Scott Alexander 2021 Predictions: Market Prices 2021-04-27T14:03:10.995Z
Never Go Full Kelly 2021-02-25T12:53:50.618Z
Kelly isn't (just) about logarithmic utility 2021-02-23T12:12:24.999Z


Comment by SimonM on An Introduction to Prediction Markets · 2021-06-14T20:03:22.730Z · LW · GW

Generally, running the Olympics comes with a lot of local economic activity to make the event happen and various actors benefit from being able to plan ahead. 


I agree with this, I just don't think hotel rooms are a particularly good example since supply is fixed there is little hotel operators can do with knowledge of the probability of the event. (They can "change" prices, but prices are effectively driven my a market equilibrium (which is going to effectively be a prediction market on the Olympics going ahead))

Comment by SimonM on An Introduction to Prediction Markets · 2021-06-14T19:50:33.950Z · LW · GW

Having access to an accurate probability about whether the Olympics will tell local hotels about how important it is to have a lot of beds available

Aside from changing pricing on the rooms (which is already an implicit prediction market on the Olympics) I'm not really sure what the hotels are supposed to do. Individual hotels can't exactly increase supply overnight. (Unlikely your example with Airbnb)

Comment by SimonM on An Introduction to Prediction Markets · 2021-06-14T19:48:15.112Z · LW · GW

To give one further example of uses of prediction markets, Scott Sumner has the idea of using NGDP futures as a way to have market driven monetary policy.

That said, whenever these things become more "useful" I can't help but worry that "information discovery" and "institutional hedging" act against each other. Ultimately if something becomes correlated to people's view of the world, pricing becomes less about "forecasting" and more about "risk premium". To take a concrete example, if there were NGDP futures, you should expect them to be biased low / you should earn a premium for being long GDP. Specifically because if GDP falls so will the rest of your assets, which makes it "painful" to hold - hence a premium to own it.

Comment by SimonM on What to optimize for in life? · 2021-06-06T11:52:47.543Z · LW · GW

Byrne Hobart has the counter-take: Optionality is for Innumerate Cowards

Comment by SimonM on Can you improve your intelligence with these types of exercises? · 2021-06-03T06:46:21.272Z · LW · GW

I would do additional conditioning. So P(opera | farmer), P(museum | opera, farmer), P(chess | museum, opera, farmer), etc.

My guess would it would look something like:

P(opera | farmer) = 5% (does anyone actually like opera?)
P(museums | opera, farmer) = 95%
P(chess | m, o, f) = 40%

So 5% * 95% * 40% = 1.9% of farmers...

P(o | t) = 80%
P(m | o, t) = 50%
P(c | m, o, t) = 20%

So 80% * 50% * 20% = 8% of trumpet players...

Which is a likelihood ratio ~.25 so I end up with something like 125 to 1 that we're talking to a farmer.

Comment by SimonM on Can you improve your intelligence with these types of exercises? · 2021-06-02T07:31:12.096Z · LW · GW

For farmers: 10% enjoy opera, 20% enjoy visiting museums, 5% grew up playing chess = 0.001

I doubt these are independent.

Total number of trumpets in a symphony orchestra ~500

I realise you have the math right further down, but this should be ~5000. (I assume typo)

Comment by SimonM on The Reebok effect · 2021-05-22T11:44:40.541Z · LW · GW

Prior to carbon plates I would disagree with this. It would suggest the shoe manufacturer has the largest marketing spend. (In a post-carbon-plate world where people are covering over the swoosh so that (not-Nike) sponsored athletes can race in Nike shoes I think it's pretty clear which shoe is best)

Comment by SimonM on Why quantitative finance is so hard · 2021-05-08T10:24:14.592Z · LW · GW

The title is "Why quantitative finance is so hard" but it misses the main reason why quantitative finance is hard:

The competition is brutal.

Comment by SimonM on Why quantitative finance is so hard · 2021-05-08T10:19:45.933Z · LW · GW

Small nitpicks:

You should never make a trade with negative expected return.


You explain why in your post, but let me spell it out more explicitly. Diversification means that adding a negative expected return trade to a portfolio can INCREASE the return by adding a negatively returning, negatively correlated asset. Lets say we have two assets: "market" and "insurance". Market returns 11%/year 9/10 years, down 50% the other year. Insurance returns -3^%/year 9/10 and up 22% the other year. Expected market returns are: 5%/2.6% (simple mean / compounded), insurance are: -0.5%/-.7% (mean / compounded). By your logic you should never buy the insurance, and yet if we have a portfolio which maintains a 15% allocation to our insurance asset our expected (compounded) returns increase.

Here is a concrete real-world example: (60/40 + tail hedge). 

Another way to reason about this is: there's nothing special about zero nominal returns. So if you shouldn't make a trade with negative expected return, you should be able to say the same thing about ~any return and by extension you should only put your $ in the highest returning asset... but that misses the whole value of diversification!

The only free lunch in finance is diversification.

(Emphasis mine) This is a strong claim, which I would dispute. Risk premiums (not in the sense you've used the word, but in the sense I understand it to mean) are an obvious example - some assets have a positive yield just for holding them, even after accounting for volatility... Leverage would be another example. 

This is the principle behind index funds.

Kinda, sorta, maybe. It's "a" principle behind them, but if diversification were the only concern, why would you want cap-weighted index funds? Why not equal-sector weight or equal-company weights or some other weights?

Being smart is cheap.

I can only assume you've never attempted to hire people to work for a quantitative hedge fund. If anything, this claim would undercut your main claim that QF is "so hard". Unfortunately (or fortunately for your thesis) being smart is really expensive.

Comment by SimonM on Scott Alexander 2021 Predictions: Buy/Sell/Hold · 2021-05-02T07:25:28.744Z · LW · GW

It is possible that Scott believed that ETH is negatively-skewed (ie small chance of collapsing, large chance of small increase) but this would be inconsistent with his probability that ETH is going to 5k. 

I think the vast majority of people think crypto is positively-skewed.

Comment by SimonM on Scott Alexander 2021 Predictions: Buy/Sell/Hold · 2021-04-30T21:30:50.870Z · LW · GW

because any participant in the options market can hedge their position against the underlying asset.

Right, but then the underlying asset is telling you something and if you disagree with that, then you can trade the underlying asset. There's nothing special about options here. The difference comes from the fact that the underlying asset can have a return. (In the same way that a bond have a price different from par doesn't (necessarily) mean that the market is forecasting default - they are discounting the value of a future cash flow).

What evidence do you have that this is true? Your post is taking risk neutral probabilites from the market + your own opinion that risk neutral is similar to real world, then presenting that as the "market probability", which is very misleading.

