Mathematical Finance

Artificial intelligence with uncertainty quantification can plug gaps in climate science and inform multi-sector resilience
2022-09-02 14:00 - 15:00 (America/Chicago) | via Zoom
Speaker: Prof. Auroop Ganguly, Civil & Environmental Engineering, Northeastern University
Abstract: 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
arameterizations, 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.

Environmental, Social, Governance scores and the missing pillar: Why does missing information matter?
2022-09-09 12:00 - 13:00 | via Zoom
Speaker: Dr. Oezge Sahin, Mathematical Statistics, Technical University of Munich
Abstract: 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).
These 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.

ESGBERT: Language model to help with classification tasks related to companies ESG practice
2022-09-16 14:00 - 15:00 | via Zoom
Speaker: Srishti Mehra, School of Information, UC Berkeley
Abstract: 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
money 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.

The China trade shock and the ESG performances of US firms
2022-09-23 13:00 - 14:00 | via Zoom
Speaker: Prof. Hui Xu, Accounting and Finance, Lancaster University
Abstract: 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.

Cross-sectional explanatory power of ESG features
2022-10-07 12:00 - 13:00 | via Zoom
Speaker: Prof. Damien Challet, Mathematics & Computer Science, University of Paris-Saclay
Abstract: 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.

Mathematical psychology of behavioural dynamics
2022-10-14 12:00 - 13:00 | via Zoom
Speaker: Prof. Dorje C. Brody, Mathematics, University of Surrey
Abstract: 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.

Quantile diffusions for risk analysis
2022-10-21 14:00 - 15:00 | via Zoom
Speaker: Prof. Andrea Macrina, Mathematics, University College London
Abstract: 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.

ESG investments: Filtering versus machine learning approaches
2022-10-28 12:00 - 13:00 | via Zoom
Speaker: Dr. Carmine de Franco, Head of Research & ESG, Ossiam
Abstract: 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.
Bio: 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.
Ossiam: 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.

A unified Bayesian framework for pricing catastrophe bond derivatives
2022-11-04 14:00 - 15:00 | via Zoom
Speaker: Prof. Matthew Dixon, Applied Mathematics, Illinois Institute of Technology
Abstract: Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed, but none treat the uncertainty in catastrophe occurrences and interest rates in a sufficiently flexible and statistically reliable way within a unifying asset pricing framework. Consequently, little is known empirically about the expected risk-premia of CAT bonds. The primary contribution of this paper is to present a unified Bayesian CAT bond pricing framework based on uncertainty quantification of catastrophes and interest rates. Our framework allows for complex beliefs about catastrophe risks to capture the distinct and common patterns in catastrophe occurrences, and when combined with stochastic interest rates, yields a unified asset pricing approach with informative expected risk premia. Specifically, using a modified collective risk model — Dirichlet Prior-Hierarchical Bayesian Collective Risk Model (DP-HBCRM) framework — we model catastrophe risk via a model-based clustering approach. Interest rate risk is modeled as a CIR process under the Bayesian approach. As a consequence of casting CAT pricing models into our framework, we evaluate the price and expected risk premia of various CAT bond contracts corresponding to clustering of catastrophe risk profiles. Numerical experiments show how these clusters reveal how CAT bond prices and expected risk premia relate to claim frequency and loss severity.
This is joint work with Dixon Domfeh and Arpita Chatterjee.

The economic impact of ESG ratings
2022-11-11 14:00 - 15:00 | via Zoom
Speaker: Prof. Julian Koelbel, School of Finance, University of St. Gallen
Abstract: This study examines the impact of ESG ratings on mutual fund holdings, stock returns, corporate investment, and corporate ESG practices, using panel event studies. Looking specifically at changes in the MSCI ESG rating, we document that rating downgrades reduce ownership by mutual funds with a dedicated ESG strategy, while upgrades increase it. We find a negative long-term response of stock returns to downgrades and a slower and weaker positive response to upgrades. Regarding firm responses, we find no significant effect of up- or downgrades on capital expenditure. We find that firms adjust their ESG practices following rating changes, but only in the governance dimension. These results suggest that ESG rating changes matter in financial markets, but so far have only a limited impact on the real economy.

