How to Compare Actuals vs. Forecast in Tableau

 

Forecasting in Tableau uses a technique known as exponential smoothing. This where an algorithm tries to find a regular pattern in your data that can be continued into the future.

In this video I’ll share some helpful tips to help you determine which options you should select that will enable Tableau to make the most predictive forecast for your data. By the end of the video you will be able to differentiate between an additive and multiplicative data pattern and to evaluate MASE to measure the accuracy of the forecast.

I’m not talking about this Mase:

Harlem World

Rather, you’ll learn about the mean absolute scaled error (i.e., MASE) and how it helps you judge the quality of the model.

In addition, you’ll also also learn how to compare your actual data to the Tableau forecast in order to judge if the model is doing its job.

If you’ve used the forecasting capabilities in Tableau without knowing about these concepts, you might have generated an inaccurate error riddled forecast. Don’t just set a forecast and forget it. Watch this video and generate better forecasts in Tableau!

Here is additional reading from Tableau on the forecast descriptions (including MASE).

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

 

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How to Generate a Forecast in Power BI

In this video I’ll demonstrate how to use the forecasting analytics option in Power BI. Although Power BI’s forecast algorithm is a black box, it’s more than likely using exponential smoothing to generate results. At a very high level, exponential smoothing is an algorithm that looks for patterns in data and extrapolates that pattern into the future. To help exponential smoothing perform at an optimal level, it is very important to pick an accurate seasonality estimation, as this will have an outsized effect on the time series forecast.

If your data points are at the daily grain, then you’d use 365 as your seasonality value. If your data points are at a monthly grain, then you’d use 12 as your seasonality value. Generally, the more seasonality cycles (e.g., years) that you provide Power BI, the more predictive your forecast will be.

Without giving away the whole video, here is a pro and a con of using forecasting in Power BI.

Con: As I stated earlier the exact algorithm is a black box. Although based upon a Power View blog post, we can reasonably assume exponential smoothing is involved. Furthermore, the results cannot be exported into a spreadsheet and analyzed.

Pro: The ability to “hindcast” allows you to observe if the forecasted values match your actual values. This ability allows you to judge whether the forecast is performing well.

Check out the video; I predict you’ll learn something new.

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

2018 – The Most Popular Posts from AnthonySmoak.com

Two thousand eighteen was the third year in which I’ve been actively blogging here on AnthonySmoak.com. It’s interesting to look back on my first full year of activity in 2016 and compare the blog’s growth in views and visitors.  For my first year of posting back in 2016, I had a little under a 1,000 visitors for the whole year, as compared to 2018 where I had about 28.5 thousand visitors. The past year was also momentous for my Youtube channel as I hit the milestone 1,000 subscribers mark.

I thoroughly enjoy sharing what I learn with people and that’s what keeps me coming back to share more. The positive feedback and the comments I receive from readers and followers make this pastime of mine worth carrying on.

Since I finally have a good sample size of views to draw from, I wanted to share a year in review of the most popular articles here on AnthonySmoak.com for 2018. Although I have a number of Tableau video posts, my most popular posts are related to companies and/or strategy. To my surprise, there seems to be a sizable audience for technology related strategy at Wal-Mart.

I want business intelligence practitioners to come here for their Tableau fix, but I also want business school, management information systems students and anyone else occupying the dual spaces of business and technology (like I’ve done throughout my industry and consulting career) to read my posts on various companies and their uses of technology.

Without further ado, here are my 10 most popular blog posts over the past year.

1. More Than You Want to Know About Wal-Mart’s Technology Strategy Part 1

Popularity Index Score = 100

By far this was my most read blog article of the year and the most popular of all time. It is the first part of a three part series that I wrote that takes a look at a few different areas related to Wal-Mart’s use of technology. This post specifically relates to technology infrastructure and IT staffing.

2. Michael Porter’s Generic Cost Leadership Strategy Explained

Popularity Index Score = 90

This is the first of two posts where I cover the famous business professor’s generic strategies. In this specific post I describe the cost leadership strategy and its advantages and disadvantages. The cost leadership strategy is employed when a company aims to be the lowest cost producer in the market. It enables a business to reap higher than average profitability.

