Category Archives for "Research"
Something I am always thinking about is how the markets are behaving now vs the past few years vs several years ago. My edge on the strategies I trade depends on two main ideas. One, current market behavior is similar to what I tested on which is normally the last 5-10 years. Two, not too many others have found the same edge. Unfortunately for (2), more and more people are trading quant style and edges are harder to find and smaller when I do find them. The only thing I can control is continuing to research for new strategies.
This end of year rally which started on October 2023 has been strong. My trading buddy and I started wondering how this compares to the past. Is this a “normal” strong rally or an “abnormally” strong one?
Determining this is always tough because it depends on the indicators you use. Because of that, I tried lots of them. This will be a post short on words but with lots of tables.
Data is from 1/1/1980 to 12/19/2023. The close of 12/19/2023 was the 36th day of the rally. These are the stats based on these last 36 days.
Over the last 44 years, these are the average returns for all days for 5, 21, 63, 126, and 252. My main focus is on 126 & 252 days later.
For each of the above stats, we will look at when it happened in the past and how the market did later. I ignore signals that happen within 21 days of the last one.
I was working on testing a market timing indicator that I read about it. It was showing some promise and the next step was to compare it to my benchmark. My benchmark is using the 200-day moving average. But an additional rule removes a lot of the whipsaws that can happen.
After doing the comparison, the market timing indicator compared well. But then I realized I had not written a blog post about my additions. I touched on it in the Market Timing with a Canary, Gold, Copper, LQD, IEF and much more post.
For me, the goal of using the 200-day MA to trade the SPY is to get about the same CAR but with a significant reduction in MDD.
My recent research has been on the volatility Exchange Traded Products. My focus has been on long trades using VXX and XIV. Although VXX has a very strong downtrend, I am not a fan of developing short strategies on it due to the huge upside risk. I wrote about XIV here and expressed some of the dangers of trading these ETFs.
UPDATE: These original results were published on October 26, 2016. Since then there have been lots of changes in the volatility ETFs/ETNs. Scroll down to Updated Results Through June 30, 2023 to see the updated results.
While reading the January 2023 issue of Technical Analysis of Stocks & Commodities, I came across an article about Efficiency Ratio (ER) by Perry Kaufman. In the article, he discusses using ER to decide when to trade mean reversion strategy vs a trend following one.
My curiosity on this was could I use the ER to filter trades in my mean reversion strategies.
From the Volume and Mean Reversion post, a reader sent a suggestion to instead use the ratio of 10 day moving average of the Close times Volume divided by the 63-day moving average of the Close times Volume (CV10/63). I had not tried this before and wanted to see how well it would work.
Overall, I have had very little success integrating volume into any of my strategies. Either volume would have no predictive value or if it did, using it reduced the number of trades too much to be worthwhile. It has been a long while since I have looked into this and I had some new ideas.
A common question I get is whether mean reversion is still working. My response is I am still trading a mean reversion strategy but the edges seem to get smaller. Over the year I have investigated this. I was asked again recently and wanted to investigate again. Here are the results of my 2022 investigation.
Test date range 1/1/2000 to 9/30/2022. I wanted to keep the rules simple. I tested various ways with the 200-day moving average. The reason for this is that some people only trade stocks above the 200, while I like to trade without. In general, this will have more volatility but better returns.
In my last blog post, Using Historical Volatility for Parameter Adjustment, I tested using historical volatility to determine trade rules. While reading the July 2022 Technical Analysis of Stocks & Commodities, I came across an article, “Is It Too Volatile To Trade?” by Perry Kaufman. I always like his work so I was interested to see what he had to say. He uses standard deviation from the median historical volatility to decide if a stock is too volatile. He points out that even though returns may be positive during volatile times, it comes with higher risk.
From my own research, I frequently use historical volatility (HV) in my strategies. Most of the time focusing on high HV because these are the stocks that are moving. But when I am trying to tame down drawdowns, I change the focus to low HV stocks. Kaufman’s concept is similar but not something I had tried.
The AllocateSmartly website often has interesting posts. Recently I was reading the article Trending Fast and Slow and thought about other ideas to test. The article is based on research on trading the SPX and depending on the current historical volatility one would either use a 12-month or a 1-month lookback to decide whether to enter or exit the trade. I had tried similar ideas before but not this one.