better ESG scores on the price returns of small and large capitalization companies: better ESG scores are generally associated with larger price returns for the latter\, and reversely for the former.
URL:https://www.math.ttu.edu/mathematicalfinance/event/continuous-linear-algebra-and-chebfun/
LOCATION:via Zoom
CATEGORIES:Colloquia,Fall 2022
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/challet.png
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221014T120000
DTEND;TZID=America/Chicago:20221014T130000
DTSTAMP:20260411T144504
CREATED:20220920T200014Z
LAST-MODIFIED:20230109T144236Z
UID:881-1665748800-1665752400@www.math.ttu.edu
SUMMARY:Mathematical psychology of behavioural dynamics
DESCRIPTION:Speaker: Prof. Dorje C. Brody\, Mathematics\, University of Surrey \nAbstract: The behaviour of a person is dominated by their ability to process uncertain information available to them. When there is a range of alternatives to choose from\, the likelihoods assigned by the person to these different alternatives determine the state of their mind in relation to that particular choice. When new information arrives\, the person’s perspective changes\, generating behavioural dynamics. To model this behaviour\, it is highly effective to use the mathematics of signal processing. In this scheme\, it is then possible to represent (i) reliable information\, (ii) noise\, and (iii) disinformation in a unified framework. Because the approach is designed to characterise the dynamics of the behaviour of people\, it is possible to quantify the impact of information control\, including those resulting from the dissemination of disinformation. It can be shown that if a decision maker assigns an exceptionally high weight on one of the alternative realities\, then under the Bayesian logic their perception hardly changes in time even if evidences presented indicate that this alternative corresponds to a false reality. Thus confirmation bias need not be incompatible with Bayesian updating; contrary to what is widely believed in psychology. The information-based approach\, originated in financial modelling\, when applied to psychology\, also poses new challenges in stochastic analysis\, which will be discussed briefly. The talk will be an extended version of an informal article in: https://theconversation.com/the-mathematics-of-human-behaviour-how-my-new-model-can-spot-liars-and-counter-disinformation-185309.
URL:https://www.math.ttu.edu/mathematicalfinance/event/mathematical-psychology-of-behavioural-dynamics/
LOCATION:via Zoom
CATEGORIES:Colloquia,Fall 2022
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/brody.png
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221021T140000
DTEND;TZID=America/Chicago:20221021T150000
DTSTAMP:20260411T144504
CREATED:20220920T200241Z
LAST-MODIFIED:20230109T144420Z
UID:883-1666360800-1666364400@www.math.ttu.edu
SUMMARY:Quantile diffusions for risk analysis
DESCRIPTION:Speaker: Prof. Andrea Macrina\, Mathematics\, University College London \nAbstract: We develop a novel approach for the construction of quantile processes governing the stochastic dynamics of quantiles in continuous time. Two classes of quantile diffusions are identified: the first\, which we largely focus on\, features a dynamic random quantile level and allows for direct interpretation of the resulting quantile process characteristics such as location\, scale\, skewness and kurtosis\, in terms of the model parameters. The second type are function-valued quantile diffusions and are driven by stochastic parameter processes\, which determine the entire quantile function at each point in time. By the proposed innovative and simple — yet powerful — construction method\, quantile processes are obtained by transforming the marginals of a diffusion process under a composite map consisting of a distribution and a quantile function. Such maps\, analogous to rank transmutation maps\, produce the marginals of the resulting quantile process. We discuss the relationship and differences between our approach and existing methods and characterisations of quantile processes in discrete and continuous time. As an example of an application of quantile diffusions\, we show how probability measure distortions\, a form of dynamic tilting\, can be induced. Though particularly useful in financial mathematics and actuarial science\, examples of which are given in this work\, measure distortions feature prominently across multiple research areas. For instance\, dynamic distributional approximations (statistics)\, non-parametric and asymptotic analysis (mathematical statistics)\, dynamic risk measures (econometrics)\, behavioural economics\, decision making (operations research)\, signal processing (information theory)\, and not least in general risk theory including applications thereof\, for example in the context of climate change.
URL:https://www.math.ttu.edu/mathematicalfinance/event/quantile-diffusions-for-risk-analysis/
LOCATION:via Zoom
CATEGORIES:Colloquia,Fall 2022
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/macrina.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20221028T120000
DTEND;TZID=America/Chicago:20221028T130000
DTSTAMP:20260411T144504
CREATED:20220920T200446Z
LAST-MODIFIED:20230109T144505Z
UID:885-1666958400-1666962000@www.math.ttu.edu
SUMMARY:ESG investments: Filtering versus machine learning approaches
DESCRIPTION:Speaker: Dr. Carmine de Franco\, Head of Research & ESG\, Ossiam \nAbstract: We designed a machine learning algorithm that identifies patterns between ESG profiles and financial performances for companies in a large investment universe. The algorithm consists of regularly updated sets of rules that map regions into the high-dimensional space of ESG features to excess return predictions. The final aggregated predictions are transformed into scores which allow us to design simple strategies that screen the investment universe for stocks with positive scores. By linking the ESG features with financial performances in a non-linear way\, our strategy based upon our machine learning algorithm turns out to be an efficient stock picking tool\, which outperforms classic strategies that screen stocks according to their ESG ratings\, as the popular best-in-class approach. Our paper brings new ideas in the growing field of financial literature that investigates the links between ESG behavior and the economy. We show indeed that there is clearly some form of alpha in the ESG profile of a company\, but that this alpha can be accessed only with powerful\, non-linear techniques such as machine learning. \nBio: Carmine de Franco is the head of research at Ossiam\, an asset management firm specializing in systematic and quantitative ETFs\, located in Paris. Graduated in Mathematics from the University of Roma II – Tor Vergata and the University Paris VII – Denis Diderot\, he holds a PhD in Probability and a master’s degree in Financial Random Modelling from the University Paris VII-Denis. Carmine joined Ossiam in May 2012 after working for 4 years at the Faculty of Mathematics of the University of Paris VII (Université Denis Diderot). His domain of expertise spans from mathematics and probability theory to statistics\, from financial research to the design of investment strategy and cross-assets portfolio construction. More recently\, his research topics have focused on ESG themes\, low carbon approaches and biodiversity in financial investments\, machine learning and artificial intelligence. He is co-author of several research papers on portfolio insurance\, modelling and hedging with stochastic jumps\, regime switching models\, interest rates\, equity\, smart beta and factor investing\, ESG\, machine learning\, Bayesian learning and portfolio construction under uncertainty\, carbon and biodiversity. \nOssiam: is a Paris-based asset manager focused on quantitative and systematic investment solutions since 2009 with a distinct vision: providing clear\, transparent access to quantitative\, research-based strategies. Ossiam is an affiliate of Natixis Investment Managers and manages a range of ETFs\, open ended-funds\, dedicated funds and mandates across a variety of asset classes and themes. Ossiam is a signatory of the UN-supported Principles for Responsible Investment since 2016 and a signatory of the Finance for Biodiversity Pledge since 2021. As of end of July 2021\, Ossiam had 5 bn EUR in assets under management.
URL:https://www.math.ttu.edu/mathematicalfinance/event/esg-investments-filtering-versus-machine-learning-approaches/
LOCATION:via Zoom
CATEGORIES:Colloquia,Fall 2022
ATTACH;FMTTYPE=image/jpeg:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/01/de_franco-scaled.jpg
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