Analysing of financial patterns and movements is as old as stock exchanges themselves, but in recent decades the sophistication of these methods has grown exponentially as machine-led algorithms have exponentially improved at modelling the real world.
For today’s professionals, technical and fundamental analysis has become an invaluable tool, yet the rise of new technologies, and deep-learning algorithms, promise the potential to automate this process fully at a speed and rate of success far beyond the reach of human capacity.
A new report out this month – Machine Learning in Finance Market 2019-2024 – highlights the range of machine learning tools already available the finance sector, providing a detailed study of different aspects of the global market and outlining the key players, fluctuations and changing market trends.
What role will artificial intelligence and machine-learning play in financial analysis going forward as returns from algorithmic analysis improve?
Machine Learning growing
A recent project by the trading company IG, 13 of its financial writers and analysts submitted predictions describing what trading technology might look like in the year 2069. Their expectation was that algorithms will be able to highlight opportunities to a trader automatically and will be able to recommend trading strategies based on the analysis these algorithms have carried out.
According to IG, the big problem with the current form of algorithmic trading is that it requires constant monitoring and back-testing to ensure the algorithms are working correctly.
“By 2069, however, trading algorithms are predictive and can automatically find a range of opportunities, each with a different probability of success,” the report predicts.
Matthew Key, head of customer innovation, global banking and financial markets, at BT, cautions that many current algorithms remain remain limited by the logic that is built into the algorithm in the first place, potentially creating problems in a more dynamic volatile market.
“Machine-learning, on the other hand, has the ability to improve over time and should be part of any trading environment,” he notes. “These techniques also help with the speed of transactions and recent evidence shows machine-learning-based funds have higher returns than other options.”
In fact, most stock traders now depend on intelligent trading systems of one form or another to help predict prices based on various situations and conditions. Stock prices are also considered to be very dynamic and susceptible to quick changes, says Samuel Leach, director of Samuel and Co Trading.
Mr Leach adds: “Though it is very hard to replace the expertise that an experienced trader has gained, an accurate prediction algorithm can directly result into high profits for investment firms, indicating a direct relationship between the accuracy of the prediction algorithm and the profit made from using the algorithm.” In addition, he argues that the use of mathematics and algorithms eliminates
Even so, algorithms are still only able to make predictions based on the data available, says Mr Leach. “Take Brexit, for example, it has not been seen before and therefore algorithms do not know what to predict,” he explains.
Tim Kane, research fellow at the Hoover Institution, cautions: “The human ability to notice patterns or to decide which patterns make sense will always be critical. There has always been a fear that machines were going to take our place or take our jobs, but I believe this thinking is fundamentally flawed.”
“I wouldn’t trust my investment portfolio entirely to AI,” Dr Kane concludes. “Some funds do, but I expect they will have good days and bad days. If anything, computers have given us an ability to make insights that equity analysts alone weren’t capable of back in 1919. That will, I think, make the economy grow faster, but it will probably be coupled with a boom in employment in the financial sector.