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Articles

Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming

References

  • Aitken, M.; Cumming, D.; and Zhan, F. Trade size, high-frequency trading, and colocation around the world. European Journal of Finance, 2014, DOI: 10.1080/1351847X.2014.917119.
  • Baron, M.; Brogaard, J.; and Kirilenko, A. The trading profits of high frequency traders. SSRN Electronic Journal, 2012, Unpublished manuscript.
  • Biais, B.; Foucault, T.; and Moinas, S. Equilibrium fast trading. Journal of Financial Economics, 116, 2 (2015), 292–313.
  • Brogaard, J.A. High frequency trading and its impact on market quality. Kellogg School of Management Working Paper (2010).
  • Brunnermeier, M.K.; and Pedersen, L.H. Predatory trading. Journal of Finance, 60, 4 (2005), 1825–1863.
  • Budish, E.; Cramton, P.; and Shim, J. The high-frequency trading arms race: Frequent batch auctions as a market design response. Quarterly Journal of Economics, 130, 4 (2015), 1547–1621.
  • Chae, J.; Khil, J.; and Lee, E.J. Who makes markets? Liquidity providers versus algorithmic traders. Journal of Futures Markets, 33, 5 (2013), 397–420.
  • Chatterjee, S.; Laudato, M.; and Lynch, L.A. Genetic algorithms and their statistical applications: An introduction. Computational Statistics and Data Analysis, 22, 6 (1996), 633–651.
  • Chen, Y.; and Wang, X. A hybrid stock trading system using genetic network programming and mean conditional value-at-risk. European Journal of Operational Research, 240, 3 (2015), 861–871.
  • Connolly, R.A. An examination of the robustness of the weekend effect. Journal of Financial and Quantitative Analysis, 24, 2 (1989), 133–169.
  • Delaney, L. An examination of the optimal timing strategy for a slow trader investing in a high frequency trading technology. City University of London Working Paper (2015).
  • Diebold, F.X.; and Mariano, R.S. Comparing predictive accuracy. Journal of Business and Economic Statistics, 13 (1995), 253–265.
  • Dunis, C.L., Laws, J.; and Karathanasopoulos, A. GP algorithm versus hybrid and mixed neural networks. European Journal of Finance, 19, 3 (2013), 180–205.
  • Egginton, J.F.; Van Ness, B.F.; and Van Ness, R.A. Quote stuffing. Financial Management, 45, 3 (2016), 583–608.
  • Felker, T.; Mazalov, V.; and Watt, S.M. Distance-based high-frequency trading. Procedia Computer Science, 29(2014), 2055–2064.
  • Fishe, R.P.H.; Haynes, R.; and Onur, E. Anticipatory traders and trading speed. Journal of Financial and Quantitative Analysis (in press).
  • Folger, J. Beginners guide to E-Mini futures contracts: E-Mini characteristics, 2015. http://www.investopedia.com/university/how-to-trade-e-mini-futures-contracts/( Accessed on April 20 2015).
  • Foucault, T.; Kozhan, R.; and Tham, W.W. Toxic arbitrage. Review of Financial Studies, 30, 4 (2017), 1053–1094.
  • Frino, A.; Mollica, V.; and Webb, R.I. The impact of co‐location of securities exchanges and traders computer servers on market liquidity. Journal of Futures Markets, 34, 1 (2014), 20–33.
  • Goldstein, M.A.; Kumar, P.; and Graves, F.C. Computerized and high‐frequency trading. Financial Review, 49, 2 (2014), 177–202.
  • Han, J.; Khapko, M.; and Kyle, A.S. Liquidity with high-frequency market making. Swedish House of Finance Research Paper No. 14-06 (2014).
  • Hasbrouck, J.; and Saar, G. Technology and liquidity provision: The blurring of traditional definitions. Journal of Financial Markets, 12, 2 (2009), 143–172.
  • Hasbrouck, J.; and Sofianos, G. The trades of market makers: An empirical analysis of NYSE specialists. Journal of Finance, 48, 5 (1993), 1565–1593.
  • Hirschey, N. Do high-frequency traders anticipate buying and selling pressure? London Business School Working Paper (2017).
  • Jarnecic, E.; and Snape, M. The provision of liquidity by high‐frequency participants. Financial Review, 49, 2 (2014), 371–394.
  • Karlin, S.; and Taylor, H. M. A First Course in Stochastic Processes. 2d ed. New York: Academic Press, 1975.
  • Kumaresan, M.; and Krejić, N. Optimal trading of algorithmic orders in a liquidity fragmented market place. Annals of Operations Research, 229, 1 (2015), 521–540.
  • Leal, S. J.; Napoletano, M.; Roventini, A.; and Fagiolo, G. Rock around the clock: An agent-based model of low- and high-frequency trading. Journal of Evolutionary Economics, 26, 1 (2016), 49–76.
  • Lensberg, T.; Eilifsen, A.; and McKee, T. E. Bankruptcy theory development and classification via genetic programming. European Journal of Operational Research, 169, 2 (2006), 677–697.
  • Li, W. High frequency trading with speed hierarchies. John Hopkins University Working Paper (2017).
  • Manahov, V. The rise of the machines in commodities markets: New evidence obtained using Strongly Typed Genetic Programming. Annals of Operations Research, 260, 1 (2018), 321–352.
  • Meade, N. A comparison of the accuracy of short term foreign exchange forecasting methods. International Journal of Forecasting, 18, 1 (2002), 67–83.
  • Mendes, L.; Godinho, P.; and Dias, J. A Forex trading system based on a genetic algorithm. Journal of Heuristics, 18, 4 (2012), 627–656.
  • Menkveld, A. J.; and Zoican, M. A. Need for speed? Exchange latency and liquidity. Review of Financial Studies, 30, 4 (2017), 1188–1228.
  • Montana, D. J. Strongly typed genetic programming. Evolutionary Computation, 3, 2 (1995), 199–230.
  • Narang, R. K. Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading. Hoboken, NJ: Wiley, 2013.
  • Östermark, R. Genetic modelling of multivariate EGARCH processes: Evidence on the international asset return signal response mechanism. Computational Statistics and Data Analysis, 38(2001), 71–93.
  • Paddrik, M.; Hayes, R.; Todd, A.; Yang, S.; Beling, P.; and Scherer, W. An agent based model of the E-Mini S&P 500 applied to flash crash analysis. Computational Intelligence for Financial Engineering and Economics (2012), 1–8, INSPEC Accession Number:13059425.
  • Sermpinis, G.; Stasinakis, C.; Theofilatos, K.; and Karathanasopoulos, A. Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations. European Journal of Operational Research, 247, 3 (2015), 831–846.
  • Sun, E. W.; Kruse, T.; and Yu, M.-T. High frequency trading, liquidity, and execution cost. Annals of Operations Research, 223, 1 (2014), 403–432.

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