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Review

Automated trading systems statistical and machine learning methods and hardware implementation: a survey

, , , &
Pages 132-144 | Received 30 Mar 2018, Accepted 21 Jun 2018, Published online: 12 Jul 2018

References

  • Acharya, A., and N. S. Sidnal. 2016. “High Frequency Trading with Complex Event Processing.” In High Performance Computing Workshops (HiPCW), 2016 IEEE 23rd International Conference on, 39–42. IEEE.
  • Achelis, S. B. 2001. Technical Analysis from A to Z. New York: McGraw Hill.
  • Angel, J. J., and M. Douglas. 2013. “Fairness in Financial Markets: The Case of Highfrequency Trading.” Journal of Business Ethics 112 (4): 585–595. doi:10.1007/s10551-012-1559-0.
  • Barberis, N., and R. Thaler. 2003. “A Survey of Behavioral Finance.” Handbook of the Economics of Finance 1: 1053–1128.
  • Bloomberg.com. n.d.. “Trading Fortunes Depend on a Mysterious Antenna in an Empty Field.” https://www.bloomberg.com/news/articles/2017-05-12/mysterious-antennas-outside-cme-reveal-traders-furious-land-war
  • Baron, M. D., Brogaard, J., Hagströmer, B., and Kirilenko, A. A. Forthcoming. "Risk and Return in High-Frequency Trading." (November 14, 2017). Journal of Financial and Quantitative Analysis. Available at SSRN: https://ssrn.com/abstract=2433118 or http://dx.doi.org/10.2139/ssrn.2433118
  • Bowen, D., M. C. Hutchinson, and O. Niall. 2010. “High-Frequency Equity Pairs Trading: Transaction Costs, Speed of Execution, and Patterns in Returns.” The Journal of Trading 5 (3): 31–38. doi:10.3905/jot.2010.5.3.031.
  • Brasileiro, R. C., V. L. F. Souza, B. J. T. Fernandes, and A. L. I. Oliveira. 2013. “Automatic Method for Stock Trading Combining Technical Analysis and the Artificial Bee Colony Algorithm.” In Evolutionary Computation (CEC), 2013 IEEE Congress on, 1810–1817. IEEE.
  • Brogaard, Jonathan. 2010. “High Frequency Trading and Its Impact on Market Quality.” Northwestern University Kellogg School of Management Working Paper 66.
  • Broussard, J. P., and M. Vaihekoski. 2012. “Profitability of Pairs Trading Strategy in an Illiquid Market with Multiple Share Classes.” Journal of International Financial Markets, Institutions and Money 22 (5): 1188–1201. doi:10.1016/j.intfin.2012.06.002.
  • Budish, E., P. Cramton, and J. Shim. 2015. “The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response.” The Quarterly Journal of Economics 130 (4): 1547–1621. doi:10.1093/qje/qjv027.
  • Cai, Q., D. Zhang, W. Zheng, and S. C. H. Leung. 2015. “A New Fuzzy Time Series Forecasting Model Combined with Ant Colony Optimization and Auto-Regression.” KnowledgeBased Systems 74: 61–68.
  • Chaboud, A. P., B. Chiquoine, E. Hjalmarsson, and C. Vega. 2014. “Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market.” The Journal of Finance 69 (5): 2045–2084. doi:10.1111/jofi.12186.
  • Chatrath, A., H. Miao, S. Ramchander, and S. Villupuram. 2014. “Currency Jumps, Cojumps and the Role of Macro News.” Journal of International Money and Finance 40: 42–62. doi:10.1016/j.jimonfin.2013.08.018.
  • Chen, X., and Y. Fang. 2013. “Enterprise Systems in Financial Sector-An Application in Precious Metal Trading Forecasting.” Enterprise Information Systems 7 (4): 558–568. doi:10.1080/17517575.2012.698022.
  • Chlistalla, M. 2012. “High-Frequency Trading: Better than Its Reputation? Research Briefing, Deutsche Bank Research.”
  • Cont, R., and J.-P. Bouchaud. 2000. “Herd Behavior and Aggregate Fluctuations Infinancial Markets.” Macroeconomic Dynamics 4 (2): 170–196. doi:10.1017/S1365100500015029.
  • Cont, R., and A. Kukanov. 2017. “Optimal Order Placement in Limit Order Markets.” Quantitative Finance 17 (1): 21–39. doi:10.1080/14697688.2016.1190030.
  • de Brito, R. F. B., and A. L. I. Oliveira. 2014. “Sliding Window-Based Analysis of Multiple Foreign Exchange Trading Systems by Using Soft Computing Techniques.” In Neural Networks (IJCNN), 2014 International Joint Conference on, 4251–4258. IEEE.
