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Data Driven Computational Finance
DSC – Cryptica
Miodrag Janjusevic
CEO FIKS

2.
Introductory Remarks
Data Science Conference - the international character and size makes it unique venue for
all participants.
It represents great knowledge sharing platform, networking opportunity and hopefully, and
an incubator of new ideas that will generate positive impact on economic development,
local and why not, international.
We are witnessing a birth of new economy, driven by rapid technological development.

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Introductory Remarks
Computational speed & computer memory are still growing. This technological advances
enabled new way of data processing, where machine learning is used to find
patterns and regularities and use them to speed up, enhance and in some cases create
decision making process.
Financial Industry was in the forefront of innovation, regarding new technology.
Neural networks or adaptive filters are used for algorithmic trading. Today, machine
driven decision making creates buy and sell signals, and in turn represent the largest portion of
daily trading volume.
Blockchain technology is signaling a birth of new finance, speeding up velocity of money which
always have positive impact on economy.

4.
Data Driven Economy
Driving ingredient of this new industry are data. Safe, clean data that are the
fastest growing global commodity.
Data processing already consumes almost 2-3% of global EE output. That means
data centers, AI etc.
In order to utilize data we need knowledge that can process data and create
platforms for clients to get the products that we designed.
Knowing it bring us to knowledge and how to go about acquiring it. I think that
technological revolution goes hand in hand with the revolution in acquiring
knowledge about this new technologies

5.
Knowledge and its role in modern economy
• Knowledge is what can bring societies that were
economically devasted into position of economic
well being. South Korea, Japan, China etc.
• We are seeing signs of it in Serbia with IT industry. Last
year 50K or so people in IT created about 2bil USD. And
that is just a beginning. With more advanced products
and more R&D those numbers will be significantly bigger.

6.
Examples of knowledge innovation - Academia
• MCF graduate program in computational finance.
• Proper bland of academic fundamentals and practitioners insights in the latest developments
in financial industry.
• There is a big interest for the program and about 45-50 students, domestic and international
are enrolled in the program.
• we have between 45-50 people attending from Serbia and Europe. Professors are domestic and foreign. we have
between 45-50 people attending from Serbia and Europe. Professors are
domestic and fo

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Examples of customized knowledge transmission-FIKS
• There is also a way to do a customization of knowledge delivery. Attending classes
and getting academic degree is sometimes expensive time wise and financially.
• A few of us created company FIKS ( Financial Innovations, Knowledge Solutions ) where
we in collaboration with experts in fields ( Data Science, AI, Blockchain Technology,
Risk Management etc. ) provide knowledge solutions for corporates.
• We want to be Innovative in the way we deliver knowledge. We want to provide
innovative Financial solutions to clients. Our target audience for software solutions
regarding portfolio risk management are Banks, Insurance Companies , Pension Funds
and Hedge Funds.
• We also develop trading arbitrage algorithms.

8.
We create MRI ( MACRO RISK INDEX )
for specific portfolio that gives rolling
history of that specific risk.
To create it we use risk indicators for all
asset classes ( FX, Commodity, Interest
rates Equity) throughout different
geographies ( US, Europe, Asia etc )
Implied volatility is the way that
markets measure risk.
Example one – MRI Index

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Example two: Arbitrage trading system
Five-year CDS versus Vol-Credit Model
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CDS
Model
Debt/Equity arbitrage system that looks for
mispricing in default risk protection .
CDS – Credit Default Swap is standard default
protection contract
Alternative way of creating it default protection
is using equity options Credit Risk
Reversal(CRR)..
One can show that CDS and CRR are equivalent
in their insurance properties and market
dynamics.
That allow us to create a data base of the names
that can be traded and we have a way of
identifying those discrepancies.
e created Debt/Equity arbitrage system that
looks

10.
Example two: Arbitrage trading system
• The key is weather one can access data needed for construction of arbitrage driven trading
system.
• In order to ascertain trading opportunity we have to compare current spread to its historical
averages.
• Prices of accessing historical volatility surfaces surfaces is high. ( check Bloomberg data
prices ).
• Creating historical volatility surface represents combination of using existing data and
extending it via non arbitrage extrapolation to maturities and strikes not seen on the
exchanges.

11.
At the end…
• Data as the main global commodity
• Clean reliable data allow creative solutions
• Examples above give you an idea of how to go about
value added based on data available….
Good luck and have a great conference!

12.
Contact informations
Miodrag Janjusevic CEO FIKS d.o.o
+38163238212
+19149530533
www.fiksrs.com
• miodrag.janjusevic@fiksrs.com
• raisa.khamidullina@fiksrs.com
• branko.urosevic@fiksrs.com
•