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Does High-Frequency Trading Matter?

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Complex Systems Modeling and Simulation in Economics and Finance (CEF 2015)

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Abstract

Over the past few decades, financial markets have undergone remarkable reforms as a result of developments in computer technology and changing regulations, which have dramatically altered the structures and the properties of financial markets. The advances in technology have largely increased the speed of communication and trading. This has given birth to the development of algorithmic trading (AT) and high-frequency trading (HFT). The proliferation of AT and HFT has raised many issues regarding their impacts on the market. This paper proposes a framework characterized by an agent-based artificial stock market where market phenomena result from the interaction between many heterogeneous non-HFTs and HFTs. In comparison with the existing literature on the agent-based modeling of HFT, the traders in our model adopt a genetic programming (GP) learning algorithm. Since they are more adaptive and heuristic, they can form quite diverse trading strategies, rather than zero-intelligence strategies or pre-specified fundamentalist or chartist strategies. Based on this framework, this paper examines the effects of HFT on price discovery, market stability, volume, and allocative efficiency loss.

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Notes

  1. 1.

    In [15] and [19], they provide a rather detailed review and description regarding a number of different types of HAMs and show the importance of HAMs to the study of financial markets.

  2. 2.

    While the dividend’s stochastic process is unknown to traders, they base their derivation of the expected utility maximization on an Gaussian assumption. Under the CARA preference, the uncertainty of the dividends affects the process of expected utility maximization by being part of conditional variance.

  3. 3.

    For example, K = 1, 12, 52, 250 represents the number of trading periods measured by the units of a year, month, week and day, respectively.

  4. 4.

    Our framework can incorporate other types of expectation functions; however, different specifications require different information available to traders, and the choice depends on the selected degree of model validation. Unlike [1] and [20], we do not assume that traders have the information regarding the fundamental value of the stock, i.e. the price in the equilibrium of homogeneous rational expectations. The functional form used in this paper extends the designs employed in [6, 20], and [26]. It allows for the possibility of a bet on the Martingale hypothesis if fi,t = 0. For more details about applying the GP evolution to the function formation, we recommend the readers to refer to Appendix A in [27].

  5. 5.

    Similar designs were used in [1] and [20].

  6. 6.

    Although NEC, defining the time length of the periods between the occurrences of strategy evolution, is a parameter common to all traders, each trader has his first strategy evolution occurred randomly at one of the first NECperiod, so that traders are heterogeneous in asynchronous strategy learning. Because we focus on the effect of the speed difference between HFTs and LFTs, rather than the relative speed difference among HFTs nor how market dynamics drive the participants learn toward either kind of timing capability, on the market performance, a setting with endogenous evolution cycles though intriguing may blur, if any, the possible speed-induced effect by entangling the characteristics of the two trade-timing types.

  7. 7.

    One alternative is including informed HFTs so as to examine their impacts. However, in order to focus on the effects resulting from high-frequency trading alone, both LFTs and HFTs are uniformly assumed to be uninformed traders.

  8. 8.

    PT (VT) is the last transaction price (trading volume), \(P^{T}_{-l+1}\) (\(V^{T}_{-l+1}\)) is the last lth transaction price (trading volume). Bb,−1 (Ba,−1) refers to the current second best bid (ask), and Bb,−l+1 (Ba,−l+1) is the current lth best bid (ask).

  9. 9.

    Although the way we design the “jump-ahead” behavior of HFTs is not information-driven, it is neutral without subject interpretation of HFT behavior taking part in the assumptions. In light of the fact that the motives and the exact underlying triggering mechanisms of the vast variety of HFTs are still far from transparent, it would be prudent not to overly specify how HFTs would react to what information and thus have the results reflecting the correlation induced by ad hoc behavioral assumptions instead of the causality purely originated from the speed difference.

  10. 10.

    The relative speeds of information processing for different types of traders are exogenous. One may vary the speed setting to investigate the effect of technology innovation in terms of the improvement in traders’ reactions to market information.

  11. 11.

    The real characteristics of HFTs deserve further examination, in the hope of uncovering the causality between some specific trading behavior (not necessarily exclusive to HFTs) and market phenomena.

References

  1. Arthur, W. B., Holland, J., LeBaron, B., Palmer, R., & Tayler, P. (1997). Asset pricing under endogenous expectations in an artificial stock market. In W. B. Arthur, S. Durlauf, & D. Lane (Eds.), The economy as an evolving complex system II (pp. 15-44). Reading, MA: Addison-Wesley.

    Google Scholar 

  2. Bottazzi, G., Dosi, G., & Rebesco, I. (2005). Institutional architectures and behavioral ecologies in the dynamics of financial markets. Journal of Mathematical Economics, 41(1–2), 197–228.

    Article  Google Scholar 

  3. Brock, W. A., & Hommes. C. H. (1998). Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control, 22(8–9), 1235–1274.

    Article  Google Scholar 

  4. Carrion, A. (2013). Very fast money: High-frequency trading on the NASDAQ. Journal of Financial Markets, 16(4), 680–711.

    Article  Google Scholar 

  5. Cartea, Á., & Penalva, J. (2012). Where is the value in high frequency trading? Quarterly Journal of Finance, 2(3), 1250014-1–1250014-46.

    Google Scholar 

  6. Chen, S.-H., & Yeh, C.-H. (2001). Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market. Journal of Economic Dynamics and Control, 25(3–4), 363–393.

