finance.bib

@article{cont2014price,
  title = {The price impact of order book events},
  author = {Cont, Rama and Kukanov, Arseniy and Stoikov, Sasha},
  journal = {Journal of financial econometrics},
  volume = {12},
  number = {1},
  pages = {47--88},
  year = {2014},
  publisher = {Oxford University Press},
}

@inbook{inbook,
  author = {Xiong, Yibing and Yamada, Takashi and Terano, Takao},
  year = {2015},
  month = {01},
  pages = {63-74},
  title = {Exploring Market Making Strategy for High Frequency Trading: An
           Agent-Based Approach},
  isbn = {978-3-319-20590-8},
  journal = {Springer Proceedings in Complexity},
  doi = {10.1007/978-3-319-20591-5_6},
}

@article{sirignano2019deep,
  title = {Deep learning for limit order books},
  author = {Sirignano, Justin A},
  journal = {Quantitative Finance},
  volume = {19},
  number = {4},
  pages = {549--570},
  year = {2019},
  publisher = {Taylor \& Francis},
}

@inproceedings{inproceedings,
  title = {A Comparison of Different Automated Market-Maker Strategies},
  author = {Janyl Jumadinova and Prithviraj Dasgupta},
  year = {2010},
}


@article{SEZER2018525,
  title = "Algorithmic financial trading with deep convolutional neural
           networks: Time series to image conversion approach",
  journal = "Applied Soft Computing",
  volume = "70",
  pages = "525 - 538",
  year = "2018",
  issn = "1568-4946",
  doi = "https://doi.org/10.1016/j.asoc.2018.04.024",
  url = "http://www.sciencedirect.com/science/article/pii/S1568494618302151",
  author = "Omer Berat Sezer and Ahmet Murat Ozbayoglu",
  keywords = "Algorithmic trading, Deep learning, Convolutional neural networks,
              Financial forecasting, Stock market, Technical analysis",
  abstract = "Computational intelligence techniques for financial trading
              systems have always been quite popular. In the last decade, deep
              learning models start getting more attention, especially within the
              image processing community. In this study, we propose a novel
              algorithmic trading model CNN-TA using a 2-D convolutional neural
              network based on image processing properties. In order to convert
              financial time series into 2-D images, 15 different technical
              indicators each with different parameter selections are utilized.
              Each indicator instance generates data for a 15 day period. As a
              result, 15 × 15 sized 2-D images are constructed. Each image is
              then labeled as Buy, Sell or Hold depending on the hills and
              valleys of the original time series. The results indicate that when
              compared with the Buy & Hold Strategy and other common trading
              systems over a long out-of-sample period, the trained model
              provides better results for stocks and ETFs.",
}

@inproceedings{inproceedings,
  author = {Das, Sanmay and Magdon-Ismail, Malik},
  year = {2008},
  month = {01},
  pages = {361-368},
  title = {Adapting to a Market Shock: Optimal Sequential Market-Making.},
  journal = {Advances in Neural Information Processing Systems 21 - Proceedings
             of the 2008 Conference},
}

@article{article,
  author = {Li, Xiaodong and Deng, Xiaotie and Zhu, Shanfeng and Wang, Feng and
            Xie, Haoran},
  year = {2014},
  month = {08},
  pages = {596-608},
  title = {An intelligent market making strategy in algorithmic trading},
  volume = {8},
  journal = {Frontiers of Computer Science},
  doi = {10.1007/s11704-014-3312-6},
}

@inproceedings{Jumadinova2010ACO,
  title = {A Comparison of Different Automated Market-Maker Strategies},
  author = {Janyl Jumadinova and Prithviraj Dasgupta},
  year = {2010},
}

@article{article,
  author = {Abernethy, J. and Kale, S.},
  year = {2013},
  month = {01},
  pages = {},
  title = {Adaptive market making via online learning},
  journal = {Advances in Neural Information Processing Systems},
}

@article{Guéant2013,
  author = {Gu{\'e}ant, Olivier and Lehalle, Charles-Albert and Fernandez-Tapia,
            Joaquin},
  title = {Dealing with the inventory risk: a solution to the market making
           problem},
  journal = {Mathematics and Financial Economics},
  year = {2013},
  volume = {7},
  number = {4},
  pages = {477-507},
  abstract = {Market makers continuously set bid and ask quotes for the stocks
              they have under consideration. Hence they face a complex
              optimization problem in which their return, based on the bid-ask
              spread they quote and the frequency at which they indeed provide
              liquidity, is challenged by the price risk they bear due to their
              inventory. In this paper, we consider a stochastic control problem
              similar to the one introduced by Ho and Stoll (J Fin Econ 9(1):
              47-73, 1981) and formalized mathematically by Avellaneda and
              Stoikov (Quant Fin 8(3):217-224, 2008). The market is modeled using
              a reference price Stfollowing a Brownian motion with standard
              deviation s, arrival rates of buy or sell liquidity-consuming
              orders depend on the distance to the reference price Stand a market
              maker maximizes the expected utility of its P\&L over a finite time
              horizon. We show that the Hamilton-Jacobi-Bellman equations
              associated to the stochastic optimal control problem can be
              transformed into a system of linear ordinary differential equations
              and we solve the market making problem under inventory constraints.
              We also shed light on the asymptotic behavior of the optimal quotes
              and propose closed-form approximations based on a spectral
              characterization of the optimal quotes.},
  issn = {1862-9660},
  doi = {10.1007/s11579-012-0087-0},
  url = {https://doi.org/10.1007/s11579-012-0087-0},
}

@unknown{unknown,
  author = {Kim, Adlar and Shelton, Christian},
  year = {2002},
  month = {01},
  pages = {},
  title = {Modeling Stock Order Flows and Learning Market-Making from Data},
  journal = {SSRN Electronic Journal},
  doi = {10.2139/ssrn.1171370},
}

@misc{benk2020unicorn,
  title = {How to find a unicorn: a novel model-free, unsupervised anomaly
           detection method for time series},
  author = {Zsigmond Benkő and Tamás Bábel and Zoltán Somogyvári},
  year = {2020},
  eprint = {2004.11468},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
}

@inbook{Xu_2004,
  title = {TIME SERIES FORECAST WITH ELMAN NEURAL NETWORKS AND GENETIC
           ALGORITHMS},
  ISSN = {2010-295X},
  url = {http://dx.doi.org/10.1142/9789812561794_0040},
  DOI = {10.1142/9789812561794_0040},
  booktitle = {Recent Advances in Simulated Evolution and Learning},
  publisher = {WORLD SCIENTIFIC},
  author = {Xu, LiXin and Dong, Zhao Yang and Tay, Arthur},
  year = {2004},
  month = aug,
  pages = {747–768},
}