3 other recent papers related to trading ...

Discussion in 'Strategy Development' started by bhamadicharef, Feb 19, 2008.

  1. Hi,

    3 other recent papers related to trading ...

    Again provide your email if you want the PDF

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    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 CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 37, NO. 6, NOVEMBER 2007

    04342801.pdf

    Abstract—The 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.

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    A Petri-Net-Based Correctness Analysis of Internet Stock Trading Systems
    YuYue Du, ChangJun Jiang, and MengChu Zhou,
    IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 38, NO. 1, JANUARY 2008

    04359284.pdf

    Abstract—This 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.

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    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 CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 38, NO. 1, JANUARY 2008 93

    04395350.pdf

    Abstract—The 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 “beat the market” 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 pattern—the “bull flag stock chart”—which 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 “bull flag” (or “bear flag” 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.

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  2. Cliff Notes please.
     
  3. nverse

    nverse

    nverse0 at gmail.com

    thanks
     
  4. huzhen

    huzhen

  5. Corey

    Corey

  6. Done ...
     
  7. TEDW

    TEDW

  8. Hi, could you send me the papers as well?

    mishra.anshuman+trader@gmail.com

    Thanks very much!
     
  9. #10     Mar 21, 2008