Investor Sentiment and the Cross-Section of Stock Returns

By Malcolm P. Baker, Jeffrey Wurgler. 2006

5,449 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aab58756ddec2840f2a3e8d42bfdcaa3877ae037

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Citations in the corpus

The Sum of All FEARS: Investor Sentiment and Asset Prices (2015, positive): “Moreover, this reversal is strongest among stocks with high beta and high volatility, consistent with the predictions in Baker and Wurgler (2006, 2007).”
Reference-Dependent Preferences and the Risk-Return Trade-Off (2017, positive): “Indeed, Table 7 repeats the previous double-sorting portfolio analysis in references and the risk–return trade-off, Journal of Finan- 010 the low-sentiment subperiods based on the sentiment index of Baker and Wurgler (2006) .”
Cryptocurrency: A New Investment Opportunity? (2017, background): “Consistent with the prediction of Baker and Wurgler [2006], a higher sentiment index predicts lower future stock returns. The most related paper that motivates our empirical design was written by Stambaugh, Yu, and Yuan [2012], who explored sentiment-related overpricing as an explanation for the 11 ”
Does investor sentiment predict the asset volatility? Evidence from emerging stock market India (2015, neutral): “The recent empirical evidence shows that investor sentiment influences the expected stock returns and mean–variance relation of stock returns (e.g. Baker and Wurgler, 2006, 2007, Yu and Yuan, 2011).”
The Effect of Issuer Conservatism on IPO Pricing and Performance (2013, positive): “…evidence that conservatism is significantly and inversely related to the industry-adjusted ROA (return on 2 These results are also consistent with Baker and Wurgler (2006, 2007) and Hribar and McInnis (2012) who find that “soft” data such as sentiment more significantly affects the stock prices…”
The Bright Side of Managerial Over-Optimism (2016, background): “…( ) (Kasznik and McNichols, 2002), momentum using the stock return over the past four quarters (e.g. Jegadeesh and Titman, 1993; Carhart, 1997), investor sentiment (Daniel et al., 1998; Barberis et al., 1998; Baker and Wurgler, 2006), and M&A activities (Collins and Kim, 2015) in the specification.”
Market maturity and mispricing (2016, neutral): “These results appear to be consistent with noise trader-based interpretations of these variables as brought forward in, for instance, Baker and Stein (2004), Baker and Wurgler (2006), Daniel, Hirshleifer, and Subrahmanyam (1998), or Hou, Peng, and Xiong (2013).”
Investor sentiment effect in stock markets: Stock characteristics or country-specific factors? (2013, methodology): “Following Baker and Wurgler (2006) and Schmeling (2009), we include, as exogenous variables, four macroeconomic variables (Ms) (industrial output index, durable goods consumption, consumer goods consumption, and the unemployment index) to protect the results from the effect of possible changes in…”
Textual analysis in finance (2019, background): “There are evidences that sentiment predicts returns on small stocks (Lemmon and Portniaguina, 2006; Baker and Wurgler, 2006) since small stocks are held mostly by”
The Effect of Succession Taxes on Family Firm Investment: Evidence from a Natural Experiment (2012, background): “Following Baker and Wurgler (2006), I measure asset tangibility by PPE over assets, (PPE/A), the year before succession.”
Market Conditions, Default Risk and Credit Spreads (2010, neutral): “Baker and Wurgler (2006) construct a sentiment measure, but it is only available on an annual basis.”
Can economic policy uncertainty and investors sentiment predict commodities returns and volatility (2017, background): “The momentum profits arise only under investor optimism (Antoniou et al. 2013) and subsequent returns change relative to the investor sentiments (Baker and Wurgler, 2006).”
Retail investor attention and stock price crash risk: Evidence from China (2019, methodology): “…biases of investors (e.g., overconfidence and herd mentality), mispricing could be strengthened and price discovery could be further delayed (Hou et al., 2006; Baker and Wurgler, 2006; Kumar and Lee, 2006; Seybert and Yang, 2012; Wurgler, 2012; Kim et al., 2014; Stambaugh et al., 2012, 2014).”
The predictive power of public Twitter sentiment for forecasting cryptocurrency prices (2020, methodology): “Furthermore, the prices of cryptocurrencies can vary substantially across various markets and exchanges, allowing for arbitrage - a characteristic of ine cient markets - to be a pro table trading strategy (Baker and Wurgler, 2006). The informational ine ciency also suggests why the market reacts so ”