  • Do, B., and R. Faff. 2010. “Does Simple Pairs Trading Still Work?” Financial Analysts Journal 66 (4): 83–95. doi:10.2469/faj.v66.n4.1.
  • Do, B., and R. Faff. 2012. “Are Pairs Trading Profits Robust to Trading Costs?” Journal of Financial Research 35 (2): 261–287. doi:10.1111/jfir.2012.35.issue-2.
  • Feuerriegel, S., and R. Fehrer. 2016. “Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures.” In ECIS, Research-in.
  • Feuerriegel, S., and H. Prendinger. 2016. “News-Based Trading Strategies.” Decision Support Systems 90: 65–74. doi:10.1016/j.dss.2016.06.020.
  • Fixprotocol.org. n.d. “FIX Adapted for STreaming(SM) FAST Protocol(SM).” http://www.fixprotocol.org/fast
  • Gatev, E., W. N. Goetzmann, and K. Geert Rouwenhorst. 2006. “Pairs Trading: Performance of a Relative-Value Arbitrage Rule.” The Review of Financial Studies 19 (3): 797–827. doi:10.1093/rfs/hhj020.
  • Grossman, S. J. 1988. “Program Trading and Market Volatility: A Report on Interday Relationships.” Financial Analysts Journal 44 (4): 18–28. doi:10.2469/faj.v44.n4.18.
  • Groth, S. S., and J. Muntermann. 2011. “An Intraday Market Risk Management Approach Based on Textual Analysis.” Decision Support Systems 50 (4): 680–691. doi:10.1016/j.dss.2010.08.019.
  • Guilbaud, F., and H. Pham. 2015. “Optimal High-Frequency Trading in a Pro Rata Microstructure with Predictive Information.” Mathematical Finance 25 (3): 545–575. doi:10.1111/mafi.2015.25.issue-3.
  • Hafezi, R., J. Shahrabi, and E. Hadavandi. 2015. “A Bat-Neural Network Multiagent System (BNNMAS) for Stock Price Prediction: Case Study of DAX Stock Price.” Applied Soft Computing 29: 196–210. doi:10.1016/j.asoc.2014.12.028.
  • Hagenau, M., M. Liebmann, and D. Neumann. 2013. “Automated News Reading: Stock Price Prediction Based on Financial News Using Context-Capturing Features.” Decision Support Systems 55 (3): 685–697. doi:10.1016/j.dss.2013.02.006.
  • Hasbrouck, J., and G. Saar. 2013. “Low-Latency Trading.” Journal of Financial Markets 16 (4): 646–679. doi:10.1016/j.finmar.2013.05.003.
  • Hendershott, T., C. M. Jones, and A. J. Menkveld. 2011. “Does Algorithmic Trading Improve Liquidity?” The Journal of Finance 66 (1): 1–33. doi:10.1111/j.1540-6261.2010.01624.x.
  • Herrmann, F., G. Perin, J. P. Jos´e de Freitas, R. Bertagnolli, and J. B. D. S. Martins. 2009. “An UDP/IP Network Stack in FPGA.” Electronics, Circuits, and Systems (ICECS).
  • Hsieh, T.-J., H.-F. Hsiao, and W.-C. Yeh. 2011. “Forecasting Stock Markets Using Wavelet Transforms and Recurrent Neural Networks: An Integrated System Based on Artificial Bee Colony Algorithm.” Applied Soft Computing 11 (2): 2510–2525. doi:10.1016/j.asoc.2010.09.007.
  • Hu, Y., B. Feng, X. Zhang, E. W. T. Ngai, and M. Liu. 2015. “Stock Trading Rule Discovery with an Evolutionary Trend following Model.” Expert Systems with Applications 42 (1): 212–222. doi:10.1016/j.eswa.2014.07.059.
  • Huck, N., and K. Afawubo. 2015. “Pairs Trading and Selection Methods: Is Cointegration Superior?” Applied Economics 47 (6): 599–613. doi:10.1080/00036846.2014.975417.
  • Jiang, Y., X. Lida, H. Wang, and H. Wang. 2009. “Influencing Factors for Predicting Financial Performance Based on Genetic Algorithms.” Systems Research and Behavioral Science 26 (6): 661–673. doi:10.1002/sres.967.
  • Jones, C. M. 2013. “What Do We Know about High-Frequency Trading?” doi:10.2139/ssrn.2236201.
  • Kaya, O., J. Schildbach, and D. B. Ag. 2016. “High-Frequency Trading.” https://www.dbresearch.com/PROD/RPS_ENPROD/PROD0000000000454703/Research_Briefing%3A_High-frequency_trading.pdf
  • Kazemian, S., S. Zhao, and G. Penn. 2014. “Evaluating Sentiment Analysis Evaluation: A Case Study in Securities Trading.” In WASSA ACL, 119–127. doi:10.1016/j.jecp.2013.08.005.
