NDLI: A Q-learning based approach to design of intelligent stock trading agents

Content Provider	IEEE Xplore Digital Library
Author	Lee, J.W. Hong, E. Park, J.
Copyright Year	2004
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Sungshin Women's Univ., Seoul, South Korea (Lee, J.W.; Hong, E.)
Abstract	The portfolio management for trading in stock market poses a challenging stochastic control problem of significant commercial interests to finance industry. To date, many researchers have proposed various methods to build an automated portfolio management system that can recommend financial decisions for daily stock trades. However, most previous attempts to predict future returns of stocks have been largely unsuccessful due to the high complexity of integrating price prediction results with trading strategies. Motivated by this, this paper presents a new stock trading system, named Q-trader, that can overcome the limitations of the existing approaches through the joint optimization of the prediction results and the corresponding trading strategies. Specifically, the proposed framework employs four cooperative Q-learning agents that adaptively interact with their environments by use of feedforward neural networks. In order to achieve computational efficiency for global trend prediction, a novel data structure is proposed for compact representation of long-term dependencies of stock price changes. Experimental results on KOSPI 200 show that the proposed approach outperforms the trading systems trained by supervised learning both in profit and risk management.
Starting Page	1289
Ending Page	1292
File Size	264493
Page Count	4
File Format	PDF
ISBN	0780385195
DOI	10.1109/IEMC.2004.1408902
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent agent Portfolios Financial management Stock markets Stochastic processes Automatic control Finance Electrical equipment industry Industrial control Neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Multiagent Approach to $Q$-Learning for Daily Stock Trading

A multiagent approach to q-learning for daily stock trading.

Dynamic trading strategies with stochastic and nonlinear price impact

Stock market prediction using neural networks: Does trading volume help in short-term prediction?

A neural module to estimate context information for sequence learning

Some issues with the intelligent control of industrial processes

A holonic ontology-based multi-agent system for the distributed scheduling and monitoring of industrial processes

Sequential Learning for Adaptive Critic Design: An Industrial Control Application

A neurocomputer based learning controller for critical industrial applications

A Q-learning based approach to design of intelligent stock trading agents

Similar Documents

A Multiagent Approach to $Q$-Learning for Daily Stock Trading

A multiagent approach to q-learning for daily stock trading.

Dynamic trading strategies with stochastic and nonlinear price impact

Stock market prediction using neural networks: Does trading volume help in short-term prediction?

A neural module to estimate context information for sequence learning

Some issues with the intelligent control of industrial processes

A holonic ontology-based multi-agent system for the distributed scheduling and monitoring of industrial processes

Sequential Learning for Adaptive Critic Design: An Industrial Control Application

A neurocomputer based learning controller for critical industrial applications

A Q-learning based approach to design of intelligent stock trading agents