Items where Author is "Scholz, M."
Marchese, M. 
ORCID: 0000-0001-6801-911X, Martinez-Miranda, M. D., Nielsen, J. P. ORCID: 0000-0001-6874-1268 & Scholz, M. (2024).
Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data.
Financial Innovation, 10(1),
article number 138.
doi: 10.1186/s40854-024-00657-9
Kyriakou, I. ORCID: 0000-0001-9592-596X, Mousavi, P., Nielsen, J. P. ORCID: 0000-0002-2798-0817 & Scholz, M. (2021).
Short-term exuberance and long-term stability: A simultaneous optimization of stock return predictions for short and long horizons.
Mathematics, 9(6),
article number 620.
doi: 10.3390/math9060620
Kyriakou, I. ORCID: 0000-0001-9592-596X, Mousavi, P., Nielsen, J. P. ORCID: 0000-0002-2798-0817 & Scholz, M. (2020).
Longer-Term Forecasting of Excess Stock Returns—The Five-Year Case.
Mathematics, 8(6),
article number 927.
doi: 10.3390/math8060927
Mammen, E., Nielsen, J. P. ORCID: 0000-0002-2798-0817, Scholz, M. & Sperlich, S. (2019).
Conditional variance forecasts for long-term stock returns.
Risks, 7(4),
article number 113.
doi: 10.3390/risks7040113
Kyriakou, I. ORCID: 0000-0001-9592-596X, Mousavi, P., Nielsen, J. P. & Scholz, M. (2019).
Forecasting benchmarks of long-term stock returns via machine learning.
Annals of Operations Research, 297(1-2),
pp. 221-240.
doi: 10.1007/s10479-019-03338-4
Scholz, M., Sperlich, S. & Nielsen, J. P. (2016). Nonparametric long term prediction of stock returns with generated bond yields. Insurance: Mathematics and Economics, 69(July 2), pp. 82-96. doi: 10.1016/j.insmatheco.2016.04.007
Scholz, M., Nielsen, J. P. & Sperlich, S. (2015). Nonparametric Prediction of Stock Returns Based on Yearly Data: The Long-Term View. Insurance: Mathematics and Economics, 65(Novemb), pp. 143-155. doi: 10.1016/j.insmatheco.2015.09.011