The evidence would be something akin to "the historic sharpe for risk assets is <1" so the order magnitude of risk premia is "small enough" relative to the volatility.

I don't think there is anything misleading about taking the market prices, constructing a bet and presenting that as a market probability, any more than taking showing betting odds and saying that's the betting market probability. Sure, there might be some subtleties depending on the market (eg long-shot bias, fees, etc), but fundamentally that's the price the market is offering. If you disagree, BET.

Edit: Maybe a better framing is that in order for option probabilities to give us a ~real world pdf of asset price at a given time, the asset needs to be approximately a martingale from now to the time in question. Many people would strongly disagree that BTC/ETH are even approximately a martingale on this time scale (they think there's large positive drift).

I agree with this, all I'm saying is that the degree to which those assets fail to be a martingale is small relative to their volatility.

You are making a strong claim that is contrary to the view of many or most of the top crypto traders in the market, and yet you don't make this clear but instead claim it's a "market probability", with the implication that people should defer to it unless they have strong domain knowledge. 

I assume all those people are long crypto, which fundamentally means they disagree with the underling price and are long... I don't see any inconsistency between that and what I'm saying. I would be more interested if you could find me someone who thinks both that 

  • option prices are wrong 
  • they shouldn't have a position in options
  • they shouldn't have a position in the underlying

because of some kind of risk-neutral vs real-world probability considerations.

Comment by SimonM on Scott Alexander 2021 Predictions: Buy/Sell/Hold · 2021-04-30T15:20:54.811Z · LW · GW

tl;dr "some sort of median vs mean distinction"

No, there's two things going on which act against each other:

  • Riskier assets have higher returns on average
  • Riskier assets are more skewed (mean higher than median)

I've made the (I think safe) assumption that the skewness of the assets are more important than the relative differences in their expected return. 

You can have a play with some toy models for this, for example, fixed Sharpe, lognormal assets  you will have something which looks like:

log(X) ~ N(sharpe * vol - vol^2/2, vol)

P(return larger than r) = P( Z*vol + (sharpe*vol - vol^2/2) > r) = P(Z  > (-sharpe + vol/2 + r/vol))

If we're interested in r = 0 (outperformance of current price) then as vol increases, the probability goes down. 

(There's actually quite a bit of intuitive stuff which drops out of this model (if we're required to hit a given r, then increasing volatility makes it easier (up until vol = sqrt(2*r)))

Comment by SimonM on Scott Alexander 2021 Predictions: Buy/Sell/Hold · 2021-04-30T07:34:29.513Z · LW · GW

Actually this is the other way around. If ETH is less risky than BTC then the median performance of ETH will outperform BTC and his probability could be consistent with EMH

This is neither consistent with historical realised volatility (ETH is more volatile than BTC), nor is it consistent with the options market (ETH implied vols are all higher than the equivalent moneyness BTC implied vols)

Comment by SimonM on Scott Alexander 2021 Predictions: Buy/Sell/Hold · 2021-04-29T09:25:33.635Z · LW · GW

I detailed a few of them which are already on Metaculus here. If there are others which you are particularly keen to see added I'm sure they could be written

Comment by SimonM on Scott Alexander 2021 Predictions: Market Prices · 2021-04-28T15:03:10.822Z · LW · GW

I just ball-parked the numbers from a the tightest call spread currently tradable in the market. If precision was important I'd do something more sophisticated.

Yeah not really sure why you'd divide the option price by current spot?

Comment by SimonM on Scott Alexander 2021 Predictions: Buy/Sell/Hold · 2021-04-27T21:58:48.782Z · LW · GW

I'm now confused as to what you mean by "real world" in this context?

Zvi is giving a credence for the event (p_zvi).

The market is offering a bet which implies some probability for the event (p_market).

All I am noting is p_zvi is different from p_market. I don't think there's anything special about the fact that options are involved here. (Unless I'm the one inferring you were specifically talking about options when you talk about "risk neutral measures". All market probabilities are in some sense in risk neutral probabilities. If you're complaint is about me talking about market probabilities then I guess this post wasn't really for you?)

EDIT: To be more concrete about this, the places where "risk neutral" vs "real world" probabilities end up mattering is places where there is a concrete risk premium. (ie what the options market implies about stocks in 1y's time doesn't account for the fact that "stocks tend to go up over time"). In all the examples we're talking about, those risk premiums are tiny relative to the numbers involved so they don't make a significant difference to how we should be calculating the "market implied" odds.

Comment by SimonM on Scott Alexander 2021 Predictions: Market Prices · 2021-04-27T21:39:25.193Z · LW · GW


Comment by SimonM on Scott Alexander 2021 Predictions: Buy/Sell/Hold · 2021-04-27T21:38:27.489Z · LW · GW

I think there's ~80-85% chance the Olympics happen on time. I think there's a ~90% chance that the Olympics go ahead this year.

I think the case against them going ahead this year is roughly:

  • Current state of COVID in Japan, potential for it getting worse
  • "Cancellation is possible" statements from government
  • Public opinion is against the games

I don't think it's very likely, but do I think there's a ~15-20% chance that COVID flares up in Japan in the next 3 months in a particularly bad way? Doesn't seem crazy to me.

(FWIW, I don't think betting on the Olympics at the FTX odds is a bad bet, I just don't think it's a sure thing)

Risk neutral vs Real world measure isn't really a meaningful distinction the way you think it is. You can construct a binary bet in terms of options, and the price is the market price for that bet and that's the market probability. It's no different than betting on any other event. If you don't like market pricing, then sure, ignore everything I've written here, but don't think "risk neutral measure" is some magic phrase which lets you ignore the options market. If you think the odds are different, you can always place that bet.

I'm not saying Zvi is wildly wrong. Indeed he says he wouldn't trade with anything in 40-60% (and the market being at 60% means he's technically not "off" it), but I given it's close to what he'd consider trading, I think that's an interesting difference worth noting.

