“There’s a global race to discover a viable solution to create more reliable – and better performing – investment decisions in financial trading. Our model offers higher returns compared to others developed to date consistently,” says Dr Arman Hassanniakalager of the university’s School of Management. Hassanniakalager, who will present the research at the Financial Management Association meeting in Glasgow this week, says his model has been shown to lead to a 3% higher return than the standard U.S. Federal Reserve Funds rate, predicated on evidence from 12 currency markets indices from around the globe.
An improvement of 0.5-1.0% would be thought to be significant. The search for an all-powerful investment algorithm has stepped up lately and early results have been mixed. The challenge is to create a level of dependability that regularly outperforms investment bankers and financiers and a tool that can function equally well in rising and falling marketplaces. The continued development of algorithms and their recognized benefits are increasing optimism and hopes among many in the marketplaces.
But the increasing reliance on the tools in addition has created some nervousness in the top tiers of the world’s financial systems – and some scepticism from those who believe there will always be a job for the motivated human touch. Hassanniakalager, whose knowledge is in developing novel artificial intelligence and statistical methods for financial decision making, said his algorithm has already reached the stage where it is consistently outperforming both typical ways of investment and algorithmic tools.
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“There’s a great deal of theoretical thinking and aspirations around about such investment tools however the key question is resolving steps to make them work in real life. We think we’ve attended to that question,” Hassanniakalager said. The algorithm can be associated with artificial intelligence, which will learn from investment decisions and fine-tune itself automatically. He envisages a black-box solution for investment managers who will have the ability to run complex choice investment scenarios in real time.
The primary use would maintain trading rooms, in particular in the technical analysis field, evaluating how stock markets react to company information or in gauging the performance of derivative equipment and offering different investment paths to managers. There could be a question mark over the future of decision-makers themselves even.
“Whoever succeeds in it has the potential to change financial markets and especially investment bank and equities trading. You will see winners and losers – it isn’t hard to assume the radical effect on employment at the best bank levels if investment decisions are more and more automated,” Hassanniakalager says. The algorithm, which Hassanniakalager describes as universal, may have applications beyond financial marketplaces. “If you learn what’s changing statistically, you can apply that to other areas, such as genetics. That’s the wonder of figures,” he says.
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