Artificial Intelligence (AI) technologies are creating a predictive power that presents the opportunity for autonomous machine learning. It is dramatically enhancing our ability to recognize patterns in data, predict what will happen in the future, create rules, make decisions and communicate with others. Ultimately, there is potential to completely reshape existing operation models with new kinds of value.
One sector that is benefitting hugely from AI adoption is trading and investment management. According to Deltec Bank, Bahamas – “AI is allowing firms to do things they never imagined were possible and augmenting the intelligence of a human workforce.” In this post, we will long at some use cases of AI within investments.
Automated trading and investment management.
Over recent years there has been a gradual shift into automated trading, leveraging AI and its applications such as machine learning (ML) and natural language processing (NLP). These systems are able to gather and process data at speeds which would be impossible for humans, making them very intuitive. In fact, they can make trading decisions in the blink of an eye.
Fingent list several benefits that AI could bring to the sector. Some of the key points are shown below.
• Critical Insights – datasets can derive critical insights from the patterns they generate and use these to form investment decisions. This can include both structured and unstructured data. For example, in a report by Flextrade, the VP at Greenwich Associates, Richard Johnson, talks about how trading firms are overwhelmed with voice, email, chat and news data. NLP can analyze all these sources and provide valuable insight e.g. are people being positive or negative about Company X.
• Risk Management – machine learning algorithms can transform existing security frameworks and mitigate potential risks. Automated monitoring of fraud and reduction of administrative tasks that lead to human error help investors make more accurate decisions.
• Removing investment bias – AI bases decision on data meaning decisions are not reflective of any bias from the advisor or investor. These investments are not made with “gut feel” but always have the backing of data and statistics.
Each of these items removes the need for human intervention, creating greater workflow efficiency. Traders and investment managers can spend more time reviewing the decisions rather than gathering and analyzing data.
Case Study – Sentient
Firms in this sector are ripe for AI disruption given their troves of historical data. For example, Sentient Investment Management has a platform that uses deep learning techniques to dive into masses of historical and current trading datasets and arrive and successful investment strategies.
AI algorithms create virtual traders which the platform tested for a period of one year after they were randomized in their trading strategies. The traders could only create a specific number of actions such as choosing to buy or sell for the hedge fund. As some virtual traders performed better than others, they were chosen as a basis for the next batch that was created. Once there had mean many generations of traders, the best strategies were combined and transferred to the next generation.
Sentient tested 40 trillion virtual trading strategies over a 12-month period and then selected only the top 2 to represent the trading strategy of the fund.
The patented technology could allow for vast amounts of historical investment data to find patterns that might have never been identified by humans.
Case Study – BlackRock
American global investment management firm BlackRock has turned to AI in order to better understand liquidity risk. This was accomplished by incorporating internal trade data into existing liquidity models. Machine learning techniques were designed to more accurately calculate the cost of liquidating fund positions in the case of redemptions.
Moreover, BlackRock has developed a unique operating system for investment managers called the Aladdin Risk Platform. This platform is making the best use of machine learning algorithms to provide its users with risk analytics which monitor risk in their portfolios. BlackRock says that the platform is capable of scanning as many as 2,000 risk factors per day. The machine will continue to learn from each of these over time as well as testing portfolio performance under different economic conditions.
For investment management firms, they might be able to augment the capabilities of human managers using Aladdin by providing the capability of predictive portfolio performance. This can be done much faster than if done manually. The platform will be loaded with data such as the historical performance of securities in a fund to help predict future performance.
Case Study – Greenkey Technologies
Greenkey Technologies’ AI for trading is using speech recognition and NLP to ensure traders have not got to spend their time going through conversations, financial data, and notes. Using the platform, financial professionals can sift through this information using AI algorithms and generate real-time market insights. Other firms are starting to follow suit.
In June 2019, Liquidnet was reported to have acquired Prattle, an AI start-up that uses machine learning and NLP to analyze communications from central banks and earnings calls. Prattle can be used to generate alpha for investment managers, analysts, and institutional traders.
Tejas Shastry, the chief data scientist at Greenkey Technologies, says there is so much actionable intelligence inside unstructured data sources that using NLP to better understand them is becoming pivotal to remain competitive. Applications like that provided by GreenKey extract value from this data, ignoring the noise around it that can distract human counterparts.
Firms with large volumes of quality data are making the best use of their resources for efficiency, productivity and ultimately, better decisions. AI is a major asset when it comes to analyzing the market and processing that information faster and more accurately than humans have ever been able to achieve.
However, this does not mean AI is set to replace traditional traders and investment managers. Experts think although AI is amazing at identifying patterns, it cannot be sophisticated enough to apply insights to other areas that we don’t know about yet. Experienced traders make their strategies profitable by applying findings to unrelated datasets and exploiting the factors. What we mean here is that machines can only base their insight on data they know whereas humans still lead the way in intuition.
AI is bringing a wealth of benefits to the trading and investment sector but it is important to view is as an augmentation for human knowledge, not a replacement.
Disclaimer: The author of this text, Robin Trehan, has an Undergraduate degree in Economics, Masters in international business and finance and MBA in electronic business. Trehan is Senior VP at Deltec International www.deltecbank.com. The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries and/or employees.
About Deltec Bank
Headquartered in The Bahamas, Deltec is an independent financial services group that delivers bespoke solutions to meet clients’ unique needs. The Deltec group of companies includes Deltec Bank & Trust Limited, Deltec Fund Services Limited, and Deltec Investment Advisers Limited, Deltec Securities Ltd. and Long Cay Captive Management.