Hi,
3 other recent papers related to trading ...
Again provide your email if you want the PDF
=======================================================================
A Multiagent Approach to Q-Learning for Daily Stock Trading
Jae Won Lee, Jonghun Park, Jangmin O, Jongwoo Lee, and Euyseok Hong
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSâPART A: SYSTEMS AND HUMANS, VOL. 37, NO. 6, NOVEMBER 2007
04342801.pdf
AbstractâThe portfolio management for trading in the 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 intelligent
portfolio management system that can recommend financial decisions for daily stock trading. Many promising results have been
reported from the supervised learning community on the possibility of building a profitable trading system. More recently, several
studies have shown that even the problem of integrating stock price prediction results with trading strategies can be successfully
addressed by applying reinforcement learning algorithms. Motivated by this, we present a new stock trading framework that
attempts to further enhance the performance of reinforcement learning-based systems. The proposed approach incorporates multiple
Q-learning agents, allowing them to effectively divide and conquer the stock trading problem by defining necessary roles for
cooperatively carrying out stock pricing and selection decisions. Furthermore, in an attempt to address the complexity issue when
considering a large amount of data to obtain long-term dependence among the stock prices, we present a representation scheme
that can succinctly summarize the history of price changes. Experimental results on a Korean stock market show that the proposed
trading framework outperforms those trained by other alternative approaches both in terms of profit and risk management.
=======================================================================
A Petri-Net-Based Correctness Analysis of Internet Stock Trading Systems
YuYue Du, ChangJun Jiang, and MengChu Zhou,
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSâPART C: APPLICATIONS AND REVIEWS, VOL. 38, NO. 1, JANUARY 2008
04359284.pdf
AbstractâThis paper shows how temporal Petri nets (TPNs) can be used to specify and analyze an Internet stock trading system.
The dynamical behavior of the system and causality between events can be explicitly described by temporal formulas. The functional
correctness of the modeled system is formally verified by using the inferential rules in temporal logic. Important properties of
the system are analyzed based on its TPN model such as liveness, eventuality, and fairness properties. This paper demonstrates that
TPNs can provide significant advantages in the design and analysis of business processes.
=======================================================================
Trading With a Stock Chart Heuristic
William Leigh, Cheryl J. Frohlich, Steven Hornik, Russell L. Purvis, and Tom L. Roberts
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSâPART A: SYSTEMS AND HUMANS, VOL. 38, NO. 1, JANUARY 2008 93
04395350.pdf
AbstractâThe efficient market hypothesis (EMH) is a cornerstone of financial economics. The EMH asserts that security
prices fully reflect all available information and that the stock market prices securities at their fair values. Therefore, investors
cannot consistently âbeat the marketâ because stocks reside in perpetual equilibrium, making research efforts futile. This flies
in the face of the conventional nonacademic wisdom that astute analysts can beat the market using technical or fundamental stock
analysis. The purpose of this research is to partially assess whether technical analysts, who predict future stock prices by analyzing
past stock prices, can consistently achieve a trading return that outperforms the stock market average return. This is tested using
knowlege engineering experimentation with one price history patternâthe âbull flag stock chartââwhich signals technical analysts
of a future stock market price increase. A recognizer for the stock chart pattern is built using a template-matching technique
from pattern recognition. The recognizer and associated trading rules are then tested by simulating trading on over 35 years
of daily closing price data for the New York Stock Exchange Composite Index. The experiment is then replicated using the
horizontal rotation or mirror image pattern of the âbull flagâ (or âbear flagâ stock chart) that signals a future stock market
decrease. Results are systematic, statistically significant, and fail to confirm the null hypothesis based on a corollary to the EMH: that
profit realized from trading determined by this heuristic method is no better than what would be realized from trading decisions
based on random choice.
=======================================================================
3 other recent papers related to trading ...
Again provide your email if you want the PDF
=======================================================================
A Multiagent Approach to Q-Learning for Daily Stock Trading
Jae Won Lee, Jonghun Park, Jangmin O, Jongwoo Lee, and Euyseok Hong
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSâPART A: SYSTEMS AND HUMANS, VOL. 37, NO. 6, NOVEMBER 2007
04342801.pdf
AbstractâThe portfolio management for trading in the 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 intelligent
portfolio management system that can recommend financial decisions for daily stock trading. Many promising results have been
reported from the supervised learning community on the possibility of building a profitable trading system. More recently, several
studies have shown that even the problem of integrating stock price prediction results with trading strategies can be successfully
addressed by applying reinforcement learning algorithms. Motivated by this, we present a new stock trading framework that
attempts to further enhance the performance of reinforcement learning-based systems. The proposed approach incorporates multiple
Q-learning agents, allowing them to effectively divide and conquer the stock trading problem by defining necessary roles for
cooperatively carrying out stock pricing and selection decisions. Furthermore, in an attempt to address the complexity issue when
considering a large amount of data to obtain long-term dependence among the stock prices, we present a representation scheme
that can succinctly summarize the history of price changes. Experimental results on a Korean stock market show that the proposed
trading framework outperforms those trained by other alternative approaches both in terms of profit and risk management.
=======================================================================
A Petri-Net-Based Correctness Analysis of Internet Stock Trading Systems
YuYue Du, ChangJun Jiang, and MengChu Zhou,
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSâPART C: APPLICATIONS AND REVIEWS, VOL. 38, NO. 1, JANUARY 2008
04359284.pdf
AbstractâThis paper shows how temporal Petri nets (TPNs) can be used to specify and analyze an Internet stock trading system.
The dynamical behavior of the system and causality between events can be explicitly described by temporal formulas. The functional
correctness of the modeled system is formally verified by using the inferential rules in temporal logic. Important properties of
the system are analyzed based on its TPN model such as liveness, eventuality, and fairness properties. This paper demonstrates that
TPNs can provide significant advantages in the design and analysis of business processes.
=======================================================================
Trading With a Stock Chart Heuristic
William Leigh, Cheryl J. Frohlich, Steven Hornik, Russell L. Purvis, and Tom L. Roberts
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSâPART A: SYSTEMS AND HUMANS, VOL. 38, NO. 1, JANUARY 2008 93
04395350.pdf
AbstractâThe efficient market hypothesis (EMH) is a cornerstone of financial economics. The EMH asserts that security
prices fully reflect all available information and that the stock market prices securities at their fair values. Therefore, investors
cannot consistently âbeat the marketâ because stocks reside in perpetual equilibrium, making research efforts futile. This flies
in the face of the conventional nonacademic wisdom that astute analysts can beat the market using technical or fundamental stock
analysis. The purpose of this research is to partially assess whether technical analysts, who predict future stock prices by analyzing
past stock prices, can consistently achieve a trading return that outperforms the stock market average return. This is tested using
knowlege engineering experimentation with one price history patternâthe âbull flag stock chartââwhich signals technical analysts
of a future stock market price increase. A recognizer for the stock chart pattern is built using a template-matching technique
from pattern recognition. The recognizer and associated trading rules are then tested by simulating trading on over 35 years
of daily closing price data for the New York Stock Exchange Composite Index. The experiment is then replicated using the
horizontal rotation or mirror image pattern of the âbull flagâ (or âbear flagâ stock chart) that signals a future stock market
decrease. Results are systematic, statistically significant, and fail to confirm the null hypothesis based on a corollary to the EMH: that
profit realized from trading determined by this heuristic method is no better than what would be realized from trading decisions
based on random choice.
=======================================================================
