big data analytics for investor relations @ niri 2016 conference
TRANSCRIPT
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 (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 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
• Big data and financial markets
• Academic big data research for
capital market communication
• Implications of big data for IROs
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
SOURCE: Feuerriegel et al., 2014
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?
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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
SOURCE: Feuerriegel et al., 2015
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
• Big data and financial markets
• Academic big data research for
capital market communication
• Implications of big data for IROs
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