=Paper=
{{Paper
|id=Vol-1240/feosw2014-paper2
|storemode=property
|title=Analyzing Stock Market Fraud Cases Using a Linguistics-Based Text Mining Approach
|pdfUrl=https://ceur-ws.org/Vol-1240/feosw2014-paper2.pdf
|volume=Vol-1240
|dblpUrl=https://dblp.org/rec/conf/esws/AliT14
}}
==Analyzing Stock Market Fraud Cases Using a Linguistics-Based Text Mining Approach==
https://ceur-ws.org/Vol-1240/feosw2014-paper2.pdf
Analyzing Stock Market Fraud Cases Using a Lin-
guistics-Based Text Mining Approach
1,* 2
Mohamed Zaki and Babis Theodoulidis
1
Cambridge Service Alliance, Department of Engineering, University of Cam-
bridge, Cambridge, UK
mehyz2@cam.ac.uk
2
Manchester Business School, University of Manchester, Manchester, UK
b.theodoulidis@manchester.ac.uk
ABSTRACT. The paper proposes a linguistics-based text mining approach to
demonstrate the process of extracting financial concepts from the Security Exchange
Commission (SEC) litigation releases (LR). The proposed approach presents the ex-
tracted information as a knowledge base to be used in market monitoring surveillance
systems. Also, it facilitates users’ acquisition, maintenance and access to financial
fraud knowledge and improves search results in the SEC enforcement portal. Answer-
ing questions such as: who are the agents involved in the manipulation? Which pat-
terns are associated with this manipulation? When was this manipulative action per-
formed? This paper used the financial ontology for fraud purposes introduced by [19]
to provide underlying framework for the extraction process and capture financial
fraud concepts from the SEC-LR. In particular, text mining analysers have been de-
veloped to extract metadata concepts (e.g. ‘LR No.’, ‘dates’) and stock market fraud
concepts (e.g. agents and manipulation types) from the actual SEC fraud case.
Keywords
Text mining, stock market fraud, stock market fraud ontology, stock market moni-
toring and surveillance system.
1 Introduction
The finance domain has suffered from a lack of efficiency in managing vast
amounts of financial data, a lack of communication and knowledge sharing between
analysts. Particularly, with the growth of fraud in financial market, cases are challeng-
ing, complex, and involve huge information that needs to be analyzed similarly to
other legal cases. Gathering facts and evidence from the information available is often
a very complex process. The impetus to effectively and systematically address stock
financial market efficiency, including factors such as stock price manipulation, has
long presented a very dynamic challenge to academia, the industry and relevant au-
thorities. Interestingly, since 1960 the Security Exchange Commission (SEC) has
prosecuted more than 24,525 fraud cases.
Many authors [1,10,12,15] have tested the impact of non-competitive behaviour in
the stock market, and verified the possibilities of market manipulation. [1] presented a
theoretical framework for profitable market manipulations, and provided empirical
evidence using a comprehensive dataset of manipulation cases which occurred in the
US stock markets and were published in SEC litigation releases from 1990 to 2001.
Manipulators can artificially increase securities prices and make profits using various
strategies, from classic manipulative trading practices that influence prices, to the
sophistication of spam and scam manipulation using various internet channels [1].
Since the passing of the Securities Act in 1930, there is evidence that market manipu-
lation has a significant impact on the efficiency of the securities market [11].
Despite the existence of many authoritarian regulations such as the Securities Ex-
change Act of 1934 and European Market Abuse Directive 200, prosecutors find it
challenging to prepare a case with appropriate evidence, and the gain for an exchange
of a successful prosecution would be small in comparison to the efforts and resources
necessary to bring a criminal case. Fraud cases can be extremely complex and diffi-
cult to demonstrate to juries. That is why regulators only select certain cases for pros-
ecution and prioritize instances of organized manipulation. Furthermore, few courts
have experience in trying securities fraud cases.
There is an urgent need to develop a novel approach that could help regulators and
relevant authorities in managing vast quantities of financial data, providing better
communication and knowledge sharing among analysts, providing a mechanism to
demonstrate knowledge of the processes of financial fraud, understanding and sharing
financial fraud logic operations, managing relevant facts gathered for case investiga-
tions, and allowing reuse of these knowledge resources in different financial contexts
[8].
