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# Week in Financial Education (2021-03-29)

#### David Harper CFA FRM

##### David Harper CFA FRM
Staff member
Subscriber
Hello valued visitors! We're bringing back Week in Risk but calling it Week in Financial Education until we find a better title. Maybe you noticed our exciting news? We recently joined CeriFi and look forward to broadening this forum's reach. The idea is to share a brief blog-like update from the week including: selected forum threads that highlight interesting exam-related topics, curated links related to our domain(s), and--if I have something to add--my musings as a passionate student of risk, finance and data science (my practice also informs my equities portfolio construction which I sporadically write about over on Seeking Alpha). Please let me know what you think! You know where to find me ...

In the forum (beginners and new learners)
• Somebody else's CAPM practice question contains an instructive misunderstanding about a key difference between the CML and the SML https://www.bionicturtle.com/forum/threads/mean-variance-analysis.23736/
• We got two questions about the commodity lease rate which perennially vexes new learners. GARP's new material takes a backward step from previous authors (McDonald remains better) and doesn't attempt numerical reconciliation with the other cost of carry factors. In the basic ("naïve") version, the least rate is quasi-synonymous with the convenience yield; in this naïve version, as both subtract from the forward price (via conferring benefits to commodity ownership) either explains the entire difference between the observed futures price and the constructed cost of carry. In the technical ("sophisticated") version, the lease rate is a net convenience yield. My strong preference is to view the lease rate as the net convenience yield; i.e., L = y - u. This will reconcile dicey applications, in particular when gold has simultaneously both a convenience yield and a lease rate. See https://www.bionicturtle.com/forum/...-carry-theory-hull-chapter-5.10611/post-87786
• Rohit asks if anybody is pursuing the CFA while in university at https://www.bionicturtle.com/forum/...ertificial-chartered-financial-analyst.23747/.
• How much does the FRM want you to know about post-2008 regulatory responses to the global financial crisis (GFC), including Basel and Dodd-Frank? My opinion is that GARP has always asked for too much review of the Basel regulations. Literal compliance with the learning outcomes (LOs) will probably lead you to regulatory overexposure https://www.bionicturtle.com/forum/...responses-and-best-practices.23058/post-87759
• For some reason, we got several questions this week about using the classic Z-lookup table; e.g., https://www.bionicturtle.com/forum/...-deviate-in-spectral-measure.23708/post-87714. GARP's Z-lookup table displays only negative values, Z = {-3.00 to zero}. FRM candidates need to be totally comfortable with distributional lookup tables. These lookup tables are partly how we talk about distributions. Much of risk is applied math, and much of that applied math is distributions. Distributions are how we numerically capture uncertainty. To be comfortable with the lookup table requires two skills. First, we need to be able to apply the the normal distribution's symmetrical truth that N(Z) = 1 - N(-Z) in order to retrieve any of the values on the un-displayed right side. For example, what's the Pr(Z<1.65)? It is given by 1 - N(-1.65) = 1 - 0.0495 ≅ 5%. Second, we need to be able to invert: N(2.33) = 99.0% is an instance of the symbolic N(Z) = p. That's solving for a probability given an quantile. To invert is to solve for a quantile given a probability: N^(-1)(p) = Z, which in this instance is given by N^(-1)(99.0%) = 2.33. Once we get this, it's easy to see how the lookup table is also giving us p-values (when the test statistic is normal). You can't read (or talk) about this to master it, sorry. You have to practice it.
In the forum (practitioners or experienced candidates)
Curated links (items you might like)
Personal musings

Upstart ($UPST) emerges as a fintech (credit risk) star. As an investor (and student of risk), my most compelling read last week was Upstart's quarterly earnings call. The market rewarded its performance with a +39% pop. This company ($UPST) is disrupting the credit business with an artificial intelligence (AI) lending platform. By applying data science to alternative (and big) data, they not only double/triple approval rates (while holding losses constant) but they enable banks to extend credit to underserved, perhaps even unbanked, populations. The application of machine learning to underwriting feels like the inexorable future. Said the CEO, "while most lenders consider only a handful of variables as part of a lending decision, Upstart's model considers more than 1,000 variables about each applicant. You can think of these as the columns in a spreadsheet. And as of December 31, 2020, our model was trained on more than 10.5 million unique repayment events." When I took my first machine learning class (in person, a few years ago), upon learning random forests--which are a black box to most of us--the class had a debate about the implications of data science on lending practices. My initial reaction, in all sincerity, was a bit of horror: how can banks make lending decisions based on black boxes algos? (If you think I exaggerate, the winner of our contest literally could not articulate why his algo outperformed). But participants smarter than me argued how algos can be more fair (should I say more equitable?). In any case, in the FRM of course we learn about traditional credit analysis and scoring (e.g., borrower capacity and willingness). And that's good. We need those fundamentals. But the future of credit analysis looks very different and understanding it will require additional skills.

Interest rate (or is it inflation?) risk is the market risk everyone is talking about. Howard Marks of Oaktree writes that the market's biggest risk is the possibility of risking interest rates. Ben Carlson says that inflation matters more than interest rates. Professor Aswath Damodaran explains the relationship between interest rates and inflation. The Atlanta Fed has a cool Underlying Inflation Dashboard at https://trtl.bz/atlanta-fed-inflation-dash. Fisher Investments has this Q&A on inflation. Where do I learn more? One source is the phenomenal Lynn Alden Schwartzer who regularly dazzles the grizzled Seeking Alpha veterans. Here she is on the three types of inflation (monetary, asset price, and consumer goods/services) and here she is on Interest Rate Risk (and the equity risk premium).

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#### lushukai

##### Active Member
Subscriber
Dear David,

Thank you for your kind words... this forum has been nothing short of amazing and yourself, a brilliant teacher. Looking forward to learn from you and the intelligent people from Bionic Turtle!

#### David Harper CFA FRM

##### David Harper CFA FRM
Staff member
Subscriber
Hi Lu Shu Kai, Oh geez thank you! I really appreciate your smart contributions. If I seem to know stuff, it's mostly because I've had the privilege of years of candidate feedback here in the forum .

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