    Article  Google Scholar 

  7. Cliff, D., & Bruten, J. (1997). Zero is Not Enough: On the Lower Limit of Agent Intelligence for Continuous Double Auction Markets. HP Technical Report, HPL-97-141.

    Google Scholar 

  8. Gsell, M. (2008). Assessing the Impact of Algorithmic Trading on Markets: A Simulation Approach. Working paper.

    Google Scholar 

  9. Hagströmer, B., & Lars Nordén, L. (2013). The diversity of high-frequency traders. Journal of Financial Markets, 16(4), 741–770.

    Article  Google Scholar 

  10. Hanson, T. A. (2011). The Effects of High frequency Traders in a Simulated Market. Working paper.

    Google Scholar 

  11. Hasbrouck, J., & Saar, G. (2013). Low-latency trading. Journal of Financial Markets, 15(4), 646–679.

    Article  Google Scholar 

  12. He, X.-Z., & Li, Y. (2007). Power-law behaviour, heterogeneity, and trend chasing. Journal of Economic Dynamics and Control, 31(10), 3396–3426.

    Article  Google Scholar 

  13. Hendersgott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity? The Journal of Finance, 66(1), 1–33.

    Article  Google Scholar 

  14. Hill, B. M. (1975). A simple general approach to inference about the tail of a distribution. Annals of Statistics, 3(5), 1163–1174.

    Article  Google Scholar 

  15. Hommes, C. H. (2006) Heterogeneous agent models in economics and finance. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of computational economics (Vol. 2, Chap. 23, pp. 1109–1186). Amsterdam: Elsevier.

    Google Scholar 

  16. Jacob Leal, S., Napoletano, M., Roventini, A., & Fagiolo, G. (2014). Rock around the clock: An agent-based model of low- and high-frequency trading. In: Paper Presented at the 20th International Conference on Computing in Economics (CEF’2014).

    Google Scholar 

  17. Jarrow, R. A., & Protter, P. (2012). A dysfunctional role of high frequency trading in electronic markets. International Journal of Theoretical and Applied Finance, 15(3), 1250022-1–1250022-15.

    Article  Google Scholar 

  18. Kirman, A. P. (2006). Heterogeneity in economics. Journal of Economic Interaction and Coordination, 1(1), 89–117.

    Article  Google Scholar 

  19. LeBaron, B. (2006). Agent-based computational finance. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of computational economics (Vol. 2, Chap. 24, pp. 1187–1233). Amsterdam: Elsevier.

    Google Scholar 

  20. LeBaron, B., Arthur, W. B., & Palmer, R. (1999). Time series properties of an artificial stock market. Journal of Economic Dynamics and Control, 23(9–10), 1487–1516.

    Article  Google Scholar 

  21. Prewitt, M. (2012). High-frequency trading: should regulators do more? Michigan Telecommunications and Technology Law Review, 19(1), 1–31.

    Google Scholar 

  22. Sornette, D., & von der Becke, S. (2011). Crashes and High Frequency Trading. Swiss Finance Institute Research Paper No. 11–63.

    Google Scholar 

  23. Wah, E., & Wellman, M. P. (2013). Latency arbitrage, market fragmentation, and efficiency: A two-market model. In Proceedings of the Fourteenth ACM Conference on Electronic Commerce (pp. 855–872). New York: ACM.

    Chapter  Google Scholar 

  24. Walsh, T., Xiong, B., & Chung, C. (2012). The Impact of Algorithmic Trading in a Simulated Asset Market. Working paper.

    Google Scholar 

  25. Westerhoff, F. (2003). Speculative markets and the effectiveness of price limits. Journal of Economic Dynamics and Control, 28(3), 493–508.

    Article  Google Scholar 

  26. Yeh, C.-H. (2008). The effects of intelligence on price discovery and market efficiency. Journal of Economic Behavior and Organization, 68(3-4), 613–625.

    Article  Google Scholar 

  27. Yeh, C.-H., & Yang, C.-Y. (2010). Examining the effectiveness of price limits in an artificial stock market. Journal of Economic Dynamics and Control, 34(10), 2089–2108.

    Article  Google Scholar 

  28. Zhang, X. F. (2010). High-Frequency Trading, Stock Volatility, and Price Discovery. Working paper.

    Google Scholar 

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Acknowledgements

Research support from MOST Grant no. 103-2410-H-155-004-MY2 is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Department of Information Management, Yuan Ze University, Taoyuan, Chungli, Taiwan

    Chia-Hsuan Yeh

  2. Department of Computational and Data Sciences, College of Science, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA

    Chun-Yi Yang

Authors
  1. Chia-Hsuan Yeh
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Correspondence to Chia-Hsuan Yeh .

Editor information

Editors and Affiliations

  1. AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen

  2. AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan

    Ying-Fang Kao

  3. Institute of Management Studies, Goldsmiths, University of London, London, UK

    Ragupathy Venkatachalam

  4. Regional Development Research Center, Taiwan Institute of Economic Research, Taipei, Taiwan

    Ye-Rong Du

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Yeh, CH., Yang, CY. (2018). Does High-Frequency Trading Matter?. In: Chen, SH., Kao, YF., Venkatachalam, R., Du, YR. (eds) Complex Systems Modeling and Simulation in Economics and Finance. CEF 2015. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-99624-0_4

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