Using Artificial Intelligence to Improve Returns in Asset Management

Can machine learning-based technology overcome the many human behavioural biases to improve investment portfolio returns?

Hedge funds and other asset management companies today deploy varying degrees of use of technology.

At some firms things still feel fairly conventional. Portfolio managers make investment decisions, manually enter trades at the desk, where dealers will size up the incoming order and call up a reliable bank counterparty to get it done on optimum terms.

Within more technologically advanced firms, there may be none of this dialogue, as almost everything, from portfolio management to order entry and execution, is done electronically. The humans who remain are merely guardians of the machines, stepping in when markets get dicey or trades need rerouting. Though these machines are most likely not learning, with the goal of optimising trading and improving returns. They are simply automating trading.

Automated trading was just the beginning

This is a gradual shift that has played out over recent years as automated trading has increased, leveraging artificial intelligence (AI) and machine-learning techniques to build more intuitive systems that can make trade decisions faster than the blink of an eye. Some investment firms have embraced technology faster than others, but within the next few years, many more could find themselves moving towards the advanced end of the spectrum.

AI offers the potential to automate very low-value repetitive tasks and provide data-driven insights on liquidity and execution, all of which can be very valuable to an investment management trading desk in seeking the best possible deal for investors.

Automated trading is not new, of course. It has been on the rise among the top investment banks for many years, with significant investment in algorithmic tools that can be used to execute trades according to certain pre-defined criteria. Traders have found they can make cost-savings and reduce market impact in certain circumstances by using smart algorithms rather than relying on human reactions.

The study of behaviour finance focusses on how human biases can negatively effect investment decisions. One such bias is anchoring, where investors place too much importance on information such as historical investment price in making future decisions. Investors hate to lose money and will hold onto a losing trade even at the risk of further loses. Can machines be used to help overcome these biases and, in the end, make better investors than humans? Some traders of managed futures are using algorithms to do this with good results.

AI is investing’s new buzz word

Given the potential for technology to create a competitive advantage, financiers often tend to create a hype bubble around particular concepts. After a long period exploring the possibilities of blockchain, AI now dominates conference agendas. If machines can be programmed to be smarter than humans, they ask, what impact might this have on trading?

As in other sectors, AI can be used simply to save costs it could be used more aggressively to beat the competition. While AI might have the potential to replace human decision-making in fast-moving financial markets, it remains to be seen to what extent financial institutions are willing to hand over the reins to machines.

AI can be used simply to save costs and or it could be used more aggressively to beat the competition

The potential for machine-based decision-making on the trading desk is now widely accepted, but some believe the investment management function may be the next frontier.

It is at the asset managers and hedge funds that the portfolio decision-making takes place and there is an opportunity to automate that process, but this segment of the market is far more complex and fragmented than the exchanges and banks, so the transformation will take longer.

How AI can improve investment management

Bringing AI to investment management would seem to be a natural progression, however. Automation began originally at the stock exchanges where technology was first used to match buyers and sellers, and after that it extended gradually to banks with the development of automated trading strategies. But with so many more asset managers than banks, it could take many years for automation to pervade the buy side.

The trading world is already highly automated, yet applying AI to investment management is much more complicated. Markets are always changing, so the strategies that worked yesterday won’t necessarily work tomorrow, and data is much more limited.

A lack of reliable data could well be the sticking point in the deployment of AI beyond the trading desk, as machines rely on real-time data to make effective decisions. Without that data, an algorithm would be like an expensive sports car that remains in the garage without any fuel.

Running AI effectively depends on having expertly designed algos that are constantly reiterated to get an optimised outcome. Data must be used ultimately to make better decisions than humans.

Increased adoption of AI will ultimately rely on individual firms building the necessary knowledge base and embedding a culture that embraces innovation to progress and compete in modern financial markets. This could well be a gradual, generational shift.

Jeff Holland is a financial professional and entrepreneur with a focus on the technology and investment management sectors. Mr. Holland is currently a Senior Advisor to Afiniti, a global technology firm focussed on artificial intelligence and advanced data analytics. Mr. Holland was a founding member of Liongate Capital Management, an investment management firm with $7 billion under management and advisory at the time of the sale of the firm to Principal Global Investors in 2013. He has been named by Institutional Investor as a “Rising Star of Hedge Funds” and by Financial News among the top “40 Under 40 in Hedge Funds”. Mr. Holland previously worked with Deutsche Bank as Vice President within Investment Banking. Mr. Holland is a Certified Public Accountant (CPA) in the US. He studied at Baylor University in the US where he graduated summa cum laude. Mr. Holland holds a Masters in Finance from London Business School.