learning and market musings (part 3)
Paleologo's portfolio management, Podcasts, and I need a new name for these write-ups lol...
Happy New Years and I wish everyone a blessed 2024. 明けましておめでとうございます。今年もよろしくお願いいたします。2023 was a long, eventful year—and this year is lined up similarly. Onwards and upwards
The following writeup includes insights from Giuseppe A. Paleologo, Paul Graham, Stanley Druckenmiller, Mike Rockefeller, Jeff Bezos, tips when traveling with United, and more.
Giuseppe A. Paleologo’s Advanced Portfolio Management: A Quant’s Guide for Fundamental Investors ( or, ‘the blue book’—thanks Dylan for the primer)
book on portfolio management/factor models written “with a quirky Italian sense of humor” (Chi non risica non rosica = those who don’t take risk don’t eat)
there is much more to this book but hopefully this serves as a brief introduction; the next step is to build out a basic risk model myself
Remember y = mx + b from Algebra class? Pick a stock, and linearly regress it against the “market” (i.e., an index or ETF index)—the “sensitivity” (slope) towards that “market” is beta. Thus, returns = (beta*market) + (alpha + ε). Here, y = total returns, mx = (beta*market), alpha = y-intercept (b) and ε is noise. ← this is for a single-factor model
(beta*market) = “systematic returns” and (alpha + ε) = “idiosyncratic returns” (“Alpha is the expected value of idio. returns and ε is the noise masking it” - Paleologo)
So, stock returns = systematic return + idiosyncratic return, where both returns are independent. Idiosyncratic (idio) returns are also known as “stock-specific” returns. With this separation, investors can see where returns come from—i.e. from “the market” moving up/down (systematic risk), or something company-specific impacting returns (idiosyncratic risk). Investors are paid to find and capitalize on “alpha” (i.e. idiosyncratic risk)—systematic risk can be purchased cheaply via index ETFs, etc.
The portfolios of market-neutral funds aim for 70-75% idio. risk and 25-30% systematic risk. For example, if an investor has a company-specific insight but no view on the whole “market,” a high idio. portfolio allows them to separate unwanted risks (systematic) from specific insight-driven risk (idiosyncratic). Investors can “hedge” systematic risks by selling/buying index futures—isolating idiosyncratic risks. Interestingly, there are diminishing returns to higher idio: “beyond 75%, benefits of higher idio. variance % may be offset by higher costs [hedging costs.]” (p. 110)
The above example only uses the “market” as one factor, but it’s a great place to start when learning about multi-factor risk models. With additional “factors,” investors can refine their portfolio construction, ensuring that returns come from mostly idiosyncratic risks, and not from systemic risk (like “levered beta”!). Examples below:
Dr. Stephen Ross’s arbitrage pricing theory1 (an extension of CAPM): returns = (α + ε) + (β1*f1) + … + (βn*fn) where n = # of systematic factors (f), βn is a “factor loading” (sensitivity of an asset to factor “f”)
Multi-Factor Model: Rt = (α + εt) + Bt*ft — given “n” assets, “m” factors, and discrete time (t), Rt = n-dimensional vector of asset total returns, α = n-dimensional vector of expected returns, εt = n-dimensional vector of asset idio returns, Bt = n X m matrix of factor loadings, and ft = m-dimensional vector of factor returns (page 168, Appendix 11.1)
Taking one step further, systematic risk is the sum of style and industry factors. Style factors: Country (i.e. investment environment of USA, Japan, EU, China, etc.), Momentum (things that go up, tend to go up + vice versa), Short Interest (what % of a company’s shares are being shorted), Ownership (what other funds own, using 13F), etc. Industry factors: Industry/Sub-Industry (i.e. tech = software, HW, semi, etc), GLP-1 factor risk for healthcare companies, etc. Also, Style/Industry factors are positively correlated with one another and NOT independent like Idio. risk. A portfolio example below:
Next, “risk” is measured in terms of volatility. Volatility* (“vol”) = standard deviation of an asset’s return. In other words, take the average return of a stock, and the volatility—vol—is the ± distribution/dispersion around that average return. The purpose of “vol” is to model a “reasonable estimate of extreme losses”; this method assumes returns are “normally distributed” (sounds faulty but its a “useful reference point”). And remember, all models are wrong but some are useful.
Portfolio managers are given an “allowance” of volatility; ex: A $10,000 portfolio with a 15% “annualized vol.” has an annual portfolio variation of ±$1500 “vol. dollars.” From this, “weekly vol.” is ~$208 (annualized vol. divided by sqrt(52)) and “daily vol.” is ~$94 (annualized vol. divided by sqrt(252)) (# of trading days/year = ~ 252).
