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.
That is true if you sell more when the market is down, you will have less to donate later. But I would think that the higher expected return would overwhelm this. This is what the Princeton endowment argued-I think their portfolio got cut in half around 2008, and then they did a bigger payout as a percentage. But they said that because they had invested with high expected return, they were still in much better situation than investing cautiously. It would be great in your next project to have some visualizations of how the investments perform over time and what the payouts are. Then we could see how much charity smoothing there would be for the primary donor (given some value function, which I would argue should have a larger downside than logarithmic because of inefficient cutting), and consequently how much more valuable it is for small donors to be uncorrelated. I’m looking forward to reading about your new model.
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.
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.
That is true if you sell more when the market is down, you will have less to donate later. But I would think that the higher expected return would overwhelm this. This is what the Princeton endowment argued-I think their portfolio got cut in half around 2008, and then they did a bigger payout as a percentage. But they said that because they had invested with high expected return, they were still in much better situation than investing cautiously. It would be great in your next project to have some visualizations of how the investments perform over time and what the payouts are. Then we could see how much charity smoothing there would be for the primary donor (given some value function, which I would argue should have a larger downside than logarithmic because of inefficient cutting), and consequently how much more valuable it is for small donors to be uncorrelated. I’m looking forward to reading about your new model.
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.