In this paper we present an algorithm based on AI and machine learning techniques that estimates the average trading behavior of a trader by modeling the transactions performed in response to the observed state of the market and the expected profits and loses made with respect to each transaction. Through this modeling we can compare between the behaviors of different traders in addition to capturing the actions of individual traders in response to market conditions. Through this we aim to infer activities that provide certain participants an unfair advantage over others, allowing us to learn newer ways of market manipulation.

1. 1
Markov Decision Processes in Market Surveillance
Authors:
Asoka Korale1, Fuard Ahamed1, Kaushalya Kularatnam2, Liam Smith2
1: Millennium Information Technology
2: London Stock Exchange

2. 2
Market Surveillance
• Build and maintain confidence in the market
• Ensure a level playing field & fairness to all market participants
• Proper functioning of the market essential to
• Raise capital for new investments
• Barometer of the economy
• Illegal activities lead to
• Market inefficiencies
• Higher costs
• Loss of investor trust / confidence
• Intelligent Software Algorithms employed to deter / detect market manipulation
• Understand / Characterize Trader Behavior
• Employ Machine Learning / Artificial Intelligence techniques

3. 3
Modeling Trader Behavior
• Day Traders / automated algorithms place orders based on the current state of the
order book, market or economy.
• Sequence of Trader actions capture their individual strategy with relation to market
conditions & Order Book
• Compare trading behavior
• By a Trader – against himself – over time
• Across traders – comparing average behaviors against each other
• Deviations from “average” behavior may signal need for further investigation
• Correlate this signal with other market / economic events / price movements
• Group Traders based on their strategy – Clusters / Networks
• Insights in to collusion

4. 4
Trading Modeled as a Markov (Stochastic) Processes
• Markov Property )/().../( 1121   nnnnn XXPXXXXP
)...()...( 121121 llnlnlnnnn XXXXPXXXXP  • Stationarity
• State Transition Matrix for a sequence of events
A B C B C B C A A
Events in a non overlapping window of Fixed Number of Events
)(
)(
)/(
j
ji
ji
XP
XXP
XXP


)(
)&(
)/(
j
ji
ji
XN
XXN
XXP 
Second State
First
State A B C Total
A 1 1 0 2
B 0 0 3 3
C 1 2 0 3
Transition Matrix
1/2
State A B C
A 1/2 1/2 0
B 0 0 1
C 1/3 2/3 0

5. 5
The Order Book
• A measure of the Supply and Demand for a security at any point in time

6. 6
Finite State Representation on a single (Trading) “Side”
Maximum -
Mean
Mean -
Minimum
High Low
Volume
Price High Low
High 1 2
Low 3 4
1
HP / HV
5
Cancel
6
Fill
2
HP / LV
4
LP / LV
3
LP / HV

7. 7
“State” of an Order – Relative to the “State” of the Order Book
• Captures the relative position of an order in the Order Book
second
state
Buy sell
first state 1 2 3 4 5 6 7 8 9 10 11 12
Buy
1
2
3
4
5
6
sell
7
8
9
10
11
12
State Transition Matrix

8. 8
Principal Component Analysis
• PCA a self-organizing learning algorithm - another “view” of a multivariate system
• like clustering - finds patterns without a training sequence / teacher.
• PCA identifies groups of correlated (redundant – no new information) variables
• replaces those groups with a new variable (a principal component).
• Principal components are orthogonal (“uncorrelated” ) to each other –
• Each component contains as much of the variance in original data as possible
• Original multivariate data - represented by a smaller number of principal components
• but contains much of the information in the original data set.
• Once the principal components are found - the data points of the original data set are
“transformed” to new coordinate system defined by the principal components

9. 9
Anomaly Detection in Multivariate Data – via PCA
• The transformed data points – used to identify outliers in the original data set –
• transformed data points grouped according to their similarities (correlation) in
the original data space
• outliers visible in the transformed space are those that show less correlation
with the samples in original space
• data grouped together in the transformed space are the data points that are
correlated in the original data space

10. 10
Characteristics of a Single Trader over Time (a day)
• Similar behavior observed most of the day
• A few anomalous trading periods (9, 14, 32, 42, 46)

11. 11
Comparison of the Characteristics of Several Traders over Time
• Clustering of traders with similar behavior (2-10)
• Traders with anomalous behavior
• Outliers (1,11)

12. 12
Conclusion
• Modeling trader characteristics via Markov Stochastic Processes gives insights
in to
• Manipulative behavior
• Collusive behavior
• Anomalous trading behaviors can be detected by comparing within individual
strategies and between trader strategies
• Anomalous groups can be flagged for further investigation
• Profiling Trader strategy a key component of overall understanding and profiling
of traders – an important surveillance requirement