From research findings in Machine Learning that we are comfortable sharing to opinion pieces on the state of Machine Learning in finance, to open challenges on the way to an AI revolution in investment management, you will find below some of our thoughts and works. Whether you are an investor, a mathematician or a Machine Learning researcher, we hope they shed some light on what we do, how we think, guide you as you try to make sense of the transformative impact AI is having in finance, and ignite a 'finance-first' Machine Learning research movement.

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What Makes An Asset Useful?

What Makes An Asset Useful?

In this 46 pages foundational paper, we develop an information-theoretic protocol for systematically quantifying how useful new assets or trading strategies are to investment managers. Among other contributions, we revisit age-old problems such as measuring financial risk and portfolio diversification, addressing well-known restrictions such as linearity and i.i.d. Gaussian assumptions and we provide a framework for quantifying the memory/predictability of a time series of returns.

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