Financial Services Analytics: Non-Exhaustive list of theory questions
Caveat: Some of the theory questions at the exam are based on the below list of questions. They are
not identical. The list is not exhaustive.
Lecture 1: Introduction to the course
1. Make sure to understand how data brings value by transforming a business problem into a data
problem and data solution. This may be part of a case study where you need to write down a data
science pipeline that solves a problem.
- Business problem
→ Real problem in the firm wherefor a solution is desired
→ Example insurance: which premium is sufficient to cover all the claims, business costs & a
reasonable profit?
- Data problem
→ Which data/models are useful?
→ Example: which data can be used to avoid fraud?
- Data solution
→ We use this data & this model to make decisions
→ Example: collect data & client characteristics
- Business solution
→ The solution that’s really used in the company
→ Example: questionnaires in an interactive way & calculate the premium as such
, 2. What have been the six drivers of the trend towards digitalization in financial services?
- Technology has changed
More communication on a digital advice leads to more data to store and process. Because of this
more and more decisions can be data-driven and automated
- Customers have changed
They are more demanding in terms of user experience, require personalization and low costs.
They embrace digitalization: they have a high trust in information technology firms, they interact with
an increasing number of digital devices, they accept that user data is used for corporate purposes,
and they accept to interact with robots.
More demanding in terms of user experience → Simple purchasing process → Quick response
→ Require personalization
→ Require low costs
→ Embrace digitalization → High trust in information technology (IT) firms → Interact with an
increasing number of digital devices (PC, laptop, smartphone, tablet, smartwatch…) → Accept that
user data is used for corporate purposes → Accept to interact with robots (chatbots, automated
investment advice…)
- Regulation has changed
Payment Services EU Directive = PSD2
Banks are required to provide access to payments accounts for Third Party Providers (TPPs)
on the condition that the TPP has received permission from the bank customers to whom
the accounts belong.
- Bank’s profitability is under pressure
Return on equity below the cost of capital is unsustainable in the long run.
→ The profitability for banks is under pressure
→ ROE is declining year after year due to the overcapacity & the low margins
→ Solutions are mergers & digitalization
→ Aiming for state-of-the-art technology → Stay relevant (grow the business, what do consumers
really want?) → Be efficient (reduce operating costs) → Success in digitalization (more revenues &
less costs to create more profits)
- Competition has increased
New entrants that specialize in 1 service. Ex. Apple Pay, Google Pay, …
- Covid-19
Drop in use of cash, no physical contact with bank
Speed up investments in digitalization
, 3. What are the hurdles in the transformation from a traditional physical bank to a phygital or digital
bank? (Phygital: Different branches in one network e.g. Belfius mobile banking + interaction platform
for customer services)
- Costs
→ Transformation costs a lot of money (IT, data, new staff…)
→ To be balanced with gains from automation and digitalization
→ Less personnel, less brick-and-mortar branches…
- People
→ War for talent
→ People who can make the data tell its story are high in demand
- Technology
→ Fast evolving
→ Staying up-to-date is challenging
→ High costs, right people, good vision…
- Vision
→ Proactively & persistence (as it takes time)
→ C-suite at banks have a CDO’s and CIO’s
→ Chief data officer, Chief information officer, Chief digital officer
4. Explain the term “augmented workforce”
A blend of human employees and technology workers on tasks together e.g. routine tasks by robots,
other jobs are created to monitor, install and maintain these robots
, Lecture 2: From data to insights
1. Explain with a plot what insights a candlestick chart provides about the performance of a financial
asset.
The top part of the figure shows us different candlestick for each day. Green is used for a up day and
pink for a down day. The bottom part is the amount of volume traded that day. Pink if it is more sold
then bought and green if more bought then sold.
2. What are the most useful transformations of price data to obtain insights about the risk of the
investment and why?
- The price data themselves need to be transformed to yield insights. These transformations are
done repeatedly and are best programmed as functions.
- Useful transformations are:
- Smoothening the price data by taking averages
- Studying financial risk and reward by computing returns
- Time series aggregation: from individual trades (intraday) to daily, weekly,… observations
- Time series aggregation = transformation from transaction data to a summary value per day
- Time series averaging = summarize data by taking average
- Differencing = working with the change in the price data
3. Suppose a trader is interested in market timing. How can (s)he use the daily prices to determine
the investment position according to Faber? Include a formula in your answer.
