Florian Berg

Bio: Florian Berg is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Divergence (statistics) & Feature selection. The author has an hindex of 3, co-authored 3 publications receiving 203 citations.

Aggregate Confusion: The Divergence of ESG Ratings

Abstract: This paper investigates the divergence of environmental, social, and governance (ESG) ratings. Based on data from six prominent rating agencies - namely, KLD (MSCI Stats), Sustainalytics, Vigeo Eiris (Moody's), RobecoSAM (SP Global), Asset4 (Refinitiv), and MSCI IVA- we decompose the divergence into three sources: different scope of categories, different measurement of categories, and different weights of categories. We find that scope and measurement divergence are the main drivers, while weights divergence is less important. In addition, we detect a rater effect where a rater's overall view of a firm influences the assessment of specific categories.

Aggregate Confusion: The Divergence of ESG Rating

Abstract: This paper investigates the divergence of environmental, social, and governance (ESG) ratings based on data from six prominent ESG rating agencies: KLD, Sustainalytics, Moody’s ESG (Vigeo-Eiris), S&P Global (RobecoSAM), Refinitiv (Asset4), and MSCI. We document the rating divergence and map the different methodologies onto a common taxonomy of categories. Using this taxonomy, we decompose the divergence into contributions of scope, measurement, and weight. Measurement contributes 56% of the divergence, scope 38%, and weight 6%. Further analyzing the reasons for measurement divergence, we detect a rater effect where a rater’s overall view of a firm influences the measurement of specific categories. The results call for greater attention to how the data underlying ESG ratings are generated.

Is History Repeating Itself? The (Un)Predictable Past of ESG Ratings

Abstract: The explosion in ESG research has led to a strong reliance on ESG rating providers. We document widespread changes to the historical ratings of a key rating provider, Refinitiv ESG (formerly ASSET4). Depending on whether the original or rewritten data are used, ESG-based classifications of firms into ESG quantiles and tests that relate ESG scores to returns change. While there is a positive link between ESG scores and firms’ stock market performance in the rewritten data, we fail to observe such a relationship in the initial data. The ESG data rewriting is an ongoing rather than a one-off phenomenon.

GINA: Group Gender Identification Using Privacy-Sensitive Audio Data

Abstract: Group gender is essential in understanding social interaction and group dynamics. With the increasing privacy concerns of studying face-to-face communication in natural settings, many participants are not open to raw audio recording. Existing voice-based gender identification methods rely on acoustic characteristics caused by physiological differences and phonetic differences. However, these methods might become ineffective with privacy-sensitive audio for two main reasons. First, compared to raw audio, privacy-sensitive audio contains significantly fewer acoustic features. Moreover, natural settings generate various uncertainties in the audio data. In this paper, we make the first attempt to identify group gender using privacy-sensitive audio. Instead of extracting acoustic features from privacy-sensitive audio, we focus on conversational features including turn-taking behaviors and interruption patterns. However, conversational behaviors are unstable in gender identification as human behaviors are affected by many factors like emotion and environment. We utilize ensemble feature selection and a two-stage classification to improve the effectiveness and robustness of our approach. Ensemble feature selection could reduce the risk of choosing an unstable subset of features by aggregating the outputs of multiple feature selectors. In the first stage, we infer the gender composition (mixed-gender or same-gender) of a group which is used as an additional input feature for identifying group gender in the second stage. The estimated gender composition significantly improves the performance as it could partially account for the dynamics in conversational behaviors. According to the experimental evaluation of 100 people in 273 meetings, the proposed method outperforms baseline approaches and achieves an F1-score of 0.77 using linear SVM.

Resiliency of Environmental and Social Stocks: An Analysis of the Exogenous COVID-19 Market Crash

Abstract: The COVID-19 pandemic and the subsequent lockdown brought about an exogenous and unparalleled stock market crash The crisis thus provides a unique opportunity to test theories of environmental and social (ES) policies This paper shows that stocks with higher ES ratings have significantly higher returns, lower return volatility, and higher operating profit margins during the first quarter of 2020 ES firms with higher advertising expenditures experience higher stock returns, and stocks held by more ES-oriented investors experience less return volatility during the crash This paper highlights the importance of customer and investor loyalty to the resiliency of ES stocks

Corporate Green Bonds

Abstract: I examine corporate green bonds, whose proceeds finance climate-friendly projects. These bonds have become more prevalent over time, especially in industries where the environment is financially material to firm operations. I document that investors respond positively to the issuance announcement, a response that is stronger for first-time issuers and bonds certified by third parties. The issuers improve their environmental performance post issuance (i.e., higher environmental ratings and lower CO2 emissions), and experience an increase in ownership by long-term and green investors. Overall, the findings are consistent with a signaling argument—by issuing green bonds, companies credibly signal their commitment towards the environment.