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.
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:
CAGR
Stdev
Ulcer
Trades/Yr
B&H
9.5
16.8
23.0
Monthly
9.3
11.7
14.4
1.4
Daily
10.7
11.0
9.6
5.1
Sell-Daily
9.7
10.3
9.2
2.3
Buy-Daily
10.6
12.3
12.3
1.8
(“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.
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.
I wonder if it makes sense to rebalance more frequently when volatility (or trading volume) is high.
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.)