Therefore, the continuous improvement and development of financial market moni-
toring and surveillance systems with high analytical capabilities to capture the fraud is
essential to guarantee and preserve an efficient market [14]. Currently, these systems
are used in limited fashion and act as a reporting archive for multiple functions within
the exchange. Thus, this paper aims to provide significant cross-fertilization between
financial research studies and information technology as it attempts to incorporate text
mining techniques for the analysis as one of the most appropriate technological area,
allowing analysis of stock market fraud documents through the development of lin-
guistic and non-linguistic patterns. In this context, text mining is used to extract fi-
nancial concepts from the SEC litigation releases and thus, provide an appropriate
knowledge base about financial market manipulations. The paper provides empirical
evidence of how text mining could help the market monitoring surveillance systems
to explore the potential efficiency and effectiveness benefits for analysing litigation
releases.
2 Previous Work
This section provides a review on existing market monitoring surveillance systems
and fraud detection studies that used data mining and text mining to investigate and
2
detect fraudulent behaviors and ascertaining evidence of potential cases of fraud with-
in different financial markets. In fact, these systems could support financial organiza-
tions to proactively detect transactions where market abuse is suspected.
Focusing specifically on the market manipulations domain, [9] described how the
National Association of Securities Dealers (NASD) Inc. used a fraud detection system
[called Advanced Detection System (ADS)] to monitor trades, to detect and identify
any suspicious trading behavior for further investigation in the NASDAQ stock mar-
ket. [6] work describe the Securities Observation, News, Analysis and Regulation
system (SONAR), also developed by NASD. The system’s main purpose is monitor-
ing NASDAQ transactions in the stock market to detect and identify any potential
insider trading and any falsification of news stories for the purposes of fraud. The
work of [13] introduced a monitoring system used on the Thai Bond Market, which
was commissioned by the Thai Bond Market Association (ThaiBMA). The market
uses a real time approach to monitor transactions, investigate any unusual ones, and
notify regulators where enforcement action is required. [4] proposed a market moni-
toring framework, comprising of the analysis components, tasks and flows of infor-
mation of a complete financial market monitoring system. The framework is designed
to have a past time or reactive monitoring engine, which is fed with either structured
or unstructured data sources.
Many studies show how data mining and text mining techniques can be used in such
domain. For example, [5] utilized data mining techniques (C4.5, decision tree, neural
network, K-mean clustering, and logistic regressions) for the early detection of insider
trading manipulation schemes before the news broke within the option market. [16]
generated a conceptual framework to identify the individuals (and their communities)
involved in trade-based manipulation using data mining such as Euclidian Distance
(ED), Shared Nearest Neighbour (SNN), density-based algorithm (DBSCAN) and
graph-partitioning algorithm (METIS). [2] employed a data mining approach to ana-
lyze two cases of manipulation in the New York Stock Exchange. The researcher used
the decision trees technique to distinguish between manipulations and normal trading
and to improve organizational fraud detection systems. [18] described a case study on
fraud detection using data mining techniques that help analysts to identify possible
instances of touting based on spam emails in the Pink Sheets market. Various data
mining techniques such as decision trees, neural networks and linear regression are
utilized in this emerging domain. [17] presents an exemplar case study of text mining
and data mining to analyze the impact of ‘stock-touting’ spam e-mails and misleading
press releases on trading data a real case from the over-the-counter (OTC) market,
and which was prosecuted by the SEC. [3] presents a high frequency trading analysis
of a particular trading scenario and discusses how quote stuffing can affect the func-
tion of trading systems.
This research contributes to the development of a comprehensive domain ontology
for stock markets. Currently, the existing market monitoring systems lack a compre-
hensive financial knowledge base [19]. This research contributes to the development
by providing an additional context for the evaluation of the domain ontology and
furthermore, demonstrates how data sources such as the SEC litigation releases could
be analysed using a text mining approach and could support fraud analysts in the in-
vestigation process. Finally, the paper uses SEC cases as the data source that has not
been addressed in previous work.
