A Multi-objective Deep Reinforcement Learning Approach for Stock Index Future’s Intraday Trading

By Weiyu Si, Jinke Li, Peng Ding, Ruonan Rao. 2017

52 citations recorded by the source corpus.

Subfields: Reinforcement Learning, Economics & Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Reinforcement Learning

1. Ann Nowé
2. Diederik Marijn Roijers
3. Ze Hu
4. A. Ozbayoglu
5. Omer Berat Sezer
6. M. Ugur Gudelek
7. Matloob Khushi
8. Patrick Mannion

In Economics & Operations Research

1. A. Ozbayoglu
2. M. Ugur Gudelek
3. Ze Hu
4. Omer Berat Sezer
5. Matloob Khushi
6. Yiqi Zhao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [153] used MODRL+LSTM method to predict Chinese stock-IF-IH-IC contract indices.”
• Deep Learning for Financial Applications : A Survey (2020, methodology): “[43] implemented Chinese intraday futures market trading model with DRL and LSTM.”
• Multi-objective multi-agent decision making: a utility-based analysis and survey (2019, background): “In the last couple of years, interest in Deep MORL has intensified, although primarily in single-agent settings (see e.g. [1,33,47,70,74,82,106,111,112]).”
• A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “The LSTM network made continuous decisions and could change positions at the right time, which reduced the transaction cost, and the multi-objective structure made good profits within the acceptable risk [82].”