Very few can pay no attention to the presence of Artificial Intelligence and Machine Learning in today s world, and even less so if you work in quantitative finance. Here, Michael Harris, quant methodical and optional trader and best selling author, examines the influence these technologies are having on trading and investing. Below are selections from a presentation I gave last year in Europe, as an invited speaker to a group of low profile but high net worth investors and traders. The topic was determined by the organizer to be about the influence of artificial intelligence and machine learning on trading and investing. The excerpts below are organized in four sections and cover about 50 of the original presentation.
General influence of artificial intelligence and machine learning on trading
Artificial Intelligence AI permits supplanting humans with machines. In the 1980s, AI research focused fundamentally on skilled systems and fuzzy logic. With computational power becoming cheaper, using machines to solve large-scale optimization problems became economically feasible. As a result of the propels in hardware and software, at the present time AI centers on the use of neural networks and other learning strategies for recognizing and analyzing indicators, also known as features, or factors, that have economic value and can be used with classifiers to develop profitable models. This particular use of AI frequently goes by the name Machine Learning ML .
The application of strategies for creating trading strategies based on AI, both in short-term periods and for longer-term investing, is picking up fame and there are a few hedge funds that are very lively in this meadow. In any case, broad approval of this new expertise is slow due to various factors, the most vital being that AI requires investment in new tools and human talent. The majority of funds use crucial analysis since this is what managers learn in their MBA programs. There are not many hedge funds that rely solely on AI. Application of AI is growing at the retail level but the majority of traders still use methods that were planned in mid twentieth century, as well as traditional technical analysis, since they are effortless to learn and relate.
Note that AI and ML are not only utilized to build up trading strategies but also in other areas, for instance in creating liquidity looking algos and proposing portfolios to clients. Therefore, with AI applications picking up ground, the number of humans included in trading and asset choices diminishes and this clearly influences markets and charge action. It is early to speculate on the overall impacts this new technology will have on the industry but it is likely that extensive use of AI will result in more effective markets with lower volatility for extended periods of time followed by occasional volatility spikes due to regime changes. This is achievable because the influence of subjective assessment of data by humans will be minimized and with that the associated noise. But that remains to be seen in exercise.