big data analytics for investor relations @ niri 2016 conference

BIG DATA ANALYTICS IN IR:

Latest Academic Research and its Implications for IROs

Objectives of this session

Understand the concept of big data analytics

and its impact on financial industriesI

Review latest academic big data research

relevant to IROsII

Learn how to leverage big data as an IROIII

33

Information is the oil of the 21st century, and analytics is the combustion engine.”

Warm-up: Facts about big data

What is big data exactly about?

Big Data quiz (I)

How long does it take to produce more data

than in the whole previous human history?

~5 yearsA.

~2 yearsB.

~2 monthsC.

Insights into data generation

Big Data quiz (II)

How much of the collected digital data do

we effectively analyze?

About 10 percentA.

About 4 percentB.

About 0.5 percentC.

Insights into digital data usage

• Big data and financial markets

• Academic big data research for

capital market communication

• Implications of big data for IROs

Contents

1010

Data volume CAGR of >40% each year…

44.0

7.91.2

2015 2020E

+43% p.a.

2010

37x

Global digital data production

In zettabytes

SOURCE: CSC, IDC

… or 5 DVD stacks to the moon in 2015

Moon

Earth

SOURCE: CSC, IDC, analysis of University of Freiburg

So big data = big data volumes?

1313

Big data is not just about big volumes

Idea: IBM’s 5 V’s

1414

Do you recognize the data footprint you leave in your daily work?

In 2015, the average person produced 3GB of data – per day

Financial data as an ‘insight mine’ for all capital market stakeholders

• Stricter regulation (e.g. BCBS 239, SSM, …) of data quality and availability

• Big data transforms how some investors analyze companies; i.e. investor

interaction

• Provides deeper insights in how investors process information

• FinTechs as new competitors to bank

• Machine learning allows to refine risk and customer modeling

• Algorithmic trading accounts for more than 50 percent of trading

• Large-scale back-testing of investment strategies

Investors

Regulators

Inter-

mediaries

Stock-

listed

companies

The Big data revolution launched a whole new FinTech industry

As of 2016, the product range offered by these FinTechs is already broad

▪ Student and personal loans

▪ Business loans

▪ Microcredit

▪ Consumer banking

▪ Microinsurance

▪ Merchant Cash Advance

New FinTech players have emerged, making use of those data sets

Financial transactional data

Cash flow data

Social network data

Mobile phone usage data

Browsing/behavioral data

Academic performance data

Ambient data

Various sources of data are being leveraged for customer analysisTrends

The explosion of big data had a profound impact on many industries

In the financial industry, data has always been a strategic asset. Banks had a natural monopoly

Since 2007, the banking industry has faced increasing disruption by a phenomenon called ‚FinTech companies‘

The usage of ‚big data‘ is at the heart of this FinTech trend

…..▪ …

EXAMPLE FINTECH

FinTechs are more advanced in data use than traditional banks…

Mo

re p

red

icti

veLe

ss p

red

icti

ve

BanksSource

▪ Data from social media, company reviews, shipping data, satellite images

→ Highly volatile, but viewed holistically can be a predictor of riskiness

▪ Information on past performance and repayment history

→ Limited business risk profile information; rather weak predictor

▪ Volume, volatility, seasonality, industry comparison, trend, etc.

→ Best measure of business health

Ambient

Credit Bureau

Cash flow

Static data

▪ Revenue and profit level, firm size, age, industry, assets, geography, etc.

→ Basic but limited indicator of riskiness

Example of factors used Fintech

EXAMPLE FINTECH

… and drive insights in equity research

Big data analytics allows to analyze more data and potentially increase the number of companies covered per analyst

“Morgan Stanley's AlphaWise Smartphone Tracker has been more accurate than consensus estimates” - Forbes

EXAMPLE INVESTORS

Big data opens new avenues for trading

High Frequency Trading (HFT)

Goal

High Intelligence Trading (HIT)

Speed Speed

Best predictivealgorithm

Big Data

From… … to

EXAMPLE INVESTORS

Investors design algorithms to combinemore and different data sources…

SOURCE: Infosys

EXAMPLE INVESTORS

… e.g. MarketPsych trading on sentiment

EXAMPLE INVESTORS

SOURCE: MarketPsych





Contents

We derive academic insights from bigdata on how investors decide

How do different topics resonate withinvestors?

SOURCE: Feuerriegel et al., 2014

Investors reward forward-lookinginformation

0.2

Company development

Management changes

0.7

0.2Strategy

Topics with positive stock price reaction

In percent; unexpected returns


Extracted topics from stock market announcements and their impact on stock prices

-0.2

-0.2

-0.2

M&A

Financial figures

Share issue

Topics with negative stock price reaction

In percent; unexpected returns

What‘s therelevance of a cowboy for bigdata?

| 27

Corporate disclosures

$$$

Machine learning

algorithms

(LASSO & Ridge)

Word lists with positive

and negative words

Unexpected stock market

returns

Machine learning identifies the tone of words based on their stock market effect

SOURCE: Pröllochs et al., 2014, Alfano et al. 2015

28

21

92

100

5 X

Standard

(static dictionaries)

Ridge

LASSO

Coefficients of dictionary regression on returns

Normalized to 100

Statistically selected dictionaries Benchmark approach

Machine learning

outperforms

predictive accuracy

of standard approach

by a factor of 5

Statistical dictionaries increase predictiveaccuracy by a factor of five

SOURCE: Pröllochs et al., 2014, 2015

Bottom 25%

1.9

AverageBenchmark

A negative tone leads to a lower unexpected return

Unexpected return depending on tonality1

In percent

▪ Negative tonality

yields an up to 2.7%

lower unexpected

return

▪ For a company with a

market capitalization

of USD 10 billion, this

means up to USD 270

million in lower

stock valuation

1 Control variables account for company performance (market-to-book value, market value, cumulative abnormal returns,

market model alpha), sector performance (sector index) and market performance (market index)

-2.7

-1.2


In addition, a lasting long-term effect of tonality on stock prices is observed

Ad hoc announcement day

Positive tonalityNegative tonality Neutral tonality

-4

-2

0

2

4

-2 -1 0 1 2 3 4 5

Average cumulative unexpected return of selected stocksIn percent

Trading days relative to ad hoc announcement day

SOURCE: Liebmann, Tetlock

SOURCES: Liebmann et al., 2011, Tetlock 2008

Better tone = better investor mood

Source: Survey conducted in Q1 2016 (intermediate results); TonalityTech, Finance Research Group – University of Freiburg

More favorable ‘Sell/Buy’ recommendation

Number of responses per ‘A/B’ test

Altered tone leads to better ‘Sell/Buy’ recommendations by finance professionals1

We alter the tone of published press releases based on statistical dictionaries

1 P-value for statistical significance < 0.001

The study

The results

Implication

76% of analysts give

better recommendations

based on tone-enhanced

disclosures

66

212





Contents

Implications of big data for IROs

• Investors rely increasingly on big data support systems; relationship vs. facts gets even more important for IROs

Changing

investor

behaviors

1

Deep insights

enable pro-

active action

• Big data analytics enables IR to get insights from more and unstructured information and incorporate big data knowledge into your decision-making and best practice

2

Faster

insights at

lower cost

• Gauge investor sentiment through big data technology at lower cost

3

Summary of how big data transformscapital markets

Data volume continues to grow 40+% p.a. in the mid-term and companies need to find innovative ways to generate value from this data

IROs should be aware of the degree how much investors use big data to inform their decisions

Big Data allows for predictive analytics of future market events for investors and IROs

big data analytics for investor relations @ niri 2016 conference

Investor Relations