Comment by SimonM on Scott Alexander 2021 Predictions: Buy/Sell/Hold · 2021-04-27T14:16:37.068Z · LW · GW

I've copied this, but only taking the market forecasts. Doing this meant I've spotted a couple of things which I think Zvi missed:

8. Olympics going ahead on schedule

Zvi cites the Metaculus forecast, but it actually isn't specific to the schedule. Other prediction markets are half-way between his forecast and Scott's. (Although my personal forecast is the ~same as Zvi)

12. Netayahu. I'm not sure if Zvi meant to, but he missed that PredictIt does have an end of year market.

14. Zvi seems somewhat off the option market forecast. (Judging him slightly on hard mode as I think he's pretty skilled)

17. I'm not sure this was quite his "biggest" fuck you to EMH. The Dow predictions and ETH to 5k both seem significantly worse

Comment by SimonM on Best empirical evidence on better than SP500 investment returns? · 2021-04-26T08:22:47.459Z · LW · GW

Right, so the back of the envelope calculation for what I think you are calling volatility drag is:

geometric return = arithmetic return - volatility^2 / 2

If you have constant leverage (for example like most constant-leverage ETFs) then you effectively multiply your arithmetic return by a constant and your volatility by the same constant so your new geometric return is:

leverage * arithmetic return - leverage^2 *volatility^2 / 2

Your example is correct.

There is a sense in which all three (leveraged-ETFs, margin, futures) are all equivalent, the main difference is in how active you need to be to maintain you need to maintain your strategy. In terms of "closest to buy-and-hold" I think they go in this order:

  • Margin (buy less than your broker allows you too, maintain cash in your brokerage, periodically adjust)
  • Futures (make sure you hold significantly more cash than your brokerage, roll your futures appropriately)
  • Leveraged-ETFs (hold cash to rebalance, you will need to do so regularly)

There is a sense in which they also go in the opposite order in terms of effort. (For example, if you do want to maintain constant leverage (which is of course the concrete recommendation for juicing returns) then leveraged ETFs are the way forward as tryactions explained)

Comment by SimonM on Best empirical evidence on better than SP500 investment returns? · 2021-04-25T17:20:32.987Z · LW · GW

Leverage is not more complicated than it looks. "Borrow money to invest". (Or more usually in finance "borrow money using your investments as collateral to invest more").

Futures aren't the only way to invest with leverage. Probably the easiest way for a retail investor would be something along the lines of owning ETFs on margin.

  1. Treasury futures and cash treasuries are pretty much exactly the same amount of volatile. Even when the cash/futures basis blows up, we are talking tiny amounts relative to the volatility of the underlying. You can absolutely leverage treasuries via treasury futures and assuming that treasuries outperform your cost of funding then you will "juice" your returns. 
  2. Futures prices are priced so there is no arbitrage - nothing more, nothing less
  3. The price of the futures account for this. (Otherwise there would be an arbitrage, see 2.)
  4. I don't really understand your question here? 

Yes, you definitely can let your leverage ratio float around a bit, in fact I would strongly recommend this. Just because someone will offer you X amount of leverage, doesn't mean you should take it all. In practice you should be able to avoid margin calls in a well managed position, although it is a risk you are taking with leverage, and you need to appreciate that before going down this path.

Comment by SimonM on Best empirical evidence on better than SP500 investment returns? · 2021-04-25T08:09:03.390Z · LW · GW

The traditional finance theory way to acquire more risk would be to increase leverage in your portfolio

(I explain more here and that thread is full of other ideas you might like)

Comment by SimonM on Are there opportunities for small investors unavailable to big ones? · 2021-04-23T11:04:03.258Z · LW · GW

You've given an example which is already 10x what I asked for, and you could have plausibly done another 5x your size... I'm glad you made some money, but I don't think this is what I'm talking about 

Comment by SimonM on Superrational Agents Kelly Bet Influence! · 2021-04-17T10:46:08.627Z · LW · GW

I also looked into this after that discussion. At the time I thought that this might have been something special about Kelly, but when I did some calculations afterwards I found that I couldn't get this to work in the other direction. I haven't fully parsed what you mean by:

(And since payoffs of the bet-against-yourself strategy are exactly identical to Kelly betting payoffs, a bunch of Kelly bets at house odds rearrange money in exactly the same way as this.)

But this is clearly equivalent to how hypotheses redistribute weight during Bayesian updates!

So, a market of Kelly betters re-distributes money according to Bayesian updates.

So take the following with a (large) grain of salt before I can recheck my reasoning, but:

Everything you've written (as I currently understand it) also applies for many other betting strategies. eg if everyone was betting (the same constant) fractional Kelly.

Specifically the market will clear at the same price (weighted average probability) and "everyone who put money on the winning side picks up a fraction of money proportional to the fraction they originally contributed to that side". 

Comment by SimonM on Are there opportunities for small investors unavailable to big ones? · 2021-04-16T08:49:20.795Z · LW · GW

The reason that I think articulating why strategies might exist is that I'm a dogmatic EMH fundamentalist. When a person gives investment advice, I have historically ignored it, the same why I ignore it when somebody makes an argument for why I should consider using heroin, or committing suicide. Those behaviors are on my "no" list. I don't intellectually engage with arguments in favor of them, and short-circuit the updating of my priors

Likewise, the EMH has put investment advice (aside from "invest in index funds") on my "no" list. I follow an iron law of not engaging with investment advice.


I would highly recommend reading the full introduction to Section 20 of Cochrane's "Asset Pricing". (The whole course is excellent) Roughly he takes you through the progress academic finance has made since the 1970s, whilst not repudiating EMH, finding reasons why "invest in index funds" isn't necessarily the whole story for investors. 

Comment by SimonM on Are there opportunities for small investors unavailable to big ones? · 2021-04-15T20:43:56.049Z · LW · GW
  • Are there investment strategies, besides index fund investing, that make the amount of money you have, your level of political access, and your accumulated knowledge infrastructure, relatively unhelpful, or even actively harmful, for evaluating certain specific types of investments - even if those resources are also extremely helpful for accessing and evaluating other trades?

I don't understand this sentence?

  • In other words, are there investment strategies where individual, 'one-off episodes' of rational thought - as opposed to accumulated rational reflection over long periods of time - is more important than any other factor for accurately predicting price change for a subset of possible trades?

... still lost

  • In other other words, are there investment strategies that are unattractive to hedge funds, but work well for small, smart, creative, and hard-working analyst-investors?

Fine, interesting question, I don't really see how it links to what you wrote in your post? You seem to posit reasons why strategies might exist, but really this is a concrete problem. "Name some strategies". 


And in addition (I try to give a tentative guess to establish plausibility):

  • If these investment strategies exist, can we identify them?
  • How easy is it to end up with a compelling, yet wrong answer?

You're proposing a third, important question (this I don't know the answer to):

  • If we can identify such a strategy, how many such opportunities can we expect to find, and how much competition will we face from other investors in our reference class?

I don't really see where you ask how to identify strategies? Nor whether or not you could delude yourself?

I don't really think LW is short of people suggesting strategies for small investors:

Comment by SimonM on Are there opportunities for small investors unavailable to big ones? · 2021-04-15T10:57:25.905Z · LW · GW

Extrapolate across the employees in the whole fund, and that amounts to something a bit like "inertia." Another way of putting it is that smaller investors face lower opportunity costs by exploring novel investment types.