3. Michael Porter’s Generic Differentiation Strategy Explained

Popularity Index Score = 84

At the opposite side of the generic cost leadership strategy is the differentiation strategy. A differentiation strategy advocates that a business must offer products or services that are valuable and unique to buyers above and beyond a low price.

4. How to Conditionally Format Text Cell Color in Tableau

Popularity Index Score = 82

Of all of my Tableau related videos, this is my personal favorite. I spent many hours researching how to perform this trick for a thoroughly ungrateful party I should add (but I won’t get into that). Tableau is not Excel and table data should be used sparingly in Tableau, but if you have to display table data then do it with style. The upside of my struggle to solve this problem is that I was left with a great video to share with my followers. This video is the 2nd most viewed video on my Youtube channel.

5. How to Fix an Import Specification Error in Microsoft Access

Popularity Index Score = 75

This post hearkens back to my days as a data analyst for General Motors where I heavily used Microsoft Access. I have a love/hate relationship with Access in that it can be an effective tool for light data work but it has the ability to frustrate you with seemingly nonsensical errors. In this post I share my findings regarding how to overcome an Import Specification Error (Run-Time error ‘3625’).  One would think that “import steps” and an import specification can be referenced and used the same way in code, but that is not the case.

Articles 6 -10

6. Strategic Analysis of ADP (Popularity Index: 69)
7. Costco’s Underinvestment in Technology Leaves it Vulnerable to Disruption (Popularity Index: 68)
8. More Than You Want to Know About Wal-Mart’s Technology Strategy Part 2 (Popularity Index: 63)
9. The Definitive Walmart E-Commerce and Digital Strategy Post (Popularity Index: 45)
10. More Than You Want to Know About State Street Bank’s Technology Strategy Part 3
 (Popularity Index: 40)

I want to thank everyone who follows AnthonySmoak.com and who also subscribes to me on youtube for their visualization fix. May you have a prosperous year in the making!

Since I’m writing this post on the Martin Luther King holiday I’ll have to close with a quote from Dr. King.

Life’s most persistent and urgent question is, ‘What are you doing for others?” – Dr. MLK 

I repost some of my articles on Medium
And of course you can subscribe to my Youtube channel

Row and Column Highlighting in Tableau

In this post you’ll learn how to highlight values in your Tableau table using set actions. The dashboard in this video displays the number of total points scored by NBA teams by position in the 2017-2018 season. I will give you step by step instructions on how to implement row and column highlighting on this dataset downloaded from basketballreference.com.

I’ve only made a few minor tweaks but this technique was developed by Tableau Zen Master Matt Chambers. You can check out his blog at sirvizalot.com and follow him at Big shout out to Matt for sharing this technique with the Tableau community!

You can interact with my visualization on Tableau Public:

If you find this type of instruction valuable make sure to subscribe to my Youtube channel!

Make Flashy Maps in Tableau with Mapbox

The default maps in Tableau are just fine but sometimes you need to kick up the flamboyancy factor in your visuals. Integrating maps from Mapbox with Tableau is the perfect way to add some Liberace flash to your development game.

Mapbox is an open source mapping platform for custom designed maps.  By creating an account with Mapbox, you can either design your own maps on the platform or use their preset maps, which are all more impressive than the out of the box option in Tableau.

All you need to do is enter your generated API token (provided by Mapbox) into Tableau’s Map Services interface and you’ll have access to some pretty impressive mapping options.

If you’re interested in Business Intelligence & Tableau subscribe to my  Youtube channel.

 

 

Starbucks, Digital and Analytics: A Perfect Blend

Starbucks is a differentiator, an early adopter in regards to technology and a savvy user of data analytics. Employing the “Starbucks Experience” differentiation strategy (e.g., customer service, ambiance, interior aesthetics, prime locations), the company is able to command above market prices for a commodity product.

Surprisingly, for an organization that was not born in the digital era, Starbucks embraces new technology like a forward-looking digitally native company. The company has demonstrated a willingness to take risks on its road to digital maturity which helped it acquire a unique value proposition in loyalty and customer satisfaction. From a company strategy perspective, the organization is aware that early adopters can acquire advantages.

