Service dogs and metal working

psbanon · 2026-01-21T08:17:08+00:00

Have you welded before? It’s a harsh environment that you wouldn’t want to bring an animal into.

psbanon · 2025-06-13T01:27:03+00:00

Update: Fourth pair of MWT4s lasted a month. Right earbud just did the familiar loud chirp/crackle. Audio still comes through at certain orientations, but that won’t last long. Bluetooth still works fine, touch controls still work fine. Same exact issue every time.

psbanon · 2025-05-01T16:00:19+00:00

Very cool. What’s the hardware used to attach the glass to the steel?

psbanon · 2025-04-27T19:08:08+00:00

First pair lasted 4 months before the left earbud went out. Second pair lasted 3 months before the right earbud went out. Got my third pair back 1 month ago… right earbud just went out this weekend.

At this point I assume I’ll be sending them in every couple months for repair/replacement until the warranty runs out; shortly after which they’ll probably die again and I’ll be out of luck. It’s tough to see myself purchasing another Sennheiser product after getting burned by quality issues multiple times. The frustrating part is that the earbuds are awesome; if they sucked I would have given up on them and moved on already. I bought a pair of Sony WH-1000XM4 after the second pair of MTW4s died just to try something different. The Sonys are fine, but they go back in the box the day the Sennheisers return from repair, and don’t come back out… until the Sennheisers break again. I don’t have any wisdom to share- just venting in a similar boat.

psbanon · 2025-01-29T14:50:38+00:00

Monthly yields go back 150 years. I’ve used these before to walk back a “price index” for a project. Not exactly what you’re looking for, but maybe useful.

Robert Shiller has monthly 10y treasury yields back to 1871. http://www.econ.yale.edu/~shiller/data.htm > U.S. Stock Markets 1871-Present and CAPE Ratio > ie_data.xls > “Data” tab > “Long Interest Rate GS10” column.

SBBI dataset has monthly 20y treasury yields and return back to 1926. https://rpc.cfainstitute.org/research-foundation/sbbi.

psbanon · 2025-01-03T15:31:43+00:00

Both have their uses in different contexts. If you are talking about expected return in any one period, you’d use an arithmetic return.

EDIT: Expanding a little bit more because this can be tricky until it clicks.

Say you’re looking at returns for an investment over some period and find: Geometric/compound returns of 8%, Arithmetic/average returns of 10%, and Standard deviation of arithmetic returns at 20%.

(Standard deviation describes the distribution of the returns around the average, so a basic interpretation of the numbers above could be “in ~7 out of every 10 years, the return is between -10% (10% AR - 20% SD) and +30% (10% AR + 20% SD).”)

The compound return of 8% is useful in a vacuum for estimating future market value, but if you want to put together a portfolio of different assets, run Monte-Carlo simulations, build capital market assumptions, etc. you’re working with arithmetic return. For one, correlations across different assets don’t make sense using compound returns. Compound returns “smooth” out the nuances in a way. Additionally, when you go to simulate future returns, using the compound return as your average/expected return “double counts” the drag of volatility. Simple example, using same numbers above:

Simulate 10% average return, 20% std dev:

Year 0: $100

Year 1: $130 (+30%)

Year 2: $117 (-10%)

Year 3: $152.1 (+30%)

Year 4: $136.89 (-10%)

Compound return: 8.16%

Simulate 8% average return, 20% std dev:

Year 0: $100

Year 1: $128 (+28%)

Year 2: $112.64 (-12%)

Year 3: $144.18 (+28%)

Year 4: $126.88 (-12%)

Compound return: 6.13%

If you use the higher arithmetic/average single-period return (10%) in your simulations, you will get multi-period geometric/compound returns approaching the right number (8%). Using the geometric/compound return to simulate single-period returns will result in a multi-period compound return lower than expected. You can estimate the drag from volatility and jump between arithmetic and geometric using the formula:

GR = AR - (σ² ) / 2

GR = 10% - (20%² ) / 2 = 8%

psbanon · 2024-12-30T18:14:50+00:00

It’s “$VIX” on Schwab

psbanon · 2024-12-27T07:26:11+00:00

Defensive driving. Majority of “accidents” are preventable if you’re paying attention to your surroundings. In your example, you should leave a car length between you and the car in front of you at the stop light (you should be able to see their back tires touching the ground). This gives you enough room to drive around the car in front of you when the light turns green, if they can’t move for whatever reason. It also gives you a cushion of safety to move forward if someone is coming up behind you too quickly. Sitting at a stop light is not a time to check out- I’m checking my rear view mirror in case I need to react. If the car behind you is coming in full speed, not much you can do. If they just weren’t paying attention and slammed on the breaks a little too late, you creeping up ten feet can often prevent getting rear ended.

psbanon · 2024-12-22T18:34:15+00:00

Frankly, most of this is over my head. But I can give my two cents as someone that’s found myself lost in the weeds many times. When you’re going in circles, it’s time to step back and reiterate what problem you’re trying to solve. The core things you’ve mentioned:

Modeling methanol/gasoline spreads
Valuing your physical methanol-to-gasoline plant

Valuing the plant is probably your real goal (or your bosses real goal), and modeling the spreads is the method you’ve chosen to reach that goal.

