If wild animals have bad lives on net, then indiscriminately increasing wild animal populations is bad under any plausible theory of population ethics.

Thanks for sharing your experience with Vanguard! That aligns with anecdotes I've heard about Vanguard's brokerage service.

• Are there any simple ways of making investments in these accounts that offer 2x leverage or more? Are there things here that you'd recommend?

I just published something about DAF investing strategies: https://mdickens.me/2021/04/06/investing_strategies_DAF/ In this section, I talk about leveraged ETFs. I believe the only way to invest with leverage in a DAF is through a leveraged ETF or mutual fund, although I've heard conflicting things about what the actual legal requirements are. In general, I don't think leveraged ETFs are good investments.

• Do you have an intuition around when one should make a Donor-Advised Fund?

If you want to use leverage, probably never. (Or just use it to convert stock into cash for donations, as akrolsmir described.) Otherwise, you want to have at least $10,000 or so, otherwise the minimum fee will eat too large a % of your assets each year. (Schwab and Fidelity both have a $100 minimum fee.)

• How easy is it for others to invest in one's Donor-Advised Fund?

It's definitely possible. I personally don't have my own DAF, I use my parents' DAF. I'm a full authorized user on the account, which means I had to connect my Fidelity account to the DAF. If you don't care about managing anything and just want to donate to the DAF, I would think that should be pretty easy, but I haven't tried it. I think it should be as simple as writing a check to Fidelity Charitable with a note that the money is for that particular DAF.

Yeah this is for US only. I actually thought I had said that in the post, but looks like I forgot to! I'll edit it.

I think a lot of people feel this way, and it's something I've experienced. I don't have any great solutions but I generally do two things:

1. Set reasonable expectations. The application process has a lot of randomness, and almost all applications will get ignored even if they're good, so I should expect any particular application to have a very low chance of getting a response.
2. Spend less time on individual applications; apply to a lot of things; use commonalities across applications to copy/paste things I wrote on previous applications.

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.