It's easy to be dismissive of this, especially by working from the popular Warren-Buffet-index-funds-beat-hedge-funds story that makes the rounds. It's true that most hedge funds and active traders lose money (or at least, underperform the market). But as I am fond of pointing out, there are a non-negligible number of funds and traders who consistently and demonstrably earn significantly stronger returns than the market benchmark, even net of fees.

I am skeptical of Numerai for different reasons. If someone can consistently churn out profitable and novel equity pricing insights, it would be more rational for them to work for a more well established hedge fund in quantitative research. Perhaps more importantly, I'm skeptical of how they judge accuracy in their participant-volunteered insights.

Check their board of directors -- all very smart and established people.

A former tournament winner did well on both the public and private leaderboard. It is very difficult to do this by luck. He was also a student from Bangladesh who got the opportunity to play with hedge fund data with just the cost of an internet connection and zero risk for messing it up. Should he start working for a hedge fund now, without any finance experience? Could be a good bet. Numerai would still beat him, because they can aggregate all the top models into an ensemble. It is hard to beat 50 individuals, but near impossible to beat a team of 50 competitors. Compare the variance of a single decision tree with a Random Forest.

They are betting that there exist people who are capable of performing that work who cannot work at a hedge fund, for whatever reason.

Perhaps they live in the wrong location -- it's hard/impossible to get a quant job if you don't live in a major market center. Not everyone is 23 years old and unattached and prepared to move around the world for a job.

Perhaps they can do the predictions but they didn't go to a high end university and have no track record -- just try even getting an interview at a hedge fund without one of those.

Or people who do work at hedge funds, but want to moonlight on the side and get paid via bitcoin anonymously

Really smart monkeys. And monkeys which will (hopefully) be self-correcting to converge on the bulls-eye.

Most people don't realize that the "markets" are 49% random, 48% sentiment driven, and 3% fundamentals. If you approach the problem with that assumption held true, monkeys throwing darts isn't such a horrible mechanism for investing.

See: "Monkeys Are Better Stockpickers Than You'd Think: Why dart-throwing primates demolish S&P 500 returns and most active fund managers don't even come close." http://www.barrons.com/articles/SB50001424053111903927604579...

> Most people don't realize that the "markets" are 49% random [...] If you approach the problem with that assumption held true, monkeys throwing darts isn't such a horrible mechanism for investing.

Maybe I haven't understood that part of data science but I never got why throwing more unpredictability on an already unpredictable data source would somehow make it more predictable.

It doesn't make it "more predictable". You're simple achieving better fit to the sample data distribution.

I guess another way of saying it is that your mess is starting to look more like their mess.

Anything that deals with the future is inherently non-predictable. Using chaos theory as a framework, we say it is unpredictable because we are unable (and will always be unable) to model the currently system completely. There will always be data that was not captured hiding between the data that was captured. Follow the arrow-of-time far enough out into the future and that non-captured data will manifest itself in the captured data, thereby (usually) creating a deviation from the modeled future.

To get around this we use statistics and probability. We rely on the law of large numbers and regression to the mean. In other words, we hope that the future won't get too weird and will be similar enough to the past, within some confidence interval.

So, we're not really predicting a specific outcome, we're predicting that the outcome will be some point within some confidence interval.

The reason we can get better at this, is as we capture more data we can better guess the inputs and assumptions we use to create the model. We throw out stuff that didn't happen to be relevant. We discover stuff that we should have considered relevant. If we're lucky the model closely follows the physical laws of our reality and we can apply the frameworks so arduously worked out by chemists, physicists, biologists, etc. If we're not so lucky we're dealing with sentiment, conjecture, or any of the other human inputs of the financial markets, and we are forced to make up formulas that work until they fail spectacularly.

Investing in S&P500 is a strategy, which can be optimised. You can ask if it is better to invest in top 100 or top 1000 instead of a top 500 index. Why even go after the top index? There are all sorts of inactive strategies available.

So in a sense, the "active" manager is just as much a monkey throwing darts as the "inactive", yet one of them is better than the other.

Why can't a third monkey be even better?

Buffet's wager is a trick, which works on simple people who don't understand statistics.

When you take a group of hedge funds, they end up being a very good proxy for the market. At about 20, they will be almost indiscernible.

So - a passive fund, buying the market, with lower fees, should outperform.

The Protégé basket of funds contains five hedge funds, not twenty.

The returns are far from indiscernible from S&P 500. At the end of 2015, the S&P 500 portfolio had seen 3x the profits of the Protégé portfolio.

In his case, these were 5 funds of hedge funds. Which underneath could be much more than 20 funds.

You're also forgetting how much the compounding of the fees can cost, and you have two layers of them. This could easily make the S&P at least 3x higher in returns after 10 years.

Aha, I had skimmed past the fund of fund parts.

In CFA Institute's estimate, the difference in fees only made up about half of the underperformance at the end of 2014, especially considering that 2008 was an easy win for hedge funds:

https://blogs.cfainstitute.org/investor/2015/02/12/betting-w...

Note that at the end of 2015, eight years had elapsed, not ten.

Fair enough, there is a slight bit more to the buffet bet than I trivialized.

The short portion of the hedge fund will have been losing as we are in a bull market. The full length of a cycle should see this effect reverse (and indeed 2008 shows this).

So Buffet did have to pick a bull market in making this bet, but he still had better than 50/50 odds regardless.

You also have other costs specific to hedge funds over active funds, such as higher brokerage from more frequent trades, and short interest. These have the same drag as the management fees, and I should have included that also in the description.

> Mr. Buffett ended up winning handily.

The bet isn't over until December 31, 2017: http://longbets.org/362/