Memes, Brain Warmups, and Roth Conversions w/ Kevin Bruns [Episode 2]

Kevin Bruns is back to talk memes, tv, books, warming up your brain, when to best answer emails, taxes, and more.

Topics & Timestamps:

1:10 Foreign Accents

2:05 Netflix & TV Shows

2:50 Internet & Memes

9:15 Books (Taleb)

10:05 Frisbee Interlude

13:00 Brain Warmups

29:40 Taxes (Roth, DISC)

34:05 Scott Sumner Post

36:30 Philippines Lose Major

Links & Notes:

Internet & Memes:

Books:

Taxes:

Link to Kitces

I used the word “sell” with regards to putting the DISC in the Roth, that’s probably not the correct terminology.

Scott Sumner:

Why is Inflation so Low?

Also mentioned: EconLog

Philippines:

Valve Pulls Major

Chinese Censorship

 

Buck Talk – Episode 1 with Kevin Bruns

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Topics & Timestamps:

0:40 Social Security Trustees Report: https://www.ssa.gov/oact/tr/2017/VI_F_infinite.html#1000194

http://www.heritage.org/social-security/report/misleading-the-public-how-the-social-security-trust-fund-really-works

6:00 State Lotteries (which suck)

11:15 2018 Tax Changes

  • Charitable Gifts
  • Property Taxes
  • State Tax Deduction

https://www.forbes.com/sites/timtodd/2018/01/05/substituting-charitable-contributions-for-state-taxes/#69361c8c2279

Cliff Asness:

21:45 Roth IRA for Tax Diversification

23:30 Corporate AMT

26:00 Interest Deductions

31:40 Property Taxes

34:20 Casualty Losses

35:00 Gambling Losses

36:35 Charitable Deductions (cash, appreciated stock, QCD)

37:45 Season Tickets & Stadiums & Sports Teams

43:00 CRUTs

48:15 Educator Deduction

49:45 Schedule C Story

52:45 Itemizing Deductions

 

 

Robot Tax

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What is this daft discussion about taxing robots as if they are people? Companies are already taxed on the efficiency gains if a robot takes a human’s job.

Company A employs one human to whom they pay $100 to produce one widget that costs $100 and that they sell for $1000. The company earns $800 and pays taxes on it. The human earns $100 and pays taxes on it.

Company B fires its last employee, replacing him with a robot. The robot produces one widget that costs $100 and they sell it for $1000. The company earns $900 and pays taxes on it. Sure, you get to depreciate the robot, but you had to buy it in the first place.

What will rising rates cause?

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I get asked almost every week what will happen when rates finally rise.

This is something that most people think they kind of understand, but almost nobody has actually thought through.

The correct answer is that it depends why the rates are going up. Rates, you see, are pretty much always a dependent variable. They do not simply rise randomly and cause chaos in the world. Now, that doesn’t mean they are predictable, since the factors that affect them may not be predictable.

For example, if rates rise because the economy keeps doing wonderfully, unemployment is minuscule, wages are rising, and inflation is at 3%, then I’d guess you’d look at the correlation between rates and the market, and say what a good thing rising rates are, both lines are up and to the right.

However, as we said before, interest rates are essentially a dependent variable. People get confused about this because the Fed can change a couple of rates (either by setting them directly or with open market operations), but working ideally, the Fed isn’t making those decisions in a vacuum.

If we looked at two worlds going forward, one where rates slowly march up as the economy does well and inflation grows, and one where the Fed decides that “just because” we’re going to raise rates by 3%, you can be pretty confident the stock market and economy are going to react relatively poorly to the 3% raise (think taper tantrum), and that the ‘steady as she goes’ timeline is the better one to live in.

Remember, the next time someone asks you what will happen when rates rise, the smart answer is always “that depends on what is causing the rates to rise.”

Goals vs. Systems & Wanting It

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People often get confused between goals and systems. If you have goals, you read about how what you really need to get where you want to go is a system. If you have a system, you read about how what you really need are some specific, measurable goals.

The trick that nobody talks about is that what you actually need is both. Setting specific goals that you can check off and feel accomplished by completing is important, and once you have the goal, you have to build a plan to get there, that’s the system.

The piece that people get wrong is when they set the goal, it may be measurable, specific, etc., but it isn’t what they actually want. Setting a goal to run a certain amount of miles in a certain time or make a certain amount of money might sound like what you want, but lots of times it isn’t. And if your goal isn’t really what you want to do, you aren’t going to stick with the system.

In my experience, when it comes to goals (and excellence in general), there are four kinds of people. Those who do ‘it’, those who want ‘it’, those who want to want ‘it’, and everybody else. Surrounding yourself with people who know what they want and then just do what it takes to get there is the ideal. But those people are rare. People who want it can, with the right tools and resources, become people who do it. The most dangerous group isn’t everybody else, they are obviously not who you want to be with. The most dangerous group is those who want to want it. It can be easy to confuse them with people who actually want it — for years at a time. But they don’t want it, they might think they are supposed to want it, and trap themselves into pretending they want it until their whole self-image is as somebody who wants it.

Serial Correlation: Patriots Win

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The Patriots (that’s my team) beat the Falcons, Tom Brady is the GOAT, Bill Belichick is the GOAT.

How could this have happened?

ESPN’s model had the Patriots as having 0.3% chance of winning at one point, at <1% at more than 20 points.

However, ESPN’s model and many others aren’t properly factoring in serial correlation. Or at least I doubt it, I don’t actually know the inner workings of the model, other than that they use other games in similar situations to project the results.

Serial correlation is essentially the momentum effect. The application in football is thus: it’s unusual for equally matched teams to be separated by a lot of points early in a game, but because it’s a high variance sport, that can happen whether the teams are equally matched or whether one team or the other is superior.

Once one of the teams begins a comeback, if they are actually better than their opponent, they’ll be much more likely than statistics based on other similar games would show to finish the comeback. And there’s a snowball effect, if a team comes back from down 10, they’re likely to be better than their opponent, so more likely to be able to come back from down 14.

That’s because all of the actions the Patriots had to take to come back are related to each other, they’re all team A vs team B, but a sample of similar games played by various teams is going to include more closely matched teams or outmatched teams. But once team A has come back vs team B, it’s more likely that team A is actually beating team B, and will continue to do so. The probabilities have fat tails.