“In 1998, it was one of the first companies to launch a website; in 2002, it began offering WiFi to its customers, helping to start the transition from quick coffee stop to all-day hangout; and a full decade ago, Starbucks was establishing its social media presence. Now, while others are setting up mobile payment terminals and struggling to start a loyalty program, Starbucks is seeing 11 percent of its sales from mobile order and pay, and 14.2 million Starbucks Rewards members accounting for 37 percent of U.S. company-operated sales.” [1]

As the importance and impact of data has come to prominence in today’s business environment, the company relies heavily upon analytics and digital technologies to enhance business performance. Starbucks processes approximately 90 million transactions per week, [2] therefore the company has to have the requisite culture and resources in place to wring optimal value from data and analytics.

Starbucks Technology Leadership

Any company’s drive for a culture of digital, data and analytics is greatly influenced by senior leadership. As such, Starbuck’s senior leadership increasingly reflects a silicon valley background and mindset. Former chairman and CEO Howard Schultz has been referred to as the “Steve Jobs” of coffee. Schultz’s handpicked successor Kevin Johnson is a veteran technology player having previously served as CEO of Juniper Networks and as President of Microsoft’s Windows division.

Another high level technology hire was that of Gerri Martin-Flickinger who has held technology leadership roles at Adobe, VeriSign and McAfee Associates. Ms. Martin-Flickenger serves as the company’s first Chief Technology Officer, which is a title change from the previous CIO position held by Curt Garner. Incidentally Garner left Starbucks to become the CIO of Chipotle, as his new suitor was impressed with Starbuck’s use of online ordering and mobile pay capabilities.

The ultimate technology name drop belongs to Satya Nadella who serves on Starbuck’s board of directors (as of 2017) and is currently the CEO of Microsoft. Coincidentally, Starbucks is a prominent customer leveraging the Microsoft Azure cloud platform.

Mobile Order / Mobile Pay

One of Starbuck’s most laudable achievements has been its execution of mobile ordering and mobile pay via its iOS and Android applications. Customers who are short on time can pop open the Starbucks app on a smartphone and then place and pay for their order. Purchases lead to an accumulation of loyalty “stars” which can be redeemed for free products. This digital relationship has helped drive demand in physical stores and increase ticket spend per customer.

“They’re actually broadening the footprint of their stores with technology. If everything was a walk-in order, you’d only be able to sell what people could drive up and wait around to get. But by having mobile pay and drive thru, they can extend that store footprint out for miles.”
 [1]

This functionality has been so popular that it has caused operational challenges in the physical stores as customers have walked out after seeing large crowds waiting on mobile orders. The company has beefed up staffing and changed store layouts where applicable to accommodate the increased demand. Starbucks has also based loyalty program rewards on total spend as opposed to number of transactions. The latter approach allowed customers to game the system by sub-dividing orders into multiple transactions which caused bottlenecks.

 Future enhancements to the application include a personalization engine that will allow stores to target customers for differentiated treatment (e.g., birthday rewards, discounts for previously purchased items). To increase customer satisfaction and operational efficiencies, geolocation can be used to track a customer’s presence near the store and allow the point of sale terminal to pre-assemble the customer’s typical order. The barista can then confirm the order once the customer arrives and then submit at the press of a button.

Additionally, voice ordering capabilities via Amazon’s Alexa platform will allow customers to order and pay for food and drinks as if they were speaking to a human barista. The company demonstrated this capability at an investor conference when it placed an order for a “Double upside down macchiato half decaf with room and a splash of cream in a grande cup.” [3]

Due to the company’s investment in this digital capability, Starbucks is in a position to capitalize on its homegrown mobile order and payment processing technology via licensing to other retailers. The decision would have to be weighed against the advantages of keeping its technology and processes proprietary.

Geolocation

Starbucks is a savvy user of data and analytics to help determine where to place its next retail locations. The organization has a real estate analytics team (amongst many others) that spearheads its site selection strategy.

Using an in-house mapping and business intelligence platform called Atlas, the organization can combine data from various internal and external sources to create models which help drive decision making.

“Through a system called Atlas, Starbucks links to as many external and internal APIs as possible, connecting the data with R to build cannibalization models that can determine impact to existing stores if a new store enters the area. This model drives decision-making in cities across the US and world.” [4]

As part of its growth strategy, Starbucks is planning to open 3,000 stores in mainland China through 2022. That is approximately 600 stores per year, or 1 every 15 hours [6]. Choosing the correct site locations is absolutely critical to store success and the correct mix of data and analyses must be conducted to enable a successful rollout.

