Investment philosophy

Our approach

Investment dicipline

We believe that market prices are not random and there are persistent sources of alpha in markets that can be identified and harvested systematically.

We see the world in a constant period of major economic and political transition, with the investment landscape shifting year by year. Our strategies are built to profit from this dynamic environment.

To capture these uncorrelated returns, we use deep learning to create proprietary systematic investment strategies. Through our deep learning robots we analyse, forecast and rank all assets classes on a daily basis. The robots are independent of each other, hence no consensus forecasting.

While far from high-frequency, Innolab is sampling data, adjusting signals and return forecasts continuously, 24 hours a day, in light of the stream of new data. All strategies are deployed in a highly-resilient, scalable, zero-downtime environment in the cloud.

Deep learning

Deep Learning can extract information from the markets that humans can’t.

Inefficiencies

Inefficiencies exist across asset classes that can be exploited.

Improvement

Constant And Never-ending Improvement (CANI) is of great importance.

Robustness

Strategies are rigorously tested before they are allowed into the portfolio.

Self-healing

Strategies are deployed as autonomous self-healing systems to ensure maximal stability.

Engineers

Engineering experience can be used in financial applications with great success.

Deep learning seek to uncover tendencies in historical data more efficiently than could be done by humans. These techniques can uncover robust and statistically significant patterns that would have been entirely impossible for a human being to uncover.

Peter Smedegaard
CEO AND FOUNDER

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