sampleReport.bib

@book{Vince,
  author    = {Ralph Vince},
  publisher = {Wiley},
  title     = {Portfolio management formulas},
  year      = {1990},
  langid    = {english}
}

@article{Escobar,
  title    = {A Technical Analysis Indicator Based On Fuzzy Logic},
  journal  = {Electronic Notes in Theoretical Computer Science},
  volume   = {292},
  pages    = {27-37},
  year     = {2013},
  note     = {Proceedings of the XXXVIII Latin American Conference in Informatics (CLEI)},
  issn     = {1571-0661},
  doi      = {https://doi.org/10.1016/j.entcs.2013.02.003},
  url      = {https://www.sciencedirect.com/science/article/pii/S1571066113000054},
  author   = {Alejandro Escobar and Julián Moreno and Sebastián Múnera},
  keywords = {stock market, technical analysis, technical indicator, fuzzy logic, simulation},
  abstract = {In this paper an indicator for technical analysis based on fuzzy logic is proposed, which unlike traditional technical indicators, is not a totally objective mathematical model, but incorporates subjective investor features such as the risk tendency. The fuzzy logic approach allows representing in a more “human” way the decision making reasoning that a non-expert investor would have in a real market. Such an indicator takes as input, general market information like profitability and volatility of the stock prices, while the outputs are the buy and sell signals. In addition to present the detailed formulation of the indicator, in this paper a validation for the same is presented, which makes use of a multi-agent based simulation platform within which the behavior and profits obtained by agents that used traditional technical indicators such as MA, RSI and MACD, are compared against those obtained by agents that use the fuzzy indicator for the decision making process.},
  langid   = {english}
}

@book{Sansanee,
  author = {Sansanee Auephanwiriyakul, Ph.D.Associate Professor},
  title  = {Introduction to Computational Intelligence for Computer Engineering},
  year   = {2013},
  langid = {english}
}

@book{Engelbrecht,
  author    = {Andries P. Engelbrecht},
  publisher = {John Wiley and Sons, Ltd},
  title     = {Computational Intelligence: An Introduction},
  year      = {2002},
  langid    = {english}
}

@article{Zadeh,
  author = {L. A. Zadeh},
  title  = {Fuzzy Sets, Information and Control},
  volume = {8},
  year   = {1965},
  langid = {english}
}

@book{Klir,
  author    = {G. J. Klir and B. Yuan},
  publisher = {Prentice Hall Inc.},
  title     = {Fuzzy Set and Fuzzy Logic: Theory and Applications},
  year      = {1995},
  langid    = {english}
}

@book{Kruse,
  author    = {Rudolf Kruse and Joan E. Gebhardt},
  publisher = {John Wiley and Sons, Ltd},
  title     = {Foundations of Fuzzy Systems},
  year      = {1995},
  langid    = {english}
}


@article{Rodrigo,
  author   = {Naranjo, Rodrigo and Meco, Albert and Arroyo, Javier and Santos, Matilde},
  title    = {An Intelligent Trading System with Fuzzy Rules and Fuzzy Capital Management},
  journal  = {International Journal of Intelligent Systems},
  volume   = {30},
  number   = {8},
  pages    = {963-983},
  doi      = {https://doi.org/10.1002/int.21734},
  url      = {https://onlinelibrary.wiley.com/doi/abs/10.1002/int.21734},
  eprint   = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.21734},
  abstract = {In this work, we are proposing a trading system where fuzzy logic is applied not only for defining the trading rules, but also for managing the capital to invest. In fact, two fuzzy decision support systems are developed. The first one uses fuzzy logic to design the trading rules and to apply the stock market technical indicators. The second one enhances this fuzzy trading system adding a fuzzy strategy to manage the capital to trade. Additionally, a new technical market indicator that produces short and long entry signals is introduced. It is based on the moving average convergence divergence indicator. Its parameters have been optimized by genetic algorithms. The proposals are compared to a classical nonfuzzy version of the proposed trading systems and to the buy-and-hold strategy. Results favor our fuzzy trading system in the two markets considered, NASDAQ100 and EUROSTOXX. Conclusions suggest that the use of fuzzy logic for capital management is promising and deserves further exploration.},
  year     = {2015},
  langid   = {english}
}