Bionic Turtle’s Week in Risk (ending April 9th)

Here is a note from David to start the week! 🙂 “Last week our incredibly capable Ivan migrated us to a higher-capacity fully-managed dedicated server (Intel Xeon E3-1270 v5 Quad-Core). Thank you to Nicole Seaman for managing the transition! We have hosted BT for several years at liquidweb. Before them, I tried at least four different hosts, but nobody matches the “heroic” service at The new configuration boosted our site speed noticeably. We want the forum to be a responsive place for candidates to get help and discuss issues. A quick site is fundamental to that experience.

And now, here are some great risk articles for all of you! We hope that you enjoy our blog as much as we love preparing it for you!

In the forum last week (selected only)

Linda Allen

Gregory CVA

Bank and banking

JP Morgan Chase shareholders

Political and regulatory risk, including Systemic Risk (including BIS)


Not a Dot Com Bubble


  • New Currency Peg Is No Panacea for Iceland by Mohamed A. El-Erian “Managing a small and open economy is becoming a lot harder in a world facing considerable trade and economic fluidity and, to use Federal Reserve Chairman Ben Bernanke’s insightful phrase, unusual uncertainty … Such exchange-rate volatility complicates the management of both business and government activities. It also threatens disruptive currency overshoots
  • Europe, Middle East, India and Africa Fraud Survey 2017 by EY

Fraud survey

Technology, including FinTech and Cybersecurity

cyber insurance cycle

  • Banks Turn to Virtual World to Modernize Physical Commodities Trading (Commodities players are trying out blockchain, the technology behind bitcoin, to help buy and sell raw materials)
  • PwC report: Redrawing the lines (FinTech’s growing influence on Financial Services) “One driving factor behind these partnerships [between global financial services firms and FinTech innovators] is an increasing fear within the industry that revenue is at risk to standalone FinTechs, with 88% of financial services respondents seeing it as a real threat (83% in 2016). On average, up to 24% of revenue is thought to be at risk.” Report here

Fintech survey

cyber risk types

Data science (primarily R), including Alternative Data

  • Will Using Artificial Intelligence To Make Loans Trade One Kind Of Bias For Another? part of the loan application process, some lenders have prospective borrowers download an app that uploads an extraordinary amount of information like daily location patterns, the punctuation of text messages or how many of their contacts have last names. In the credit industry, this is called alternative data, and it’s mostly used to make decisions on short-term, high-interest loans. But that’s likely to change soon. In 10 years, there will hardly be a credit decision made that does not have some flavor of machine learning behind it, says Dave Girouard, the CEO of Upstart, an online lender.” Here is one of the cool companies mentioned

Natural Science, including Climate and Energy

Exams, Financial Associations (GARP, FRM, CFA Institute) and Careers, including CRO Interviews

active hedge fund manager

Personal finance

  • Comparing Mortgage Applications and Originations by Credit Score Distribution 3 shows how the credit score distributions have shifted from 2006 to 2016 for both applications and originations. The share of applications and originations with a less-than-pristine credit score has declined. The difference is more pronounced for applications than for originations. The share of credit scores below 700 for applications has declined and has been offset by a greater share of credit scores above 750. From a credit space perspective, the similarity of the two density distributions for 2016 suggests that lenders were largely meeting the demand of borrowers applying for a loan when compared to 2006.

mortgage applications and credit score

Learning in the 21st century

Enterprise risk management (ERM) including Governance

tech founders

Case Studies and Companies, including Strategic or Reputation risk

Quantitative Analysis (FRM P1.T2)

Financial Markets and Products, including Interest Rates, Commodity Risk, and Foreign Exchange (FX)(FRM P1.T3)

wall street quiet

Valuation and Risk Models, including Country risk (FRM P1.T4)

  • Diversification, Adaptation, and Stock Market Valuation can arbitrarily separate asset risk into three different types: price risk, inflation risk, and fundamental risk” and an argument as to why the equity risk premium has fallen over time.
  • The Incredible Shrinking Universe of Stocks (the latest must-read by Michael J. Mauboussin) by Fascinating breakdown of the rather dramatic drop in supply of US-listed equities over the last 20 years: “while the number of listings fell by roughly 50% in the U.S. from 1996 through 2016 [i.e., from >7,000 to about 3,700], it rose about 50% in 13 developed countries that have complete data … Because the number of listings shrank in the U.S. and expanded in the rest of the world, the U.S. now has a listing gap of more than 5,800 companies. A model of how many companies should be listed, based on GDP, GDP growth, population growth, and measures of corporate governance, suggests that the U.S. should have more than 9,500 listings.

The Incredible Shrinking Universe of Stocks

Investment risk, including Pensions (FRM P1.T8)

  • With 125 Ph.D.s in 15 Countries, a Quant Alpha Factory Hunts for Investing Edge (Igor Tulchinsky’s WorldQuant is part of the forefront of a new quantitative renaissance in investing) “Alpha Factory” breaks up the process of investing into a quantitative trading assembly line. The inputs are data acquired by a special group that scours the globe for interesting and new data sets, including everything from detailed market pricing data to shipping statistics to footfall in stores captured by apps on smartphones.

Quant Alpha Factory Hunts for Investing Edge

Corporate Pension Funds About to Become Huge Players in Bond Markets

  • The $90 Billion Investor Who’s Out to Fire Wall Street, 58, became the state’s first Republican treasurer in 140 years partly on the strength of a simple investment strategy he proposed: Instead of paying money managers big fees, the state should use a slim menu of cheap, mostly indexed investments and manage them in-house when possible. The state paid $600 million in outside managers’ fees, incentives, and related costs last year, seven times more for every dollar under management than it paid in 2000.


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