EFMA Customer Week - April 2013

Tomás Pariente Lobo – Atos Spain

FIRSTEuropean research for web information extraction

and analysis for supporting financial decision making

Vision

Innovation

Tools

Motivation

Why FIRST? - Motivations

3

The most reliable data sources today…

…also have their weakness!They do not consider unstructured data, rumors, market sentiments, etc.

Why FIRST? - Motivations

4

Example: Apple iPhone 1 Announcement on 2007-01-09

Stock prices were skyrocketing after the announcement. However, the announcement could be sensed before…

Why FIRST? - Motivations

5

Example: Market surveillance via FIRST (the Google news case)

September 2008: Google news announced “United Airlines bankruptcy”.

Within 12 minutes stock price decreased 75% wiped out US $ 1bn.

The “news” was actually 6 years old…

Plausibility checking will help in identifying hoaxes: consistence with regulatory news and other sources.

Why FIRST? – MotivationsA growing universe of unstructured data

… how to separate the wheat from the chaff ?

6

Vision

Innovation

Tools

Motivation

FIRST Project

Project facts

Running from October 2010 until

September 2013

9 partners

More than 30 people

Preliminary results available

More to come...

Stay tuned (http://project-first.eu/)

8

European-funded research project

Who is behind FIRST?

Industrial partners

SMEs

Academic/Research

FIRST Vision

Vision

is to make available the relevant information

of the entire financial information space

(including unreliable, unstructured, sentiment sources)

to the decision maker in near-real time

in an automated way

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FIRST Vision

Structured

UnstructuredBlog, analysis, bulletin boards… Unreliable, poor quality, noisy…

AUTOMATION

Acquisition AnalysisProcessing Decision support

Financial Resources

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Automated data processing

Overall Goal: Mixing structured information and unstructured web data in specific decision making processes

Four steps in the macro-process of converting data into information are tackled, in our solutions tailored to the financial services market:

Data stream acquisition

Real-time processing

Sentiment building

Decision Support System

Sentiment from web data streams

Sentiment is extracted from data streams and correlated with events

Sentiment in Financial Services?

In Sept 2011, the sentiment turns from a long time of positive values to negative. A big plunge in the price happens shortly after, accompanied by a series of negative events (lost deals etc)

Sentiment cross-over

Sentiment cross-over happens

before price plunge

Vision

Innovation

Tools

Motivation

Mining the Web for financial texts

Data Acquisition pipeline: Web mining

Streaming

Cleaning

Natural Language preprocessing and entity

extraction

Financial terms, Companies,

Intruments …

Data acquisition after one year

Some numbers176 Web sites2,671 RSS sources~40,000 documents per day>10.000.000 documents by end of 2012o And growing

Essential for future evaluation and analysis

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Analysing sentiments in Web texts

Document with

sentiment sentences

Aggregatedsentiments

SENTIMENTAGGREGATION

per object and feature

The Analytical Pipeline: Identify, extract, classify, aggregate

Documentwithbasic

annotations

SENTIMENTCLASSIFICATION

per object and feature

Sentiment Sentences

ObjectIndicators

Positive sentiment

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Supporting the decision making process

Qualitative Modeling

Knowledge Base

Outputs:

Forecasts of volatility or returns, Alert on pump and

dump, Reputation change of a counterpart

Signals,Charts,

Topic Spaces,Topic Trends,

Reports…

Machine Learning

Techniques

Visualization Techniques

FIRSTAcquisition &

Analytical Pipelines Forecasting

Models

The Decision Support techniques: Analysis and visualization

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Glassbox model

Document

sentencesObjectsFeatures

Sentiment

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Drill down

Sentiment analysis & decision making

The integrated model of FIRST and its innovations

In the following slides we will rapidly check results from incorporating sentiments in retail brokerage, investment management and reputational risk scenarios

Main areas of research

•Sentiment analysis

•DSS models

•Stream visualization

•Scaling strategy

Early adopters

•Slovenian presidential elections

•GAMA Perception Analytics

Vision

Innovation

Tools

Motivation

The three FIRST use cases & their relevance for the industry

Market Surveillance Capital markets compliance can be automated today using structured data, but

the automation does not take unstructured data into account FIRST will

make use of large volumes of unstructured data into financial compliance; develop automated techniques to better detect market abuse/insider

trading..

