Skip to main content
SpringerLink
Log in

Market Movements at High Frequencies and Latency in Response Times

  • Conference paper
  • First Online:
Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1 (FTC 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 358))

Included in the following conference series:

  • 1126 Accesses

Abstract

Financial trading requires ever increasing data and compute resources principally to produce estimations of abnormal trading in the next unit interval within a complex environment involving an unlimited number of stakeholders dealing with a large number of financial assets. Machine-aided and AI assisted technologies are currently involved in the so-called high frequency trading (HFT). The HFT systems are used to compute unconditional stylised facts of price returns to investigate co-movements within and across the market and to apply trading rules for buying and selling before the next set of price returns are due - now increasingly in the next micro or pico seconds. Unconditional statistics cannot be applied to markets with variance breaks, so it is equally important to calculate conditional stylised facts, especially a measure of variance that can be related to volatility; such iterative calculations can overload both data and compute resources but are essential for trading. A key contributor to market volatility is the impact of the so-called market sentiment expressed by the market stakeholders - this slow speed signal has to be integrated with much faster moving price returns and this will impose a new set of constraints on data and compute resources. In this paper we focus on the latency in the computation of (a) unconditional and conditional stylised facts and the impact on trading decisions; (b) the latency which is inherent in aggregating high speed signals (price movement at intra-day/hour level) and the discrete but arbitrarily timed sentiment. We describe the use of cloud-computing services to alleviate problems of latency in (a) and (b) and compare the results with that of local desktop solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hou, Y.G., Li, S.: Volatility and skewness spillover between stock index and stock index futures markets during a crash period: New evidence from china. Int. Rev. Econ. Finance 66, 166–188 (2020). https://doi.org/10.1016/j.iref.2019.11.003

  2. Khurshid, A., Nicholas, D., Vanessa, L.: What is new? news media, general elections, sentiment, and named entities. In: Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011), pp. 80–88 (2011). https://www.aclweb.org/anthology/W11-3712.pdf

  3. Ahmad, K., Han, J.G., Hutson, E., Kearney, C., Liu, S.: Media-expressed negative tone and firm-level stock returns. J. Corporate Finance 37, 152–172 (2016). https://doi.org/10.1016/j.jcorpfin.2015.12.014

    Article  Google Scholar 

  4. Khurshid, A., Rogers, M.A.: Corpus linguistics and terminology extraction. In: Sue-Ellen, W., Gerhard, B. (eds.) Handbook of Terminology Management, vol. 2, chapter 8.4.1, pp. 725–760. John Benjamins Publishing Company, Amsterdam and Philadelphia (2001)

    Google Scholar 

  5. Andersen, T.G., Bollerslev, T., Das, A.: Variance-ratio statistics and high-frequency data: testing for changes in intraday volatility patterns. J. Finance (Wiley-Blackwell) 56(1), 305–327 (2001). https://doi.org/10.1111/0022-1082.00326

  6. Andersen, T.G., Bondarenko, O.: Vpin and the flash crash. J. Financial Markets 17, 1–46 (2014). https://doi.org/10.1016/j.finmar.2013.05.005

    Article  Google Scholar 

  7. Yacine, A.-S., Celso, B.: High frequency traders and the price process. J. Econ. 217, 20–45 (2020). https://doi.org/10.1016/j.jeconom.2019.11.005

  8. Tanmay, B.: Estimation of clusters based on decision latency in high frequency trading. Master’s thesis, Trinity College, Dublin (2020). https://www.scss.tcd.ie/publications/theses/diss/2020/TCD-SCSS-DISSERTATION-2020-061.pdf

  9. Matthew, B., Jonathan, B., Björn, H., Andrei, K.: Risk and return in high-frequency trading. J. Financ. Quant. Anal. 54, 993–1024 (2018). https://doi.org/10.1017/S0022109018001096

  10. Bruno, B., Thierry, F., Sophie, M.: Equilibrium fast trading. J. Financ. Econ. 116, 292–313 (2015). https://doi.org/10.1016/j.jfineco.2015.03.004

  11. Tim, B.: Generalized autoregressive conditional heteroskedasticity. J. Econ. 31, 307–327 (1986). https://doi.org/10.1016/0304-4076(86)90063-1

  12. Jonathan, B., Kevin, R.: Prices and price limits. SSRN Electronic Journal (2015). https://doi.org/10.2139/ssrn.2667104

  13. Chan, K., Chan, K.C., Karolyi, G.A.: Intraday volatility in the stock index and stock index futures markets. Review Financial Studies, vol. 4, no. 4 (1991). https://doi.org/10.1093/rfs/4.4.657

  14. Joseph, D.: High frequency trading. Salem Press Encyclopedia (2019)

    Google Scholar 

  15. Engle, R.F.: Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica 50, 987 (1982). https://doi.org/10.2307/1912773

  16. Andrey, F., Yu, H., Shashi, K.: Statistical significance of the netflix challenge. Stat. Sci. 27(2), 202–231 (2012). https://doi.org/10.1214/11-STS368

  17. Figini, S., Giudici, P.: Credit risk assessment with bayesian model averaging. Commun. Stat. Theory Methods 46(19), 9507–9517 (2017). https://doi.org/10.1080/03610926.2016.1212070

