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Big data & AI pose challenges for quant investors

Big data & AI pose challenges for quant investors

03-07-2018 | インサイト

The advent of big data and artificial intelligence (AI) has emerged as a major game-changer for the financial industry. In the asset management sector, although the adoption of these innovations is still in its infancy, a growing number of players are examining how they can be used to design better investment strategies.

  • David Blitz
    David
    Blitz
    Head of Quant Research
  • Rob van Bommel
    Rob
    van Bommel
    Managing Director, Portfolio Manager

Speedread

  • Big data and AI could enable investors to take better decisions
  • Data fitting and short histories raise robustness concerns
  • We apply our cautious pioneering philosophy to this field

But while it is true that improvements in data analytics may bring valuable insights for investors, there is little hard evidence that big data and artificial intelligence actually work in practice, at least for now. Concrete cases of investment managers able to deliver consistent outperformance in real life using these techniques are still absent.

A solid investment strategy requires extensive empirical testing and falsification on broad samples of data and over long periods of time, but the evidence for big data and artificial intelligence is largely anecdotal. Strategies based on big data and artificial intelligence may also lack the necessary backing by a clear economic rationale. In fact, most investment ideas solely rely on paper back-test results, which should always be considered with caution.

We always strive to identify factors that are rewarded with superior risk-adjusted performance over the longer term. We also look beyond mere statistical patterns and aim to understand the economic drivers of returns.

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Technical challenges

As a result, managing money based on an untested AI algorithm that scrutinizes exotic datasets raises a lot of concerns, even though big data and artificial intelligence have become a popular discussion topic among investors. Moreover, these innovations pose a number of challenges for asset managers looking to incorporate them into their investment process.

The first of these technical challenges is that ‘big data datasets’ generally have a short or even very short history. The second issue has to do with the lack of breadth, or at least the very fragmentary nature, of most big data signals. Another concern is that many signals provided by big data and AI tend to be very short-term focused. Finally, high quality datasets are not easy to obtain and can be very expensive.

For now, we see more potential in analyzing data signals available from credit, option and lending markets, using proven and transparent techniques

Given all the caveats mentioned above, we consider the current trend around big data and AI as a very interesting development, but one that should be treated with caution. For now, we see more potential in analyzing data signals available from credit, option and lending markets, using proven and transparent techniques, than in analyzing exotic big data variables with complex algorithms.

At the same time, we acknowledge the potential disruption this kind of innovation may lead to in the future. In the past, the datasets that are currently widely used by quantitative asset managers were subject to similar issues as big data today. Over the years, the quality, breadth and history of these datasets improved, and they became usable. Now, as time passes and more data becomes available, big data will probably also become increasingly usable.

Not necessarily one or the other

Finally, the fundamental issue for investors may not necessarily be about choosing between one type of data or the other. There is a wide array of possibilities between sticking to traditional price and financial statement information, and solely relying on things like satellite imagery of parking slots. For example, big data and AI signals could be very useful to fundamental credit and equity analysts. This would feed through into our quantitative strategies, that take analyst revisions into account. In this case, we would be using big data and AI information in an indirect manner.

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