Reputational Risk Management No off-the-shelf solutions or methodologies for reputational risk management. FIRST will

provide a sustainable tool for reputational risk monitoring; contribute to break new ground in this field of dramatically high impact in FSI.

Retail Brokerage Today, mainly based on quantitative analysis and key figures. FIRST will

use unstructured data to leverage both information for private investors and sophisticated tools for professional users.

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The three usecases in the words of the FIRST UC-Owners

UC#1 – Market Surveillance “The development of surveillance scenarios based on unstructured information will allow the

compliance offices to better investigate on unusual and suspicious trading activities and to better understand trends and patterns” – Stefan Queck, Business Dev. Manager at NEXT.

“Especially in time of financial crises, new regulatory requirements and reputation loss risks, the financial industry is interested in new methods and approaches to detect abuse trading behaviour”– Wolfgang Fabisch, CEO at NEXT.

UC#2 – Reputational Risk Management “From the early prototype release, we are looking forward to utilising in a real-life

environment the FIRST solution” – Maria Costante, Responsible for reputational risk modelling and Pillar 3 at Gruppo Montepaschi.

“We already discussed the tool we are setting up in European contexts, and we are looking forward to presenting the first results, already in 1H/2012” – Giorgio Aprile, Head of Reputational and Operational Risks at Gruppo Montepaschi.

UC#3 – Retail Brokerage “When presenting the usecase to potential customers, they showed interest in this kind of

data and the resulting tools” – Michael Diefenthäler, Director of Product Mgmt at IDMS. “We are looking forward to present the FIRST results to a variety of customers.” – Peter

Heister, Head of Sales EMEA at IDMS.

Reputational risk

Query• on-demand• routinary

Sentiment analysis

IE

Unstructured sources

Risk reporting:•reputational trends for each counterpart• events/topic•data sources drill-downs• …

What-if scenarios:• events• probability-weighted risk scenarios• …

Ontology

Customer an d product data

(internal sources)

PerformanceMismatchingVolumesNr. Customers

Structured sources

….. Need for integrating online unstructured data analysis with the current analysis on financial structured data

Reputation cockpit

Reputational Risk Index (RI)

Model

Application scenarios

Goal: to measure and to report, in quasi-real time, on reputational risk, using internal as well external data sources, to be integrated into a single reputation engine and application scenario

Retail brokerage @ work

Sentiments: Leverage of the investment process by assessment of unstructured information

1. Unburden the actor of reviewing various sources repeatedly by automation of this task

2. Provide different levels of sentiments, e.g. for single instruments and sectors

3. Support individual decision making by incorporating sentiments

Market surveillance @ work

1) Buying

Typically thinly-traded stocksBlog A

Blog B

Twitter

Blog C

4) Selling

On artificial price level

p

t

2) Disseminating inaccurate

or misleading information

3) Waiting for changesof market price

• Identification and classification of unstructured information

• Quite understandable generation of alerts

• Functionalitites to handle alerts

• Comparison of market, institute specific and unstructured information

Decision support in evaluation of suspicious constellations

Market surveillance @ work

Structured InformationStructured Information

Transactiondata

Employee data

Instrument Reference data

Ad-hoc news

Market data

Order data

Unstructured InformationUnstructured Information

Discussion Forums

„News“

Blogs

Social Networks

Sentiment Analysis

Scenario Analysis

Analytic Models

Visualisation

Sentiment Analysis

Scenario Analysis

Analytic Models

Visualisation

Broadend approach of detecting suspicious trading behaviour

Early recognition of trends and patterns

Decision support in investigation and escalation

Benefits for the market

Real-life implementation @ B-NEXT, Germany.Contact: Markus.Reinhardt@b-next.com

29Stay tuned (http://project-first.eu/)

AcknowledgementThe research leading to these results has received funding from the

European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n°257928.

THANKS