This leaves investors with a burning question, though. Other uses of AI and alternative and big data reported by asset managers and other investment service providers include the analysis of earnings conference calls, equity trading volumes predictions, and the use of publicly available geospatial data to estimate local market share in the aggregates industry – the mining of sand, gravel and crushed rock for the production of concrete. A case in point is the ‘news sentiment signal’ derived from advanced event-based text analytics, which is now used to enhance the momentum factor in our quantitative equity strategies. In contrast, almost half of them indicated that they had used regression analysis to find a linear relationship.īut while most of these techniques are still in their infancy, a growing number of players – primarily but not exclusively hedge funds – have taken important steps to investigate how they can be used in an effort to design better quantitative investment strategies, heralding what some experts have called “the next wave of quant investing”.Īt Robeco, for instance, we have invested significant resources over the past few years, leading to concrete advances in the integration of these innovative technologies into our investment processes. Other domains, in particular investments, still stand to benefit more broadly from this kind of innovation.Īccording to a 2019 survey by the CFA Institute among global investment professionals, 3 only 10% of the portfolio managers who responded had used AI or machine learning 4 (ML) techniques to improve their investment process in the previous 12 months. In asset management, although many players have publicly embraced these innovations and been beating their chests about it, practical applications have so far remained focused on areas such as process automation, and sales and marketing. 2 Many of these parties expected the number of areas in which they use it to more than double in the next three years. A 2019 survey by the Bank of England and the UK’s Financial Conduct Authority found, for instance, that two-thirds of all British financial firms were already using machine learning. New tools such as big and alternative data, AI, 1 and cloud computing have emerged as major developments for the financial industry.
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