How ML Gains Ground in Quantitative Investments?

Capital Markets CIO Outlook | Friday, June 11, 2021

FREMONT, CA: ML’s ability to augment predictive capabilities for proprietary investment models has been positively influencing the investors.

Machine learning has immense potential in quantitative investment. Conventionally, researchers have been providing the computers with patterns and expecting the computers to find these patterns in as many markets as they can. But now, ML offers a more modernized approach to systems allowing them to identify predictable patterns in price data along with guiding them on what those underlying relationships may look like. An ML data-driven approach that leverages decision trees and neural networks has exciting applications for quantitative investment.

ML extracts quantitative data inputs from qualitative data akin to text-based sentiment analysis. While developing technical signals, ML can extract richness in historical returns better than traditional forecasting approaches. Further, the self-learning approach allows ML-based algorithms to improve based on historical data. Further, the progressive approach of ML algorithms allows them to adapt to the constantly changing institutional and economic environment. Here are some of the practices that can guide the investors or the asset owners who are eyeing to incorporate ML.

Initially, the asset owners must have a clear understanding of the problem at hand based on which ML can be incorporated. Context-specific investment knowledge will allow the asset holders to direct the capabilities of ML as per their requirements. The domain knowledge will result in informed decisions regarding the selection and formulation of the data inputs, management of the available historical data, and choice of the algorithm.

Secondly, ML algorithms need to be carefully controlled by feeding them with deliberately restricted version input that resonates with the immediate business needs. With such an approach, asset owners can better utilize the capabilities of ML algorithms while eliminating the possibility of generic recommendations or outcomes, as is the case when the algorithms are fed with random selections of historical data.

Thirdly, understanding the drivers of the ML algorithm can offer crucial insights to the asset holders on how to better utilize the algorithms. Based on the above understanding, investors can assess a trained algorithm’s likely response to a particular scenario by feeding it with the context-specific data. It includes comparing an ML-based forecaster’s behavior to transparent signals and analyzing whether it loads on known risk factors or not.

Apart from these, there are other ways in which ML can support systematic investment strategies that are at the core of quantitative investment. ML has invaluable potential in trade execution, where the focus is always on achieving the lowest cost market access at the cost of minimal market impact. There are ML algorithms that can also assist in decision making while routing between different avenues to market.

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ML is the key technology that is aiding quantitative investment while minimizing risk factors. Asset holders and investors can power their proprietary models with ML and enhance their chances of getting an edge in the market. As the success of the quantitative strategy is dependent on the model underlying it, ML offers a high level of authenticity with respect to the highly dynamic market scenarios. Thus, ML is the primary consideration for investors who are eyeing to benefit from quantitative investment models.

Machine Learning