Identifying sparse signals for the Ibovespa
Published:
Jan 22, 2026
Volume:
24
Keywords:
LASSO
Predictability
Regularization
Sparsity
Abstract
In this paper, we make 1-step-ahead predictions of the IBOVESPA returns at the 1-, 5- and 10-minute frequencies using past returns on the index constituents. We impose a LASSO regularization penalty to reduce the dimensionality of the model and to identify sparse signals across stocks. Our forecasting model yields statistically significant gains in predictive ability, which translate into a moderately profitable trading strategy even after controlling for trading costs at B3.
How to cite
Ricardo Buscariolli, Evandro Amorim, Marcelo Fernandes. Identifying sparse signals for the Ibovespa. Brazilian Review of Finance, v. 24, n. 1, 2026. p. e202601. DOI: 10.12660/rbfin.v24n1.2026.94372.
References
Alexandre, H., Arismendi-Zambrano, J. and De Genaro, A. (2024) Intraday returns forecasting using machine learning: Evidence from the Brazilian stock market. University of São Paulo; University College Dublin School of Business; São Paulo School of Business Administration, FGV.
Bergmeir, C., Hyndman, R.J. and Koo, B. (2018) A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics and Data Analysis, 120, 70–83.
Campbell, J.Y. and Thompson, S.B. (2008) Predicting excess stock returns out of sample: Can anything beat the historical average? Review of Financial Studies, 21(4), 1509–1531.
Chinco, A., Clark-Joseph, A.D. and Ye, M. (2019) Sparse signals in the cross-section of returns. Journal of Finance, 74(1), 449–492.
DeMiguel, V., Garlappi, L., Nogales, F.J. and Uppal, R. (2009) A generalized approach to portfolio optimization: Improving performance by constraining portfolio norms. Management Science, 55(5), 798–812.
Feng, G., Giglio, S. and Xiu, D. (2020) Taming the factor zoo: A test of new factors. Journal of Finance, 75(3), 1327–1370.
Freyberger, J., Neuhierl, A. and Weber, M. (2020) Dissecting characteristics nonparametrically. Review of Financial Studies, 33(5), 2326–2377.
Gabaix, X. (2014) A sparsity-based model of bounded rationality. Quarterly Journal of Economics, 129(4), 1661–1710.
Harvey, C.R., Liu, Y. and Zhu, H. (2015) ... and the Cross-Section of Expected Returns. Review of Financial Studies, 29(1), 5–68.
Kozak, S., Nagel, S. and Santosh, S. (2020) Shrinking the cross-section. Journal of Financial Economics, 135(2), 271–292.
Linnainmaa, J.T. and Roberts, M.R. (2018) The history of the cross-section of stock returns. Review of Financial Studies, 31(7), 2606–2649.
McLean, R.D. and Pontiff, J. (2016) Does academic research destroy stock return predictability? Journal of Finance, 71(1), 5–32.
Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.