RESEARCH

Big & Alternative data is a rich complement to traditional data used by asset managers. Research and practice have shown that integrating these data with more traditional ones and incorporating them into investment management decisions improves the performance of portfolios and strategies by significant margins. Traditional quantitative tools and techniques are still a dominate part of standard investment approaches, but cannot accommodate or extract signals from Big data. AI/ML algorithms, on the other hand, provide a variety of effective approaches to process big data sets and identify important investment signals. Hitherto, the shortfall of AI/ML algorithms is that they were developed for the disciplines of computer science and engineering and are not customized to the financial world with its challenging signal-to-noise ratios.

The silver lining is that after years of R&D and collaboration between financial practitioners and computer scientists, FinAIx has developed and continues to refine a new class of AI/ML generic models for portfolio management. We disseminate examples of these models via our open public portal and a research initiative to develop AI in Finance expertise in Montreal.

We also present our generic AI models at some of the world’s most prestigious conferences for academics and industry. This provides world class peer-validation, demonstrating the competitiveness, transparency and validity of our novel approach.