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. =======================================================================