Researchers from the University of Michigan and Georgia Tech, in collaboration with the AWS Research Initiative, have developed new techniques to identify financial market manipulation in high-volume, high-velocity market data streams. They are using a combination of data-driven and model-based techniques to identify financial market manipulation. In this session, we discuss the use of machine learning using Amazon SageMaker to study, process, and analyze huge volumes of data to prevent financial market manipulation.

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Detecting Financial Market Manipulation Using Machine Learning
Michael Wellman
Associate Dean for Academic Affairs
University of Michigan
A I M 3 4 7
Sanjay Padhi
AWS Research Initiatives, WWPS
Amazon Web Services

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BigData Market Manipulation Project
• Collaboration between University of Michigan and Georgia Tech
• Sponsored by NSF BIGDATA program, computational support from AWS
• Interdisciplinary: Computer science (AI/ML), finance, law, and public policy
Goal: New techniques for detecting and mitigating manipulation of markets

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Market manipulation

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Spoofing
Definition: Practice of
submitting large spurious orders
to buy or sell some security to
mislead other traders about
market state
True buy
order
True sell
order
Spoofing sell
order
Spoofing buy
order
Source: UK Financial Conduct Authority

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Approach
Model spoofing and other manipulation strategies
Develop new surveillance and audit algorithm based on signatures extracted from model-augmented data streams
background
trading model
market
data
calibration
spoofing
strategy
optimization
signature
extractor
surveillance/audit
algorithms
machine learning
Generate realistic financial order streams

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An agent-based model of spoofing
• Simulate financial markets as complex multi-agent
systems
• Elucidate strategic choices faced by market
participants
• Evaluate market performance and the impact of
spoofing given agent interactions at Nash equilibrium

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Machine learning

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Generative adversarial network (GAN)
Interleave training of two deep neural net

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Order book evolution
Generator outputs next order, conditional on order book state and history

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Wasserstein generative adversarial network (WGAN)
Discriminator computes
Earth-Mover distance
between real and
generator distributions

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Generator NN architecture
LSTM layer
Noise input
Convolutional layers
after a single
fully connected layer
Pre-trained
CDA network
Time
History xj of length

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One-click training
for ML, DL, and
custom algorithms
Easier training with
hyperparameter
optimization
Highly optimized
machine learning
algorithms
Deployment
without
engineering effort
Fully-managed
hosting at scale
BuildPre-built
notebook
instances
Deploy
Train
Amazon SageMaker

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Managed distributed training with flexibility
Training code
• Matrix factorization
• Regression
• Principal component analysis
• K-means clustering
• Gradient boosted trees
• And more!
Amazon provided algorithms
Bring Your Own Script (IM builds the container)
Bring Your Own Algorithm (you build the container)
I
ML training service
Fetch training data
Save model artifacts
Fully
managed –
Secured–
Amazon ECR
Save inference image
IM estimators in
Apache Spark
CPU GPU HPO

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Results
Fake means generated order
Results compare—Real and
fake order distributions
Training
Simulated financial market
Thinly traded stock
- PN, 20k orders/day
Thickly traded stock
- GOOG, 230k order/day

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Results

18. Thank you!
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Michael Wellman – wellman@umich.edu
Sanjay Padhi – sanpadhi@amazon.com

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