# Combining Machine Learning Classifiers for Stock Trading with Effective Feature Extraction

arXiv: https://arxiv.org/abs/2107.13148 · HTML: https://mlsanity.com/paper/2107.13148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 54 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [A Machine Learning Model for Stock Market Prediction](https://mlsanity.com/paper/1402.7351/) (2014)

## Abstract (arXiv)

The unpredictability and volatility of the stock market render it challenging to make a substantial profit using any generalised scheme. Many previous studies tried different techniques to build a machine learning model, which can make a significant profit in the US stock market by performing live trading. However, very few studies have focused on the importance of finding the best features for a particular trading period. Our top approach used the performance to narrow down the features from a total of 148 to about 30. Furthermore, the top 25 features were dynamically selected before each time training our machine learning model. It uses ensemble learning with four classifiers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1 regularization, and Stochastic Gradient Descent, to decide whether to go long or short on a particular stock. Our best model performed daily trade between July 2011 and January 2019, generating 54.35% profit. Finally, our work showcased that mixtures of weighted classifiers perform better than any individual predictor of making trading decisions in the stock market.