Statistical analysis and stochastic interest rate modelling for valuing the future with implications in climate change mitigation
2022-11-18 14:00 - 15:00 | via Zoom
Speaker: Prof. John Geanakoplos, Economics, Yale University
Abstract: Statistical analysis and stochastic interest rate modelling for valuing the future with implications in climate change mitigation High future discounting rates favor inaction on present expending while lower rates advise for a more immediate political action. A possible approach to this key issue in global economy is to take historical time series for nominal interest rates and inflation, and to construct then real interest rates and finally obtaining the resulting discount rate according to a specific stochastic model. Extended periods of negative real interest rates, in which inflation dominates over nominal rates, are commonly observed, occurring in many epochs and in all countries. This feature leads us to choose a well-known model in statistical physics, the Ornstein-Uhlenbeck model, as a basic dynamical tool in which real interest rates randomly fluctuate and can become negative, even if they tend to revert to a positive mean value. By covering 14 countries over hundreds of years we suggest different scenarios and include an error analysis in order to consider the impact of statistical uncertainty in our results. We find that only 4 of the countries have positive long-run discount rates while the other ten countries have negative rates. Even if one rejects the countries where hyperinflation has occurred, our results support the need to consider low discounting rates. The results provided by these fourteen countries significantly increase the priority of confronting global actions such as climate change mitigation. We finally extend the analysis by first allowing for fluctuations of the mean level in the Ornstein-Uhlenbeck model and secondly by considering modified versions of the Feller and lognormal models. In both cases, results remain basically unchanged thus demonstrating the robustness of the results presented.

A simulation of the insurance industry: The problem of risk model homogeneity
2022-12-02 14:00 - 15:00 | via Zoom
Speaker: Dr. Juan Sabuco, Smith School of Enterprise and the Environment, Oxford University
Abstract: We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from clients in return for ensuring them against intermittent, heavy-tailed risks. Firms manage their capital and pay dividends to their investors, and use either reinsurance contracts or cat bonds to hedge their tail risk. The model generates plausible time series of profits and losses and recovers stylized facts, such as the insurance cycle and the emergence of asymmetric, long tailed firm size distributions. We use the model to investigate the problem of risk model homogeneity. Under Solvency II, insurance companies are required to use only certified risk models. This has led to a situation in which only a few firms provide risk models, creating a systemic fragility to the errors in these models. We demonstrate that using too few models increases the risk of nonpayment and default while lowering profits for the industry as a whole. The presence of the reinsurance industry ameliorates the problem but does not remove it. Our results suggest that it would be valuable for regulators to incentivize model diversity. The framework we develop here provides a first step toward a simulation model of the insurance industry for testing policies and strategies for better capital management.

On ESG Investing: Heterogeneous Preferences, Information, and Asset Prices
2023-01-13 14:00 - 15:00 | via Zoom
Speaker: Prof. Lin Shen, Department of Finance, INSEAD, Fontainebleau, Fr.
Abstract: We study how environmental, social and governance (ESG) investing reshapes in-formation aggregation by prices. We develop a rational expectations equilibrium model in which traditional and green investors are informed about ﬁnancial and ESG risks but have diﬀerent preferences over them. Because of the preference het-erogeneity, traditional and green investors trade in the opposite directions based on the same information. We show that the equilibrium price may not be uniquely determined. An increase in the fraction of green investors and an improvement in the ESG information quality can reduce price informativeness about the ﬁnancial payoﬀ and raise the cost of capital.

