A speaker (from a YouTube video titled "Learnings From Trading Money for a Hedge Fund") reflects on their hedge-fund trading experience, focusing on leverage and risk management. They describe reducing leverage after unexpected downside moves, balancing statistical edges (e.g., momentum/seasonality strategies) with emotional capacity to hold risk, and acknowledge that over-managing risk can reduce returns. Concrete thresholds mentioned include a long-side leverage cap of about 1.3–1.4 and a drawdown avoidance target of not going below -15%.
Questions this video answers
About 1.3 to 1.4.
Minus 15% ("below minus 15%").
Very reluctant to take leverage now. Very, very reluctant. Like this index strategy in leverage of Factor 3 was usual because you leverage up very high, you go down. Go leverage very high, you go down. So now my leverage factor on the long sign is maybe the max is 1.3, 1.4. I got itchy. It's like.
A lot of downside risk unexpected down that risk. And sometimes especially as if you have a momentum and trend following strategy and you like to add up, for example seasonality strategy, I mean reverse strategy, it makes sense to take the extra risk for a short time period because the statistical edge is so high. But on the other hand I also need to be capable of holding it emotionally. So yeah, my, my willingness to take, to take leverage as way it's decreased way down. And also my willingness to take risk. Like I think if I would have been a little bit more risk seeking the last couple of years, I would have made so much more money. Like yes, but I was very concerned about my risk return ratio. Like very much concerned. Like so instead of having like 50% annual return, I could have easily made maybe 25% but I didn't because I put my investment level low and it's like yeah, I didn't want to go below, below minus 15%. So yes, so that's a. Learning to know. Okay, managing risk is important.
And on the other hand you can also over manage your risk and you lose a lot of.
Capacity to make the money or returns you can actually make. So yes, that's that Big learning, two big learnings.
