Our journey of Humans and Machines in managing Uncertainty — Part I

Our journey of Humans and Machines in managing Uncertainty — Part I

Our journey of Humans and Machines in managing Uncertainty — Part I 800 600 Efi Pylarinou

Our journey of Humans and Machines in managing Uncertainty — Part I

The conversation around the interaction between data, algorithms, and humans never stops.

Once again, A crisis like no other!

The COVID19 induced crisis, of course, provided us with an ideal setting to use more data, test and train algorithms, and experience our and their strengths and weaknesses in the investing arena.

Once again, A crisis like no other is actually the title of an AQR article at the end of March. The 20+yrs old quant king has suffered after a very difficult 2018 (the so-called Red October) which led to large outflows and over 1000 layoffs- Investors pull billions from quant king AQR as performance slumps — Financial News.

There has never been a better time to bring up the conversation on whether Data (fundamental, conventional and alternative) made a difference during this crisis. Did algorithms help and in what way? And 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 the time lag and that not all data sources are trustworthy. We need to build a real-time information system aggregating trustworthy data, that starts with fundamental data and extends to the currently coined `alternative data`. Eventually, we will get rid of the alternative data term, all together.

Lykke recently launched the Open Initiative project which is a competition with a CHF50k funding award. This is led by Richard Olsen (founder & CEO of Lykke), Christopher Giancarlo (Cofounder of the Digital Dollar and ex-Chairman of CFTC), and Ashkan Nikeghbali (Chair in Mathematics at UZH)

One of the four different thematics of the competition is focused on building a Real-time information system. Lykke proposes a Wikipedia like system (in certain aspects) with revenue streams built on Blockchain and accessed via APIs. This vision merits a separate article.

In May, as an advisor to AxessThinkTank the Geneva-based ecosystem with a vision of becoming a distributed knowledge hub, we embarked on the first step of a journey focused on `Humans and Machine to manage Uncertainty`[1]

I had the pleasure to moderate a 50 minutes digital discussion with a diverse group of experienced professionals that work as quants with data and algorithms focused on investing intelligently. Watching the recorded discussion will provide you with the full insights and color from each participant.

There was a consensus more or less than the demand for alternative data, as expected, spiked during this crisis because everybody needed to access the situation in real-time and needed real-time measures. Investors, traders, portfolio managers, pensions, 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.

The panelists uniformly confirmed the increased need to manage thematic and narrative risks. In plain words, 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. And on and on….

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 in certain asset classes (e.g. commodities) 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.

The panelists were from the following companies:

Ravenpack is a leading data analytics platform headquartered in Spain. I was using their free Coronavirus dashboard during the lockdown which had lots of alternative indicators (panic, media hype, fake news etc).

You can check out their research around sentiment impact and sentiment investment strategies — here. Ravenpack highlights that negative sentiment has more predictive power for asset prices than positive sentiment.

CueMacro, a UK based alternative data consulting practice. Saeed Amen, the founder of Cuemacro, is the co-author of the upcoming book on The Book of Alternative Data. It is a book covering ways of leveraging alternative information sources in the context of investing and risk management.

RAM is a macro hedge fund in Geneva that emphasizes the complexity and alpha generation potential of combining structured and unstructured data (see article here).

Macrosynergy is a UK macro hedge that is currently acting as a fiduciary quant house for long term institutional investors. They are combining fundamental data and insights with algorithmic trading.

Predictive Layer is a Swiss company focused on predictive Analytics and forecasting applications. Their algorithms adapted to this crisis and offered insights that only machine learning can produce in a timely fashion. Their value add was clear in the commodities space during this crisis.

The journey `Humans and Machine to manage Uncertainty` continues. Yves Carnazzola, the president of AxessThinkTank, and myself are leading this initiative in the spirit of building a distributed knowledge hub around this thematic.

This will include a variety of additional digital conversations around this thematic, surveys and storytelling landscape reports of the space. If you are a stakeholder in this space and would be interested in taking part in this journey with us, please email us so we can provide you with additional information and details. 



[1] I have been a moderator in certain tracks of the Axess Think Tank biannual physical conferences

Challenges and opportunities — Big Data, Alternative Data and AI in Finance — January 2020

Challenges and opportunities — Diving deeper into alternative Risk premia — June 2019

Challenges and opportunities in Crypto investing — Spring 2018

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