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CISC 849 : Applications in Fintech BIG DATA IN FINANCE SERVICES Markets Customers Channels Products Regulations Competitors Suppliers Employees
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CISC 849 : Applications in Fintech
ARUNPRABHU SADAIYAPPANDept of Computer & Information Sciences
University of Delaware
BIG DATA APPROACH TO ANALYZING MARKET VOLATILITY
CISC 849 : Applications in Fintech
WHY BIG DATA TECHNOLOGY?
CISC 849 : Applications in Fintech
BIG DATA IN FINANCE SERVICES• Markets
• Customers
• Channels
• Products
• Regulations
• Competitors
• Suppliers
• Employees
CISC 849 : Applications in Fintech
MARKET LIQUIDITY
Market's ability to facilitate the purchase or sale of
an asset without causing drastic change in the asset's
price
CISC 849 : Applications in Fintech
LIQUIDITY INDICATOR• VOLUME SYNCHRONISED PROBABILITY OF
INFORMATION TRADING
CISC 849 : Applications in Fintech
TECHNIQUES FOR BIG DATA ANALYSIS
• EFFICIENT FILE ORGANISATION FOR STORING TRADING RECORDS
• EFFICIENT ALGORITHM FOR COMPUTING VPIN
• PARALLELIZATION OF COMPUTATIONAL TASKS
CISC 849 : Applications in Fintech
FILE ORGANIZATION
• 67 Months worth of data (Liquid futures trade information)
• File Format : CSV (Comma separated value)
• File Size : 140 GB
Processing time for computing VPIN values : 142 seconds
CONVENTIONAL METHOD:
CISC 849 : Applications in Fintech
FILE ORGANIZATION
• Same amount of data (for 67 months)
• File Format : HDF5 (Hierarchical Data Format)
• File Size : 41 GB
Processing time for computing VPIN values : 0.4 second
PROPOSED METHOD:
CISC 849 : Applications in Fintech
FILE ORGANIZATION
• 29% gain in memory storage
• Less time in reading/writing the file
• Better organization for data
Reason behind the Efficiency: HDF5 files store data in binary form
IMPROVEMENT:
CISC 849 : Applications in Fintech
ALGORITHM FOR COMPUTING VPINInitially, Data arrives at irregular frequency
Volume BarsVolume Bars
Bulk Volume Bulk Volume ClassificationClassification
BucketsBuckets
VPINVPIN
CISC 849 : Applications in Fintech
VOLUME BAR• SHELL SORT – To order trades in a volume
bar
• To compute Median Prices
CISC 849 : Applications in Fintech
SHELL SORT ANALYSIS
• BEST CASE (LOWER BOUND): O(N(log N )^2) • WORST CASE (UPPER BOUND): Θ(N^2)
In this paper, the runtime is mentioned as O(log N )
In-place Sorting (Space complexity is O(N) )
CISC 849 : Applications in Fintech
ALGORITHM FOR COMPUTING VPIN
Time Taken to construct Volume bars with different nominal prices
CISC 849 : Applications in Fintech
BULK VOLUME CLASSIFICATION• Trades are classified as “Buyer-initiated” or
“Seller-initiated”
• BVC assigns a fraction of volume as buys
The remainder as sells based on normalized sequential price change
CISC 849 : Applications in Fintech
BUCKETS
• When forming buckets Each volume bar is considered as a single trade with the nominal price
• 30 Volume bars in a buckets (maximum upto 50)
• Most recent buckets with buy and sell volumes Kept in Fixed Memory
CISC 849 : Applications in Fintech
BUCKETS
Pseudocode for storing Volume in Buckets
CISC 849 : Applications in Fintech
CALCULATING VPIN
FORMULA:
PSEUDOCODE:
CISC 849 : Applications in Fintech
EXPERIMENTAL RESULTS
Statistics on prices for volume bars and the resulting VPIN:
(Overall trades of ES)
CISC 849 : Applications in Fintech
EXPERIMENTAL RESULTS
CISC 849 : Applications in Fintech
PARALLELIZATION
PREFERRED IMPLEMENTATION: POSIX THREADS
Instrument with large number of trades assigned first
CISC 849 : Applications in Fintech
HEDGE ANALYTICS – SWISS BASED STARTUP
•RISK FACTOR ANALYSIS
•PERFORMANCE ANALYSIS
•NEW CUSTOMER WIN RATE ANALYSIS
Universal Meta data Search Engine
Incorporated NoSQL Technology to avoid inconsistency
CISC 849 : Applications in Fintech
THANK YOU!