• Corpus ID: 238531424

Protecting Retail Investors from Order Book Spoofing using a GRU-based Detection Model

@article{Tuccella2021ProtectingRI,
  title={Protecting Retail Investors from Order Book Spoofing using a GRU-based Detection Model},
  author={Jean Tuccella and Philip Nadler and Ovidiu Serban},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.03687}
}
Market manipulation is tackled through regulation in traditional markets because of its detrimental effect on market efficiency and many participating financial actors. The recent increase of private retail investors due to new low-fee platforms and new asset classes such as decentralised digital currencies has increased the number of vulnerable actors due to lack of institutional sophistication and strong regulation. This paper proposes a method to detect illicit activity and inform investors… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 10 REFERENCES
Microstructure-based manipulation: Strategic behavior and performance of spoofing traders
We examine how investors strategically spoof the stock market by placing orders with little chance of being executed, but which mislead other traders into thinking there is an imbalance in the order
Stock price manipulation detection using a computational neural network model
TLDR
This paper constructed mathematical models that use level 2 data for both pump-and-dump and spoof trading, and implemented feedforward neural network models that have level 1 data, containing less-detailed information, but is more accessible to investors as input.
Is Bitcoin Really Untethered?
This paper investigates whether Tether, a digital currency pegged to the U.S. dollar, influenced Bitcoin and other cryptocurrency prices during the 2017 boom. Using algorithms to analyze blockchain
Robust multivariate autoregression for anomaly detection in dynamic product ratings
TLDR
This work model the base behavior of users regarding a product as a latent multivariate autoregressive process and this latent behavior is mixed with a sparse anomaly signal finally leading to the observed data.
Dodd-Frank and the Spoofing Prohibition in Commodities Markets
The Dodd-Frank Act amended the Commodity Exchange Act and adopted an explicit prohibition regarding activity commonly known as spoofing in commodities markets. This Note argues that the spoofing
Long Short-Term Memory
TLDR
A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Adam: A Method for Stochastic Optimization
TLDR
This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification
TLDR
This empirical study showed that the proposed GRU-FCN model also outperforms the state-of-the-art classification performance in many univariate time series datasets without additional supporting algorithms requirement.
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
TLDR
These advanced recurrent units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU), are found to be comparable to LSTM.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
TLDR
Qualitatively, the proposed RNN Encoder‐Decoder model learns a semantically and syntactically meaningful representation of linguistic phrases.