Presentation held at the 5th Risk Summit organized by Center for Risk Studies at the University of Cambridge. See http://www.risk.jbs.cam.ac.uk/news/events/risksummits/risksummit2014.html
Presented at the annual Financial Risk and Network Theory conference in Cambridge, I discuss recent work by FNA on addressing various financial risks with the help of network analysis.
As the financial system becomes more complex, new methods to understand the inherent risks and dynamics are needed. Kimmo Soramäki will discuss how network analysis of large‐scale financial transaction data can be used to improve our understanding systemic risk. He will also show case studies how visual analytics and accurate data driven maps of asset correlations and tail risks can enable a stronger intuition of market dynamics.
Global Trends in Large Value Payment SystemsKimmo Soramaki
We discuss 10 main trends in the settlement of large-value interbank payments;
1. Diffusion of Real Time Gross Settlement
2. Take Off of Hybrid Systems
3. Emergence of Cross Border and Offshore Payment Systems
4. The Rise of CLS
5. Increasing Settlement Values and Volumes
6. Shrinking Average Payment Size
7. Falling Number of System Participants
8. Extended Operating Hours
9. Declining Transaction Fess
10. Adoption of Common Standards for LVPS
Presentation at FSC-PSSC Workshop "Systemic risk analysis: interconnectedness within the financial system and market infrastructures", Frankfurt, 17 October 2012
The paper presented here will be published in Journal of Economic Behavior and Organization (http://www.fna.fi/papers/jebo2012gs.pdf)
Presented at the annual Financial Risk and Network Theory conference in Cambridge, I discuss recent work by FNA on addressing various financial risks with the help of network analysis.
As the financial system becomes more complex, new methods to understand the inherent risks and dynamics are needed. Kimmo Soramäki will discuss how network analysis of large‐scale financial transaction data can be used to improve our understanding systemic risk. He will also show case studies how visual analytics and accurate data driven maps of asset correlations and tail risks can enable a stronger intuition of market dynamics.
Global Trends in Large Value Payment SystemsKimmo Soramaki
We discuss 10 main trends in the settlement of large-value interbank payments;
1. Diffusion of Real Time Gross Settlement
2. Take Off of Hybrid Systems
3. Emergence of Cross Border and Offshore Payment Systems
4. The Rise of CLS
5. Increasing Settlement Values and Volumes
6. Shrinking Average Payment Size
7. Falling Number of System Participants
8. Extended Operating Hours
9. Declining Transaction Fess
10. Adoption of Common Standards for LVPS
Presentation at FSC-PSSC Workshop "Systemic risk analysis: interconnectedness within the financial system and market infrastructures", Frankfurt, 17 October 2012
The paper presented here will be published in Journal of Economic Behavior and Organization (http://www.fna.fi/papers/jebo2012gs.pdf)
Financial Cartography at Bogazici UniversityKimmo Soramaki
As the financial system becomes more complex, new methods to understand its inherent risks and dynamics are needed. Kimmo Soramäki will discuss how network analysis of large‐scale financial transaction data can be used to improve our understanding systemic risk. He will also show case studies how visual analytics and accurate data driven maps of asset correlations and tail risks can enable a stronger intuition of market dynamics.
Global Network of Payment Flows - Presentation at Commerzbank Cash ForumKimmo Soramaki
The presentation summarizes results from the research paper by Samantha Cook and myself on "The Global Network of Payment Flows" and discusses other applications of payment data for gaining business insights or for improving risk models.
Applications of Network Theory in Finance and ProductionKimmo Soramaki
In recent years, network theory has proved useful in applications ranging from cancer research to the social graph. Applications of network theory are becoming ever more present also in economics and finance, with network analysis providing answers to questions where traditional analysis methods are weak, and leading to improved models across wide types of risks. This presentation discusses three real-world applications of network theory: identifying pivotal countries and payment corridors from the global network of payment flows, using industry level value chains for casualty risk modeling, and using asset correlation networks for detecting emerging and systemic risks.
Slides from a PRMIA Webinar broadcast on 9 October 2013 by Alan Laubsch and me.
Description from PRMIA Website:
This webinar will apply advanced network visualization techniques to detect emerging systemic stress scenarios.
We will start with an introduction of the Adaptive Stress Testing framework, which harnesses network intelligence in the stress testing process. We'll show how Adaptive Stress Testing can be used to design credible scenarios and monitor emerging risks.
We review historical case studies, and then discuss potential emerging threats in the current market environment by using network visualization.
System shock analysis and complex network effectsKimmo Soramaki
Joint presentation with Michelle Tuveson and Dr Andrew Coburn from Cambridge Risk Center at the Conference Board Global Risk Conference in New York, 8 May 2013.
Links to conference website: http://www.conference-board.org/conferences/conferencedetail.cfm?conferenceid=2456
Financial Cartography at Bogazici UniversityKimmo Soramaki
As the financial system becomes more complex, new methods to understand its inherent risks and dynamics are needed. Kimmo Soramäki will discuss how network analysis of large‐scale financial transaction data can be used to improve our understanding systemic risk. He will also show case studies how visual analytics and accurate data driven maps of asset correlations and tail risks can enable a stronger intuition of market dynamics.
