Combining supervised and unsupervised learning methods to predict financial market movements

TL;DR

This study combines supervised and unsupervised machine learning techniques to extract novel features from financial data, improving the prediction of market movements across different assets.

Contribution

It introduces new feature extraction methods using Gaussian Mixture Models and compares their effectiveness with traditional features in predicting market trends.

Findings

GMM filtering improves model generalization.

KNN and RF outperform random trading strategies.

Machine learning models can leverage novel features for better predictions.

Abstract

The decisions traders make to buy or sell an asset depend on various analyses, with expertise required to identify patterns that can be exploited for profit. In this paper we identify novel features extracted from emergent and well-established financial markets using linear models and Gaussian Mixture Models (GMM) with the aim of finding profitable opportunities. We used approximately six months of data consisting of minute candles from the Bitcoin, Pepecoin, and Nasdaq markets to derive and compare the proposed novel features with commonly used ones. These features were extracted based on the previous 59 minutes for each market and used to identify predictions for the hour ahead. We explored the performance of various machine learning strategies, such as Random Forests (RF) and K-Nearest Neighbours (KNN) to classify market movements. A naive random approach to selecting trading decisions was used as a benchmark, with outcomes assumed to be equally likely. We used a temporal cross-validation approach using test sets of 40%, 30% and 20% of total hours to evaluate the learning algorithms' performances. Our results showed that filtering the time series facilitates algorithms' generalisation. The GMM filtering approach revealed that the KNN and RF algorithms produced higher average returns than the random algorithm.