Does sustainability generate better financial performance? Review, meta-analysis, and propositions
2023-01-20 14:00 - 15:00 | via Zoom
Speaker: Ulrich Atz, Stern School of Business, NYU
Abstract: Sustainability in business and ESG (environmental, social, and governance) in finance have exploded in popularity among researchers and practitioners. We surveyed 1,141 primary peer-reviewed papers and 27 meta-reviews (based on ∼1,400 underlying studies) published between 2015 and 2020. Aggregate conclusions from a sample suggest that the financial performance of ESG investing has on average been indistinguishable from conventional investing (with one in three studies indicating superior performance) – in contrast with research in the wider management literature as well as industry reports. Until recently top finance journals did not publish climate change related studies, yet these studies capture the frontier of corporate risk and ESG investment strategies. We developed three propositions: first, ESG integration as a strategy seems to perform better than screening or divestment; second, ESG investing provides asymmetric benefits, especially during a social or economic crisis; and third, decarbonization strategies can potentially capture a climate risk premium.

Sustainable Finance and E, S, and G Issues – Values versus value
2023-01-27 14:00 - 15:00 | via Zoom
Speaker: Prof. Laura T Starks, G. Kozmetsky Distinguished University Chair and Professor of Finance; University of Texas at Austin
Abstract: TBA

Cross-dispersion bias-adjusted ESG rankings
2023-02-03 12:00 - 13:00 | via Zoom
Speaker: Prof. Jean-Charles Garibal, Grenoble School of Management
Abstract: We study the formation of ESG scores and rankings. In particular, we investigate the impact of aggregation rules when combining information on firms across categories, notably the E, S and G categories, into single ESG scores. Usual aggregation rules may bias scores toward the most dispersed category. We suggest a correction for this dispersion bias. We apply this correction to scores provided by two of the main score providers: Refinitiv and Bloomberg. We also provide simulation evidence. We show that the cross-dispersion bias may have a significant impact on ESG scores formation and that our proposed adjustment tends to weather it.

International market exposure to sovereign ESG
2023-02-10 14:00 - 15:00 | via Zoom
Speaker: Christian Morgenstern, School of Public Health, Imperial College London
Abstract: We quantify equity and bond market sensitivity to sovereign ESG scores and their variations which, theoretically, is equivalent to evaluating the demand for ESG at the global scale. We do so by estimating a longitudinal model, at the issue level, that captures exposures to sovereign ESG factors for both equity and fixed income indices. In spite of the surging interest in ESG investing, our results do not support a strong impact of ESG factors on the returns of international markets, implying that the demand for ESG at the country level is not a significant driver of prices. Nevertheless, we document a strong association between GDP growth and ESG scores at the country level.

The dilemma between ‘comply or explain’ and SRI, ESG methodology; transitional terminology
2023-02-17 16:00 - 17:00 | via Zoom
Speaker: Prof. Kazuyuki Shimizu, School of Business Administration, Meiji University
Abstract:This paper tries to find out what the difference is between ESG and SRI. ESG developed from the SRI concept with “comply and explain” which was introduced in 1992, and it creates difficulties between their concepts and also can make difficulties for their methodological development. ESG and SRI had different concepts from each other before, but they mix their methodologies after this introduction. Both concepts need to be rechecked against the pure (principle) model. ESG and SRI have different investment strategy which tries to capture both financial returns and societal good. The fundamental question of this dilemma between SRI and ESG is analysed with three steps.

At first, the difference between the SRI investment approaches was investigated. The logical implication of SRI refers to a segmentation (Euler diagram). It contains three segments. The economic segment forms the smallest circle in the core and the social segment embedded within the environmental component. The Euler diagram is showing a clear stance for the limitation of environmental resources, compared to Venn’s idea. The Venn diagram reveals an interactional relationship between the economy, society and the environment but is not interdependent, that is why stocks were selected on the basis of investment assessment in favour of unlimited inclusion rather than limited exclusion.