3 Methodology
This paper used the financial ontology for fraud purposes introduced by [19] to
provide underlying framework for the extraction process and capture financial fraud
concepts from the SEC litigation releases. The ontology has a comprehensive finan-
cial concept system for fraud purposes, which utilized to semantically rich the
knowledge base of market monitoring surveillance systems to potentially help fraud
analysts to understand different manipulation patterns from prosecuted cases. In this
context, this paper evaluates this ontology through a specific text mining instantiation
that demonstrates the published prosecuted case in an appropriate fraud knowledge
base. The role of the analysers is to identify the knowledge that lies in the prosecuted
cases to be able to answer questions similar to those asked by users and analysts read-
ing the cases themselves. Some key questions that the text mining analysers should
answer include: Who is (are) the agent(s) involved in the manipulation? Which asset
is being targeted? In which venue is the manipulation taking place? Which action has
been performed or is planned? Which patterns are associated with this manipulation?
When was this manipulative action performed? Where is the manipulator getting his
profit?
3.1 Data Source
This paper used the SEC published litigation releases which are prosecuted fraud
cases for the US stock markets as a main data source of fraud knowledge to be ana-
lyzed. The litigation releases are concerning civil lawsuits brought by the Commis-
sion in federal court. Each litigation release has a release number, release publication
date, and action that include the defendants’ names; and most of the releases have an
external link to SEC complaint.
The SEC complaint documents are the reports produced by the US district court
that describe violations cases of securities laws. In these documents the court de-
scribes in detail all the evidence and facts that make its decisions to prohibit the acts
or practices that were the results of violation of the law or commission rules. The SEC
complaint document structure consists of five main sections: ‘Civil case action no’
which is the file number the court allocates to the document; ‘district court name’,
‘court clerk’s office stamp’ which includes his name, signature, title and filing date,
‘title’ including the names of the defendant and the plaintiff, and the main ‘document
sections’ which generally include a summary of the complaint, list of defendants and
relevant person entities involved in the violation, jurisdiction and the venue of the
court, facts that describe the manipulation scheme and all the evidence that has been
collected by the Commission to prosecute the defendants, ‘fraud for reliefs and viola-
tion’ includes all the acts and laws that defendants violated in such case.
4
Different textual sources were collected from the SEC website, such as RSS for-
mat, HTML and PDF files. The RSS format is used to download the recent litigation
releases published in the SEC website. Most of the litigation releases have an HTML
link that contains a short description as a summary of the case followed by a detailed
description of the cases (SEC Complaints). The SEC complaints are PDF documents
produced by the district courts to provide a full description of the prosecuted cases.
Based on the case, the average size of these documents could vary from 10 to 60 pag-
es. In particular, the text-mining application analyzed the third quarter of 2012 that
contains 62 litigation releases with total size of the corpus is 185.1 MB.
3.2 Text Mining Application Design
This section demonstrates the design of text mining, which is constructed for the
SEC fraud cases. As shown in figure 1, this study adapted the information extraction
application layer introduced by the stock market fraud ontology [19]. The application
layer can process all document types such as text, audio and video. Thus the Textual-
Format class will have concepts related to documents used in the application, such as
the SEC case study, the SEC litigation release and RSS feed. In addition, each docu-
ment instance could be related to one or more annotations used to develop the linguis-
tic patterns. Two main types of resource are used in the text-mining components,
namely language resources and processing resources. Language resources contain
resources such as a thesaurus, list of terms, concepts, synonyms, and types (semantic
groupings of concepts). Processing resources incorporate analyzers, generators, rec-
ognizers (e.g. speech transcribers, handwriting recognizers), and retrievers (e.g.
search engines).
Fig 1 Text Mining Application Layer [19]
The ‘corpus’ concept has the actual litigation releases of the third quarter of 2012
that have been analyzed in the application, such as ‘LR-22420’ and ‘LR-22421’. The
‘document’ class contains the ‘insider trading’ case study of Bio-Medicus, Inc.1,
which was originally a PDF file but has been converted to the ‘docx’ extension. This
case will be used to demonstrate how text mining could automate the process of clas-
sifying financial concepts in the proposed classes in the financial fraud ontology.