    Article  MathSciNet  MATH  Google Scholar 

  18. Thierry, F., Johan, H., Ioanid, R.: News trading and speed. J. Finance 71, 335–382 (2016). https://doi.org/10.1111/jofi.12302

  19. Gabrys, R., Hormann, S., Kokoszka, P.: Monitoring the intraday volatility pattern. J. Time Ser. Econ. 5(2), 87–116 (2013). https://doi.org/10.1515/jtse-2012-0006

    Article  MathSciNet  MATH  Google Scholar 

  20. Michael, A., Goldstein, P., Frank, C.G.: Computerized and high-frequency trading. Financ. Rev. 49, 177–202 (2014). https://doi.org/10.1111/fire.12031

  21. Joel, H., Gideon, S.: Low-latency trading. J. Financ. Market. 16, 646–679 (2013). https://doi.org/10.1016/j.finmar.2013.05.003

  22. Hastie, T.J., Tibshirani, R.J., Friedman, J.H.: The elements of statistical learning: data mining, inference, and prediction. Springer (2011). https://dlib.hust.edu.vn/handle/HUST/19661

  23. Peter, H.: A dynamic limit order market with fast and slow traders. J. Financ. Econ. 113, 156–169 (2014). https://doi.org/10.1016/j.jfineco.2014.04.002

  24. Hofmarcher, P., Grün, B.: Bayesian model averaging Macroeconomic Forecasting in the Era of Big Data. Springer (2020). https://doi.org/10.1007/978-3-030-31150-6

  25. Paul, H., Stefan, K., Bettina, G., Michael, S., Kurt, H.: Model uncertainty and aggregated default probabilities: new evidence from austria. Appl. Econ. 46, 871–879 (2014). https://doi.org/10.1080/00036846.2013.859378

  26. Gregory, L., Anthony, A., Joseph, G.: Information transmission between financial markets in chicago and new york. Financ. Rev. 49, 283–312 (2014). https://doi.org/10.1111/fire.12036

  27. MacDonald, A.: Lse leads race for quicker trades. The Wall Street Journal Europe, June 19 (2007)

    Google Scholar 

  28. Opschoor, A., Taylor, N., van der Wel, M., van Dijk, D.: Order flow and volatility: an empirical investigation. J. Empirical Finance 28, 185–201 (2014). https://doi.org/10.1016/j.jempfin.2014.07.002

    Article  Google Scholar 

  29. Poudineh, R., Jamasb, T.: The case of the Norwegian electricity distribution networks. Energy Economics, Determinants of Investment Under Incentive Regulation (2014). https://doi.org/10.17863/CAM.5750

  30. Rathinasamy, M., Adamowski, J., Khosa, R.: Multiscale streamflow forecasting using a new bayesian model average based ensemble multi-wavelet volterra nonlinear method. J. Hydrol. 507, 186–200 (2013). https://doi.org/10.1016/j.jhydrol.2013.09.025

    Article  Google Scholar 

  31. Carl, S., Varian, H.R.: Information rules : a strategic guide to the network economy. Harvard Business School Press (2013). https://kcmit.edu.np/Uploads/information-rulesLarge20210211052224.pdf

  32. Sneha, S.: Impact of low-latency architecture on high-frequency big data. Master’s thesis, Trinity College, Dublin (2020). https://www.scss.tcd.ie/publications/theses/diss/2020/TCD-SCSS-DISSERTATION-2020-077.pdf

  33. Steel, M.F.: Bayesian model averaging and forecasting. Bull. EU US Inflation Macroeconomic Anal. 200, 30–41 (2011). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.377.943&rep=rep1&type=pdf

  34. Stone, P.J., Dunphy, D.C., Smith, M.S., Ogilvie, D.M.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge (1966). https://psycnet.apa.org/record/1967-04539-000

  35. Sun, M.: Quantitative methods in high-frequency financial econometrics: Modeling univariate and multivariate time series (2007). http://www.gcma-ev.de/PDFs/JT2007_Vortraege/JT2007_SunWei.pdf

  36. Stephen, J.T.: Asset price dynamics, volatility, and prediction. Princeton University Press (2011). https://doi.org/10.1515/9781400839254

  37. Adam, T.: Can methods of conventional sentimental analysis work for high frequency data? Master’s thesis, Trinity College, Dublin (2021). https://doi.org/10.13140/RG.2.2.31463.78248

  38. Böhmelt, T., Bove, V.: Forecasting military expenditure. Research & Politics, vol. 1 (2014). https://doi.org/10.1177/2053168014535909

  39. Varian, H.R.: Big data: New tricks for econometrics. The Journal of Economic Perspectives, pp. 3–27 (2014). https://doi.org/10.1257/jep.28.2.3

Download references

Author information

Authors and Affiliations

  1. Trinity College Dublin, Dublin, Ireland

    Jiachuan Wang, Tanmay Bagla, Sneha Srivastava, Adam Teehan & Khurshid Ahmad

Authors
  1. Jiachuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tanmay Bagla
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sneha Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adam Teehan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Khurshid Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khurshid Ahmad .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, J., Bagla, T., Srivastava, S., Teehan, A., Ahmad, K. (2022). Market Movements at High Frequencies and Latency in Response Times. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1. FTC 2021. Lecture Notes in Networks and Systems, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-030-89906-6_60

Download citation

Publish with us

Policies and ethics

Search

Navigation