Secondly, as far as the SRI and ESG investment approach is concerned, the stocks should serve as a screen in the evaluation process. The screen can be applied with either exclusionary (negative) or inclusionary (positive) methodology. According to GSIA, The largest sustainable investment strategy globally is exclusionary screening ($15.02 trillion). However, the “exclusionary strategy” that several index firms are using still includes the stocks dealing with Alcohol such as Diageo plc, and others exclude industries such as Gambling, Tobacco, Military Weapons, Civilian Firearms, Nuclear Power, Adult Entertainment and Genetically Modified Organisms. Therefore, this paper gave a trial difference between SRI and ESG from the historical and methodological point of view. SRI needs to be in place before the introduction of the “comply or explain” idea in 1992. After that, the index used might be ESG, which assesses more through the “included exclusion” criteria.

Finally, the performance of ESG and SRI are investigated, compared with a known-ESG index such as the MSCI world index. The DJSI World index is applied as an SRI category and the FTSE4GOOD index as the ESG group. There is skepticism between social responsibility and financial performance. Then, we found stability in SRI in the long-term capital performance, especially during the crisis. However, ESG methodology reveals almost the same movement, like the MSCI world index. Values are changing due to crises such as Lehman and the Covid19 shock. Therefore, I would like to consider ethics and stock performance by comparing the performance of SRI and ESG with stocks excluded by the Norwegian Pension Fund’s own ethical standards. Ethics and stock performance are levelled by countervailing power of the values of various AIs and DAOs with human values.

Equity costs and risks in emerging markets: Are ESG and Sharia principles complementary?
2023-02-24 12:00 - 13:00 | via Zoom
Speaker: Stefan Pisera, Dept. of Economics & Statistics, University of Udine
Abstract: By proposing a novel continuous and time-varying measure of Sharia compliance, we investigate whether it enhances the effects of corporate social responsibility, proxied by Environmental-Social-Governance scores, on firms’ equity costs and market risks in emerging countries. We construct a large dataset of non-financial listed firms incorporated in eighteen emerging markets, both Sharia-compliant and conventional (4612 firm-year observations from 2002 to 2018), finding a consistent, statistically significant, and negative association between the interaction of ESG scores and the Sharia sensitivity with the cost of equity. Moreover, we reveal that this negative relationship is mediated by firms’ market risk (risk channel).

Do lower ESG rated companies have higher systemic impact? Empirical Evidence from Europe and the United States
2023-03-03 12:00 - 13:00 | via Zoom
Speaker: Prof. Sandra Paterlini, Economics and Management, University of Trento

Abstract: In recent years, companies have increasingly been characterized by environ- mental, social, and governance (ESG) scores, and investors and academics have raised questions concerning financial performance and investment risks. Now, as the European Banking Authority has acknowledged that ESG risks can potentially impact the economic and financial system, the debate on systemic risk has gained traction. Understanding the relationship between ESG merit and systemic risk is of utmost importance for the stability of the eco- nomic and financial system, still, research is limited. Relying on real-world European and US data, we quantify systemic risk by means of QL-CoVaR. Empirical analyses of the entire period from 2007-2021 show that compa- nies with high ESG scores tend to exhibit low QL-CoVaR values indicating a positive effect of ESG scores. Such evidence is confirmed by clustering the individual companies into ESG portfolios and focusing on COVID-19. Additional insights using the individual pillars are also provided.

Risk-return performance of optimized ESG equity portfolios in the NYSE
2023-03-10 09:00 - 10:00 | via Zoom
Speaker: Prof. Javier Lopez Prol, Economics & Environmental Finance, Yonsei University (Mirae)
Abstract: The literature on the risk-return performance of equity portfolios depending on their ESG score is mixed. While most studies use some variant of Fama-French, we optimize equity portfolios of the NYSE between 2018-2019 according to the Markovitz mean-variance framework depending on their ESG scores to better reflect the behavior of financial agents. We then systematically analyze the optimal and efficient portfolio performance and find that high ESG portfolios have lower volatility and even lower returns, resulting in lower Sharpe ratios. The lower performance of high ESG portfolios is homogeneous across the three ESG components and robust across specifications.

