advanced-algorithmic-trading

Chapter 15
Introduction to Machine Learning
Machine learning is a relatively new area of research that couples statistical analysis with computer
science. It encompasses extremely effective techniques for forecasting and prediction that
in many cases currently exceed human ability.
In quantitative finance machine learning is used in various ways, which include prediction
of future asset prices, optimising trading strategy parameters, managing risk and detection of
signals among noisy datasets.
15.1 What is Machine Learning?
Machine learning employs algorithms that learn how to perform tasks such as prediction or
classification without explicitly being programmed to do so. In essence, the algorithms learn
from data rather than prespecification.
Such algorithms are incredibly diverse. They range from more traditional statistical models
that emphasise inference through to highly complex "deep" neural network architectures that
excel at prediction and classification tasks.
Over the last ten years or so machine learning has been making steady gains in the quantitative
finance sector. It has attracted the attention of large quant funds including Man AHL, DE Shaw,
Winton, Citadel and Two Sigma.
Machine learning algorithms can be applied in many ways to quantitative finance problems.
Particular examples include:
• Prediction of future asset price movements
• Prediction of liquidity movements due to redemption of capital in large funds
• Determination of mis-priced assets in niche markets
• Natural language processing of equity analyst sentiment and forecasts
• Image classification/recognition for use in commodity supply/demand signals
Unfortunately much of the work on applying machine learning algorithms to trading strategies
in quantitative finance is proprietary. Hence it is difficult to gain insight into the latest techniques.
With practice, however, it can be seen how to take certain datasets and find consistent ways to
generate alpha.
219