Is there an ESG anomaly? Using textual analysis to detect market mispricing

This thesis investigates the relationship between stock returns and ESG scores on primary listed firms on the three major Scandinavian stock exchanges between 2017 and 2021. In conducting the analysis, we have collected ESG scores from Refinitiv and designed two scores based on textual analysis of the companies’ annual reports from 2015 to 2019. A two-year lag is applied between the ESG scores and the financial data to ensure that ESG information is known before the returns are produced, and the connection can be explained. We analyze the difference in stock returns on firms with high and low ESG score by constructing long-short portfolios. Then, we apply the Fama-French three-factor and five-factor models to control for different risk exposures. The ESG-based portfolio shows no abnormal return using either ESG scores, which suggests that the ESG risk is priced in the market.nhhma

A vortex-based model for the subgrid flux of a passive scalar

A model for the flux of a passive scalar by the subgrid motions in the large-eddy simulation of turbulent flow is proposed within the framework of the stretched-vortex subgrid stress model. The model is based on an analytical solution for the winding of a scalar field by an elemental subgrid vortex. This gives a tensor gradient-diffusion expression for the local flux of the scalar with subgrid turbulent diffusivity which depends upon the subgrid energy, the local cell size, and the vortex orientation in space. The scalar-flux subgrid model is tested by comparison of the results of 323 large-eddy simulation of passive-scalar transport by forced isotropic turbulence in the presence of a mean scalar gradient, with the direct-numerical simulation results of Overholt and Pope [Phys. Fluids 8, 2128 (1996)]. The present large-eddy simulation results predict that at large Taylor–Reynolds numbers, the ratio of the scalar variance to the squared product of the scalar gradient with the dissipation length of the turbulence, is asymptotic to a nearly constant value c[prime]2/(alpha1 Lepsilon)2[approximate]0.36

Does corporate social performance improve bank efficiency? Evidence from European banks

This paper analyses the impact of corporate social performance (CSP) on bank efficiency in a sample of 108 European listed banks across 21 countries over the period 2011–2019. Simar and Wilson’s two-stage approach (Simar and Wilson in J Econom 136:31–64, 2007) has been applied, specifically using data envelopment analysis (DEA) at the first stage to estimate efficiency scores and then truncated regression estimation with double-bootstrap to test the significance of the relationship between bank efficiency and CSP as well as its different dimensions. Our results suggest evidence of a U-shaped relationship between CSP and efficiency, indicating that banks with either high or low corporate social performance levels are the most efficient. Considering the isolated effect of environmental, social, and governance dimensions, the same conclusion can be drawn for the latter two, while the former does not appear to have any effect on a bank’s efficiency. Our work contributes to the existing literature by providing a holistic procedure for assessing CSP in terms of efficiency, allowing us to study the separate effect of each component on bank efficiency. Our results have strong implications for regulators, policymakers, bank managers and investors supporting the changes in the EU Regulatory Taxonomy that lead banks to align their activities and strategies with the Sustainable Development GoalsOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer NatureS

Green Stocks and How to Find Them: Identifying environmentally sustainable IPO firms using textual analysis and assessing their profitability

Sustainable investing has seen exponential growth among investors in recent years. To this end, ESG ratings are the tool used by investors to gauge environmental sustainability in firms. Recently listed firms lack ESG ratings, creating a market imperfection. Additionally, researchers found no correlation between carbon emissions and ESG ratings. This thesis proposes a textual analysis methodology using cosine similarity for computing environmental sustainability scores (E-scores) based on firm activities. Primarily, we provide E-scores for 366 recently listed US firms. We use selection criteria established in IPO literature. Additionally, we compute E-scores for all publicly listed US firms in 2020, approximately 10000 firms. We indirectly verify the proposed method through an event study of the 2020 US presidential election. Firms with high E-scores had cumulative abnormal returns of 2 percent, while firms with low E-scores achieved only -0.28 percent. Through significance testing, we conclude that this difference in cumulative abnormal returns is statistically significant. Due to the lack of ESG ratings of recently listed firms, little research has been conducted on the relation between environmental sustainability and underpricing. In this thesis, we use the proposed E-scores to establish this link. During the sample period between 2019 and 2021, we found that firms with high E-scores were underpriced by 1.44 percent, while firms with low E-scores had underpricing equal to -0.73 percent.nhhma

The cost of doing nothing: Testing the benefits of water disposal risk reduction with water management Activism investing in Latin America

El costo de hacer nada: Una prueba de los beneficios de la reducción del riesgo de disposición de agua por medio de inversión activista en aguaObjetivo: Se responde la pregunta ¿cuál sería el desempeño de un inversionista si invierte solo en compañías con un adecuado manejo de agua en Latino Amética? Metodología: Se emplea la razón de consumo de agua entre ingresos (WTR) para medir la calidad de las políticas de manejo de agua en la empresa. También simula el desempeño de un inversionista invertido principalemente en empresas con el mejor WTR (del 6 de enero del 2005 al 20 de abril del 2022). Resultados: Al comparar el portafolio simulado contra el de mercado, los resultados sugieren que ambos tienen un desempeño similar en el corto plazo. En el largo plazo, las pruebas evidencian que el portafolio WTR tiene un riesgo sistemático menor (beta de 0.26), y su desempeño es más estable (media-varianza eficiente). Los resultados controlan el impacto de fluctuaciones cambiarias en algunos paises latinoamericanos. Conclusiones: Los resultados pueden ser de utilidad para inversionistas activistas de consumo de agua, motivar mejores prácticas de manejo de agua en las empresas y reducir el riesgo de disposición de agua en años venideros.Objective: This paper answers What would the performance of an investor be if she or he invested only in public companies with proper water management practices in Latin America (LATAM)? Methodology: The research uses the water-to-revenues (WTR) ratio to measure water management quality. It simulates the performance of an investor invested mainly in companies with the best WTR (from January 6th, 2005, to Abril 20, 2022). Results: Comparing the simulated portfolio’s performance against a broad market portfolio, the results suggest that both portfolios have similar performance in the short term. In the long term, the tests found that the WTR has a low systematic (market) risk (beta of 0.26), and its performance is more stable (mean-variance efficient) than the market portfolio. The tests also control the impact of some LATAM currencies’ depreciation. Conclusions: The results could be useful for investors to engage in water management activism through investing, motivate companies to engage in better water management practices, and reduce the future risk that water disposal represents to the world in years to come