Right, but then my point is individual funds don't matter. What matters is the ecosystem. Are there funds in your area? Another way of of putting your argument is: "Individual investors have an advantage because they can scan across all investments" but my counter to this is:

  • You already accept LARGE FIRMS can't cover the space of all investments - how can an individual investor manage?
  • You aren't competing with one firm, you're competing with ALL FIRMS

This doesn't quite make sense to me, taken alone. Any $1mm opportunity is also a $10 opportunity, but not all $10 opportunities are $1mm opportunities. There must be more, say, $1,000 opportunities. Hard to give a firm reason to explain what the ratio would be.

Lots of investments are $1mm investments and aren't $10 investments. (Try and see if you can get a $10 allocation in an IPO / VC / PE type investment).

Humans might differentiate themselves from such algorithms by focusing on an approach to investing that considers it more along the lines of "superforecasting"-style questions. Figuring out how to ask and answer questions with good judgment, and determine how the answers bear on the market, is something that an algorithm is not cut out for.

And you think hedge funds aren't doing this? I'm not sure exactly what edge you're ascribing to small investors here.

No (I'm assuming you're talking about 20% gains on a low-volume stock). But I have modest confidence that these opportunities might exist, and that I'd discover historical examples if I took the time to look. Right now, I'm trying to focus more on "how the EMH could be wrong (enough) under not-too-unreasonable assumptions to justify doing more research to verify or falsify the model," rather than making an argument that the model is valid or that the EMH is right or wrong. As I said, this is highly speculative, and I am inexperienced but trying to learn.

I like the way of framing EMH as "Is EMH true for you?" and there may or may not be reasons to believe it's true for you. Personally I have rather dim views of people who claim that EMH is not true in some generality. If you want to convince me EMH isn't true for you, develop a track record.

(Also to be clear a 20% gain on even the lowest volume stock wouldn't count for me - very low volume stocks would still allow investors to invest many times more than $1000...)

And this is my key point. Within this class of opportunity, you're not competing with hedge funds. You're competing with other small investors. One thing I really don't understand is what fraction of small investor wealth is tied up in things like index funds and managed retirement funds. Of small investor wealth, what fraction is people actively trying to play the small-cap low-volume weird-investment stock market? What level of sophistication does their thinking rise to?

I would recommend you spend some time on [insert any online investing forum]. There are loads of people doing it. I'm not sure the "fraction" is particularly meaningful. One thing which I think is mechanically true is more of the investments in small caps are active (since fewer indices invest in small caps).

Comment by SimonM on Are there opportunities for small investors unavailable to big ones? · 2021-04-15T09:18:55.972Z · LW · GW

Inertia vs. agility

I think this section makes two arguments:

  1. The investment universe is very large; it's hard for one fund to cover all of it
  2. Large investment funds have inertia associated with any large company

I think both points are ~wrong / unimportant for EMH.

  1. Whilst the investment universe is large, so are the number of eyeballs on it. There are many funds all specialising so whilst you might not be competing with Bridgewater in your microstock world, you may be competing with some other funds.
  2. Whilst each fund is structured differently, typically individual portfolio managers have a lot of autonomy within their mandate. If they change their mind about a position they can move very quickly. (Ignoring the size issue from the next paragraph). Bureaucracy can be a factor in some funds, but that's typically not what limits people.

Investment size floors

I think the argument (as you've written it) doesn't really make a huge amount of sense. (Saying that each person needs to find $x/per hour etc). Whilst not all trades scale, lots do, so finding a $10 opportunity is not necessarily easier or harder for a large fund than a $1mm opportunity. Some other advantages of scale which you're glossing over:

  • Access to investments not available to smaller investors (allocations in issuance, private deals, products unavailable to retail investors, co-location etc)
  • Execution edge - your trade is typically being managed by hand, theirs can be managed by algos written by professionals; they can watch the market 24/7 to find the small edge in some weird basis you know nothing about.
  • A seat at the table - don't like the way the board is handling a company you own? Phone them up / get your own board seat. Want to understand something about a company you might invest in? Have a chat with the head of IR / CEO etc
  • Small edges being meaningful - squeezing out another basis point on a big investment makes a big difference in $ terms. (You sort of address this later)

meaning that they'd need to be 20x as efficient as you in identifying them. 

Consider the HFT - they are trying to place trades to earn pennies and they are much more than 20x as efficient as you at identifying those opportunities. (I don't think HFTs are particularly interesting to consider when thinking about "competition" in an EMH sense, but I think it's a good illustration about how much more efficient funds can be than you). 

To be sure, a small investor still must compete against other smart, small investors for whatever edge there is to be found. But now we're basing the EMH, with respect to these small-time and strange investments, on the intelligence of people more or less like you and me.

I don't think this is quite the right interpretation. Can you describe to me a concrete trade which looks like: 20% return on $1000. (All the ones I can think of tend to be just as amenable to professionals). The other issue of playing in the "micro-investment" pool, is typically liquidity is much lower, so costs are higher.


EDIT: To be clear viz-a-vis your title "Are there opportunities for small investors unavailable to big ones?" I think the answer is "Yes, but there are lots more small investors"

Comment by SimonM on Tales from Prediction Markets · 2021-04-04T11:41:14.656Z · LW · GW

Whilst these stories are wild lets not forget that in the "real" markets also have had a lot of wild stories over the same time period:

  • GME
  • Archegos
  • Greensill
  • Citi/Revlon
  • Texas Electricy

My point is less "these stories aren't interesting" (they are) and more "interesting things happen in lots of markets". Try to avoid weighting too much on "prediction markets are weird" and more "markets are weird".

Comment by SimonM on Speculations Concerning the First Free-ish Prediction Market · 2021-04-01T16:08:29.415Z · LW · GW

TIPS aren't great for betting on inflation:

  • Embedded option on deflation
  • Liquidity issues
  • Difficult to short for a retail investor

If you have the kind of ability to short TIPS, you have the kind of ability to trade inflation swaps which are a much purer way to bet on inflation.

(That's not to say TIPS aren't a useful product - as an investment vehicle with the risks it mitigates they are excellent, but for betting on inflation they aren't especially useful for retail)

Comment by SimonM on Systematizing Epistemics: Principles for Resolving Forecasts · 2021-03-30T19:19:15.801Z · LW · GW

(I realise everything I'm commenting seems like a nitpick and I do think that what you've written is interesting and useful, I just don't have anything constructive to add on that side of things)

I don't like litigating via quotes, but:

I haven't said, and I don't think, that the majority of markets and prediction sites get this wrong.


