Paper: arXiv 2312.16190

Abstract

Accurately forecasting the direction of financial returns poses a formidable challenge, given the inherent unpredictability of financial time series. The task becomes even more arduous when applied to cryptocurrency returns, given the chaotic and intricately complex nature of crypto markets. In this study, we present a novel prediction algorithm using limit order book (LOB) data rooted in the Hawkes model, a category of point processes. Coupled with a continuous output error (COE) model, our approach offers a precise forecast of return signs by leveraging predictions of future financial interactions. Capitalizing on the non-uniformly sampled structure of the original time series, our strategy surpasses benchmark models in both prediction accuracy and cumulative profit when implemented in a trading environment. The efficacy of our approach is validated through Monte Carlo simulations across 50 scenarios. The research draws on LOB measurements from a centralized cryptocurrency exchange where the stablecoin Tether is exchanged against the U.S. dollar.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced stochastic processes (Hawkes models) with detailed mathematical formulations and derivations, indicating high mathematical complexity. It is heavily data-driven with specific empirical validation using real limit order book data, Monte Carlo simulations, and reported trading performance, showing high empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Predict cryptocurrency return signs using LOB data"] --> B{"Methodology"}
  B --> C["Hawkes Process Model"]
  B --> D["Continuous Output Error COE Model"]
  C & D --> E["Data Input<br>Cryptocurrency LOB Data Tether USD"]
  E --> F["Computational Process<br>Monte Carlo Simulations 50 Scenarios"]
  F --> G["Key Findings Outcomes"]
  G --> H["Superior Prediction Accuracy vs Benchmarks"]
  G --> I["Higher Cumulative Profit in Trading"]

Cited References (31)

  1. Continuous-time modeling of financial returns based on Limit Order Book data (2023)
  2. Buy low, sell high: A high frequency trading perspective (2014)
  3. The information content of an open limit-order book (2009)
  4. Predictive Methods for H awkes Processes in High Frequency Finance (2022)
  5. State-dependent H awkes processes and their application to limit order book modelling (2022)
  6. Modelling Limit Order Book Dynamics Using H awkes Processes (2017)
  7. High-dimensional H awkes processes for limit order books: modelling, empirical analysis and numerical calibration (2018)
  8. Spectra of some self-exciting and mutually exciting point processes (1971)
and 23 more
  1. A cluster process representation of a self-exciting process (1974)
  2. The information content of the limit order book: evidence from NYSE specialist trading decisions (2005)
  3. Price jump prediction in limit order book (2012) — link
  4. Forecasting stock prices from the limit order book using convolutional neural networks (2017)
  5. The role of volume in order book dynamics: a multivariate H awkes process analysis (2017)
  6. Modeling foreign exchange market activity around macroeconomic news: H awkes-process approach (2015)
  7. Hawkes model for price and trades high-frequency dynamics (2014)
  8. A time-modulated H awkes process to model the spread of COVID-19 and the impact of countermeasures (2021)
  9. Inference of functional connectivity in neurosciences via H awkes processes (2013)
  10. Flexible spatio-temporal H awkes process models for earthquake occurrences (2023)
  11. Longitudinal modeling of social media with H awkes process based on users and networks (2017)
  12. Stablecoins and cryptocurrency returns: What is the role of T ether? (2020)
  13. Cryptocurrency, a successful application of blockchain technology (2020)
  14. Forecasting cryptocurrency volatility (2022)
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  16. High Frequency Trading Strategies (2021)
  17. Hawkes processes (2015) — link
  18. An introduction to the theory of point processes: volume I: elementary theory and methods (2003)
  19. Tutorial on maximum likelihood estimation (2003)
  20. Deeplob: Deep convolutional neural networks for limit order books (2019)
  21. Hawkes process for understanding the influence of pathogenic social media accounts (2019)
  22. Cryptocurrencies and stablecoins: a high-frequency analysis (2022)
  23. Market impact and efficiency in cryptoassets markets (2023)

0 of 31 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.