Pricing climate change exposure

We estimate the risk premium for firm-level climate change exposure among S&P 500 stocks and its time-series evolution between 2005 to 2020. Exposure reflects the attention paid by market participants in earnings calls to a firm’s climate-related risks and opportunities. When extracted from realized returns, the unconditional risk premium is insignificant but exhibits a period with a positive risk premium before the financial crisis and a steady increase thereafter. Forward-looking expected return proxies deliver an unconditionally positive risk premium with maximum values of 0.5%–1% p.a., depending on the proxy, between 2011 and 2014. The risk premium has been lower since 2015, especially when the expected return proxy explicitly accounts for the higher opportunities and lower crash risks that characterize high-exposure stocks. This finding arises as the priced part of the risk premium primarily originates from uncertainty about climate-related upside opportunities. In the time series, the risk premium is negatively associated with green innovation; Big Three holdings; and environmental, social, and governance fund flows and positively associated with climate change adaptation programs

Electric vehicle X driving range predition – EV X DRP

Projeto Final para obtenção do Grau de Mestre em Engenharia Informática e de ComputadoresThe electric vehicle use as a reliable and eco-friendly means of transportation has in creased rapidly over the past few years. When choosing an electric vehicle, its driving range capacity is a decisive factor to be taken into account as it minimizes driver’s anxiety while driving. An electric vehicle driving range depends on multiple factors that must be taken into account when attempting its prediction. Machine learning has become a widely used approach for highly complex problems, in which eRange prediction, being one of them, provides benefits such as becoming more accurate, the more the user drives his vehicle. This thesis compares, through standard metrics, implementations of machine learn ing based regression models (Linear regression and Ensemble Stacked Generalization) when training with publicly available datasets. The results of this work show the effects of different training sample sizes on machine learning model’s accuracy and training time, presenting more favorable results for the Linear Regression algorithm, as the algorithm was more resistant to overfitting for commonly trained data. The results can be replicated with the implemented Python application, allowing for future testing and study of the topic.A utilização veículos elétricos como um meio de transporte confiável e ecológico tem vindo a aumentar nos últimos anos. Ao escolher um veículo elétrico, o range elétrico de condução é um fator decisivo a ser levado em consideração, pois minimiza a ansiedade do utilizador durante a condução. A autonomia de um veículo elétrico depende de vários fatores que devem ser conside rados ao estimar a sua previsão. A aprendizagem automática tem sido uma abordagem amplamente utilizada para problemas altamente complexos, dos quais a previsão da autonomia do veículo, é beneficial para o consumidor ao tornar-se mais preciso quanto mais o veículo é utilizado. Esta tese compara, através de métricas padrão e validação cruzada, implementações de modelos de regressão aprendizagem automática (Linear Regression e Ensemble Stacked Generalization) ao treinar com conjuntos de dados disponíveis publicamente. Os resultados desta tese demonstram as alterações da qualidade de previsão e de tempo de treino que os modelos de aprendizagem automática sofrem quando são usa das configurações dos dados diferentes e demonstrando resultados mais favoráveis para o algoritmo de Linear Regression, pois este demonstra melhor resistência a sobrea justar aos dados mais comuns presentes no conjunto de treino. Utilizando a aplicação desenvolvida em Python, é possível a replicação resultados, promovendo estudos fu turos no tema.info:eu-repo/semantics/publishedVersio

Market Reactions to ESG News : How do negative firm specific ESG news affect the market value of public companies in Europe and the US?

In this master thesis, we examine how negative ESG news specific to individual firms impact the market value of publicly traded companies in the US and Europe over a short term period. We assess news events of 329 listed companies in the period between 2010 and 2020. Initially, we conduct an event study with the full sample to test a general but fundamental hypothesis and analyze whether we can find negative abnormal returns that are significantly different from zero following firm specific negative ESG news. Moreover, to provide further insight, we perform three additional event study analyzes with split samples. To examine whether companies with higher ESG commitment are being penalized by the market, we split the sample using UN Global Compact membership as a proxy. Furthermore, we split the sample based on the reach of the media source that reported the incident, and lastly whether the incident was new or recurring for the individual firms. Our analysis reveals a significant negative abnormal return for ESG incidents that were considered novel for the companies. Additionally, incidents reported by limited reach media sources also show a marginally significant negative impact. Moreover, our findings indicate that companies operating within specific industries tend to face more pronounced negative abnormal returns when the news incidents are related to environmental issues. Furthermore, we find marginally significant results of companies with higher ESG commitment experiencing larger negative abnormal results. However, concerning our general research question, we cannot find evidence suggesting that ESG news has a significant negative effect on a public company’s market value in the short term.nhhma