  • Kearney, C., and S. Liu. 2014. “Textual Sentiment in Finance: A Survey of Methods and Models.” International Review of Financial Analysis 33: 171–185. doi:10.1016/j.irfa.2014.02.006.
  • Kearns, M., and Y. Nevmyvaka. 2013. “Machine Learning for Market Microstructure and High Frequency Trading.” In High Frequency Trading: New Realities for Traders, Markets and Regulators. Risk Books, edited by David Easley, Marcos Lopez de Prado, and Maureen O’Hara . http://riskbooks.com/book-high-frequency-trading
  • Kirilenko, A., A. S. Kyle, M. Samadi, and T. Tuzun. 2011. “The Flash Crash: The Impact of High Frequency Trading on an Electronic Market.” Available at SSRN 1686004.doi:10.2139/ssrn.1686004.
  • Krauss, C. 2017. “Statistical Arbitrage Pairs Trading Strategies: Review and Outlook.” Journal of Economic Surveys 31 (2): 513–545. doi:10.1111/joes.2017.31.issue-2.
  • Kwon, Y.-K., and B.-R. Moon. 2007. “A Hybrid Neurogenetic Approach for Stock Forecasting.” IEEE Transactions on Neural Networks 18 (3): 851–864. doi:10.1109/TNN.2007.891629.
  • Lahaye, J., S. Laurent, and C. J. Neely. 2011. “Jumps, Cojumps and Macro Announcements.” Journal of Applied Econometrics 26 (6): 893–921. doi:10.1002/jae.1149.
  • Leber, C., B. Geib, and H. Litz. 2011. “High Frequency Trading Acceleration Using FPGAs.” In Field Programmable Logic and Applications (FPL), 2011 International Conference on, 317–322. IEEE.
  • Lev, B., and S. R. Thiagarajan. 1993. “Fundamental Information Analysis.” Journal of Accounting Research 190–215. doi:10.2307/2491270.
  • Li, F. 2010. “The Information Content of Forward-Looking Statements in Corporate filingsA Na¨Ive Bayesian Machine Learning Approach.” Journal of Accounting Research 48 (5): 1049–1102. doi:10.1111/j.1475-679X.2010.00382.x.
  • Liew, R. Q., and W. Yuan. 2013. “Pairs Trading: A Copula Approach.” Journal of Derivatives & Hedge Funds 19 (1): 12–30. doi:10.1057/jdhf.2013.1.
  • Lo, A. W., H. Mamaysky, and J. Wang. 2000. “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation.” The Journal of Finance 55 (4): 1705–1765. doi:10.1111/0022-1082.00265.
  • Lobo, N., V. Malik, C. Donnally, S. Jahne, and H. Jhaveri. 2012. "Evaluating the Latency Impact of IPv6 on a High Frequency Trading System." Master degree, University of Colorado.
  • Lockwood, J. W., A. Gupte, N. Mehta, M. Blott, T. English, and K. Vissers. 2012. “A Low-Latency Library in FPGA Hardware for High-Frequency Trading (HFT).” In High-Performance Interconnects (HOTI), 2012 IEEE 20th Annual Symposium on, 9–16. IEEE.
  • Lux, T. 1995. “Herd Behaviour, Bubbles and Crashes.” The Economic Journal 881–896. doi:10.2307/2235156.
  • Manning, Christopher D, Hinrich Sch¨Utze. 1999. Foundations of Statistical Natural Language Processing. Vol. 999. London: MIT Press.
  • Miao, G. J. 2014. “High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach.” International Journal of Economics and Finance 6 (3): 96. doi:10.5539/ijef.v6n3p96.
  • Morris, G. W., D. B. Thomas, and W. Luk. 2009. “FPGA Accelerated Low-Latency Market Data Feed Processing.” In High Performance Interconnects, 2009. HOTI 2009. 17th IEEE Symposium on, 83–89. IEEE.
  • Nath, P. 2003. “High Frequency Pairs Trading with Us Treasury Securities: Risks and Rewards for Hedge Funds.” (November 2003).  Available at SSRN: https://ssrn.com/abstract=565441 or https://doi.org/10.2139/ssrn.565441
  • Nuij, W., V. Milea, F. Hogenboom, F. Frasincar, and U. Kaymak. 2014. “An Automated Framework for Incorporating News into Stock Trading Strategies.” IEEE Transactions on Knowledge and Data Engineering 26 (4): 823–835. doi:10.1109/TKDE.2013.133.
  • Park, C.-H., and S. H. Irwin. 2007. “What Do We Know about the Profitability of Technical Analysis?” Journal of Economic Surveys 21 (4): 786–826. doi:10.1111/j.1467-6419.2007.00519.x.