More than that, I think the balance should be better and more explicitly be addressed.

I read the bit I've emphasised as saying "prediction sites have got this balance wrong" contradicting your comment saying you think they have it right.

Still, excessive focus on making rules on the front end, especially for longer-term questions and ones where the contours are unclear, rather than explicitly being adaptive, is not universally helpful. 

I think it's really hard for this adaptive approach to work when there's more than a small group of like minded people involved in a forecast. (This is related to my final point):

1) When the wording of a question seems ambiguous, the intent should be an overriding reason to choose an interpretation.
2) When the wording of a question is clear, the intent shouldn't change the resolution.

The problem for me (with this) is what is "clear" for some people is not clear for others. To give one example of this, the language in this question was completely unambiguous to me (and it's author) but another predictor found it unclear. (I don't think this is a particularly good example, but it's just one which I thought of when trying to think of an example of where some people thought something was ambiguous and some people didn't).

Comment by SimonM on Systematizing Epistemics: Principles for Resolving Forecasts · 2021-03-30T08:00:08.366Z · LW · GW

This seems pretty reasonable. In particular I think you've very clearly articulated a set of principles which are valuable for prediction.  However, I feel like your principles for forecasting seem to explicitly lead to most forecasting needing to be rules based. For example, to begin with you say:

In short, when making rules, one can front-load intentions by writing details upfront, or back-load work by stating high-level principles and having procedures to decide on details on an as-needed basis*. [...] I think that the question shouldn’t be implicitly decided by front loading assumptions, which is often the current default. More than that, I think the balance should be better and more explicitly be addressed.

But then later on you say:

This requires that they be resolvable.

  • The resolution criteria should be well-specified.
  • If relevant or possible, the intent of the question, or guidance for how resolution will occur, should be clear.
    • Ambiguity should be avoided, but by default, when (inevitable) ambiguity arises, intent should guide the resolution. Any guidance about the motive or intent of the question can therefore be an asset. This is especially true when resolutions are based on expert opinion.
  • By default, forecasts should be assumed to be about object-level issues.

My first take reading "decide on details on an as-needed basis" was that we could somehow avoid most of the pain which goes alongside writing forecasting questions, but that doesn't seem to be the case.

Another example would be the need to have explicit rules for scoring. 

To give a concrete example, I think Metaculus is pretty far to the "Rules" end of the spectrum in your frame. Despite this, I can't really see what you're suggesting would change if they shifted to a more "Principles" based approach. If I had to speculate, I would guess your only "real" change would be something like:

"Where the spirit and letter of a question conflict, the question should be resolved based on the spirit"

This seems pretty reasonable to me (although I don't feel that you've made a compelling case for it).


But for prediction markets to be useful, there should be a balance between principles and rules. 

So I don't fully agree with the logic in this section. I agree that often the spirit and letter of a question can disagree, but I don't necessarily think that prediction markets will be "more useful" if they have a more principles based approach. (At least, I think the downsides from potentially uncertain resolution outweigh the upside from potentially resolving "more correctly"). [To give a concrete example, my comfort level using Polymarket decreased substantially when they (in my opinion) changed their resolution criteria on their US Election market. (When the on-ramp and off-ramp started to creak that was the nail in the coffin as far as I was concerned)]

Comment by SimonM on Improvement for pundit prediction comparisons · 2021-03-28T19:20:45.640Z · LW · GW

I don't think this is an especially good idea for a bunch of reasons:

  1. It's hard enough getting pundits to put numbers on their forecasts,
    1. adding a bunch of additional demands on them seems counterproductive
    2. making their forecasting competitive may put them off
  2. It's extremely unlikely they'll answer enough questions whereby you can statistically significantly tell the difference between them (unless they are really terrible)

Ideally Metaculus (or other prediction platforms) should be asking sufficiently many interesting questions about future years that the questions which the pundits choose to forecast on are already predicted on, and we can make comparisons from there.

I would recommend this article from the EA forum which also lays out a bunch of additional issues around prediction contests

Comment by SimonM on Exploiting Crypto Prediction Markets for Fun and Profit · 2021-03-13T11:39:01.418Z · LW · GW

The payoff per trade is in the region of 4% with a roughly 30 - 60 day turnaround time. Because of various costs, it's probably not economical to trade on less than 2k USD as the fixed costs will eat up your margin.

To what degree do you think this is just the "fair price of risk" for doing things on crypto platforms? The returns I've seen are roughly inline with various other DeFi opportunities or the contango of the BTC futures curve. 

Whilst I think that prediction markets have had some issues this year, I think a large part of that was less about "crypto" and more about "broken prediction markets". (In fact, the "price of risk" in the spread between (say) Augur / Polymarket and Betfair for the same contract still existed in this period).

Comment by SimonM on A Primer on United States Treasuries · 2021-03-10T09:44:12.321Z · LW · GW

There are several other key points relating to treasuries (and nominal rates in general) which haven't made it into your article, but are probably more important than anything you've mentioned:

  • Correlation with the real economy
  • Inflation (aka nominal vs real rates aka UST vs TIPS)
  • Hedging properties of treasuries

There is a general thread running through this whole article whereby you are alternating between "Treasuries" and "Risk-free rates". Given that treasuries are considered the risk-free rate, there's a degree to which mixing these things up "doesn't matter". Contrary to this, I think it is important to distinguish between these things, especially when talking about how these things relate to other assets.

Roughly speaking, there are two ways in which the risk-free rate affects pricing of other securities:

  • As an arbitrage bound / discount rate for pricing things in the future
  • As an "alternative" in an investment portfolio

(I associate the first one more with RFR and the latter more with treasuries)

Most of your examples are in reference to RFR. (In fact, everything you wrote could be replaced with "everything is discounted using DCF and the discount factors come from Treasury prices" (which somewhat misses the point that treasuries are priced via DCF...))


The key question here is "which fx rate moves". (Ie Forward rate vs Spot rates). The forward rate for an FX pair is defined by an arbitrage bound. (Actually there is much more which could be said about this relating to cross-currency basis, but this is not the place for that). The forward exchange rate (how many EUR can I buy with 100 USD in a years time) is defined by an arbitrage. I can either buy my EUR today and put them in a EUR bank account, or I can put my USD in a USD bank account today and exchange them for EUR in a year. If EUR rates are -50bps and USD rates are 0bps, then I will have either current_exchange_rate * .995 in a years time or forward_exchange_rate * 1 in a years time. These two values must be equal (otherwise there's an arbitrage), so the forward exchange rate is fully determined by the differential interest rate in the two countries.

