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15.4 Machine Learning Applications
Machine learning is used heavily in quantitative finance, particularly in quantitative strategy
development.
15.4.1 Forecasting and Prediction
Machine learning techniques naturally lend themselves to asset price prediction based on historical
pricing data. The accuracy of such techniques depends greatly on the quality and availability
of historical data, the particular market or asset under consideration, the time frame of the
prediction and the machine learning algorithm chosen for forecasting.
Predictions can be made for a single time point ahead or for a set of future time points.
Examples of such predictions include prediction of daily S&P500 returns, prediction of spreads
in intraday forex prices and predictions of liquidity based on order-book dynamics.
15.4.2 Natural Language Processing
Natural language processing (NLP) involves quantifying structured language data in order to
derive inferential or predictive insight. One of the most widely utilised NLP domains is Sentiment
Analysis, which attempts to apply a "sentiment" to a set of text, such as "bullish" or "bearish".
Such analysis can be used to produce trading signals.
Another area of NLP is known as Entity Extraction. This involves identifying "entities" or
"topics" being discussed in a particular set of text. When Sentiment Analysis is combined with
Entity Extraction it is possible to produce strong trading signals. Such approaches are often
applied in equities markets, using social media data.
15.4.3 Factor Models
Factor modelling is a statistical technique that attempts to describe the variation among a set
of correlated observed variables via a reduced set of unobserved variables known as factors. This
is achieved by modelling the observed variables as a linear combination of the reduced factors
along with error terms.
There are three types of factor models in use for studying asset returns[96]. The first makes
use of macroeconomic data such as GDP and interest rates in an attempt to model asset returns.
The second makes use of fundamental data for a particular firm or asset, such as book value or
market capitalisation to produce factors. The third type uses statistical methods to treat these
factors as "latent" or unobservable, which need to be estimated from the asset returns.
Factor analysis is strongly related to the unsupervised dimensionality reduction technique
Principal Component Analysis.
15.4.4 Image Classification
Image classification is an application that spans the fields of machine learning and computer
vision. It has recently become more popular due to the explosion of deep learning algorithms–
particularly Convolution Neural Networks–that have significantly reduced the classification error
rate in many of the leading image classification benchmark datasets.