advanced-algorithmic-trading

Chapter 21
Unsupervised Learning
The previous chapters have all discussed supervised learning techniques. This chapter introduces
unsupervised learning, which will allow analysis of unlabelled datasets.
Supervised learning involves working with feature/response pairs. Its goal is to try and predict
the response from the associated features. It is "supervised" because in the training phase of
the learning process the algorithm has access to the ground truth via known responses to certain
input features. It uses these to adjust its model parameters such that when exposed to new
features it can make an estimate of the response.
In unsupervised learning the features are still present but there is no associated response.
Instead interest lies solely in attributes of the features themselves. This might include whether
the features form specific clusters or sub-groups in feature space. It might also include whether
very high-dimensional data can be described in a much lower-dimensional setting.
Unsupervised learning techniques are often motivated by the fact that it can be prohibitive
in terms of time and/or money to "label" feature data, which would permit analysis using supervised
techniques. An additional motivation is due to the fact that images, video, natural
language documents and scientific research data (such as gene expressions), once quantified, possess
very high dimensionality. Such high dimensionality requires supervised learning techniques
with many degrees of freedom, potentially leading to overfitting and thus poor test performance.
Unsupervised learning techniques are a partial solution to these problems.
Unfortunately the lack of ground truth or supervision for unsupervised techniques often leads
to subjective assessment of their performance. There are no widely agreed approaches for quantifying
how effective unsupervised algorithms are. Performance is largely determined on a case-bycase
basis using heuristic approaches. Such judgement-based assessments might seem unscientific
to quantitatively trained individuals, but unsupervised techniques have proven to be extremely
useful in many research areas.
Unsupervised learning techniques are often deployed in the realms of anomaly detection,
purchasing habit analysis, recommendation systems and natural language processing. In quantitative
finance they find usage in de-noising datasets, portfolio/asset clustering, market regime
detection and trading signal generation with natural language processing.
301