The developed text-mining application uses different components to develop the
advanced linguistic patterns, which could contain the following components: sub-
classes from the developed library, synonyms, macros, and word gaps. ‘Finan-
cialFraud Library’ is another sub-class added to the ‘lexicalResource’ concept which
has 20 classes.This library was developed to help the text-mining application to ex-
tract concepts from the litigation releases, especially the fraud-related concepts. Based
on the [19], the library has over 223 concepts that are classified and mapped to clas-
ses.
This library is used to develop and construct the advanced linguistic patterns. For
example, “Confidentiality” includes a list of terms related to confidential information
such as “confidential information, confidentiality, confidential advice, confidentiality
agreement, confidentiality policy, code of ethical conduct, confidential, etc”. “Opera-
tionType” contains terms related to trading operation such as “acquired, sold, ob-
tained recommended, sell, buy, purchase, etc”. “AgentNetwork” includes all terms
representing the type of relatives who help manipulators to execute the fraud, such as
“son, cousin, friend's wife, friend, relative, etc”. “Employees” has all terms related to
employees such as “employee, manager, chairman, board, office manager”. “District
Court Name” holds a list of different state courts such as “federal district court, east-
ern district of New York, middle district of Florida, district of New Jersey”.
Furthermore, the library includes synonyms, which associate two or more concepts
that have the same meaning. In particular, synonyms have been used to resolve the
issue of misspelled concepts, and concepts having the same meaning, e.g. “Securities
and Exchange Commission” and “Commission”.
The macros is another class added to the lexicalResource concept, which represents
reusable patterns; it is used to simplify the appearance of literals and word strings
needing to be extracted, e.g. prepositions, articles, and verbs. Overall, the application
includes six macros that support the extraction process and pattern development. For
example, “mPreposition” includes a list of tokens related to prepositions such as “to,
from, for, of, on, at, with, about, into, etc”. “mArticles” includes a list of tokens relat-
ed to English language articles such as “a, the, an, etc”. “mVtobe” is another macro
that holds tokens related to concepts concerning the verb ‘to be, e.g. “is, are, was,
were”, , etc.
Process resource concept demonstrates the text-mining process and the advanced
linguistic pattern approach employed on the basis of natural language processing
(NLP) in order to linguistically analyze the litigation releases. 60 advanced linguistic
patterns were developed to analyze the litigation releases sentence-by-sentence and to
apply focus group participants’ recommendations. This section demonstrates the ‘In-
sider Trade’ analyzers developed to automate the process of extracting information in
the context recommended by the ontology to explain the fraud cases
1
Case web link at http://www.sec.gov/litigation/complaints/2006/comp19929.pdf
6
4 Text Mining Analysis
This paper used the IBM-SPSS Modeler14 data and text mining workbench to de-
velop the text mining analyzers [7]. The text mining application contains two compo-
nents metadata analysers and SEC Complain Document Analysers.
4.1 Metadata Analysers
The text-mining metadata analysers aim to extract key metadata information from
the data source. The target concepts are ‘litigation release number’, ‘release publica-
tion dates’, ‘agents’ which are the defendants’ (individual or organization) names,
‘document format type’, the ‘document link’, ‘civil case no.’ which is the allocation
number issued by the court, ‘district court no.’ including state courts or federal courts,
and the ‘plaintiff’. The text-mining application used some of the predefined libraries
incorporated in the IBM PASW 14 software, such as date, time, person, organization.
However, these patterns did not capture all related concepts within the document.
Thus, extra patterns have been developed to cover the gap and to increase the accura-
cy of extraction. Furthermore, other analyzers were developed from scratch to match
the specific structure of linguistic patterns.
Table 1 explains the patterns developed by the ‘Civil Case No’ analyzer. Each
court has a different pattern and in order to capture most of them, 18 linguistic pat-
terns using regular expressions were developed. These patterns have been numbered
sequentially from 1–18 to avoid any break in numbering which might cause suspen-
sion or conflict when processing the document. For example, in the first pattern in
Table 1, “6-12-CV-00932-JA-GJK”, the regular expression was developed as [0-9]{1,
2} to match a digit repeated exactly one or two times [e.g. 6], followed by specific
character “–“, similarly, s[0-9]{1, 2} to match the two digits, followed by “-”, fol-
lowed by two character [case sensitivity is considered] [a-zA-Z] {1,2}, followed by
five numbers [0-9]{3, 5}, followed by 2 characters [a-zA-Z]{1,2}, followed by three
character [a-zA-Z]{1,3}. The “Civil Case No” analyzer successfully captured 100%
from the 62 litigation releases used in the application.
