... and the Cross-Section of Expected Returns
By Campbell R. Harvey. 2013
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Nicholas Barberis
- Philipp Krueger
- Jean-Philippe Bouchaud
- A. Landier
- Jingyu He
- David Thesmar
- Guido Giese
- Nicholas G. Polson
Citations in the corpus
- Deep Learning for Predicting Asset Returns (2018, methodology): “Improved methodologies have been suggested by Campbell and Thompson (2007), Rapach et al. (2010) and Harvey et al. (2016) and, more recently, Feng et al. (2017) and Kozak et al. (2017).”
- Deep Learning for Forecasting Stock Returns in the Cross-Section (2018, background): “In particular, many authors attempt to explain the cross-section of stock returns by using various factors, such as earnings–price ratio, company size and stock price momentum, and the efficacy of using such factors [1-3].”
- Liquidity Biases and the Pricing of Cross-Sectional Idiosyncratic Volatility around the World (2015, unknown): “Johnson (2004), Nagel (2005), and Fama and French (2008) have used 14Han and Lesmond (2011) theorize that the difference in variance estimated using the transactionprice-based returns and the quote-midpoint-based returns is related to the square of the bid–ask spread.”
- Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks (2017, unknown): “Further overviews are provided by Ilmanen (2011) and Harvey et al. (2013).”
- Empirical Asset Pricing via Machine Learning (2018, unknown): “, He et al. (2016)). (16)The corresponding number of weight parameters are 29505, 30017, 30145, 30177, and 30185 for NN1 to NN5, respectively. (17)See, e.g., Jarrett et al. (2009), Nair and Hinton (2010), and Glorot et al.”
- Shrinking the Cross Section∗ (2017, unknown): “Green et al. (2017) and Harvey et al. (2015) adjust significance thresholds to account for such data mining.”
- The History of the Cross Section of Stock Returns (2016, unknown): “The second set in the middle column contains anomalies identified as having statistically significant Fama-French three-factor model alphas based on a t-statistic threshold of 3.00, as suggested by Harvey et al. (2015).”