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

Описание: 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....

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