From the 62 litigation releases, the analyzers’ ‘litigation release number’, ‘release
publication dates’, ‘actions’, ‘document format type’, ‘document link’, ‘short descrip-
tion’, ’detailed description’, and ‘plaintiff’ have a high accuracy level of almost 98 %
precision. Regarding ‘Civil case no.’ the analyzer achieved 98.93% precision, and for
‘District court Name’ 93.55% precision. In five releases the court names were not
included due to the pending status of the allegation or administrative proceeding sta-
tus, or were missed.
Table 1. 'Civil Case No' Analyzer
Despite the size of the documents, the analyzers are good enough to demonstrate
how text mining could be used for automating the analysis of SEC litigation releases.
Table 2 shows the final output of the target concepts captured by the data source ana-
lyzers. For example, in litigation release number ‘LR-22396’ published on 20th June
2012, the defendants are ‘Gary J.Mortal’, ‘Martel Financial Group’, and ‘MFG Fund-
ing’. The user can find short or detailed descriptions of the release via the link
http://www.sec.gov/litigation/litreleases/2012/lr22396.htm. Therefore, the only in-
formation provided by the SEC is through the link as the release does not yet have a
complain file (See also has null value) issued by the respective court. The Security
and Exchange Commission, the plaintiff in this release, sent the case to the federal
district court. The civil case number of the release is ‘12-cv-11095’.
Table 2 Metadata Data Source Ontology Text Mining Analyzers’ Output
4.2 SEC Complain Document Analysers
The text-mining application analyzed the SEC complaint document produced by
the US district courts. 11 analyzers have been developed to extract the annotated
financial concepts. The developed analyzers analyzed the document sentence-by-
sentence. In total, 60 advanced linguistic patterns were developed to extract infor-
8
mation related to financial fraud and classify this information in the appropriate on-
tology classes, as guided by the financial ontology [19].
In particular, manipulation participants’ analyzer represents the manipulator who
performs the manipulation, and whether the manipulator acts by him or has networks
of other agents who helped him to execute the manipulation. Furthermore, the analyz-
er extracts the information describing benefits behind such manipulation, whether
they accrue to the manipulator or to others. Finally, it checks whether the manipulator
has any previous records or history of manipulations or violations. In total, the ana-
lyzer has 9 linguistic patterns to answer these questions and describe the manipulator
and his social network profile.
The first three patterns show the agent who performed the violation and the manip-
ulation activity type, as shown in figure 2. The patterns automatically analyze the
sentence, extract the concept ‘Robert J. Gallivan’ and classify it under the
sub-category. The concept ‘defendant’ is classified under the sub-
category, the concepts ‘breached a duty of trust and confidence’ and ‘insider trading
activity’ under the sub-category, and the concept ‘the C&B con-
sulting Firm’ under ‘organization’. Using the regular expression the analyzer retrieves
the dates corresponding to the manipulation activity. The patterns automatically clas-
sify these sentences as the manipulator who performed the manipulation and map it to
the ‘ManipulationParticipants\Agent\AgentCharacteristics\Individual’. The line width
and node sizes in a concept graph represent the global frequency counts of the ex-
tracted concepts from the document. For example, apparently the concepts ‘Robert J.
Gallivan’ and ‘breached a duty of trust and confidence’ were mentioned in the docu-
ment several times, represented by the thickness (Global count 5) of the line as shown
in figure 2. In order to check whether the manipulator has a previous violation record,
three patterns are developed to automatically analyze the complaint document and
extracts the concepts that describe the manipulation history of the manipulator. The
Commission found that Gallivan, who was affiliated with a broker-dealer at the time
of the scheme, wilfully violated Section 17(a) of the Securities Act of 1933, in 1975,
without admitting or denying the Commission's findings, Gallivan consented to the
entry of a Commission order against him in the Proceeding File No. 3-4425.
