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Cracking the Code:
Data ScienceTackles
Investment
Management
Sharala Axryd,
Founder and CEO of
The Center of Applied
Data Science

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We need to understand the business
goals
‒ What are your strategic KPIs
‒ What are the challenges you are
facing as a business
‒ How can data and analytics support
delivering these objectives
We explore the data and
analytic capability we have to
deliver these insights
‒ Do we have the data
‒ Can we get the data
‒ How sophisticated do we
need to be with the analytics
We consider do we have the people
to deliver this capability
‒ Can we train our people
‒ How do we augment capability with
external resource
‒ Are we leveraging skills across the
organization
Finally, we look at the tools
‒ Do these align with our
business goals
‒ Are they complimentary
with the skills and talent we
haveWhat does it mean
to be data-driven?
Advanced Analytics: A Powerful Edge
As innovation triggers an explosion of options, expertise becomes paramount

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Smart advisors (robo advisors):
• Relies on algorithms for
management strategies.
• Considers various customer data –
risk tolerance, behaviour, legal
benchmarks, preferences – and make
recommendations.
• Allows portfolio managers to suggest
tailored investment plans to clients in
both B2B and B2C operations.
Fraud detection powered by
neural network
• Anti-money laundering and fraud-
detection models that helps
identify suspicious activities.
• The system is trained and
developed that can track and
assess the behaviour of all the
individuals involved in the process.
• Detects fraud and implicit link
between customer-potential fraud.
Predictive / Scenario-based
analytics
• Predictive analytics - historical
data to determine the relationships
of data with outputs and build
models to check against current
data.
• Scenario-based analytics - analyse
possible future events by
considering alternative possible
outcomes, presents several
alternative future developments.
Data Driven Asset Management
Here are the main operations that can be enhanced with a data driven approach

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The Rise and Rise of Data
Investors will be more regularly informed,
analysis will be more granular, and
investment opportunities will ultimately be
unearthed more frequently.
Alternative Data – Don’t have to rely
so heavily – or perhaps even at all – on
traditional, cyclical financial data.
The very fact that we could have fresh data
consistently at our disposal that allows us to
make assessments in real-time, rather
than periodically, is a profound evolution.
Through increasingly sophisticated
techniques that can capture huge quantities
of data, we now have the means to reveal
behaviour, trends, and patterns of
enormous relevance when gauging the
appeal of a potential investment.

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Using Big Data to Avoid Bias
Machine learning is opening up new avenues in behavioural finance – but human curiosity and
intelligence are still essential
Behavioural finance theory has identified three main types of bias, which can explain many central facts
about markets, including average returns, time series, predictability, momentum and reversals, bubbles,
and trading volume:
Over-extrapolate the past,
putting too much weight on
the recent past (such as recent
performance) while making
decisions and forming beliefs
about the future.
Over-confident in our
own ability over other
people’s, as well as in the
accuracy of our beliefs.
More sensitive to the
prospect of loss over
gain, and to give too
much weight to low-
probability outcomes.
Another bias is the influence of
narratives and their contagion.
The current biggest influencer on the US
stock market is the narrative of Donald
Trump as cutter of taxes and regulations
giving a boost to earnings, despite a
rational understanding being that these
effects will only be temporary.

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How millennials are changing the face of finance & investing

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PI 2018702181™

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Characteristics of different maturity model
Strategy
Analytics
Talent
Mature data and strategyMature in data and technology
These organisations rely heavily on
technology to deliver their services.
As a result have access to large
quantum of data.
Being technology led, they are
slow to movers from a strategy,
organisational culture change and
analytics adoption perspective
Organisations that operate in
heavily regulated industries are
motivated to manage and use their
data as an asset but at the same
time crippled by legacy systems
and data regulations.
Typical sectors: Retail banks, Utilities.
• Data
• Mature across all areas
• Newly formed in the information age
with little or no legacy systems and
regulations.
• Innovation based on data and
analytics culture drives the
organisation.
• Typical sectors: Online gaming,
consumer services (Uber etc).
Typical sectors: Telco, media.
Mature strategy
Traditional monoliths with large
amount of data. The appetite to
understand and make use of the
data is there but the execution at
the organisational level is low.
Limited internal technology
knowledge is seen as a limitation
Typical sectors: Healthcare,
Newsprint.
© 2016 ANSYS SDN BHD; the operating company for ADAX. All rights reserved.

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CADS Approach
Analytics Framework
Data Management
Is data available?
Diagnostic
Analytics
Why did it happen?
ValuetoBusiness
Source :
Adapted from Gartner’s
Analytics Ascendancy Model
Difficulty
Descriptive
Analytics
What happened?
Predictive
Analytics
What will happen?
Prescriptive
Analytics
How can we make it happen?
Hindsight
Identify Revenue
Shortfall
Insight
Revenue Shortfall
caused by shortage of
key inventory
Insight
Forecast future supply
and demand of
inventory
Foresight
Optimize pricing
Visualize your
Data
Get
Hindsight
Insight
Foresight
Make Fact
Based
Decisions
Collect data Maintain dataValidate data Store data

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“The illiterate of the 21st century will not be
those who cannot read and write, but those who
cannot learn, unlearn, and relearn.”
— AlvinToffler, American Writer, Futurist, Businessman

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ThankYou