This morning I placed an interfund transfer request in my TSP account. This is a discretionary override of my systematic TSP strategy. I changed allocations to 25% C 75% G funds for my existing balance. I will keep a 40% C 60% G fund allocation for new biweekly contributions.
I’m getting more and more bearish for the rest of the year. What does this mean? Probably that the market is about to soar and you should load up on stocks. Well, maybe not…
We’re pretty far above the 200 day average and near all time highs and in a very mature bull market. I could be wrong but am playing it safe(r) here.
The allocation displayed in the top right of the site will continue to reflect the 40/60 allocation until an official strategy change occurs.
On a side note, XIV daily mean reversion (long only) has continued to crush daily follow through since my March post. I’ve been keeping an eye on it and playing it occasionally. I’m sure it will stop working eventually but am enjoying it (in small sizes) while it lasts.
On another side note, my life has been pretty hectic recently and I haven’t been able to do much of the market research I love. I’m hoping things will calm down by the summer.
Question: What’s one way you could have nearly doubled your money since last September with minimal drawdown?
Answer: XIV daily mean reversion! Well, a long only approach that is.
Daily mean reversion just means holding XIV if it declined in price the previous day. Daily follow through would be holding XIV if it increased in price the previous day.
Daily mean reversion was hardly a good strategy in recent years. In fact, since 2009 when VXX came into existence, this strategy performed relatively poorly while a daily follow through strategy did fantastic. That changed last September. I have no clue why.
Check out the chart below illustrating the recent performance of the two series.
One note, if one looks at simulated XIV before 2009 you can see this isn’t the only time mean reversion would have dominated. The same thing would have happened in 2007/2008. Is there anything to be read into that? Possibly not. Could just be random changes in the market.
There’s no telling how transitory this change will be. Just thought it was worth sharing.
Technical note: I just used inverse changes in VXX to create the series for the chart above. Good enough for illustrative purposes.
Every so often I come across a post or comment on some kind of XIV/VXX pair trade. I wrote one such post in 2011 toward the beginning of my volatility research. I thought I’d write another post now to shed more light on some of the ideas that get thrown around.
I’ll examine 3 types of trades here:
- Long XIV and long VXX
- Short XIV and short VXX
- Long XIV and short VXX
For each type of trade I’ll examine a 50/50 allocation (e.g. 50% short XIV/ 50% short VXX) and examine all the past outcomes of holding this trade 60, 90, and 120 days out. I’ll leave it to readers to try other combinations of weights, time lengths, and any fancy embelishments.
Note: Values before product inception are simulated based on the VIX futures .
Long XIV Long VXX
This pair goes long both products. The hope is that one of these breaks out and rises significantly from the purchase price. As an example, assume XIV rises around 100% over an extended period and VXX falls around 50% over that same period. In this case, the pair trade would be up 25%. If the returns are flipped around, the pair would still be up 25%.
This chart shows the return profile of purchasing the pair on the specified date and selling 60, 90, and 120 days later.
I was surprised by a couple findings while doing this analysis. I’m sure others have thought of them but they are new to me. These findings include:
- This pair only does well when a long XIV position outperforms a short VXX position over the specified period. In a sense, this pair is betting on that to happen.
- The returns of this pair are slightly negatively correlated to equities and nearly uncorrelated to government bonds. This presents promise as a portfolio diversifier.
Of the three pairs, I like this one best. Negative returns are subdued while positive returns can be quite high. It’s also the easiest trade to execute consistently as you don’t have to worry about finding shares to short.
Short XIV Short VXX
This pair is short both products. The trade is profitable when neither product rises that much from the purchase date. The return profile is inverse to the long/long pair. This pair only does well when a short VXX position outperforms a long XIV position over a specified period.
Below is a chart of returns for this pair. Because of the nature of short positions, it’s possible to lose more than 100% on a trade. Upside returns are also limited due to the decreasing leverage a short position provides as a shorted product declines in value.
I don’t like this pair very much. It can be profitable but really requires a lot more active management to avoid the issues of varying leverage shown in the chart.
Long XIV Short VXX
This pair is not neutral like the other two. You’re basically shorting volatility in two different ways. This means you better have a system for entering/exiting the trade. When the trade is working well it will outperform the other pairs given its directional bias.
Here is a chart showing past returns for the pair.
The short part of the trade presents the same leverage issues as the short/short pair shown previously. I personally prefer using a simple long XIV position for shorting volatility.
Pair returns compared to equities and bonds
I mentioned earlier that the long/long pair had slightly negative correlation to equities and near zero correlation to bonds. Here I’ve posted a chart comparing the long/long pair to returns of SPY and IEF over 120 day holding periods. There appears to be some nice diversification going on.
I’ve charted below differences in open, high, low, and close values between Yahoo Finance and PI Trading for the SPY ETF between 12/30/02 and 11/9/12 (last date in my PI dataset).
I haven’t seen this kind of comparison done elsewhere (let me know if you have). I’ve done this to illustrate just how different various data sources can be. Enjoy!
(Note: Reader James reminded me of a similar comparison made here – http://www.quantifiedstrategies.com/the-importance-of-good-data-sets/ that compared Yahoo finance to IB. Check it out as well.)