Caveat: Some of the theory questions at the exam are based on the below list of questions. They are
not identical. The list is not exhaustive.
Lecture 1: Introduction to the course
1. Make sure to understand how data brings value by transforming a business problem into a data
problem and data solution. This may be part of a case study where you need to write down a data
science pipeline that solves a problem.
- Business problem
→ Real problem in the firm wherefor a solution is desired
→ Example insurance: which premium is sufficient to cover all the claims, business costs & a
reasonable profit?
- Data problem
→ Which data/models are useful?
→ Example: which data can be used to avoid fraud?
- Data solution
→ We use this data & this model to make decisions
→ Example: collect data & client characteristics
- Business solution
→ The solution that’s really used in the company
→ Example: questionnaires in an interactive way & calculate the premium as such
, 2. What have been the six drivers of the trend towards digitalization in financial services?
- Technology has changed
More communication on a digital advice leads to more data to store and process. Because of this
more and more decisions can be data-driven and automated
- Customers have changed
They are more demanding in terms of user experience, require personalization and low costs.
They embrace digitalization: they have a high trust in information technology firms, they interact with
an increasing number of digital devices, they accept that user data is used for corporate purposes,
and they accept to interact with robots.
More demanding in terms of user experience → Simple purchasing process → Quick response
→ Require personalization
→ Require low costs
→ Embrace digitalization → High trust in information technology (IT) firms → Interact with an
increasing number of digital devices (PC, laptop, smartphone, tablet, smartwatch…) → Accept that
user data is used for corporate purposes → Accept to interact with robots (chatbots, automated
investment advice…)
- Regulation has changed
Payment Services EU Directive = PSD2
Banks are required to provide access to payments accounts for Third Party Providers (TPPs)
on the condition that the TPP has received permission from the bank customers to whom
the accounts belong.
- Bank’s profitability is under pressure
Return on equity below the cost of capital is unsustainable in the long run.
→ The profitability for banks is under pressure
→ ROE is declining year after year due to the overcapacity & the low margins
→ Solutions are mergers & digitalization
→ Aiming for state-of-the-art technology → Stay relevant (grow the business, what do consumers
really want?) → Be efficient (reduce operating costs) → Success in digitalization (more revenues &
less costs to create more profits)
- Competition has increased
New entrants that specialize in 1 service. Ex. Apple Pay, Google Pay, …
- Covid-19
Drop in use of cash, no physical contact with bank
Speed up investments in digitalization
, 3. What are the hurdles in the transformation from a traditional physical bank to a phygital or digital
bank? (Phygital: Different branches in one network e.g. Belfius mobile banking + interaction platform
for customer services)
- Costs
→ Transformation costs a lot of money (IT, data, new staff…)
→ To be balanced with gains from automation and digitalization
→ Less personnel, less brick-and-mortar branches…
- People
→ War for talent
→ People who can make the data tell its story are high in demand
- Technology
→ Fast evolving
→ Staying up-to-date is challenging
→ High costs, right people, good vision…
- Vision
→ Proactively & persistence (as it takes time)
→ C-suite at banks have a CDO’s and CIO’s
→ Chief data officer, Chief information officer, Chief digital officer
4. Explain the term “augmented workforce”
A blend of human employees and technology workers on tasks together e.g. routine tasks by robots,
other jobs are created to monitor, install and maintain these robots
, Lecture 2: From data to insights
1. Explain with a plot what insights a candlestick chart provides about the performance of a financial
asset.
The top part of the figure shows us different candlestick for each day. Green is used for a up day and
pink for a down day. The bottom part is the amount of volume traded that day. Pink if it is more sold
then bought and green if more bought then sold.
2. What are the most useful transformations of price data to obtain insights about the risk of the
investment and why?
- The price data themselves need to be transformed to yield insights. These transformations are
done repeatedly and are best programmed as functions.
- Useful transformations are:
- Smoothening the price data by taking averages
- Studying financial risk and reward by computing returns
- Time series aggregation: from individual trades (intraday) to daily, weekly,… observations
- Time series aggregation = transformation from transaction data to a summary value per day
- Time series averaging = summarize data by taking average
- Differencing = working with the change in the price data
3. Suppose a trader is interested in market timing. How can (s)he use the daily prices to determine
the investment position according to Faber? Include a formula in your answer.
