We examine the relation between an institution's stock ownership and its tendency to support corporate management through the “Say-on-Pay” (SOP) executive compensation vote. Institutional advisors are more likely to oppose management on the SOP vote for their small-scale investments, i.e., investments that comprise a small fraction of an institution's aggregate stockholdings across its funds, or, alternatively, investments that comprise a small fraction of the total equity market capitalization of a corporation. We find evidence indicating that this voting pattern reflects an institutions’ overall sentiment for the stock, and is particularly prevalent when institutions have limited attention to monitor their investments.

We use Word2vec to develop a financial sentiment dictionary from 3.1 million Chinese-language financial news articles. Our dictionary maps semantically similar words to a subset of human-expert generated financial sentiment words. In validation tests, our dictionary scores the sentiment of articles consistently with human reading of full articles. In return association tests, our dictionary outperforms and subsumes previous Chinese financial sentiment dictionaries, such as direct translations of Loughran and McDonald’s (2011, Journal of Finance, 66, 35–65) English-language financial dictionary. We also generate a list of politically related positive words that is unique to China; we find that this list has a weaker association with returns than does the list of other positive words. We demonstrate that state media uses more politically related positive and fewer negative words, and exhibits a sentiment bias. This bias renders the state media’s sentiment as less return-informative. Our findings demonstrate that dictionary-based sentiment analysis exhibits strong language and domain specificity.