One caveat is that these results do not allow us to infer how often or how extensively these data sources are being used in industry and company analysis. However, these results do suggest that the investment industry is generally in the early stages of adopting AI techniques and related technologies, and a few investment professionals currently use big data applications in their daily investment processes. Overall, given the low usage of AI and big data techniques, coupled with the large number of data scientists entering the investment industry, it is reasonable for asset allocators to expect to embrace a growth in modern approaches to investment decision-making.
The effects of the COVID-19 pandemic ushered in a new era in March 2020, as traditional models failed in the systemic economic shifts caused by the pandemic, and alternative data came into play as a provider of real-time data to derive insights for immediate action. This change triggered an interest in using AI and alternative data to both increase signal accuracy and generate a competitive edge as asset managers reset their strategies. Asset managers realise that inputs and data should not include only structured data like the clean time series such as prices, PE’s or economic indicators. To gain an edge, the current development or implementation in the investment industry has increasingly been predictive models using unsupervised learning to understand data structure, supervised learning algorithms for prediction and reinforcement learning to learn actions. Another popular technique is the use of neural network architecture taking in price data such as FX, equity indices, yields, commodity prices and so forth. Using the most efficient search technologies such as cloud and/or distributed scaling, global traffic congestion monitoring, real-time inflation trackers and using deep learning to gauge sentiment on social media and the news are also some techniques that are gaining popularity in increasing the predictive power of models.
As Absa Multi Management, we are always looking for innovative ways to combine the collective power of both artificial and human intelligence. The path of adoption begins with routine, rudimentary tasks that analysts perform, that AI and big data have some advantage over human beings in the breadth of information that they can process at high speeds. Incorporating AI and data science techniques can augment human intelligence to enable investment professionals to reach a higher level of efficiency, minimising behavioural bias and making investment decisions that leverages the collective intelligence of machines and humans. The differentiating factor among investment teams of the future will excel in collective intelligence through cognitive diversity (artificial and human) to achieve a competitive edge.
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