Starbucks weaves together various data points such as weather, auto traffic, consumer demographics, population density, income levels, coffee purchase patterns, current store locations and even levels of mobile phone ownership to construct its site selection strategy. The company analyzes all pertinent data points overlaid on a visualization map powered by a spatial data warehouse.

Weather and sales data individually can tell two different stories but blended together they can offer the company new insights. For example, if it’s forecasted to be hotter than average in a location, Starbucks can geo-design a localized promotion for cold beverages [6].

Store Labor Analytics

In a presentation given by Leslie Hampel (VP, Store Operations) at Dartmouth’s Tuck School of business, she described the process of balancing the company’s top down strategic store scheduling decisions with emergent strategies from store level managers. If an improvement suggestion is offered by one store for use in all others, then its impact has to be modeled on a statistically significant group of stores first.

Starbucks is very risk averse in regards to making labor related changes. If you consider that labor is about a third of Starbuck’s cost base, a decision that is wrong by 1% will be wrong by about 40 million dollars [7].

In addition to taking suggestions from stores, the company will use analytics to systematize best scheduling practices by examining the best operating 10% of stores. These localized processes are then tested on a statistically significant group of 100 stores. An internal analytics team will assess the impact of the changes to the company’s financials, as well as impacts to store sales and the customer experience. At the end of the 90 day testing period the new process will either be rolled out to multiple stores, tweaked and re-tested, or simply abandoned [7].

As an aside, it’s worth noting that the creation of Starbuck’s famous Frappuccino’s were the result of an emergent strategy from a store level manager in California. Although Starbucks stores are only supposed to sell company approved drinks, the manager sold them in her store against the company mandate. Despite corporate management’s initial reluctance to stock and sell the drink (until seeing the local sales data), Frappuccino’s are now a billion dollar business for Starbucks [8]. Always listen to your ground level employees, as they are the closest to the customer.

Starbucks & Bitcoin

No post about digital technology would be complete without a nod to Bitcoin.

To burnish its tech bona fides, Starbucks has been in talks with Microsoft, the parent of the New York Stock Exchange and the Boston Consulting Group to develop a crypto-initiative. Bakkt (pronounced “backed”) will allow consumers to store and convert digital currencies to dollars that can be used for in-store purchases.

The idea has the backing of major corporations and could help lend some Fortune 500 legitimacy to the somewhat murky and volatile world of cryptocurrencies. Although cryptocurrencies have yet to reach mainstream appeal, Starbucks is showing its willingness to be an early adopter of a new payments solution as it did with its mobile ordering and payment initiatives.

References:

[1] 5 Ways Starbucks is Innovating the Customer Experience. https://www.qsrmagazine.com/consumer-trends/5-ways-starbucks-innovating-customer-experience

[2] Starbucks’ CTO brews personalized experiences  https://www.cio.com/article/3050920/analytics/starbucks-cto-brews-personalized-experiences.html

[3] Starbucks Adds Voice Ordering to iPhone, Amazon Alexa http://fortune.com/2017/01/30/starbucks-alexa-voice-ordering/

[4] Data Analytics in the Real World: Starbucks https://www.northeastern.edu/levelblog/2016/03/04/data-analytics-in-the-real-world-starbucks/

[5] China is getting nearly 3,000 new Starbucks https://money.cnn.com/2018/05/16/news/companies/starbucks-in-china-store-expansion/index.html

[6] Esri 2014 UC: Starbucks Coffee and IT. Coffee beans and business strategy. https://www.esri.com/videos/watch?videoid=3654&isLegacy=true

[7] Using Data to Create & Maintain the Starbucks Experience https://www.youtube.com/watch?v=sUkQwhMwOig @32:42

[8] Rothaermel, Frank T. 2015. Strategic Management 2nd Edition. New York: McGrawHill, Irwin (2nd edition).