Now if interest rates go up in the US, then dollars vs euros today needs to move OR dollars vs euros in a years time needs to move.

This is [mostly] an arbitrage relationship rather than anything to do with USTs as an asset class.

There is a level on which these things are more complicated. mostly driven by the action of large foreign investors who typically have a maturity mismatch between their bonds (often long bonds) and FX hedges (often 3m-1y rolling). This generally means that spot rates bear the brunt of the moves.

Corporate Bonds

What you've written is technically true. (Although you could re-write the whole paragraph replacing "corporate bonds" with "US treasuries" and convey as much information).

This is [kinda] an "alternative" example. If I'm choosing between lending my money to the US Government and Acme Corp, then I expect to earn a rate slightly higher than if I lend my $ to the government.

When thinking about corporate bonds (at least higher rated ones) the important bit is (as you say) the credit spread. The more interesting question is "how does the credit spread relate to interest rates". In general, they are positively correlated, because as interest rates go up, their credit burden goes up, so their creditworthiness goes down (and vice versa). [There's some complications in all of this but lets leave aside for now]

Fixed Rate Mortgages and other Fixed Rate Loans

US fixed rate mortgages are a messy product. To first order, what you're saying is true, but only in the same sense as everything else. "Everything is discounted using DCF and the discount factors somehow come from treasuries". (Which is pretty circular).


That chart is a chart-crime on so many levels.

One of the most common ways to value a company is The Discounted Cash Flow Model. One of the inputs of the model is the discount rate. The higher the UST rate, the higher the discount rate. The higher the discount rate, the lower the present value of the company according to the model.

This misses the woods for the trees. Why do we use DCF? Because of alternatives. If a company is offering me $100 in cashflow in a years time, how much should I be willing to pay for that? Well, a decent starting point is "how much can I buy that (future) $100 from the government for. So another way of saying the same thing is: "If the return on owning treasuries increases (ie bond prices down, rates up) then the return on owning equities should increase (since people can get the a better return elsewhere otherwise)".

Relatedly, you might find Fight The Fed (Asness) interesting.

Commodities, Futures, Options

Pretty much all of these fall squarely in the "risk-free rate as arbitrage" camp. (At least to a first order approximation). 


Some important nitpicks:

One of the most common UST is the UST 10 year bond.

I'm not sure what you mean by "common" here. Some senses in which the 10y isn't the "most common": 

  • Not the highest outstanding notionals
  • Not the weighted average maturity (or duration) of the whole stock
  • Not the highest volume traded (either by notional or duration (or as a derivative))

I'm guessing you mean something like "most commonly cited by the financial media".

You can buy treasuries that mature in most months and many different years, but only a select few are tracked by the markets, and are the reference rates that are used by the markets to drive expectations and investment decisions.

Err... what? I'm not even sure what you're trying to say? Perhaps you mean something like: "A few benchmark points are observed more closely", but generally people making investment decisions will just take the whole yield curve. Or perhaps you mean "The US only issues bonds at a certain subset of durations"? Not really clear to me.

All else being equal, when USTs are rising across the board, it means that the United States dollar will strengthen, assuming that rates in the foreign currency in question are stable or dropping

This is a massive problem people have when talking about bonds. USTs are treasury bonds not treasury bond rates.  When USTs are rising, rates are falling. Here you mean something like "When USTs sell-off / fall and US rates rise ...". 

Comment by SimonM on What is the VIX? · 2021-03-10T07:55:36.350Z · LW · GW

If you could trade it directly, this suggests a simple strategy: Buy when it's steady, and then sell when it spikes. Even if you can't time the spikes perfectly, you'll make a lot of money.

So can you do this with the futures? No, because someone has to take the other side of the trade, and they know it might spike, so they'll price that in! It's like buying insurance. You have to pay a "risk premium" to the market to hold a long vol position.

It's actually more subtle than this. There are two things going on (which I believe you understand based on "Wrong side of risk") but you have conflated here:

  1. The VIX exhibits behaviour whereby there are strategies where if you could trade it you could guarantee a profit: "Buy when it's steady, and then sell when it spikes"
  2. "You have to pay a "risk premium" to the market to hold a long vol position"

The reason you have to pay a "risk premium" to hold a long vol position is because the "spikes" are negatively correlated to the market portfolio / other assets / the economy. This means that if the expected value for holding a long vol position was zero (or positive) you could improve your portfolio risk-adjusted returns (and hence by leverage total returns) by simply adding a "long vol" position to your portfolio. Another way of saying this is "You are only compensated (positive return) for systemic risks, and long vol as is inversely correlated to systemic risk so must have a negative return".

If there was an analogous index where the spikes weren't uncorrelated to the broader market, the futures would still have some term structure such that you couldn't exploit future spikes and expect to make money. However, you wouldn't see the long term decay you see looking at (long) VIX ETFs that you do now.

Unless you can predict the spikes pretty well, you'd be better off taking the opposite trade (like SVXY) to collect that premium yourself

As I've said elsewhere, I think collecting vol risk premium is a poor strategy for retail investors. Vol risk premium should exist and adding expected negative return assets to your portfolio can enhance your returns. Equivalently, adding a positive expected return asset to your portfolio can reduce your returns. Caveat emptor

Comment by SimonM on Kelly *is* (just) about logarithmic utility · 2021-03-01T22:59:55.469Z · LW · GW

For sure - both my titles were clickbait compared to what I was saying.

I think if I was trying to explain Kelly, I would definitely talk in terms of time-averaging and maximising returns. I (hope) I wouldn't do this as an "argument for" Kelly. I think if I was to make an argument for Kelly which is trying to persuade people it would be something close to my post. (Whereby I would say "Here are a bunch of nice properties Kelly has + it's simple + there are easy modifications if it seems too aggressive" and try to gauge from their reactions what I need to talk about).

I will definitely be more careful about how I phrase this stuff though. I think if I wrote both posts again I would think harder about which bits were an "argument" and which bits were guides for intuition. 

I actually wouldn't make very much of a defence for the Peters stuff. I (personally) put little stock in it. (At least, I haven't found the "Aha!" moment where what they seem to be selling clicks for me).

I think the most interesting thing about Kelly (which has definitely come through over our posts) is that Kelly is a very useful lens into preferences and utilities. (Regardless of which perspective you come from).

Comment by SimonM on Kelly *is* (just) about logarithmic utility · 2021-03-01T20:16:46.439Z · LW · GW

Thanks for writing this! I feel like we're now much closer to each other in terms of what we actually think. I roughly suspect we agree:

  • Kelly is a litmus test for utilities
  • For a Bayesian with log-utility Kelly is the end of the story

You think the important bit is the utility, I think the important bit is what it says about people's utilities.

