Generating Trading Signals by ML algorithms or time series ones?

TL;DR

This paper compares machine learning algorithms and time series methods for generating trading signals, finding that Random Forests outperform Kalman Filters in online stock price prediction tasks.

Contribution

It introduces an ensemble of Random Forests for trading signal generation and compares its performance with Kalman Filters using high-frequency stock data.

Findings

Random Forests outperform Kalman Filters in online predictions

Ensemble methods improve trading signal accuracy

High-frequency data enhances model performance

Abstract

This research investigates efficiency on-line learning Algorithms to generate trading signals.I employed technical indicators based on high frequency stock prices and generated trading signals through ensemble of Random Forests. Similarly, Kalman Filter was used for signaling trading positions. Comparing Time Series methods with Machine Learning methods, results spurious of Kalman Filter to Random Forests in case of on-line learning predictions of stock prices