Image Copyright : monticello on 123rf.com

The London Whale Trading Incident

Once again I am digging into my digital crates to share an informative post. Here is a small writeup from a Syracuse graduate Enterprise Risk Management class (IST 625) I completed concerning JP Morgan Chase and the “London Whale”. The post is slightly edited from the final version I submitted. The assignment was as follows:

“This assignment requires you to research an organization that has suffered a devastating loss from a so-called ‘low frequency or low probability but-high consequence’ event, to understand what happened to that company during and after that event, and to synthesize what we can learn from their experiences.”

In gambling parlance, a whale is a high roller who bets big and has the potential to cause the house substantial losses if he/she stops betting at the wrong time. Although the whale at the center of this episode wasn’t sitting in a casino, the house (JPMorgan) experienced substantial losses when the betting stopped.

JP Morgan Overview

JPMorgan Chase is the largest financial holding company in the United Sates and has more than 2 trillion dollars in assets. It is also a global financial services firm with more than 250,000 employees (United States Senate pg. 24). The company has more than 5,600 branches and is a strong market participant in the mortgage lending, investment banking and credit card spaces (Hoover’s Company Records, 2014). JP Morgan’s principal bank subsidiary, JPMorgan Chase Banks is also the largest bank in the United States.

The current Chairman and CEO of JP Morgan Chase is Mr. James “Jamie” Dimon. Previous to 2012, Mr. Dimon’s name was associated with the eponymously named “Dimon principle”. “The ‘Dimon principle,’ as it is known, was shorthand for a safe bank with regular profits” (Ebrahimi, Aldrick & Wilson, 2012). With Mr. Dimon guiding the firm, JP Morgan held an excellent reputation with respect to risk management. The same could not be said of similar financial firms who were no longer “going concerns” as a result of the 2008 Great Recession.

“So when the US Government desperately sought someone with the balance sheet for the corporate rescue acts necessary to prevent financial meltdown, it sent for Dimon. In what were admittedly sweetened deals, JP Morgan swallowed bankrupt investment bank Bear Stearns and cash-strapped retail lender Washington Mutual” (Osborne, 2011). Emerging from the 2008 financial crisis unscathed, Mr. Dimon became more powerful and confident. He frequently railed against the need for government regulations with regard to proprietary trading in large financial firms. (Scuffham & Laurent, 2012) quote Mr. Dimon as stating, “We must not let regulatory reform and requirements create excessive bureaucracy and unnecessary permanent costs.”

Unfortunately for JP Morgan Chase and Mr. Dimon, subsequent trading events and risk management failures of 2011 would tarnish the firm and the CEO’s cultivated and highly regarded reputation.

London Whale Trades

(English, 2012) offers an analogy of the high risk/low frequency event that occurred in JP Morgan’s Chief Investment Office (i.e. CIO). Imagine a scalper who purchased 50,000 tickets to a sporting event with a capacity of 75,000 seats. The event is not nearly as popular as was anticipated by the scalper, thus the going price on the tickets plummets rapidly as would be ticket buyers wait for prices to fall further from face value. The scalper intended to hold tickets for the long term expecting high demand but with too many people on the sidelines betting that prices would fall, the scalper had to cut losses and sell at a drastic loss.

The “London Whale” trader at the center of this JP Morgan controversy was a French national and London based trader named Bruno Iksil who was known for being a successful leviathan risk taker. Mr. Iksil worked for the firm’s Chief Investment Office. Since JP Morgan has an excess of deposits after the firm makes loans available to business and consumers, this excess cash is invested by the CIO group to hedge against disparate investment actions undertaken by other areas of the bank.

The stated purpose of the CIO unit was to protect the bank from losses and interest rate risk by acting as a hedge and offsetting the bank’s other credit risks. The CIO unit is not tasked with proprietary or “prop” trading (essentially placing bets) intended to boost profits. Prop trading is the mandate of the company’s Investment Banking Division. In 2009 alone the CIO group’s Synthetic Credit Portfolio (SCP) of financial derivatives generated 1.05 billion dollars for the bank (United States Senate, pg.87). Ultimately JP Morgan would end up being a victim of its own success as it continued to conduct proprietary trades in the CIO division.

