The stock market should grow faster than GDP in the long run. Three different simple arguments for this:

1. This falls out of the commonly-used Ramsey model. Specifically, because people discount the future, they will demand that their investments give better return than the general economy.
2. Corporate earnings should grow at the same rate as GDP, and stock price should grow at the same rate as earnings. But stock investors also earn dividends, so your total return should exceed GDP in the long run. (The reason this works is because in aggregate, investors spend the dividends rather than re-investing them.)
3. Stock returns are more volatile than economic growth, so they should pay a risk premium even if they don't have a higher risk-adjusted return.

(These numbers are actually more similar than I expected—I would have predicted the top-10% portfolio to have something like 5x more value factor loading than the top-half portfolio, not 2x.)

I'm not sure how to calculate it precisely, I think you'd want to run a regression where the independent variable is the value factor and the dependent variable is the fund or strategy being considered. But roughly speaking, a Vanguard value fund holds the 50% cheapest stocks (according to the value factor), while QVAL and IVAL hold the 5% cheapest stocks, so they are 10x more concentrated, which loosely justifies a 10x higher expense ratio. Although 10x higher concentration doesn't necessarily mean 10x more exposure to the value factor, it's probably substantially less than that.

I just ran a couple of quick regressions using Ken French data, and it looks like if you buy the top half of value stocks (size-weighted) while shorting the market, that gives you 0.76 exposure to the value factor, and buying the top 10% (equal-weighted) while shorting the market gives you 1.3 exposure (so 1.3 is the slope of a regression between that strategy and the value factor). Not sure I'm doing this right, though.

To look at it another way, the top-half portfolio described above had a 5.4% annual return (gross), while the top-10% portfolio returned 12.8% (both had similar Sharpe ratios). Note that most of this difference comes from the fact that the first portfolio is size-weighted and the second is equal-weighted; I did it that way because most big value funds are size-weighted, while QVAL/IVAL are equal-weighted.

That could help. "Standard" trendfollowing rebalances monthly because it's simple, frequent enough to capture most changes in trends, but infrequent enough that it doesn't incur a lot of transaction costs. But there could be more complicated approaches that do a better job of capturing trends without incurring too many extra costs. One idea I've considered is to look at buy-side signals monthly but sell-side signals daily, so if the market switches from a positive to negative trend, you'll sell the following day, but if it switches back, you won't buy until the next month. On the backtests I ran, it seemed to work reasonably well.

These were the results of a backtest I ran using the Ken French data on US stock returns 1926-2018:

("Ulcer" is the ulcer index, which IMO is a better measure of downside risk than standard deviation. It basically tells you the frequency and severity of drawdowns.)

The AlphaArchitect funds (except for VMOT) are long-only, so they're going to be pretty correlated with the market. The idea is you buy those funds (or something similar) while simultaneously shorting the market.

And I've heard it claimed that assets in general tend to be more correlated during drawdowns.

This is true. Factors aren't really asset classes, but it's still true for some factors. This AQR paper looked at the performance of a bunch of diversifiers during drawdowns and found that trendfollowing provided good return, as did "styles", by which they mean a long/short factor portfolio consisting of the value, momentum, carry, and quality factors. I'd have to do some more research to say how each of those four factors have tended to perform during drawdowns, so take this with a grain of salt, but IIRC:

• value and carry tend to perform somewhat poorly
• quality tends to perform well
• momentum tends to perform well during drawdowns, but then performs really badly when the market turns around (e.g., this happened in 2009)

I'm talking about long/short factors here, so e.g., if the value factor has negative performance, that means long-only value stocks perform worse than the market.

Also, short-term trendfollowing (e.g., 3-month moving average) tends to perform better during drawdowns than long-term trendfollowing (~12 month moving average), but it has worse long-run performance, and both tend to beat the market, so IMO it makes more sense to use long-term trendfollowing.

We never know how this will continue in the future. For example, the 2020 drawdown happened much more quickly than usual—the market dropped around 30% in a month, as opposed to, say, the 2000-2002 drawdown, where the market dropped 50% over the course of two years. Trendfollowing tends to perform worse in rapid drawdowns because it doesn't have time to rebalance, although it happened to perform reasonably well this year.

There's a lot more I could say about the implementation of trendfollowing strategies, but I don't want to get too verbose so I'll stop there.

Monthly is fine, it's probably better for charities. I personally donate annually because it's a lot simpler. I donate appreciated stock, and transferring stock is a substantial amount of work.

At the risk of being overly self-promotional, I have written a few posts on cause candidates that I don't see listed here.

• Unknown existential risks (not exactly a cause, but conceptually similar)
• Estimating the Philanthropic Discount Rate suggests that we have some reason to believe it is extremely valuable to put more effort into estimating the philanthropic discount rate.
• Charities I Would Like to See proposes four weird and neglected ideas.

Another potential cause area that's not listed: reducing value drift (e.g., this post).

I only skimmed the linked source but my rough impression is that I'm fairly bearish on art, mainly because there's no expectation that it will appreciate. The linked article doesn't really present evidence to the contrary—the only relevant bit I saw was a graph showing appreciation from 2000 to 2010. 10 years of appreciation is almost meaningless, I'd want to see more like 50 years of data showing an asset class has positive real return.

Perhaps it would be worth buying art if you have some reason to believe you can outperform the market at predicting which pieces will be more valuable in the future. The art market is probably less efficient than more liquid financial markets, but on priors I wouldn't expect to be able to pick "winning" art pieces.

That's an interesting idea, I'm thinking about the best way to model it. I think what you'd want to do is to calculate the safe withdrawal rate for different portfolios and see which is best. The problem is, we don't have enough historical data to get good results, so we'd have to do simulations. But those simulations couldn't assume that returns follow a log-normal distribution, because the fact that assets tend to experience big drawdowns substantially affects the safe withdrawal rate.

In my experience, when the market is down a lot, the payouts would increase as a percentage, because donors would not want to have inefficient cuts in charities.

This is a good point that I hadn't thought of. This would still reduce donations overall, right? Because if people donate a larger % when markets are down, that means they have less money to donate later. It's not obvious to me off hand how this should be modeled, but that's something to think about.

I do agree that a fully market-neutral position is probably not optimal in practice. That only makes sense if you assume leverage costs the risk-free rate, you can get however much leverage you want, and you can rebalance continuously with no transaction costs. If you impose more realistic restrictions, you probably want to aim for a higher expected return with low fees rather than going for pure market neutral. I'm writing a new essay about this right now. According to my new model, the optimal allocation under realistic costs and restrictions is something like 200% long, 50% short. In my previous essay on leverage, I do think I overstated the value of reducing correlation rather than increasing expected return.