On Model Selection Consistency of Lasso
The Journal of Machine Learning Research
Fuzzy dual-factor time-series for stock index forecasting
Expert Systems with Applications: An International Journal
Evolving and clustering fuzzy decision tree for financial time series data forecasting
Expert Systems with Applications: An International Journal
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Identifying important indicating factors for expected level of stock return has been one of the central problems in modern finance. Researchers have worked on different candidate sets of indicators from different perspectives, but there has not been a consensus reached on which factors to be included in the model. In this paper, based on relative complete information from a large set of factors from US financial reports, we use least angle regression (LARS) to select a sparse and relatively stable set of indicators for predicting stock return. The use of LARS is consistent with the theoretically well developed economic theory arbitrage pricing model. The empirical results offer new insights of the well-known indicators from the previous studies and discover new important factors.