A1711
Title: Wavelet analysis and the financial performance of ESG-Strategies
Authors: Michaela Kiermeier - University of Applied Sciences Darmstadt (Germany) [presenting]
Abstract: Wavelet Analysis is applied to investigate if and for how long information with regards to ESG influences financial performance of investment strategies for European companies. This includes retrieving economic and firm specific data from LSEG Refinitiv, identifying best risk factor models for expected returns and calculate abnormal returns after an ESG-signal occurs. The strongest price movements of Global Stoxx ESG Leader indices define positive or negative signals. We use maximal overlap discrete wavelet transforms on abnormal returns within an event study. The abnormal returns are calculated using the LSEG Refinitiv ESG Ratings. The return data is analyzed on a scale-by-scale basis. Bootstrapping methods allow us to calculate thresholds to identify significant wavelet coefficients. We use daily stock price data since 2010 of European companies. Our results indicate that E signals have significant performance effects over the medium and long term.
