ARTIFICIAL INTELLIGENCE AND INVESTSMENT
Mageska Capital’s innovative vision
Roberto Marracco, CFA
Chief executive and investment officer
While maximizing returns is the primary objective of any fund manager, the strategies used to achieve this differ greatly. At Mageska Capital, we have acquired a major ally: the M-LAB investment strategy optimization platform. Using artificial intelligence (AI), this cutting-edge technological tool provides Mageska Capital’s management team with remarkable added value in terms of recommended strategies based on real-time observations of the financial markets. But is AI the way of the future in investment? Will it supplant all human judgment and if so, is this the desired outcome?
AI is defined as a field of study that artificially reproduces the cognitive faculties of human intelligence to create systems or machines capable of performing functions normally performed by
humans. Although AI attracts its share of supporters, it is also feared by many, who fear that the machine will replace or even eliminate all human judgment.
However, a closer look reveals that not only is human involvement present in many aspects of AI, but it often remains essential to decision making. According to Sam Ransbotham, editor of the renowned MIT Sloan Management Review : « A human can add value by scrutinizing a system’s results before action. But long before that, people also had a foundational role in developing the algorithms underlying the classification system and selecting the data used to train and evaluate the efficacy of the resulting system.
While far from perfect, there have been huge improvements in AI. Building off vast training data, prediction is much more accurate in many scenarios. If there are sufficient observations, humans can likely build off our breadth of experience to infer lessons from other cases in ways that machines cannot. »
At least for now, human involvement is still necessary in the development of AI’s decision-making capabilities.
AI and predictive models, where do we stand?
Over a period of several months, a fund manager can analyze up to a thousand pieces of data in making financial decisions. However, performing the same exercise in a much shorter period, during times of high stress, remains nearly impossible. Computer models help managers make better decisions, especially in times of high volatility and uncertainty in the markets.
Research around 1950 established that simple predictive models outperform the ability of human experts to make predictions and forecasts. Since then, more than 200 studies have compared expert predictions to those of algorithms, with statistical algorithms almost always outperforming unassisted human judgment.
However, once the model is built and deployed, human judgment is still required to assess the applicability of a model’s prediction in a particular case. After all, models are not omniscient, they can only do so much as combine the pieces of information presented to them. In an ever-changing economic environment, human-machine collaboration is an essential way to improve our forecasting and judgmental capabilities.
At Mageska Capital, we firmly believe in the power of this collaboration. Proven technology, experienced professionals, and a great team to deliver competitive results, regardless of the jolts in the markets.
Sources:
RANSBOTHAM, Sam. “Justifying Human Involvement in the AI Decision-Making Loop”, MIT Sloan Management Review, 23 octobre 2017, [www.sloanreview.mit.edu].
COOPER, Philip. “Why AI Drives Better Business Decision-Making”, 3 novembre 2017, [www.linkedin.com/pulse /why-ai-drives-better-business-decision-making-philip-cooper].
GUSZCZA, Jim. MADDIRALA, Nikhil. “Minds and machines: The Art of Forecasting in the Age of Artificial Intelligence”, Deloitte Review Issue 19, 26 juillet 2016.
