AFA Ph.D. Student Poster Session at the 2020 Annual Meeting(24)

Using machine learning (ML) and contrasting with simple textual sentiment score and principal components analysis (PCA) methods, we examine the time se- ries of content within over 700,000 sell-side analyst research reports from 1983 to 2017. We find that analyst reports have significantly changed across a variety of dimensions including length and content of four existing and two new dictionaries related to valuation and alternative metrics. We find that the naive net tone of reports only explains contemporaneous, not future, equity returns. On the other hand, we find that ML methods provide substantially different results from naive sentiment and PCA approaches on determining the impact of analyst reports on financial markets. We also examine the ability of reports to predict changes in firm short interest and volatility. Overall, we find that sell-side analyst reports have stronger impact on smaller cap stocks .

原文链接：

https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=AFAPS2020&paper_id=263

This paper analyses the effect of mandatory pollution abatement on U.S. corporate investment and performance and shows that environmental regulation can stimulate investment in innovation. The following set of theoretical and empirical results are presented. For financially unconstrained firms, mandatory pollution abatement leads to more current R&D investment, more future investment in pollution abatement, reduces current profits, increases future profits and reduces the market value of the firm. However, if firms are financially constrained three of the five consequences are different. It leads to less current R&D investment, less future investment in pollution abatement and lower future profits.

原文链接：

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3257675