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We examine the role sentiment plays and its manifestation in the trading behavior of investors in the U.S. stock market. Our findings support the notion that sentiment-induced buying and selling is an important determinant of stock price variation. While 'classical' asset pricing categorizes investors who trade in ways not consistent with mean-variance optimization as 'irrational,' we show that this traditional view should not hastily be evoked to characterize sentiment-driven investing. We instead show that sentiment-driven investors can trade against the herd and sell when prices are overinflated as a result of over-bullishness and vice versa. The asset pricing implications of this paper are that sentiment is linked to shifts in risk tolerance and this triggers contrarian-type behavior. In sum, we uncover the following regarding the behavior of sentimentdriven investors; firstly, they are more apt to trade on survey-based indicators rather than marketbased indicators. Secondly, they trade on the basis of information extracted from individual, rather than institutional, investor surveys. Thirdly, they respond asymmetrically to shifts in sentiment and trade more aggressively during periods of declining sentiment. Finally, there is asymmetry in the role of sentiment with respect to business conditions whereby such buying and selling is more pronounced during bear markets.

This paper examines the extent to which herding and feedback trading behaviors drive price dynamics across nine major cryptocurrencies. Using sample price data from bitcoin, ethereum, XRP, bitcoin cash, EOS, litecoin, stellar, cardano and IOTA, respectively, we document heterogeneity in the types of feedback trading strategies investors utilize across markets. Whereas some cryptocurrency markets show evidence of herding, or, 'trend chasing', behaviors, in other markets we show evidence of contrarian-type behaviors. These findings are important because they elucidate upon, firstly, what forces drive cryptocurrency markets and, secondly, how this type of trading behavior affects autocorrelation patters for cryptocurrencies. Finally, and from our intertemporal asset pricing model, we shed new light on the observed nature of the risk-return tradeoffs for each of our sampled cryptocurrencies.

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This is a repository copy of The timing of new corporate debt issues and the risk-return tradeoff.