Four building blocks, briefly explained.
i.Supervised machine learning
Supervised machine learning uses algorithms to analyse data and make decisions based on the data input. It is commonly used in classification, regression and probabilistic modelling. The premise: a dataset of inputs with corresponding known outputs trains an algorithm to predict outputs for new, unseen inputs.
The accuracy of a supervised learning algorithm depends on the quality of the training data, the type of algorithm used, and the explanatory power of the features that describe the data.
ii.Unsupervised machine learning
Unsupervised learning algorithms find patterns and relationships within a dataset without labels or human assistance. The goal is to identify the structure that exists within the data itself.
It can be used to identify outliers and clusters, construct low-dimensional representations of high-dimensional data, and detect associations within the data. Examples include clustering, association-rule learning and anomaly detection.
iii.Neural networks
Neural networks are computing systems inspired by biological neural networks: interconnected nodes that process information using a connectionist approach. They can learn and model complex, non-linear relationships between inputs and outputs — particularly effective for pattern recognition, classification and regression.
Their accuracy depends on architecture design, the quality of training data, and careful tuning of hyperparameters to avoid overfitting while preserving the ability to generalise.
iv.Recurrent neural networks
Recurrent neural networks (RNNs) are a family of neural networks for time-series and sequential data. Unlike most traditional networks, they process and learn from data in the order it is presented, maintaining memory of previous inputs through their internal state.
This makes them suited to recognising patterns over time and to predicting future events from data already seen — useful in speech and image recognition, natural language processing, and sequential decision-making.
Frequently asked questions.
What is machine learning in investment management?
The use of algorithms and predictive analytics to identify and optimise investment decisions.
What are the benefits?
Increased accuracy in predicting market movements, improved risk identification and management, enhanced data-driven decision-making, and more efficient operations.
What types of data are used?
Typically data related to assets and investment products, including stock prices and volumes, currency exchange rates, macroeconomic indicators, and company-specific financial information.
How can it help investors?
It helps investors identify trends in the market, spot opportunities, and make more informed decisions. It also helps reduce risk by making it easier to detect fraud and other suspicious activities.
What are the challenges of implementation?
The primary challenge is managing data quality, as machine learning algorithms require high-quality, accurate data to produce accurate results.
What are the risks?
The primary risk is overconfidence in the models. Over-optimistic machine learning models can lead to inaccurate predictions, improper risk-reward trade-offs, and poor investment decisions.
How can models be tested and validated?
By deploying them on a number of different data sets and evaluating the performance of the model on each.
Are there regulatory requirements?
Yes. The controls needed to validate machine learning models and ensure compliance with these regulations need to be in place prior to deployment.
Myths, debunked.
Myth: Machine learning models are knowledge-deprived
Fact: Machine learning models quickly learn from large datasets and structure valuable information with the help of supervised and unsupervised learning algorithms. This data is used to make predictions or find patterns that identify opportunities and risks in the markets.
Myth: Machine learning can replace human decision-making
Fact: Machine learning is a valuable tool for investment management and can supplement the decision-making process by monitoring market trends and identifying potential risks and opportunities. However, it cannot replace human judgement and intuition, which is an important factor in the investment management process.
Myth: Machine learning models are prone to overfitting
Fact: While models can be prone to overfitting if not properly tuned, proper tuning of parameters and features can solve the problem. Additionally, proper training and testing data, as well as selection of the right algorithms, can help reduce the risk of overfitting.
Myth: Machine learning is only good for short-term investing
Fact: Machine learning can be used not only in short-term investing but also in long-term investment strategies. Models are able to quickly learn from datasets and identify patterns that can be used to identify trends, opportunities and risks in the markets over long periods of time.
Myth: Machine learning models are expensive
Fact: Machine learning models are not as expensive as one may think. With the right resources and technology, machine learning can be implemented relatively cheaply. Additionally, the cost savings from improved decision-making and risk management often outweigh the initial investment.
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