The impact of social media sentiment on US REITs – a glimpse through the lense of ESG-conscious investors

In recent years, social media platforms have become vibrant online platforms where all kinds of market participants share their opinions on equity markets. This provision of opinions and thus of information has attracted the interest of the financial industry. Parallel to this trend, the finance and real estate industry have also recognized the importance of environmental, social, governance (ESG), due in particular to increasing pressure from the public. Bringing these developments together, this paper uses a textual analysis approach to analyze the public’s opinion on social media regarding the ESG performance of real estate related companies. The aim of the analysis is to examine how the public opinion on ESG is reflected in Twitter data and how it can be used to predict the performance of US REITs (Real Estate Investment Trusts). Therefore, using a three-step procedure, this paper first identifies ESG-related tweets, then measures the sentiment of those tweets using different natural language processing techniques and, using the results of the sentiment analysis, calculates the impact of those tweets on the performance of the corresponding company. The first step is achieved, by employing a Global Vectors (GloVe) model, which allows to select tweets based on ESG-related keywords of the corpus. In the second steps a lexicon-based method is applied to create a sentiment index, which is the baseline for the following analysis. Besides, a CNN-LSTM based sentiment index will be created, which might be more powerful in capturing the linguistic complexity of language in social media. Last, the sentiment indices are compared to the performance of the corresponding company in order to determine any correlation and predictive power. Our results not only show a significant correlation between the sentiment indices and the performance of the companies, but also a significant predictive power with positive tweets being associated with better performance and vice versa. These findings suggest that Twitter data can be a valuable source for predicting ESG performance and that using word embedding models, such as GloVe, and lexicon-based methods for sentiment analysis can improve the accuracy of the results.