Fig 2 Manipulation Participant Analyzer
The last three patterns in the manipulation participants’ analyzer are developed to
extract the information that addresses the manipulator’s social network, which helped
him to violate the securities Harbour, Mid valley and Valencia securities (Target As-
sets). In this case Gallivan recommended the purchase of different stocks to his rela-
tives, friend, and cousin to gain unlawful and combined profits reaching $58,453. The
patterns automatically classified these sentences to the ontology class as the agent was
in collusion with other agent networks (Assistance\In collusion). Furthermore, both
the manipulator and his networks received benefits from the manipulation (Bene-
fit\Own Benefit and Benefit\Third Party), as shown in figure 3.
Fig 3 Agents' Social Network with Benefits
Timeline Manipulation Events and Actions Analyzer combine three analyzers ‘Ac-
tions’, ‘Effects’, and ‘Time’. This analyser demonstrates the patterns developed for
the three analyzers. The analysis indicates a strong relationship between the three
analyzers because they describe the facts and nature of the manipulation activity exe-
cuted by fraudsters. These actions could be related information-based activities such
as obtaining non-public information and breach of confidence or trust, or could be
trade-based activities such as buying and selling stocks to stimulate the market and
violate prices. In this case, each action is associated with the temporal dimension and
explains the period in which manipulator performed these manipulative activities.
These actions have an effect on the manipulated assets represented in direct or in-
direct benefits and unlawful profits. In this case, patterns and evidence such as legal,
financial and economic unstructured information are used to trace the behavior of
manipulators and show the consequences of their behavior on the market.
This analyzer contains 50 advanced linguistic patterns to extract information relat-
ed to facts and the actions executed by the manipulator associated with the timeline.
nine patterns are classified under ‘Actions’ classes , 20 patterns extract concepts relat-
ed to the ‘Effects’ of manipulation , and 21 patterns are classified under the ‘Time’
class which describe the events before, during and after the fraud.
Figure 4 demonstrates the output of the 50 patterns developed for this analyzer
applied to the ‘insider trading’ case study. The output shows the complexity of the
manipulated activities executed by the agent. Most of the events are interconnected
and interrelated, such as date, agreements and confidentiality, stocks prices, amount
of purchases, manipulated assets, agent and his networks, amount of combined profits
collected by the agent, the way of communication and meeting to obtain the non-
10
public material to take advantage, other evidence and patterns either legal or related to
economic structure or trading used by the agent to violate the market.
In this case, the manipulator violated the prices of four stocks: Valencia Stock, Sun
Country Stock, Mid valley Stock, and Harbor Stock. The manipulation actions
between the four stocks are similar, as the manipulator ‘Robert J. Gallivan’ has
breached a duty of trust and confidence of his company ‘C&B Consulting Firm’.
Based on his role in the company, the manipulator attended meetings and set up calls
with companies and investors which gave him an opportunity to obtain non-public
material. ‘Robert J. Gallivan’ used this information to buy these stocks and
recommended them to his social network to combine profits from these trading
transactions. Indeed, the agent agreed and signed that he would keep the matter
confidential, but this was not the case and he breached this trust and violated the
securities based on the insider information he acquired.
Fig 4 Timeline Manipulation Event and action Analyser Output
5 Conclusions
This paper contributes to market monitoring surveillance systems work. A
linguistic based text mining approach is demonstrated for different market manipula-
tion types based on the SEC litigation releases. The approach provides empirical evi-
dence of how text mining could be integrated with the financial fraud ontology to
improve the efficiency and effectiveness of extracting financial concepts. However,
focusing on only a few case studies can potentially skew the outcome. This paper has
limitations regarding the coverage of the cases and datasets.
In terms of future work, it is still possible to enhance and expand the cases to eval-
uate the text mining model by including new manipulation schemes and correspond-
ing concepts on the basis of the SEC and other possible sources. For example, high
frequency trading and stuff-quoting examples of possible stock-market manipulation
cases should be included. Future work will pursue the full deployment of the text
mining solution for the SEC litigation use as fraud knowledge management portal.
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