Bruno Iksil and the London CIO office were steadily racking up daily losses in the hundreds of millions of dollars by investing in synthetic derivatives (i.e. Credit Default swaps or CDS). The trading positions that the CIO office held were not hedging against other bank investments, as was the purported charge of this office. Credit default swaps are financial derivatives the provide investors insurance on bonds against potential default. Mr. Iksil, “has been selling protection on an index of 125 companies in the form of credit-default swaps. That essentially means he is betting on the improving credit of those companies, which he does through the index—CDX IG 9—tracking these companies” (Bianco, 2012, para 5).

Needless to say, the companies in the index did not improve. The initial 100 million dollar position that the CIO office held in the CDX IG 9 index was essentially cornering the market and when there were no willing buyers, the firm had to sell at massive loss.

In April of 2012 the press began running stories about the identity of the “London Whale”. The massively large trades in credit default swaps (the same complex financial instruments that doomed A.I.G during the 2008 financial crisis) began to affect credit markets worldwide. Initially, by the end of the week on May 11, 2012 when the firm held a hastily convened conference call regarding transparency around the London Whale trades, JP Morgan suffered a loss of 14.4 billion from its market capitalization as its stock price fell 11.47% in two days (Ebrahimi, Aldrick & Wilson, 2012). By the end of May the synthetic derivatives portfolio alone had lost 2 billion dollars. By the end of June the losses doubled to 4.4 billion and eventually reached 6.2 billion by the end of the year (United States Senate, pg. 12).

Initial Management Response

Once the CIO division management learned of Bruno Iksil’s precarious investment positions, “it could have announced the maximum possible losses from the trades. Instead it said what the losses were at that moment in time, and hoped a change in sentiment and some clever trading would stop them spiralling [sic]”. To the London Whale’s credit, once he observed the potential for disaster, he suggested that the division take a loss or “full pain” which would have been an additional 100 million dollars wiped out (Farrell, 2012), far less than the eventual 6.2 billion dollar total loss number.

Amazingly, Mr. Iksil’s management began to take actions that would conceal the magnitude of losses reported. Recorded telephone calls, instant messages and a shadow spreadsheet containing actual projected losses, revealed how traders were pressured to minimize the expected losses of the SCP (Synthetic Credit Portfolio) (United States Senate Report, pg. 20).

Internal CIO management also disregarded their own risk metrics such as the VaR or value at risk, which estimates the maximum risk of loss over the course of a day. This warning sign metric was ignored and then actually raised. CEO Jamie Dimon, Chief Risk Officer John Hogan and CIO head Ina Drew, “approved the temporary increase in the Firm-wide VaR limit, and Ms. Drew approved a temporary increase in CIO’s 10-Q VaR limit.” (JPMorgan Chase & Co, 2013 pg. 79)

Senior bank management was told that potential losses were massive and no longer functioned as a hedge to the bank; management then proceeded to downplay those issues until the losses mounted into the billions of dollars (United States Senate, pg.21). On an April 13, 2102 first quarter conference call CEO Jamie Dimon dismissed the initial publicity surrounding the London Whale trades by characterizing them as a “complete tempest in a teapot” (United States Senate pg. 17). By June 2013, Mr. Dimon’s stated to a Senate Panel on the trading losses, “Let me first say, when I made that statement, I was dead wrong” (PBS NewsHour, 2012).

Remediation and Outcomes

CEO Jamie Dimon stated, “CIO will no longer trade a synthetic credit portfolio and will focus on its core mandate of conservatively investing excess deposits to earn a fair return” (JPMorgan Chase & Co., 2012(a), pg. 3). Management instituted a number of changes as a result of the CIO trading imbroglio. All CIO managers based in London with any responsibility for the Synthetic Credit Portfolio were separated from the firm with no severance and without 2012 incentive compensation. (JPMorgan Chase & Co., 2012(a), pg.22).

JP Morgan instituted significant changes for the better in the CIO Risk Management organization. A new Chief Risk Officer was empowered to hire additional senior level officers to “extend the capacity of the Risk function within CIO, Treasury and Corporate, and he has made 20 such hires since May 2012” (JPMorgan Chase & Co., 2012(a), pg.114). Along with upgraded personnel skills in the CIO Risk organization, management rightfully instituted a common sense approach to structural issues.

In the pre “Whale trades” environment, the CIO Risk Committee met infrequently and did not contain any members from outside of the CIO organization. This lack of diversity in the realm of “risk-thought” fostered a group think/rubber-stamp mentality. CIO Risk managers did not feel “sufficiently independent” from the CIO business to ask hard questions or criticize trading strategies (JPMorgan Chase & Co., 2012(a), pgs. 12-13).

