With the growth of computing power combined with the latest in artificial intelligence, machine learning and now quantum assisted machine learning a quiet revolution is occurring, changing the way we invest and trade.
Predictive analytics is the practice of extracting information from existing data sets to determine patterns and predict future outcomes and trends.
Using artificial intelligence and machine learning, algorithms can now scan vast amounts of data to quickly identify trends and anomalies within financial markets and use those to make investment decisions.
This is augmented by the rapidly increasing amount of data that is becoming available to investors. Nowadays it seems with the internet of things, like anything that can be recorded is being recorded into datasets.
Apart from the initial coding and fine tuning of the algorithms the technology has now reached a point in that it can rapidly adapt to changing market conditions without human intervention. In a computing sense the system adapts and evolves to the prevailing market environment.
To enable wealth management firms to develop approaches that incorporate AI and machine learning they need to:
- Create an environment and culture that attracts the very best data science and technology talent to build a world class investment management capability;
- Be across the latest in artificial intelligence and machine learning technology that scans large public and proprietary data sets to predict future price trends across global equity markets;
- Partner with data scientists to develop algorithms that learn and adapt to a continually changing investment market environment and enabling them to build competitive investment portfolios that generate consistent risk adjusted performance returns.
The challenge for investment management organisations is finding employees with the cross section of knowledge in AI, Investment Management, Operations, and Data and Analytics necessary to identify suitable problems, develop, and operationalise AI tools.
The use of AI and machine-learning algorithms for investing is increasing globally. Examples of overseas institutions using the technology include Renaissance Technologies, Winton Asset Management and Two Sigma.
While continually developing, the AI/ machine-learning technology is not new. It has been increasingly deployed in industries such as Telecommunications and the Retail Industry for many years. Investors are now adapting these machine-learning techniques and approaches and applying them to the financial markets.
One of many practical applications of the AI and machine learning technology could include using AI to undertake an initial screen of a stock universe and present viable candidates for further analysis by an analyst.
Like anything new and unfamiliar it takes time for the investment community to adapt and become more comfortable with the new technologies. Once wealth management firms have seen the benefits that predictive analytics can bring to their investment approaches the technology will become more common place.