Stock Return Predictability and Investor Sentiment: A High-Frequency Perspective

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Textual analysis in finance (2019, background): “…in the news stories use the output from the neural network Thomson Reuters News Analytics provides to examine its impact on stock market movements (Smales, 2015; Hendershott, Livdan, and Schrhoff, 2015; Heston and Sinha, 2017; Sun, Najand, and Shen, 2016; Araújo, Eleutério, and Louçã, 2018).”
Can economic policy uncertainty and investors sentiment predict commodities returns and volatility (2017, background): “Empirically, the investors’ sentiments have significant impact on and explanatory power for various asset’s pricing (Sun et al. 2016).”
The impact of macroeconomic news on Bitcoin returns (2020, unknown): “This is even more important in a digital age where investor’s sentiment can be derived from the social media (Renault [2017]; Siganos et al. [2017]), news wires and other internet sources (Sun et al. [2016]).”
Intraday online investor sentiment and return patterns in the U.S. stock market (2017, methodology): “Contrary to the Thomson Reuters MarketPsych Index (TRMI) used by Sun et al. (2016) as a proxy for intraday investor sentiment (a “black box” aggregate indicator), focusing on data from StockTwits allows researchers to test directly whether the predictability is driven (or not) by noise trader…”