The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Stock Return Predictability: Is it There?
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Cites
Testing the null hypothesis of stationarity against the alternative of a unit root
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992) · Journal of Econometrics · doi:10.1016/0304-4076(92)90104-y · Cited by 12,633
By Force of Habit: A Consumption‐Based Explanation of Aggregate Stock Market Behavior
Campbell, J. Y., & Cochrane, J. H. (1999) · Journal of Political Economy · doi:10.1086/250059 · Cited by 5,210
Cited by
A comprehensive look at the empirical performance of equity premium prediction
Welch, I., & Goyal, A. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhm014 · Cited by 3,987
Predicting excess stock returns out of sample: Can anything beat the historical average?
Campbell, J. Y., & Thompson, S. B. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhm055 · Cited by 2,919
Credit spreads and business cycle fluctuations
Gilchrist, S., & Zakrajšek, E. (2012) · American Economic Review · doi:10.1257/aer.102.4.1692 · Cited by 2,213
The Dog That Did Not Bark: A Defense of Return Predictability
Cochrane, J. H. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhm046 · Cited by 1,227
Chong, E., Han, C., & Park, F. C. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.030 · Cited by 834
Market expectations in the cross-section of present values
KELLY, B., & PRUITT, S. (2013) · The Journal of Finance · doi:10.1111/jofi.12060 · Cited by 584
A new anomaly: the cross-sectional profitability of technical analysis
Han, Y., Yang, K., & Zhou, G. (2013) · Journal of Financial and Quantitative Analysis · doi:10.1017/s0022109013000586 · Cited by 231
Metadata
- Authors
- Ang, A., & Bekaert, G.
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2001-04
- OpenAlex
- W3123566210
- Cited by
- 897
- References
- 2
- Type
- report
- Language
- en
- Open access
- green
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
