Welcome!

Blog Feed Post

Did an Excel error bring down the London Whale?

When JP Morgan Chase announced it had lost more than 2 billion dollars on the capital markets back in May 2012, many pointed to the actions of rogue trader Bruno Iksil as the cause. But was the "London Whale" — the nickname he was given by other traders for his outsized positions — the victim not of hubris, but a simple spreadsheet error? James Kwak, associate professor at the University of Connecticut School of Law and co-founder of the Baseline Scenario blog, noted some interesting facts in JP Morgan Chase's post-mortem investigation of the losses. Specifically, that the Value at Risk (VaR) model that underpinned the hedging strategy “operated through a series of Excel spreadsheets, which had to be completed manually, by a process of copying and pasting data from one spreadsheet to another", and "that it should be automated" but never was. This is a surprisingly common practice: through accretion and incremental advancements, an important statistical calculation somehow ends up being implemented as a convoluted series of Excel worksheets, connected by hundreds (or even thousands) of cell-reference formulas, all driven by a series of input parameters that need to be entered manually. Not only does this impose the risk of introducing errors when cutting-and-pasting the inputs, it also makes the workbook extremely fragile. As anyone who's build a budget in Excel knows, it's very easy when editing the spreadsheet to find that formulas no longer extend to their expected ranges (ever missed the bottom row from a formula when adding new data?), or point to the wrong data entirely. And then there's the possibility of errors in the formulas themselves, which seemed to have been an issue here as well: “After subtracting the old rate from the new rate, the spreadsheet divided by their sum instead of their average, as the modeler had intended. This error likely had the effect of muting volatility by a factor of two and of lowering the VaR . . .” Excel is an excellent tool for many applications, but the intertwined cross-references of formulas make errors like this hard to detect, and hard to correct even if discovered. That's why a programming language designed for data analysis such as the R language is a better platform for building the computational engines behind VaR models and other financial systems. Not only can it automate the process of importing data and inputs from other systems (and thus eliminate cut-and-paste errors), it also provides a structured, maintainable environment for the computational logic, within a framework that promotes code review and unit testing to detect errors. Excel may still be the preferred vehicle for delivering the results, but use R to generate the analytics computations (or embed real-time R computations in Excel) instead of risking an implementation in Excel formulas. Read also: How Validus Re uses Revolution R Enteprise for risk management The Baseline Scenario: The Importance of Excel (via Ben Lorica)

Read the original blog entry...

More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

Latest Stories
As data explodes in quantity, importance and from new sources, the need for managing and protecting data residing across physical, virtual, and cloud environments grow with it. Managing data includes protecting it, indexing and classifying it for true, long-term management, compliance and E-Discovery. Commvault can ensure this with a single pane of glass solution – whether in a private cloud, a Service Provider delivered public cloud or a hybrid cloud environment – across the heterogeneous enter...
"IoT is going to be a huge industry with a lot of value for end users, for industries, for consumers, for manufacturers. How can we use cloud to effectively manage IoT applications," stated Ian Khan, Innovation & Marketing Manager at Solgeniakhela, in this SYS-CON.tv interview at @ThingsExpo, held November 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA.
Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more business becomes digital the more stakeholders are interested in this data including how it relates to business. Some of these people have never used a monitoring tool before. They have a question on their mind like “How is my application doing” but no id...
@GonzalezCarmen has been ranked the Number One Influencer and @ThingsExpo has been named the Number One Brand in the “M2M 2016: Top 100 Influencers and Brands” by Onalytica. Onalytica analyzed tweets over the last 6 months mentioning the keywords M2M OR “Machine to Machine.” They then identified the top 100 most influential brands and individuals leading the discussion on Twitter.
In IT, we sometimes coin terms for things before we know exactly what they are and how they’ll be used. The resulting terms may capture a common set of aspirations and goals – as “cloud” did broadly for on-demand, self-service, and flexible computing. But such a term can also lump together diverse and even competing practices, technologies, and priorities to the point where important distinctions are glossed over and lost.
Predictive analytics tools monitor, report, and troubleshoot in order to make proactive decisions about the health, performance, and utilization of storage. Most enterprises combine cloud and on-premise storage, resulting in blended environments of physical, virtual, cloud, and other platforms, which justifies more sophisticated storage analytics. In his session at 18th Cloud Expo, Peter McCallum, Vice President of Datacenter Solutions at FalconStor, discussed using predictive analytics to mon...
All clouds are not equal. To succeed in a DevOps context, organizations should plan to develop/deploy apps across a choice of on-premise and public clouds simultaneously depending on the business needs. This is where the concept of the Lean Cloud comes in - resting on the idea that you often need to relocate your app modules over their life cycles for both innovation and operational efficiency in the cloud. In his session at @DevOpsSummit at19th Cloud Expo, Valentin (Val) Bercovici, CTO of Soli...
Information technology is an industry that has always experienced change, and the dramatic change sweeping across the industry today could not be truthfully described as the first time we've seen such widespread change impacting customer investments. However, the rate of the change, and the potential outcomes from today's digital transformation has the distinct potential to separate the industry into two camps: Organizations that see the change coming, embrace it, and successful leverage it; and...
Without a clear strategy for cost control and an architecture designed with cloud services in mind, costs and operational performance can quickly get out of control. To avoid multiple architectural redesigns requires extensive thought and planning. Boundary (now part of BMC) launched a new public-facing multi-tenant high resolution monitoring service on Amazon AWS two years ago, facing challenges and learning best practices in the early days of the new service. In his session at 19th Cloud Exp...
Extracting business value from Internet of Things (IoT) data doesn’t happen overnight. There are several requirements that must be satisfied, including IoT device enablement, data analysis, real-time detection of complex events and automated orchestration of actions. Unfortunately, too many companies fall short in achieving their business goals by implementing incomplete solutions or not focusing on tangible use cases. In his general session at @ThingsExpo, Dave McCarthy, Director of Products...
Regulatory requirements exist to promote the controlled sharing of information, while protecting the privacy and/or security of the information. Regulations for each type of information have their own set of rules, policies, and guidelines. Cloud Service Providers (CSP) are faced with increasing demand for services at decreasing prices. Demonstrating and maintaining compliance with regulations is a nontrivial task and doing so against numerous sets of regulatory requirements can be daunting task...
Fact: storage performance problems have only gotten more complicated, as applications not only have become largely virtualized, but also have moved to cloud-based infrastructures. Storage performance in virtualized environments isn’t just about IOPS anymore. Instead, you need to guarantee performance for individual VMs, helping applications maintain performance as the number of VMs continues to go up in real time. In his session at Cloud Expo, Dhiraj Sehgal, Product and Marketing at Tintri, sha...
The Internet of Things (IoT) promises to simplify and streamline our lives by automating routine tasks that distract us from our goals. This promise is based on the ubiquitous deployment of smart, connected devices that link everything from industrial control systems to automobiles to refrigerators. Unfortunately, comparatively few of the devices currently deployed have been developed with an eye toward security, and as the DDoS attacks of late October 2016 have demonstrated, this oversight can ...
Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to impr...
Join Impiger for their featured webinar: ‘Cloud Computing: A Roadmap to Modern Software Delivery’ on November 10, 2016, at 12:00 pm CST. Very few companies have not experienced some impact to their IT delivery due to the evolution of cloud computing. This webinar is not about deciding whether you should entertain moving some or all of your IT to the cloud, but rather, a detailed look under the hood to help IT professionals understand how cloud adoption has evolved and what trends will impact th...