Sensitivity Assessing to Data Volume for forecasting: introducing similarity methods as a suitable one in Feature selection methods

Battle Count: 280

The study's focus on robust feature selection for financial time series is highly relevant to quantitative trading, particularly for developing stable predictive models.

While the methods used are well-established, implementing and comparing multiple feature selection techniques across varying data volumes requires significant effort.

The study uses a specific dataset of stock prices from 100 high-income companies, which may not be easily reproducible without access to the same data.