TTU Spring Break - no mathematical finance seminar
2023-03-17

A Comparison of ESG and Credit Ratings
2023-03-24 12:00 - 13:00 | via Zoom
Speaker: Prof. Thierry Roncalli, Head of Quantitative Research, Amundi Institute; and Adjunct Prof. of Economics, University of Evry
Abstract: In this talk, we analyze the construction of ESG ratings and investigate their time dynamics. For that, we build the migration matrix of ESG ratings and assess the probabilistic properties of the associated Markov chain. We observe differences between ESG rating systems. Some of them are more reactive, others present a best-in-class or worst-in-class bias. Finally, we compare them with credit ratings, and highlight the main differences.

ESG compliant optimal portfolios: The impact of ESG constraints on portfolio optimization in a sample of European stocks
2023-03-31 12:00 - 13:00 | via Zoom
Speaker: Beatrice Bertelli, Dept. of Economics, Università degli Studi di Modena e Reggio Emilia
Abstract: The introduction of the Environmental, Social, Governance (ESG) dimensions in setting up optimal portfolios has been becoming of uttermost importance for the financial industry. Given the absence of consensus in empirical literature and the limited number of studies providing performance comparison of ESG strategies, the aim of this paper is to assess the impact of ESG on optimal portfolios and to compare different approaches to the construction of ESG compliant portfolios. Following Varmaz et al. (2022) optimization model, we minimize portfolio residual risk by imposing a desired level of portfolio average systemic risk and ESG (measured by Bloomberg ESG score) over both an unscreened and a screened sample based on the 586 stocks of the EURO STOXX Index in the period January 2007 – August 2022.

These are the main results.
• Regardless of the level of portfolio systemic risk, the Sharpe ratio of the optimal portfolios worsens as the target ESG level increases.
• The Sharpe ratio dynamics of portfolios with the highest average ESG scores follows market phases: it is very close to/higher than other portfolios in bull markets, whereas it underperforms in stable or bear markets suggesting that ESG portfolios do not seem to represent a safe haven.
• Negative screenings with medium-low threshold reduce the performance of optimal portfolios with respect to optimization over an unscreened sample. However, when adopting a very severe screening we obtain a superior performance implying that very virtuous companies allows investors to do well by doing good.

Seminar Cancelled
2023-04-07
Speaker: Prof. Nandita Das, College of Business, Delaware State University
Title:Is there a difference in ESG fund performance among different economies?
This is joint work with Prof. Aman Sunder, Dean of the Graduate School, College for Financial Planning, Centennial CO
Abstract: Public preference for Socially Responsible practices has grown exponentially over the past two decades. According to USSIF the estimated assets under management for SRMF was close to $17 trillion in 2019. Assets are poised to reach $41 trillion by the end of this year according to Bloomberg Intelligence estimates. Money held in sustainable mutual funds and ESG-focused exchange-traded funds rose globally by 53% in 2021 to $2.7 trillion according to Morningstar Inc. The AUM of ESG Funds in India is currently at Rs. 11,956 crores as per AMFI as of Mar 31, 2022.
This paper examines the risk-adjusted performance for socially responsible mutual funds (SRMF) in two different economies- US and India. Investors from different countries will probably weigh each of ESG criterion differently. The goal is to compare the performance of ESG funds based on overall score and specific criterion scores of a developed country with a developing country. We compare the results of funds with a high ESG rating in a developed country (US) with those in a developing country (India). For example, in US it appears that Environment factor is more crucial to investors, and it is possible that Governance might be a bigger factor in a developing country. As for the comparability of performance, there is no statistical difference in performance between the two economies for top-rated funds. This is not the case for the bottom-rated funds. The findings also show size to be a priced risk-factor for both the economies.

