Machine learning makes use of 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 being prespecified.
Such algorithms are incredibly diverse and range from more traditional statistical models that emphasise inference through to highly complex hierarchical "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 and has aroused the interest of large quant funds including Man AHL, DE Shaw, Winton, Citadel and Two Sigma to name a few.
Machine learning algorithms can be applied in incredibly diverse ways for quantitative finance. Particular examples include:
Unfortunately much of the work on applying machine learning algorithms to trading strategies in quant finance is proprietary and thus difficult to obtain. However, with practice it can be seen how to take certain datasets and find consistent alpha.
Machine learning is a broad area and is not a field that can be mastered quickly. The following resources will teach the basics, allowing you to dive deeper into specific areas:
Machine learning tasks are generally categorised into three main areas, which often depends on the type of data that is being analysed: Supervised learning, unsupervised learning and reinforcement learning.
The methods all differ in how the machine learning algorithm is "rewarded" for being correct in its predictions or classifications.
Supervised Learning - Supervised learning algorithms involve labelled data. That is, data that has been labelled, often manually, with categories (as in supervised classification) or with numerical responses (as in supervised regression). Such algorithms are trained on the data and learn which predictors correspond to which responses. When applied to unseen data they attempt to make predictions based on their prior training experience. An example from quantitative finance would be using supervised regression to predict tomorrow's stock price from the previous month's worth of price data.
Unsupervised Learning - Unsupervised learning algorithms do not make use of labelled data. Instead they utilise the underlying structure of the data to identify patterns. The canonical method is unsupervised clustering, which attempts to partition datasets into sub-clusters that are associated in some manner. An example from quantitative finance would be to cluster certain assets into classes that behave similarly to adjust portfolio allocations. Read more about Unsupervised Learning here.
Reinforcement Learning - Reinforcement learning algorithms attempt to perform a task within a certain dynamic environment, by taking actions inside the environment in order to maximise a reward mechanism. These algorithms differ from supervised learning in that there is no direct set of input/output pairs of data. Such algorithms have become famous recently as they have been used by Google DeepMind to exceed human performance in Atari games and the ancient game of Go. Such algorithms have been applied in quant finance to optimise investment portfolios.
Due to its interdisciplinary nature there are a large number of differing machine learning algorithms. Most have arisen from the computer science, engineering and statistics communities.
The list of machine learning algorithms is almost endless, as they include crossover techniques and ensembles of many other algorithms. However, the algorithms frequently used within quantitative finance are listed below:
Determining the "best tool for the job" is one of the trickiest aspects of machine learning as applied to quant finance. Many articles on QuantStart discuss this particular point and will guide you to applying the correct technique where appropriate.
Visit the Statistical Modelling and Machine Learning section to continue reading.