Is there any website showing ETNs NAV?
My personal experience shows 3x etf is not a fair game
- Thread starter GloriaBrown
- Start date
Is there any website showing ETNs NAV?
It usually matches their price.
A lot of people get killed by 3 times ETFs. They're not designed to follow whatever the track for any time period over 1 day. They use options and futures in order to attain the leverage, which is why they deteriorate. The more volatile, the quicker the deterioration. Because charts adjust after they do the inevitable reverse split, other wise both sides would end up at zero, most people aren't able to see how these actually perform. Unless you're day trading them, you should stay away from them.
You need to work on your math skills. A 10% roll with 3x leverage is 30% a month. Let's start with 100. Let's assume the spot price never changes over that 3 month period. At the end one month you are down to 70. At the end of month two, you are at 49. At the end of month 3 you are at 34.30. You are down 66%!!!!!!! That is with spot unchanged. But spot is not unchanged. You said it was down about $3 or 7.5% multiply that times 3 and that's another 20% drop or 6.86. Now we are down to 27.44. Down 73%!
Look, here is what many of you guys are not getting. When you buy commodities, there is a cost to storing them. Otherwise it would be like free money just averaging down until the price recovered. Same is true with averaging down in stuff like the VIX. There is a real loss that get's locked in on storage and you are not factoring that in. On top of that, you are leveraging that storage cost 3x!!!!!!! My God that is stupid. Just pay attention to the math guys. I know everyone on ET makes billions drawing lines and looking at charts, but math is important too.
An investor doesn't need to use leveraged products in order to produce alpha.
In terms of a quantitative strategy for investing in the oil sector, over the last 30 years, the oil services sector has shown a statistically significant "edge" in the winter / spring months. So far, returns for 2016 have been at the higher end of the skew.
Combining this with positioning / switching into biotech and utilities during other statistically significant seasonal periods and a risk management variable, has produced average annual returns of 29.5% over the last 30 years ( not allowed to provide link ).
Energy services winter / spring months ( Bolds = risk management variable applied / reduce to 50% allocation ).
1986 -5.5%
1987 13.0%
1988 15.0%
1989 11.4%
1990 4.7%
1991 10.2%
1992 3.3%
1993 23.8%
1994 -2.8%
1995 21.4%
1996 17.7%
1997 -5.5%
1998 30.5%
1999 65.2%
2000 16.0%
2001 3.8%
2002 14.3%
2003 2.7%
2004 3.4%
2005 4.4%
2006 5.6%
2007 17.7%
2008 9.9%
2009 25.7%
2010 9.0%
2011 5.4%
2012 -1.5%
2013 -2.5%
2014 15.6%
2015 6.4%
2016 14%
In terms of a quantitative strategy for investing in the oil sector, over the last 30 years, the oil services sector has shown a statistically significant "edge" in the winter / spring months. So far, returns for 2016 have been at the higher end of the skew.
Combining this with positioning / switching into biotech and utilities during other statistically significant seasonal periods and a risk management variable, has produced average annual returns of 29.5% over the last 30 years ( not allowed to provide link ).
Energy services winter / spring months ( Bolds = risk management variable applied / reduce to 50% allocation ).
1986 -5.5%
1987 13.0%
1988 15.0%
1989 11.4%
1990 4.7%
1991 10.2%
1992 3.3%
1993 23.8%
1994 -2.8%
1995 21.4%
1996 17.7%
1997 -5.5%
1998 30.5%
1999 65.2%
2000 16.0%
2001 3.8%
2002 14.3%
2003 2.7%
2004 3.4%
2005 4.4%
2006 5.6%
2007 17.7%
2008 9.9%
2009 25.7%
2010 9.0%
2011 5.4%
2012 -1.5%
2013 -2.5%
2014 15.6%
2015 6.4%
2016 14%
Interesting. What are you defining as winter/spring months? Nov-May?
I looked at your blog, but I didn't see anything on sector allocation, ie the oil services edge referenced above. I liked your blog and will devote additional time to it when I get a chance.
Feb - April
I don't include the sector allocation on the blog as I try to present the model as "stand alone" ( it complicates matters to have too much info )
I don't fully understand the math, but I recommend checking out pages 1-11 of Jian Zhang's 2010 PhD thesis, "Path-Dependence Properties of Leveraged Exchange-Traded Funds: Compounding, Volatility and Option Pricing" at https://www.math.nyu.edu/faculty/avellane/thesis_Zhang.pdf , especially equation 2.1.4 on page 10, which attempts to predict the price of a leveraged ETF after many rebalancing periods, given certain statistical assumptions about the movement of the underlying.
%%
S& P 500 [SPY] tends to do that also