How Do Investors Value Sustainability? A Utility-Based Preference Optimization
2023-04-14 | via Zoom
Speaker: Dr. Aydin Aslan, Department of Finance, Technical University of Dortmund
Abstract: We investigate how an investor’s preference for sustainable assets in the portfolio varies for differing levels of risk aversion. Using a sample of 411 publicly listed firms in the S&P 500, we calculate financial and sustainability returns, on which the investor’s utility depends. We approximate the investor’s preference by the exponential and s-shaped utility function and optimize with regard to the sustainability preference. We find that with increasing levels of risk aversion, both minimum-variance and maximum Sharpe ratio type investors seek to incorporate sustainable assets in the portfolio.

Did ESG Save the Day? Evidence From India During the COVID 19 Crisis
2023-04-21 10:00 - 23:00 | via Zoom
Speaker: Prof. Ved Beloskar, Modi School of Commerce, NMIMS Deemed to be University, Mumbai
Abstract: Investors have shown increasing interest in Socially Responsible Investments (SRI) in the past few years, especially during the financial crisis caused due to the outbreak of the COVID-19 pandemic. SRI are evaluated on the basis of Environmental, Social and Governance (ESG) criteria. ESG information allows investors to assess the risks associated with a particular firm and how the firm manages or intends to manage future risks. Amidst the increasing investor interest in ESG products, we attempt to study the value addition of ESG performance to investors during crisis period. Using a sample of ESG rated firms listed on the Bombay Stock Exchange (BSE), we examine the investment performance, trading volumes and return volatility of ESG stocks in an emerging market like India during the COVID-19 crisis. The results of our event study conducted around the important events that have occurred in India during the COVID-19 pandemic provide evidence that investors can use ESG information as a signal of future stock performance. Most importantly, ESG performance provides downside protection during crisis times. Our results show that ESG performance does not prove to be detrimental to investment performance during normal times. Also, ESG performance was found to reduce stock return volatility during the COVID-19 pandemic. Overall, our study attempts to establish an investment case for ESG stocks in emerging markets in India by providing support to the good management hypothesis.

Hedonic Models of Real Estate Prices with ESG Factors
2023-04-28 15:00 - 16:00 | via Zoom
Speaker: Jason Bailey, Department of Math & Statistics, Texas Tech University
Abstract: With the increasing importance of ESG factors in real estate constructions and prices, we investigate commonly-accepted factors in real estate prices through hedonic models and then apply the select ESG factors of green-home, air-conditioning, accessibility, and waterfront to evaluate their impact and significance in increasing the predictive powers of the models. In particular, we investigate the use of a P-spline generalized additive hedonic model (GAM) for real estate prices in large U.S. cities and contrast their predictive efficiency against commonly-used linear and polynomial-based generalized linear models (GLMs). Using intrinsic and extrinsic factors available from Redfin, we show that the GAM model is capable of describing 84% to 92% of the variance in the expected ln(sales price) based upon 2021 data. In contrast, a strictly-linear GLM accounted for 65% to 78% of the variance, while polynomial-based GLMs accounted for 82% to 88%. As climate change is becoming increasingly important, we utilized the GAM model to examine the significance of environmental factors in two urban centers along the Pacific Northwest. While the results indicate city-dependent differences in the significance of environmental factors, we find that multiple environmental factors were significant with their inclusion increasing the adjusted R2 of the GAM model by slightly less than 1%.

Seminar Cancelled - Deep Reinforcement Learning for ESG financial portfolio management
2023-09-11 12:00 - 13:00 | via Zoom
Speaker: Prof. Eduardo C. Garrido Merchán, Faculty of Economic and Business Sciences (ICADE), Comillas Universidad Pontifica
Abstract: 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.

ESG integration strategy for stocks portfolios based on a resampling methodology with a multivariate normal distribution
2023-09-22 14:00 - 15:00 | via Zoom
Speaker: Prof. Antonio Francisco de Almeida da Silva Jr., Department of Business Administration, Universidade Federal de Bahia, Salvador Brazil
abstract: 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.
