Deep Portfolio Theory
By J. B. Heaton, Nicholas G. Polson, Jan Hendrik Witte. 2016
28 citations recorded by the source corpus.
We construct a deep portfolio theory. By building on Markowitz's classic risk-return trade-off, we develop a self-contained four-step routine of encode, calibrate, validate and verify to formulate an automated and general portfolio selection process. At the heart of our algorithm are deep hierarchical compositions of portfolios constructed in the encoding step. The calibration step then provides multivariate payouts in the form of deep hierarchical portfolios that are designed to target a variety of objective functions. The validate step trades-off the amount of regularization used in the encode and calibrate steps. The verification step uses a cross validation approach to trace out an ex post deep portfolio efficient frontier. We demonstrate all four steps of our portfolio theory numerically.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Nikolaos Passalis
- Anastasios Tefas
- Alexandros Iosifidis
- Juho Kanniainen
- Avraam Tsantekidis
- Moncef Gabbouj
- Weiwei Jiang
- Yosi Keller
Citations in the corpus
- Financial Time Series Prediction Using Deep Learning (2017, background): “Portfolio optimization is the optimal dynamic selection of investment assets, and was studied by Heaton [28], by trying to predict a portfolio that will outperform the biotechnology index IBB.”
- Using deep learning to detect price change indications in financial markets (2017, methodology): “In Deep Portfolio Theory [12], the authors use autoencoders to optimize the performance of a portfolio and beat several profit benchmarks, such as the biotechnology IBB Index.”
- Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks (2017, methodology): “In the more advanced work of Deep Portfolio Theory [12], the authors use autoencoders to optimize the performance of a portfolio and beat several profit benchmarks, such as the biotechnology IBB Index.”