17. Wavelet Analysis and the financial performance of ESG Strategies

In this paper we apply Wavelet Analysis to investigate if and for how long information with regards to ESG influences financial performance of investment strategies of asset managers. This includes retrieving economic and firm specific data from Refinitiv, identifying best risk factor and ARIMA models for expected returns and forecasting, choosing adequate estimation techniques, and to evaluate their specific results. Various different estimation techniques are applied (pooled OLS regression, fixed effect, random effect panel data analysis) to identify the appropriate estimation technique that is in line with capital market theories. Furthermore, this approach includes the question if risk factors are evaluated by the market and therefore associated with risk premiums or if ESG-related factors can be considered as strategies that generate opportunities for alpha. Respective robust estimation techniques are applied to the collected data to discriminate between the effects of risk factors. Literature suggests that an improved financial performance of firms implementing ESG strategies only manifests itself in the long term. With Data Science methods (Wavelet Analysis) this insight is investigated within an event study. A common problem with this approach however is that expected returns have to be modelled using factor models like CAPM or APT. We use wavelet analysis in two ways. First, wavelet analysis is applied as a way to improve on estimating expected returns necessary to identify risk factors. A second application of wavelet analysis is concerned with the time period ESG-related information might be useful to generate outperformance. We therefore filter the return data and analyze the performance on a scale-by-scale basis. This approach allows to discriminate between various time periods. This topic is idealy taught within a COIL structure. Interests in forming this type of coorperation would be highly appreciated.

Mid-Level

Knowledge

Women in Statistics and Data Science 2024