Alternative Data gained more trust during the crisis

There has never been a better time to bring up the conversation on whether Data (fundamental, conventional and alternative) made a difference during the COVID19 induced crisis.

  • · Did algorithms help and in what way?

  • · what about the interaction of humans with Data and algorithms?

Fundamental macro data is essential, and this will never change. What can and should change is that fundamental macro data's time lag and that not all data sources are trustworthy. There is a need to build a real-time information system aggregating trustworthy data, that starts with fundamental data and extends to the currently coined `alternative data`.

The demand for alternative data spiked during this crisis because everybody needed to access the situation in real-time and needed real-time measures. Investors, traders, portfolio managers, pensions, lenders, who were already using some kind of alternative data, needed more and relied heavily on high-quality real-time data. The reliance on such alternative data and on actionable techniques to access exposures and make intelligent predictions, skyrocketed.

New entrants in the alternative data space, flocked as they needed to manage risks and exposures. Those in the field managing risks, uniformly confirmed the increased need to manage thematic and narrative risks.

For instance, humans needed to understand their exposure to airlines or to China during the crisis and in the new normal. They needed to ask the data and the machines what the impact of COVID19 would be in real estate.

Humans continue to be in charge of narrating the topics of interest or at stake. The machines need to be able to offer actionable insights and forecasts.

There is an increasing need for real-time and continuous re-assessment of this kind of complexity through lots of high-quality real-time data of all sorts. Machine learning and adaptive trading algorithms that reflect and retrain gave confidence to humans during this highly uncertain period.

Building trust between humans and machines has always been essential and will continue to be. The recent crisis was a painful but valuable experience that built more trust in humans as to what machines can currently do and with what inputs.

There has been an improvement in actionable techniques that allow humans to extract signals towards generating alpha by combining high-quality real-time data and adaptive algorithms. There has been a better and larger offering from providers, of data, insights, algorithms.

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