Machine Learning (ML) has become a broad technological facet encompassing countless industries, corporate interactions, and symbiotic technologies growing in tandem. ML concepts like deep learning and natural language processing (NLP) have revolutionized both the ways we learn from collected data and the ways we apply these findings to major investment decisions.
Investing management is no different; its ML application runs far and deep, covering everything from trend discovery and strategic support to the administration of new ideas. As 2019 rapidly closes in, ML-based investment management is expected to continue its recent trend of industry disruption and innovation. For now, though, the technology has already created a variety of exciting implications surrounding its potential.
ML has continues to transform the construction, interpretation, and administration of investment strategies overseen by all types of managers; this was perhaps its earliest identified benefit within the industry, and it is one that has grown immensely in recent years — to the point where the industry’s most fundamental, non-quantitative managers are using ML-generated data to formulate new ideas. Both asset management and digital asset management remain “ripe for automation through AI” thanks to their existing application of voluminous data, and ML has supplemented systematic strategies for both via market movement prediction and trade execution.
Trend discovery and analysis
When left to their own devices, many ML algorithms are becoming capable of discovering and analyzing key investing trends with which to forecast future prices; they essentially gravitate towards a trend-following pattern based on compiled historical data. This capability is incredibly significant, as most quantitative research is rooted in the “discovery of linear relationships between input data (such as historical price movements, interest rates, or company earnings) and future movements in asset prices,” and this notion continues to play a pivotal role in trend discovery. As algorithms advance to transcend what has already been established by traditional trend following, they have provided additional means of dissecting trend data. That is, they are becoming capable of identifying directional market behavior stemming from trends — and the specific path taken to reach a certain price pattern. These developments stand as natural complements to existing analytical models and forecasting methods.
The place of industry relationships
The previous sections paint a promising and ambitious future for ML-based investment management, and in order for this reality to continue its ascent, industry professionals must remain harmonious both amongst themselves and with necessary academic parties set on advancing the technology. Man Group, for instance, has forged key research relationships with the University of Oxford’s Engineering Science Department to supplement the efforts of its various technicians, scientists, and investing specialists. This is the type of comprehensive working mosaic that will allow ML management to reach new heights as investing’s potential new norm.