Comment by SimonM on What is the VIX? · 2021-02-28T21:22:05.207Z · LW · GW

The simplest product (at least from an understanding point of view) would be VIX futures. These are futures which are (to a first approximation) cash settled to the VIX Index. (You can view the specs here).

One thing to notice is that they expire. This means that if you buy a future to gain exposure, when it expires you lose your exposure. (The same is true options - when they expire, you lose your optionality. (Actually, you lose some optionality on a daily basis which is par of why you can't own / replicate the VIX Index)). This means you have to come up with a strategy to "roll" your exposure before it expires. You can have a look at the term structure of VIX futures here

Another thing to notice is that VIX futures are the expected value of the index - NOT the index. Typically when vol explodes, the VIX Index goes very high, the front future goes high, the next future less high and so on... Depending on which futures you own, you will make money, but not as much as the index will have moved.

Typically retail investors tend to trade the VIX via ETFs. These tend to formalise a strategy of buying and rolling VIX futures. Generally you can find the details in the ETF docs. 

Comment by SimonM on What is the VIX? · 2021-02-28T16:47:56.411Z · LW · GW

The VIX isn't tradeable. 

There are futures which are based off of the VIX. And there are ETFs which own have portfolios of those futures. These products are very different from "buying" the VIX and I would being very careful when "trading" or "investing" in these products. There are lots of products in this space, and they won't necessarily behave like you think they will.

Comment by SimonM on Never Go Full Kelly · 2021-02-27T09:41:20.039Z · LW · GW

Just to be pedantic, I wanted to mention: if we take Fractional Kelly as the average-with-market-beliefs thing, it's actually full Kelly in terms of our final probability estimate, having updated on the market :)

Yes - I absolutely should have made that clearer.

Concerning your first argument, that uncertainty leads to fractional Kelly -- is the idea: 

  1. We have a probability estimate , which comes from estimating the true frequency ,
  2. Our uncertainty follows a Beta distribution,
  3. We have to commit to a fractional Kelly strategy based on our  and never update that strategy ever again

Sort of? 1. Yes, 2. no, 3. kinda.

I don't think it's an argument which leads to fractional Kelly. It's an argument which leads to "less than Kelly with a fraction which varies with your uncertainty". This (to be clear) is not fractional Kelly, where I think we're talking about a situation where the fraction is constant.

The chart I presented (copied from the Baker-McHale paper) does assume a beta distribution, and the "rule-of-thumb" which comes from that paper also assumes a beta distribution. The result that "uncertainty => go sub-Kelly" is robust to different models of uncertainty.

The first argument doesn't really make a case for fractional Kelly. It makes a case for two things:

  • Strong case: you should (unless you have really skewed uncertainty) be betting sub-Kelly
  • Rule-of-thumb: you can approximate how much sub-Kelly you should go using this formula. (Which isn't a fixed

So the graph shows what happens if we take our uncertainty and keep it as-is, not updating on data, as we continue to update?

Yes. Think of it as having a series of bets on different events with the same uncertainty each time.

Also, I don't understand the graph. (The third graph in your post.) You say that it shows growth rate vs Kelly fraction. Yet it's labeled "expected utility". I don't know what "expected utility" means, since the expected utility should grow unboundedly as we increase the number of iterations.

Or maybe the graph is of a single step of Kelly investment, showing expected log returns? But then wouldn't Kelly be optimal, given that Kelly maximizes log-wealth in expectation, and in this scenario the estimate  is going to be right on average, when we sample from the prior?

Yeah - the latter - I will edit this to make it clearer. This is "expected utility" for one-period. (Which is equivalent to growth rate). I just took the chart from their paper and didn't want to edit it. (Although that would have made things clearer. I think I'll just generate the graph myself).

Looking at the bit I've emphasised. No! This is the point. When  is too large, this error costs you more than when it's too small.

I think our confusion is coming from the fact we're thinking about two different scenarios:

Here I am considering  (notice the Kelly fraction depending on  inside the utility but not outside). "What is my expected utility, if I bet according to Kelly given my estimate". (Ans: Not Full Kelly)

I think you are talking about the scenario ? (Ans: Full Kelly) 

I'm struggling to extract the right quotes from your dialogue, although I think there are several things where I don't think I've managed to get my message across:

OTHER: In those terms, I'm examining the case where probabilities aren't calibrated.

I'm trying to find the right Bayesian way to express this, without saying the word "True probability". Consider a scenario where we're predicting a lot of (different) sports events. We could both be perfectly calibrated (what you say happens 20% of the time happens 20% of the time) etc, but I could be more "uncertain" with my predictions. If my prediction is always 50-50 I am calibrated, but I really shouldn't be betting. This is about adjusting your strategy for this uncertainty.

OTHER: So all I'm trying to do is examine the same game. But this time, rather than assuming we know the frequency of success from the beginning, I'm assuming we're uncertain about that frequency.

BAYESIAN: Right... look, when I accepted the original Kelly argument, I wasn't really imagining this circumstance where we face the exact same bet over and over. Rather, I was imagining I face lots of different situations. So long as my probabilities are calibrated, the long-run frequency argument works out the same way. Kelly looks optimal. So what's your beef with me going "full Kelly" on those estimates?

No, my view were always closer to BAYESIAN here. I think we're looking at a variety of different bets but where my probabilities are calibrated but uncertain. Being calibrated isn't the same as being right. I have always assumed here that you are calibrated.

BAYESIAN: Not precisely, but I could put more work into it if I wanted to. Is this your crux? Would you be happy for me to go Full Kelly if I could show you a perfect x=y line on my calibration graph? Are you saying you can calculate the  value for my fractional Kelly strategy from my calibration graph?

OTHER: ... maybe? I'd have to think about how to do the calculation. But look, even if you're perfectly calibrated in terms of past data, you might be caught off guard by a sudden change in the state of affairs.

No, definitely not. Your calibration graph really isn't relevant to me here.

BAYESIAN (who at this point regresses to just being Abram again): See, that's my problem. I don't understand the graph. I'm kind of stuck thinking that it represents someone with their hands tied behind their back, like they can't perform a Bayes update to improve their estimate , or they can't change their  after the start, or something.

This is almost certainly "on me". I really don't think I'm talking about a person who can't update their estimate and I advocate people adjusting their fraction. I think there's something which I've not made clear but I'm not 100% I know we've found what it is yet.