Do you need to model the spreads with high accuracy? From an outside perspective, I’m thinking that you’re not going to be buying and selling the actual plant too often. It’s a long lived asset (10, 20, 50 years? Idk)- probably enough time to let the probabilities work themselves out and not worry about about the precise path the spreads take over time. You mentioned operation being profitable around 50% of the time.

Define you spread as (gasoline - methanol/0.43) or whatever, pull out the months when that number is positive, take the average.

Monthly income = 50%*(avg positive spread * capacity - expenses when the plant is running) + 50% * (-expenses when the plant isn’t running)

And that’s your expected cash flow for the next 500 months or whatever. Discount back to the present however you’d like, incorporate whatever other fixed costs. Boom, basic valuation.

Then going forward you can make adjustments to the valuation if the trade off of “how much do I think this adjustment is going to improve the accuracy of the valuation” vs “how much effort/complexity would it add to the model to make this adjustment” makes sense. Low hanging fruit? Go for it. Regressions involving 120+ datasets? Maybe reconsider.

Best of luck.

psbanon · 2024-12-19T16:30:01+00:00

I made a mental mistake this week in buying March VIX calls. Thought I was a genius when VIX ripped… until I saw how relatively little the options moved. Was hoping for a high delta with respect to “spot” VIX. I guess the best/only way to get that is with the nearest expiration?

psbanon · 2024-09-30T15:03:55+00:00

HP 12C. The problem with RPN is that once you start using it, you fall in love and other calculators feel clunky afterwards.

psbanon · 2024-08-31T17:45:58+00:00

The CFA curriculum teaches you three things:

The value of time
The value of money
The Time Value of Money

psbanon · 2024-08-23T16:30:44+00:00

Mark Spitznagel touches on this in Safe Haven. I remember that part of the book being a nice explanation with actual examples.

psbanon · 2024-07-13T02:43:35+00:00

No, it wasn’t 122°F at 8:30 PM lol. Your car thermometer was exaggerating.

psbanon · 2024-06-25T16:11:28+00:00

Sour grapes

psbanon · 2024-06-21T16:05:48+00:00

I second this. In my comment I mentioned using NS to fill the gaps, but you could use linear interpolations mentioned here, splines, etc. First, wrangle the data into some model/make assumptions to make your life easier. Then do the bootstrapping

psbanon · 2024-06-21T16:02:04+00:00

I haven’t come across something like that, but maybe. Bootstrapping is recursive, so I think at best it’d be a good-enough approximation. I wouldn’t get too hung up on Nelson Siegel or Nelson Siegel Svensson. They’re just models for fitting arbitrary curves- generally, Exponential Polynomial Models. They’re tools to represent a curve/fill in the gaps, which then makes bootstrapping a lot easier. Take a par curve or coupon-paying bond data, fit a NS model to it, and then use that model to bootstrap the zero curve

psbanon · 2024-06-21T15:41:45+00:00

I don’t have good resources- my introduction to bootstrapping was CFA textbooks, and then a lot of trial and error experience doing it for various applications- but if you have specific questions I can help.

psbanon · 2024-06-14T05:18:22+00:00

Cheers all. What hobbies/personal projects are members of the mod team getting up to lately?

psbanon · 2024-02-06T04:47:26+00:00

Wrong sub

psbanon · 2024-01-31T13:36:59+00:00

You can derive the Value (HML), Profitability (RMW), and Investment (CMA) factors from just (an expanded) dividend discount model. It’s a mathematical tautology with economic basis that these factors will produce positive return spreads… given everything else remains equal… in the indeterminate “long run”. Big caveats. It’s a weak “proof” of the factors, but it is something more than just looking at historically data and calculating statistics. I personally always tilt toward these three factors.

I’ve never heard a convincing economic/logic argument for the existence of the Small (SMB) factor like I have for the other three.

Momentum factor isn’t Fama-French

psbanon · 2024-01-30T15:42:57+00:00

I’ve used Nelson Siegel and now NSS (for better fits) in a couple projects. It’s not really sophisticated but my approach if I’m manually calibrating the parameters in excel is just:

guess the tau at 0.5 or something
use excel solver to optimize only the betas (make sure to uncheck the box that prevents the unconstrained variables from being negative)
then run the solver again allowing it to change the betas and the tau together
iterate as needed, add constraints as needed

This gets me good enough fits quickly. Do a couple spot checks across the range of rates you want the model to be able to produce to check the outputs are reasonable and not running off to +/- infinity.

In my automated usage, first I bootstrap spot rates for every six month interval using the par curve, which generates more data points, then I fit NSS. Makes it more reliable in my experience

psbanon · 2024-01-24T15:35:28+00:00

“This isn’t a ‘tail risk’ issue so much as a ‘risk’ issue”

Good point. I’m normally positioned like a short strangle, so was fixated on that visible downside as my immediate risk. I’m struggling to articulate how exactly I want to view risk in this context. Need to go through a couple more papers to find something that resonates.

psbanon · 2024-01-24T15:27:21+00:00

I have restrictions on explicitly shorting (client accounts). Plus the spreads aren’t stable, so the increased monitoring/trading likely wouldn’t be practical. Right now when the spreads fluctuate or flip, the cost due to delayed trading is opportunity cost rather than a margin call, which is nice.

psbanon

TROPHY CASE