  • Park, J., and I. W. Sandberg. 1991. “Universal Approximation Using Radial-Basisfunction Networks.” Neural Computation 3 (2): 246–257. doi:10.1162/neco.1991.3.2.246.
  • Preis, T., H. S. Moat, and H. E. Stanley. 2013. “Quantifying Trading Behavior in Financial Markets Using Google Trends.” Scientific reports 3: srep01684. doi:10.1038/srep01684.
  • Rad, H., R. K. Y. Low, and R. Faff. 2016. “The Profitability of Pairs Trading Strategies: Distance, Cointegration and Copula Methods.” Quantitative Finance 16 (10): 1541–1558. doi:10.1080/14697688.2016.1164337.
  • Ranco, G., D. Aleksovski, G. Caldarelli, M. Grˇcar, and I. Mozetiˇc. 2015. “The Effects of Twitter Sentiment on Stock Price Returns.” PloS One 10 (9): e0138441. doi:10.1371/journal.pone.0138441.
  • Salvatierra, I. D. L., and A. J. Patton. 2015. “Dynamic Copula Models and High Frequency Data.” Journal of Empirical Finance 30: 120–135. doi:10.1016/j.jempfin.2014.11.008.
  • Scholtus, M., D. van Dijk, and B. Frijns. 2014. “Speed, Algorithmic Trading, and Market Quality around Macroeconomic News Announcements.” Journal of Banking & Finance 38: 89–105. doi:10.1016/j.jbankfin.2013.09.016.
  • Schumaker, R. P., and H. Chen. 2009. “Textual Analysis of Stock Market Prediction Using Breaking Financial News: The AZFin Text System.” ACM Transactions on Information Systems (TOIS) 27 (2): 12. doi:10.1145/1462198.1462204.
  • Shi, S. M., L. Da Xu, and B. Liu. 1999. “Improving the Accuracy of Nonlinear Combined Forecasting Using Neural Networks.” Expert Systems with Applications 16 (1): 49–54. doi:10.1016/S0957-4174(98)00030-X.
  • Shi, S., L. D. Xu, and B. Liu. 1995. “Artificial Neural Network for Combining Forecasts.” Journal of Systems Engineering and Electronics 6 (2): 58–64.
  • Shi, S., L. D. Xu, and B. Liu. 1996. “Applications of Artificial Neural Networks to the Nonlinear Combination of Forecasts.” Expert Systems 13 (3): 195–201. doi:10.1111/j.1468-0394.1996.tb00119.x.
  • Shynkevich, Y., T. Martin McGinnity, S. Coleman, and A. Belatreche. 2015. “Predicting Stock Price Movements Based on Different Categories of News Articles.” In Computational Intelligence, 2015 IEEE Symposium Series on, 703–710. IEEE.
  • Subramoni, H., F. Petrini, V. Agarwal, and D. Pasetto. 2010. “Streaming, Low-Latency Communication in On-Line Trading Systems.” In Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on, 1–8. IEEE.
  • Tang, Q., M. Su, L. Jiang, J. Yang, and X. Bai. 2016. “A Scalable Architecture for Low-Latency Market-Data Processing on FPGA.” In Computers and Communication (ISCC), 2016 IEEE Symposium on, 597–603. IEEE.
  • Thawornwong, S., and D. Enke. 2004. “The Adaptive Selection of Financial and Economic Variables for Use with Artificial Neural Networks.” Neurocomputing 56: 205–232. doi:10.1016/j.neucom.2003.05.001.
  • Vidyamurthy, G. 2004. Pairs Trading: Quantitative Methods and Analysis. Vol. 217. New York: John Wiley & Sons.
  • Wang, F., L. H. Philip, and D. W. Cheung. 2014. “Combining Technical Trading Rules Using Particle Swarm Optimization.” Expert Systems with Applications 41 (6): 3016–3026. doi:10.1016/j.eswa.2013.10.032.
  • Wilson, S. W. 1995. “Classifier Fitness Based on Accuracy.” Evolutionary Computation 3 (2): 149–175. doi:10.1162/evco.1995.3.2.149.
  • Yu, H., G. V. Nartea, C. Gan, and L. J. Yao. 2013. “Predictive Ability and Profitability of Simple Technical Trading Rules: Recent Evidence from Southeast Asian Stock Markets.” International Review of Economics & Finance 25: 356–371. doi:10.1016/j.iref.2012.07.016.
  • Zhu, X., H. Wang, X. Li, and L. Huaizu. 2008. “Predicting Stock Index Increments by Neural Networks: The Role of Trading Volume under Different Horizons.” Expert Systems with Applications 34 (4): 3043–3054. doi:10.1016/j.eswa.2007.06.023.