AlphaPortfolio for Investment and Economically Interpretable AI

76 Pages Posted:

See all articles by Lin William Cong

Lin William Cong

Cornell University

Ke Tang

Institute of Economics, School of Social Sciences, Tsinghua University

Jingyuan Wang

Beihang University (BUAA)

Yang Zhang

Beihang University (BUAA)

Date Written: March 10, 2020

Abstract

We propose reinforcement-learning-based portfolio management, an alternative that improves upon the traditional two-step portfolio-construction paradigm a la Markowitz (1952), to directly optimize investors' objectives. Specifically, we enhance cutting-edge neural networks such as Transformer with a novel cross-asset attention mechanism to effectively capture the high-dimensional, non-linear, noisy, interacting, and dynamic nature of economic data and market environment. The resulting AlphaPortfolio yields stellar out-of-sample performances even after imposing various economic and trading restrictions. Importantly, we use polynomial-feature-sensitivity (and textual-factor) analysis to project the model onto linear regression (and natural language) space for greater transparency and interpretation. Such ``economic distillations'' reveal key characteristics/topics (including their rotation and non-linearity) that drive investment performance. Overall, we highlight the utility of reinforcement deep learning and provide a general procedure for interpreting AI and big data analytics in finance and beyond.

Keywords: Artificial Intelligence, Distillation, LSTM, Machine Learning, Portfolio Theory, Reinforcement Learning, Textual Analysis.

Suggested Citation

Cong, Lin and Tang, Ke and Wang, Jingyuan and Zhang, Yang, AlphaPortfolio for Investment and Economically Interpretable AI (March 10, 2020). Available at SSRN: https://ssrn.com/abstract=

Lin Cong (Contact Author)

Cornell University ( email )

Ithaca, NY 14853
United States

HOME PAGE: http://www.linwilliamcong.org

Ke Tang

Institute of Economics, School of Social Sciences, Tsinghua University ( email )

No.1 Tsinghua Garden
Beijing, 100084
China

Jingyuan Wang

Beihang University (BUAA) ( email )

37 Xue Yuan Road
Beijing 100083
China

Yang Zhang

Beihang University (BUAA) ( email )

37 Xue Yuan Road
Beijing 100083
China

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
53
Abstract Views
155
PlumX Metrics