The strawman of your argument (which I'm struggling to understand where you differ) is. "A Bayesian with log-utility is repeatedly offered bets (mechanism for choosing bets unclear) against an unfair coin. His prior is that the coin comes up heads is uniform [0,1]. He should bet Full Kelly with p = 1/2 (or slightly less than Full Kelly once he's updated for the odds he's offered)". I don't think he should take any bets. (I'm guessing you would say that he would update his strategy each time to the point where he no longer takes any bets - but what would he do the first time? Would he take the bet?)

Comment by SimonM on Never Go Full Kelly · 2021-02-26T19:16:55.289Z · LW · GW

I linked several papers, is there one in particular you are referring to and a section I could make clearer?

Comment by SimonM on What is the VIX? · 2021-02-26T17:57:02.256Z · LW · GW

Roughly speaking, it's about "when" you take square roots and what that means for the product you are trading. Here is a handy guide on a zoo of vol/var swap/forward/future products.

The key thing is less about what "volatility" and "variance" have been. (Realized volatility is the square-root of realised variance). We're talking about the expectation for the next month's volatility or variance. 

The "mathematician" way to think about this (although I think this is a little unhelpful) is . If "X" is (future) realised variance (as yet unknown), then the former is "volatility" and the latter is "square root of variance" (what I call "variance in vol units"). Therefore "expected volatility" is lower than "square root expected variance". The difference is what needs compensating

The more practical way to think about this, is that variance is being dominated much more by the tails (or volatility of volatility). When you trade a variance, you need a premium over volatility to compensate you for these tails (even if they don't realise very often).

Another way to think about this, is there is "convexity" in variance (when measured in units of volatility). If you are long and volatility goes up, you much more (because it's squared), but if it goes down, you aren't making as much less.

Comment by SimonM on What is the VIX? · 2021-02-26T08:04:04.606Z · LW · GW

What unit of information does the VIX track? the volatility of the S&P 500 index over the next 30 days, annualized. What does this mean?

VIX tracks the variance not volatility of the S&P. (Slightly more subtly, it measures the variance in vol units). (This twitter thread does a decent job of explaining the difference and why it matters)

Comment by SimonM on Kelly isn't (just) about logarithmic utility · 2021-02-23T22:25:27.474Z · LW · GW

This was fascinating. Thanks for taking the time to write it. I agree with the vast majority of what you wrote, although I don't think it actually applies to what I was trying to do in this post. I don't disagree that a full-Bayesian finds this whole thing a bit trivial, but I don't believe people are fully Bayesian (to the extent they know their utility function) and therefore I think coming up with heuristics is valuable to help them think about things. 

So, similarly, I see the Peters justification of Kelly as ultimately just a fancy way of saying that taking the logarithm makes the math nice. You're leaning on that argument to a large extent, although you also cite some other properties which I have no beef with.

I don't really think of it as much as an "argument". I'm not trying to "prove" Kelly criterion. I'm trying to help people get some intuition for where it might come from and some other reasons to consider it if they aren't utility maximising.

It's interesting to me that you brought up the exponential St Petersburg paradox, since MacLean, Thorpe, Ziemba claim that Kelly criterion can also handle it although I personally haven't gone through the math.

Comment by SimonM on Kelly isn't (just) about logarithmic utility · 2021-02-23T21:51:46.643Z · LW · GW

Yeah, I think I'm about to write a reply to your massive comment, but I think I'm getting closer to understanding. I think what I really need to do is write my "Kelly is Black-Scholes for utility" post.

I think that (roughly) this post isn't aimed at someone who has already decided what their utility is. Most of the examples you didn't like / saw as non-sequitor were explicitly given to help people think about their utility. 

Comment by SimonM on Kelly isn't (just) about logarithmic utility · 2021-02-23T19:58:59.719Z · LW · GW

Yes - I cited Peters in the post (and stole one of their images). Personally I don't actually think what they are doing has as much value as they seem to think, although that's a whole other conversation. I basically think something akin to your third bullet point.

Having read your comments on the other post, I think I understand your critique, and I don't think there's much more to be said if you take the utility as axiomatic. However, I guess the larger point I'm trying to make is there are other reasons to care about Kelly other than if you're a log-utility maximiser. (Several of which you mention in your post)

Comment by SimonM on Kelly isn't (just) about logarithmic utility · 2021-02-23T17:20:43.887Z · LW · GW

Yeah - I agree, that was what I was trying to get at. I tried to address (the narrower point) here:

Compounding is multiplicative, so it becomes "natural" (in some sense) to transform everything by taking logs.

But I agree giving some examples of where it doesn't apply would probably have been helpful to demonstrate when it is useful

Comment by SimonM on Kelly isn't (just) about logarithmic utility · 2021-02-23T17:16:23.939Z · LW · GW

Thanks! That's helpful. I definitely wrote this rather stream of consciousness and I definitely was more amped up about what I was going to say at the start than I was by the time I'd gotten halfway through. EDIT: I've changed the title an added a note at the top

I think the section where I say "it doesn't matter how you think about this" I mean it something in the sense of: "Prices and vols are equivalent in a Black-Scholes world, it doesn't matter if you think in terms of prices of vols, but thinking in terms of vols is usually much more helpful".

I also agree that having a handy version of the formula is useful. I basically think of Kelly in the format you do in your comment I highlighted and I think I would never have written this if someone else hadn't taken that comment butchered it a little but and it became a (somewhat) popular post. (Roughly I started writing a long fairly negative comment on that post, and tried to turn it into something more positive. I see I didn't quite manage to avoid all the anger issues that entails).

Comment by SimonM on Kelly isn't (just) about logarithmic utility · 2021-02-23T14:32:09.241Z · LW · GW

I’m not sure what prompted all of this effort,

The comments section here and the post and comments section here. To be completely frank, my post started out as a comment similar to yours in those threads. "I'm not sure what led you to post this". (Especially the Calculating Kelly post which seemed to mostly copy and make worse this comment). 

I’ve rarely heard Kelly described as corresponding to log utility,

I actually agree with you that aside from LW I haven't really seen Kelly discussed in the context of log-utilities, which is why I wanted to address this here rather than anywhere else.

only ever as an aside about mean-variance optimization

Okay, here our experiences differ. I see Kelly coming up in all sorts of contexts, not just relating to mean-variance portfolio optimization for a CRRA-utility or whatever.

If anything, I’d say that the Kelly - log utility connection obviously suggests one point, which is that most people are far too risk-averse (less normatively, most people don’t have log utility functions). The exception is Buffett - empirically he does, subject to leverage constraints.

So I agree with this. I'd quite happily write the "you are too risk averse" post, but I think Putanumonit already did a better job than I could hope to do on that