I was surprised how close the opening quotes tended to be between the two datasets. There are a handful of big divergences. There seems to be an even spread between higher and lower values.
When the two data sets diverged here, Yahoo finance almost always had higher highs. I think this is a result of bogus, non-tradeable quotes being recorded by Yahoo.
When the two datasets diverged here, Yahoo Finance almost always had lower lows. Again, I think this a result of bogus quotes recorded by Yahoo.
The close quotes differed more than I was expecting. The differences have been smaller though since 2009ish. As with the open comparison, the distribution of values above/below 0 seems random.
I don’t know whether the PI Trading dataset is superior to other sources for testing. It seems reasonable to me that it is more accurate at least than Yahoo Finance, especially for high and low values.
The daily OHLC data from PI was constructed from minute level data. The dataset is missing a day for 1/30/07. Part of the morning of 1/31/07 is also missing.
When comparing the close, I excluded days where the markets closed early.
The more I trade my Alpha 2 system the more data issues I become aware of. The system uses OHLC data in its inputs which can be trouble. Different sources have significantly different quotes for some days.
I’m taking a short break from trading this system. I’ll begin posting trades again when I’m more confident in the data being used for testing and live trading.
Any suggestions for sources of clean OHLC stock data are appreciated. So far I’ve compared data from PI Trading (constructed from 1-minute), Bloomberg, and Yahoo finance.
The Alpha 2 strategy went back to cash at today’s close.
I sold half of my position early (discretionary override) on Feb 5th for $21.75 (+3.6%) and the other half today for $22.50 (+7.1%). My average gain was about 5.4%. I sold half early because I did not like the expanding volatility in the market and thought I could outsmart my system by rebuying some shares lower.
**Note: made correction to Feb 5th sale price. Sale was at 21.75 not 20.75 as first reported above**
The volatility strategy went long XIV at today’s close. I got in at $21.00.
Here’s a potentially useful indicator for timing the S&P 500 on a weekly basis. Consider the following two strategies:
1. Hold SPX when VXO rose the previous week, else hold cash.
2. Hold SPX when VXO declined the previous week, else hold cash.
Here’s a chart comparing the two strategies since 1986 (not adjusted for dividends).
Not bad. Risk/reward has historically been better when VXO rose the previous week. I guess this makes sense from a mean reversion standpoint. It seems to me this indicator works best when market volatility is high (e.g. late 90′s/early 00′s and financial crisis-present). The indicator wasn’t as valuable during the early 90′s and 04-07 period.
The strategy experienced a number of big losses in this backtest. This happened when SPX fell sharply several weeks in a row while VXO continued to break out to new highs. A filter could be added to the above strategy to filter out some of the potentially big declines. One effective filter would be to avoid holding SPX when VXO closes the previous week more than 20% above its 5 week moving average. The parameters for the filter seem fairly robust. Setting the % too low will weed out a lot of profitable weeks while setting it too high will fail to avoid many of the worst losses.
The chart below compares two strategies:
1. Hold SPX when VXO rose during the previous week AND VXO closed the previous week less than 20% above its 5 week average, else hold cash.
2. Hold SPX when strategy #1 is not in play, else hold cash.
A number of large losses are filtered out without sacrificing too much performance in other time periods.
Here are some stats for the whole period and for 2009 to present.
All data is from Yahoo Finance. Using VIX would have generated similar, though not quite as good results. You can download an Excel copy of this backtest here: VXO SP500 Weekly Strategy
Here’s a trade idea
Go long EEM at the close and sell at next day’s open if:
- Today’s close is at least 1% below 10 day simple moving average
- Previous overnight gain is less than 1%
Below is an equity curve assuming $100K per trade and no commissions or slippage.
And some stats…
I’ve attached an Excel file that allows you to adjust the parameters to see how performance changes as a result. Change cells N3 and N4 to try out different combos. Data is from Yahoo finance.
Download file here –> EEM Overnight Trade
This strategy works to a degree on other indexes but not nearly as good from my tests so far.
Back in November I posted a strategy for timing SPY using the strength of recent closes. I looked at the 4 day average of a closing strength ratio reffered to by some as internal bar strength or IBS (IBS = (close-low)/(high-low)). The equity curves were pretty impressive.
I’ve recently tested a couple related strategies. One idea involved a long/short approach to timing country stock ETFs. I found some strategies that appeared to work great for a while but have become less and less effective over time.
Another idea I recently tested goes long one or more broad asset ETFs based on average IBS.
Here’s a description of one implementation.
- Basket of ETFs includes SPY, EEM, GLD, TLT, VNQ.
- Near each day’s close, calculate the 4 day moving average of IBS and rank each ETF by this value.
- At each day’s close, buy equal shares of the 2 ETFs with the lowest average IBS. Sell positions 3 days later.
- At any given time the strategy will be holding 2-5 ETFs purchased on 3 different days.
- $100K invested per trade (split equally among the 2 ETFs being purchased).
Below is an equity curve before slippage and commissions and any dividends. Average trade is .28% with 58% winners.
I’ve tested out IBS moving averages between 1-7 days and it seems pretty robust. I’m sure there are ways to improve the strategy with either some kind of equity curve timing, stops, etc. It would also be interesting to try out other combinations of ETFs. This is the first such combo I’ve tried.