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The Journal of Financial Data Science

The Journal of Financial Data Science

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Extracting Signals from High-Frequency Trading with Digital Signal Processing Tools

Jung Heon Song, Marcos López de Prado, Horst D. Simon and Kesheng Wu
The Journal of Financial Data Science Fall 2019, 1 (4) 124-138; DOI: https://doi.org/10.3905/jfds.2019.1.4.124
Jung Heon Song
is an applied math doctoral candidate at the University of Minnesota in Minneapolis, MN. songx762@umn.edu
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Marcos López de Prado
is CIO at True Positive Technologies, in New York, NY and professor of practice at Cornell University, in Ithaca, NY. mldp@truepositive.com
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Horst D. Simon
is a deputy laboratory director for Research and Chief Research Officer (CRO) at Lawrence Berkeley National Laboratory in Berkeley, CA. hdsimon@lbl.gov
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Kesheng Wu
is a staff computer scientist at Lawrence Berkeley National Laboratory in Berkeley, CA. kwu@lbl.gov
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Abstract

As algorithms replace a growing number of tasks performed by humans in the markets, there have been growing concerns about an increased likelihood of cascading events, similar to the Flash Crash of May 6, 2010. To address these concerns, researchers have employed a number of scientific data analysis tools to monitor the risk of such cascading events. As an example, the authors of this article investigate the natural gas (NG) futures market in the frequency domain and the interaction between weather forecasts and NG price data. They observe that Fourier components with high frequencies have become more prominent in recent years and are much stronger than could be expected from an analytical model of the market. Additionally, a significant amount of trading activity occurs in the first few seconds of every minute, which is a tell-tale sign of time-based algorithmic trading. To illustrate the potential of cascading events, the authors further study how weather forecasts drive NG prices and show that, after separating the time series by season to account for the different mechanisms that relate temperature to NG price, the temperature forecast is indeed cointegrated with NG price. They also show that the variations in temperature forecasts contribute to a significant percentage of the average daily price fluctuations, which confirms the possibility that a forecast error could significantly affect the price of NG futures.

TOPICS: Statistical methods, simulations, big data/machine learning

Key Findings

  • • High-frequency components in the trading data are stronger than expected from a model assuming uniform trading during market hours.

  • • The dominance of the high-frequency components have been increasing over the years.

  • • Relatively small changes in temperature could create a large price fluctuation in natural gas futures contracts.

  • © 2019 Pageant Media Ltd
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The Journal of Financial Data Science: 1 (4)
The Journal of Financial Data Science
Vol. 1, Issue 4
Fall 2019
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Extracting Signals from High-Frequency Trading with Digital Signal Processing Tools
Jung Heon Song, Marcos López de Prado, Horst D. Simon, Kesheng Wu
The Journal of Financial Data Science Oct 2019, 1 (4) 124-138; DOI: 10.3905/jfds.2019.1.4.124

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Extracting Signals from High-Frequency Trading with Digital Signal Processing Tools
Jung Heon Song, Marcos López de Prado, Horst D. Simon, Kesheng Wu
The Journal of Financial Data Science Oct 2019, 1 (4) 124-138; DOI: 10.3905/jfds.2019.1.4.124
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  • Article
    • Abstract
    • RELATED WORK
    • DATA USED FOR ANALYSES
    • PRICES OF TRADES
    • MODEL SPECTRUM
    • FFT OF ACTUAL TRADING PRICES
    • POWER LAW AT HIGHER FREQUENCIES
    • ONCE-PER-MINUTE SIGNAL
    • VOLUMES OF TRADES
    • IMPACT OF TEMPERATURE
    • ERROR-CORRECTING MODEL
    • IMPACT OF FORECAST ERRORS
    • CONCLUSION
    • ADDITIONAL READING
    • ENDNOTES
    • REFERENCES
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