A General Framework of Optimal Investment

32 Pages Posted: 10 Mar 2018 Last revised: 21 Jun 2019

See all articles by Qing Yang

Qing Yang

Fudan University - School of Economics

Tingting Ye

Boston University - Questrom School of Business

Liangliang Zhang

Dongxing Securities

Date Written: April 25, 2019

Abstract

In this paper, we propose a general framework of optimal investment and a collection of trading ideas, which combine probability and statistical theory with, potentially, machine learning techniques. The trading ideas are easy to implement and their validity is justified by full mathematical rigor. The framework is model-free and can, in principle, incorporate all categories of trading ideas into it. Simulation and backtesting studies show good performance of selected trading strategies under the proposed framework. Sharpe ratios are above 8.00 in simulation study and Sortino ratios are above 4.00 in backtesting, with very limited drawdowns, using 20 years of monthly data of U.S. equities (NASDAQ, NYSE and AMEX from 1999.1 to 2018.12) and 17 years of monthly data of China A-Share equities (Shanghai and Shenzhen Stock Exchange from 2002.1 to 2018.8).

Keywords: Active Portfolio Management, Strong Law of Large Numbers, Artificial Intelligence, Deep Learning, Backtesting

JEL Classification: C12

Suggested Citation

Yang, Qing and Ye, Tingting and Zhang, Liangliang, A General Framework of Optimal Investment (April 25, 2019). Available at SSRN: https://ssrn.com/abstract=3136708 or http://dx.doi.org/10.2139/ssrn.3136708

Qing Yang

Fudan University - School of Economics ( email )

600 GuoQuan Road
Shanghai, 200433
China

Tingting Ye

Boston University - Questrom School of Business ( email )

595 Commonwealth Avenue
Boston, MA MA 02215
United States

Liangliang Zhang (Contact Author)

Dongxing Securities ( email )

Yangshupu Rd. No. 248
Hongkou District
Shanghai, Shanghai 200433
China

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