The Sharpe Ratio (return per unit of volatility), is used to show a “risk-adjusted measure of performance compared to returns.” Paleologo writes, “Always think of volatility as an informative proxy of risk and imperfectly measured, and of the Sharpe Ratio as an imperfect, imperfectly measured, but useful performance metric” (p. 76)
Other insights
“in large drawdown events, it is always the case that the realized vol. is a multiple of the predicted vol.; investors experience 2, 3, or even 6 sigma events.” (p. 163)
Reminds me of the quote: options on strange things happening are always undervalued, because people underestimate how weird the future can be (link here)
“in a sell-off environment, all stocks become more correlated to each other and to the market… ‘in a crisis, all correlations go to 1’” (p. 51-52) → beta compression + decompression as an indicator of risk appetite (if beta falls, risk down, etc.)
“a lack of consensus among sell-side analysts is related to poor future returns” (p. 58)
Crowding/“HF hotels” leads to losses amplified “by the behavior of the actors, not by changes in asset valuations” (p. 59) → this is an OPPORTUNITY!!!
“Value stocks have high fixed costs and high levels of unproductive capital. The slack capacity generates downside risk during recessions, which is not matched by upside profitability in times of expansion” (p. 70)
“investors should consider carefully whether the investment in a stock is implicit an industry bet, a country bet, or a style bet (e.g., investing in a high-growth stock)”
[forecasted] idio. returns = total returns - industry returns → separate role of market/industry on asset specific returns!
“Vol. predicted by the risk model occasionally break. When they break, they do so spectacularly” (p. 117)
“Identifying trends in a stock’s return is a long-term, strategic skill; timing a stock’s return is inherently a short-term, tactical skill” (p. 134)
“PMs have at best very little timing skill, moderately-positive-to-moderately-negative sizing skill, and primarily selection skills” (p. 136)
“… [trading] costs increases faster than linearly in the dollar amount traded, specifically between sqrt((trade size)^3) and (trade size)^2” (p. 141)
Stanley Druckenmiller from Twitter
… [investors] get in the most trouble when they have stale longs, stale shorts.
Sizing is probably 70-80% of the equation. It’s not whether you are right or wrong, its about how much you make when you’re right and how much you lose when you’re wrong.
Barry Ritholtz’s Masters in Business podcast with Mike Rockefeller
“Do I want to go on this journey even if I never get to where I’m going?”
“Great teams should be small enough such that you can feed all of them with two pizzas” - Bezos
“No unique insight? hedge em’ out” [on factor investing]
Larger spread between winners and losers, winners by relative stock picking
“Will.I.AM envisioned himself by looking forward 30 years ahead”
Bezos reflections
I seldom watch Lex Fridman’s podcasts but this one was great: the effectiveness/clarity of write-ups, dispute resolutions (“I disagree but commit”), the power of “wandering,” two v.s. one way doors, Cost Reduction = inventing another way, and space exploration. Led me to think: What’s something that’s cheaper to do in space than on Earth? Compute in space within a few years?
*I read this last summer but these are a MUST read: Bezos’ annual letter to shareholders (open + scroll down to read 1997 to 2020)*
sports drink sold now at 7/11s and more
surprising markup though, one bottle is around $2.5-$4 and tastes exactly like Pocari Sweat from Japan but with a “fruity” aftertaste (ROIC is probs crazy)—what a biz!
United flights
Conveniently, EWR and SFO are United hubs, so I always have a direct flight home back from university. Every flight, there’s some random occurrence I learn from. This time, I sat next to a founder, and after talking to him for a while, he sternly said: “When you give yourself time, things never happen.”
Now back to 20 Dec 2023… As I usually do before flying home, I go to a local shop (Sonny’s Bagels) to get fresh lox (brined salmon), scallion cream cheese, and bagels for my family. Luggage in hand, I Ubered to the terminal at 6:40 am for a boarding time of 7:35 am. There wasn’t any traffic outside, but inside, there were hundreds of people waiting in line, suitcase in tow, trying to check-in their luggage—“shit, maybe I shouldn’t have gotten those bagels” I thought. I’ve flown out of EWR countless times (even in the holiday season), but have NEVER seen Terminal 3 that full.
Seeing the crowded line, I walked over to the baggage shortcut (which had another 200+ person line) and then the Premier Access line which was packed to the brim, with people panically yelling “I’m X status, please let me check in.” (Afterwards, I learned that United’s check-in machines were broken for the past 2-3 hours, so everything was backlogged because no one could get their luggage tags).
Looking behind those lines, there was a small “Group Check-in/Accessibility” counter with only two groups in line (and a working machine). As groups are all on one travel itinerary, I waited for the two groups, and dropped off my luggage in under five minutes. I went through security and was at my gate with time to eat a sandwich.
Later, I saw CIBO Express’ self-checkout machine which was pretty funny—no wonder people are getting annoyed with excess tipping (WSJ’s The Tipping Backlash Has Begun)
Thank you for reading this far. I’ve been at this Substack since August 2022, and now at 127 subscribers—and this is just the start. To a blessed 2024
Arbitrage Pricing Theory https://en.wikipedia.org/wiki/Arbitrage_pricing_theory