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"Honey, I Deep-Shrunk the Sample Covariance Matrix!" by Dr. Erk Subasi

Talk by Dr. Erk Subasi, Quant Portfolio Manager at ‎Limmat Capital Alternative Investments AG. From QuantCon NYC 2016. Since the seminal work of Markowitz, covariance estimates has prime importance for portfolio construction. Running naive portfolio optimizations on sample covariance estimates can be hazardous to the health of one's portfolio though. The recent developments in machine learning, in particular in deep-learning, suggest that high-level abstractions and deep architectural representations are key for success when dealing with non-linear, noisy real-life data. Motivated by this, here we demonstrate a novel form of robust-covariance estimation based on the ideas borrowed from deep-learning domain. In a pedagogical setting, we will show how to use TensorFlow, a recently open-sourced deep-learning library by Google, to build a robust-covariance estimator via denoising autoencoders. The slides for this presentation can be found at https://www.slideshare.net/Quantopian.... To learn more about Quantopian, visit us at: https://www.quantopian.com. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.