Industry Impact

“Dimonfreude” was a term coined in the wake of the trading losses, “it means taking great satisfaction in the misfortunes of the JPMorgan boss” (Foley, 2012). Yet, the fall out from JP Morgan’s episode was more than mere embarrassment for the firm and the CEO’s reputation in the area of risk management. To the chagrin of Mr. Dimon, this episode strengthened the case for more government oversight of the financial industry. In the words of then Treasury Secretary Timothy Geithner, “I think this failure of risk management is just a very powerful case for financial reform” (Shorter, Murphy & Miller, 2012, pg. 24).

References

Bianco, J. (2012). Understanding J.P. Morgan’s Loss, And Why More Might Be Coming. The Big Picture. Retrieved February 2, 2014, from http://www.ritholtz.com/blog/2012/05/understanding-j-p-morgans-loss-and-why-more-might-be-coming/

English, S. (2012). How London Whale’s errors attracted the market sharks. The Independent. Retrieved from Factiva.

Ebrahimi., H., Aldrick, P., & Wilson, H. (2012). The day JP Morgan’s Jamie Dimon lost his sparkle; Breathtaking risk failures at JP Morgan have left the bank’s reputation on the edge. The Telegraph Online. Retrieved from Factiva.

Farrell, M. (2013). JPMorgan slashes Dimon’s bonus by 53%. CNN Wire. Retrieved from Factiva.

Foley, S. Jamie Dimon Chief executive, JP Morgan Chase (2012). The Independent. Retrieved from Factiva.

Hoover’s Company Records. (2014). JPMorgan Chase & Co. Austin, U.S. Retrieved from http://search.proquest.com.libezproxy2.syr.edu/docview/230565788?accountid=14214

JPMorgan Chase & Co. (2012)(a). JPMORGAN CHASE REPORTS SECOND-QUARTER 2012 NET INCOME OF $5.0 BILLION,OR $1.21 PER SHARE, ON REVENUE OF $22.9BILLION. Retrieved February 1, 2014 from http://files.shareholder.com/downloads/ONE/2939707738x0x582870/6a286dff-ad7e-40ba-92ef-e6ff1b3be161/JPM_2Q12_EPR_Final.pdf

JPMorgan Chase & Co. (2012)(b). CIO Task Force Update. Retrieved February 1, 2014 from http://files.shareholder.com/downloads/ONE/2939707738x0x582869/df1f2a5a-927e-4c10-a6a5-a8ebd8dafd69/CIO_Taskforce_FINAL.pdf

JPMorgan Chase & Co. (2013). Report of JPMorgan Chase & Co. Management Task Force Regarding 2012 CIO Losses. Retrieved February 1, 2014 from http://files.shareholder.com/downloads/ONE/2272984969x0x628656/4cb574a0-0bf5-4728-9582-625e4519b5ab/Task_Force_Report.pdf

Osborne, A. (2012). JP Morgan $2bn loss: Dimon’s in the rough; Be careful what you wish for. The Telegraph Online. Retrieved from Factiva.

PBS NewsHour. JPMorgan Chase’s Big Losses, Big Risk: Blip on Radar or Systemic? Retrieved February 1, 2014 from http://www.pbs.org/newshour/bb/business-jan-june12-jamiedimon_06-13/

Scuffham, M. & Laurent, L. (2012). Trader known as ‘London Whale’ for his huge, hidden bets. The Globe and Mail. Retrieved from Factiva.

Shorter, G., Murphy, E., Miller, R. (2012). JP Morgan Trading Losses: Implications for the Volcker Rule and Other Regulation. Congressional Research Service. Washington, DC. Retrieved from https://www.fas.org/sgp/crs/misc/R42665.pdf

United States Senate. (2013). JPMorgan Chase Whale Trades: A Case History of Derivates Risks And Abuses. Staff Report. Washington, DC. Retrieved from http://www.hsgac.senate.gov/download/report-jpmorgan-chase-whale-trades-a-case-history-of-derivatives-risks-and-abuses-march-15-2013

Picture Copyright : Andrey Kiselev on 123rf.com