Global Network of Payment Flows - Presentation at Commerzbank Cash ForumKimmo Soramaki
The presentation summarizes results from the research paper by Samantha Cook and myself on "The Global Network of Payment Flows" and discusses other applications of payment data for gaining business insights or for improving risk models.
Applications of Network Theory in Finance and ProductionKimmo Soramaki
In recent years, network theory has proved useful in applications ranging from cancer research to the social graph. Applications of network theory are becoming ever more present also in economics and finance, with network analysis providing answers to questions where traditional analysis methods are weak, and leading to improved models across wide types of risks. This presentation discusses three real-world applications of network theory: identifying pivotal countries and payment corridors from the global network of payment flows, using industry level value chains for casualty risk modeling, and using asset correlation networks for detecting emerging and systemic risks.
Slides from a PRMIA Webinar broadcast on 9 October 2013 by Alan Laubsch and me.
Description from PRMIA Website:
This webinar will apply advanced network visualization techniques to detect emerging systemic stress scenarios.
We will start with an introduction of the Adaptive Stress Testing framework, which harnesses network intelligence in the stress testing process. We'll show how Adaptive Stress Testing can be used to design credible scenarios and monitor emerging risks.
We review historical case studies, and then discuss potential emerging threats in the current market environment by using network visualization.
System shock analysis and complex network effectsKimmo Soramaki
Joint presentation with Michelle Tuveson and Dr Andrew Coburn from Cambridge Risk Center at the Conference Board Global Risk Conference in New York, 8 May 2013.
Links to conference website: http://www.conference-board.org/conferences/conferencedetail.cfm?conferenceid=2456
Mapping Financial Landscapes @ Norges BankKimmo Soramaki
Financial market turmoil has revealed the interconnected nature of modern financial systems. Industry, regulators and academics agree on the need for better analytical tools that can help monitor and safeguard against systemic risks. Kimmo Soramaki reviews new research in financial network analysis, including how network analysis of large-scale financial transaction data can be used to improve our understanding of how the financial system functions. How can visual analytics of time-series networks bring new insights? How can cross-asset networks enable stronger intuition of market dynamics?
Identifying systemically important banks in payment systemsKimmo Soramaki
Talk at 'Systemic Risk Conference - Economists meet Neuroscientists' in Frankfurt on 18 September 2012. The conference was organized by House of Finance and Frankfurt Institute for Advanced Studies.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
1. FINANCIAL CARTOGRAPHY
1
23 June 2014
!
Risk Summit at Center for Risk Studies
The Pulse of Risk: From Big Data to Business Value
!
!
Dr. Kimmo Soramäki
Founder and CEO, FNA Ltd.
10. 10
Correlation Maps
Difficult to understand large-scale
correlation or other dependence
structures.!
!
Especially time series.!
!
How to filter signal from noise?!
!
How to put the correlations and
their changes in context with
changes/returns and volatility?!
!
!
Objective is to efficiently
represent a complex system!
…"
11. 11
Significant Correlations
Common method to visualize
large correlation matrices is
with heat maps.!
!
!
!
!
!
If we only keep statistically
significant correlations with
95% confidence level, the
resulting matrix is sparse (with
short time periods).
All correlations
(last 100 days)!
Statistically
significant
correlations
(last 100 days)!
13. 13
Correlation Networks
A sparse matrix is often well
represented as a network. !
!
We encode correlations as links
between the correlated nodes/
assets.!
!
!
Red link = negative correlation
Black link = positive correlation!
!
!
Absence of link marks that
asset is not significantly
correlated.!
14. 14
Dimensionality Reduction & Filtering
Next, we identify the Minimum
Spanning Tree (MST) and filter
out other correlations.!
!
Rosario Mantegna (1999)
‘Hierarchical Structure in
Financial Markets’
!
This shows us the backbone
correlation structure where
each asset is connected with
the asset with which its
correlation is strongest.
15. 15
Coordinate System
We use a radial tree layout
algorithm (Bachmaier &
Brandes 2005) that places the
assets so that:!
!
• Shorter links in the tree
indicate higher correlations!
!
• Longer links indicate lower
correlations!
!
As a result, we also see how
the assets cluster (analogous
to single linkage clustering).!
16. 16
Encoding non-spatial data
Node color indicates last daily
return!
!
Green = positive!
!
Red = negative!
!
Node size indicates magnitude
of return!
!
!
17. 17
“Here be Dragons”
Sornette’s Dragon King:
“Extreme events can be
predicted”!
!
Mandelbrot’s Volatility
Clustering: “Large changes tend
to be followed by large changes”!
!
!
-> Identify VaR exceptions
(return outside 95% VaR
bounds)!
!
-> Map them as bright green or
red nodes!
Track the number of outliers
each day
Highlight outliers in their context
18. 18
Correlations Maps
!
Dimensionality Reduction & Filtering!
-> Minimum Spanning Tree!
!
!
Coordinate System!
-> Radial Tree layout algorithm (correlation
distances)!
!
!
System for visual encoding of (non-spatial) data!
-> Returns and Outliers!
!
!