The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Sparse Signals in the Cross‐Section of Returns
Abstract
This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling one‐minute‐ahead return forecasts using the entire cross‐section of lagged returns as candidate predictors. The LASSO increases both out‐of‐sample fit and forecast‐implied Sharpe ratios. This out‐of‐sample success comes from identifying predictors that are unexpected, short‐lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identify predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Common risk factors in the returns on stocks and bonds
Fama, E. F., & French, K. R. (1993) · Journal of Financial Economics · doi:10.1016/0304-405x(93)90023-5 · Cited by 25,754
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
On Persistence in Mutual Fund Performance
Carhart, M. M. (1997) · The Journal of Finance · doi:10.1111/j.1540-6261.1997.tb03808.x · Cited by 17,283
Cited by
Dissecting Characteristics Nonparametrically
Freyberger, J., Neuhierl, A., & Weber, M. (2020) · The Review of Financial Studies · doi:10.1093/rfs/hhz123 · Cited by 619
Factor Models, Machine Learning, and Asset Pricing
Giglio, S., Kelly, B., & Xiu, D. (2022) · Annual Review of Financial Economics · doi:10.1146/annurev-financial-101521-104735 · Cited by 187
A New Test of Risk Factor Relevance
CHINCO, A., HARTZMARK, S. M., & SUSSMAN, A. B. (2022) · The Journal of Finance · doi:10.1111/jofi.13135 · Cited by 75
Self-citationDo Machine Learning Models Need to Be Sector Experts?
Hanauer, M. X., Soebhag, A., Stam, M., & Hoogteijling, T. (2025) · doi:10.2139/ssrn.5224253 · Cited by 2
Metadata
- Authors
- CHINCO, A., CLARK‐JOSEPH, A. D., & YE, M.
- Venue
- The Journal of Finance
- Publisher
- Wiley
- Published
- 2018-11-14
- Volume
- 74
- Issue
- 1
- Pages
- 449-492
- ISSN
- 0022-1082 (ISSN-L)
- 1540-6261
- OpenAlex
- W3122609836
- Cited by
- 284
- References
- 80
- FWCI
- 28.16
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Sparse Signals in the Cross‐Section of Returns. https://app.cochat.ai/papers/10.1111/jofi.12733
- The paper
- CHINCO, A., CLARK‐JOSEPH, A. D., & YE, M. (2019). Sparse Signals in the Cross‐Section of Returns. The Journal of Finance. https://doi.org/10.1111/jofi.12733
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
