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Market Movements at High Frequencies and Latency in Response Times

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

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.

Keywords

  • Big data
  • High frequency trading
  • Cloud computing
  • Sentiment analysis

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  • DOI: 10.1007/978-3-030-89906-6_60
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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

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