Corey’s Interview with Larry Connors Part 2

Here is the transcript from Part 2 of my recent interview with Larry Connors of TradingMarkets.com.  Be sure to read the First Part of the Interview if you have not done so already prior to reading part 2 here.

As a refresher, Larry Connors is currently CEO and founder of TradingMarkets.com, and author of many popular trading books including Street Smarts (with Linda Raschke), High Probability ETF Trading, How Markets Really Work, and Short Term Strategies that Work. His full biography page (link) is listed at Trading Markets.com.

Mr. Connors is Managing Director of Connors Research, LLC, a private research firm that specializes in quantitative analysis for trading.  He has over 28 years of experience working in the financial markets industry.

His latest book, High Probability ETF Trading:  Seven Professional Strategies to Improve Your ETF Trading, was released in June, 2009 and is already in its second printing.

In this segment, we discuss stop-loss logic, inter-market relationships, and the elusive nature of edge in trading strategies.

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Corey Rosenbloom:  Let’s talk a little about stop-losses.  You’ve written, and I’ve also found in my research, that many stop-loss strategies degrade overall system performance, especially in regard to tight stops which feel psychologically comforting.

You mention in High Probability ETF Trading about money management and stop-losses, so could you speak a little about what you mentioned and what you’ve found in regards to stop-loss strategies?

Larry Connors:  Sure.  We’ve run stops on any type of reversion to the mean trading strategy, and if anyone is using any type of overbought or oversold type of trading, they will find that in the overwhelming majority of cases that stops will wind up hurting the strategy performance.

We have run multiple test and we show one main example.  We ran it on over eight million trades going all the way back to 1995 which was just a simple pullback methodology and ultimately we showed that the edges using a pullback method without stops and then we put in stops of 1%, 3%, 5%, 7%, 10%, 25%, and 50% .

What we saw all the way through was that no matter what the stop we used, it hurt the performance.

It was somewhat eye-opening!  I don’t think any of us expected the 50% stop to show that it hurt the performance!  What we did was just put in an emergency stop.  What we saw was that even a 50% stop lowered the performance vs having no stop at all, which is very crazy.

It didn’t matter where the stop was, it lowered the performance.  We’ve seen this over and over again.

We haven’t used stops in our short-term trading with equities and ETFs in years and we’ve had positive results every year going back a number of years.  It can be done – protection can be done different ways.  Careful position sizing is one way for the best protection – it’s a form of insurance.  If you are using proper position sizing, no matter what happens, a trader wants to get to the point where “There is no way one position could blow me out here.”

CR:  Exactly.  What about diversification for managing risk as well?  But let’s first let’s talk about correlation risk in portfolio and money management.  In 2008, many of the major markets became correlated to the downside and all markets – with the exception of the US Dollar – moved to the downside, so diversification did not offer much protection.

In testing and using trading strategies, talk a little about correlation risk to the portfolio and how a trader might know how to look at market correlations and what that might mean as to whether or not to adjust the trading strategy.

Connors:  Yes, and in hindsight, we know that as the US went, so went the world in 2008.  What we do is to view Correlation as a dynamic indicator on a dynamic basis.  A lot of people will talk about correlations over multi-years, and say the Dollar Index has been correlated to this or that market over the last three or four years, but we don’t agree with that.

We use multiple look-back period, and the shortest of those periods is 21 days.  We look to see different correlations on different frames.  We’re seeing how markets are correlated to each other over a 21 day period of time to make it a dynamic process.  You’ll see some good swings in correlations themselves.

If you take a look back to October 2008 and March 2009, you’ll see that there was a 0.9 correlation across most markets and ETFs there.  So, you could be in the SPY, or in South America perhaps Brazil in EWZ, or even to China in FXI and Europe for perhaps EWG and what you would have seen looking at a 21 day correlation of greater than 0.9 over many days.

However, if you would have taken a look at that six months earlier, you would have seen that Brazil was not as correlated let’s say with the United States or SPY.  You would have seen about a .65 correlation there.

So, the correlations themselves will change and people need to be updating that along the way.  As it stands now in 2009, a lot of people wanted to believe that China was going to lead and that Chinese markets would lead the US markets, but it’s not true.  We still see the US still leads, and as the US goes, everything else goes.

Even in this rising market in mid-2009, the rest of the world is following the US – you can see it now.  There’s still a high correlation, but in times of fear, correlations rise.

CR:  What kind of decisions would an individual make when they notice that short-term correlations are rising?  What specific strategies or behaviors might they do?

Connors:  First, from an investor’s perspective, you’ll see it’s common to see in the major finance magazines like the Wall Street Journal and others where they talk about diversification, and they encourage people to have a portion of their portfolio in international equities, but at the end of the day, that did nothing in 2008.  It just made the losses larger.  The US dropped 37% and China dropped as well so there was no diversification there.

For a trader’s point of view, it all comes back to knowing that at any given time, the correlations in the markets change, and you have to use a dynamic process and allocate capital appropriately.  So, instead of saying “I have buy signals after a pullback in Germany, Brazil, China, Japan and I’ll take them all,” they’ll ultimately be better just being in the SPY or QQQQ because as the SPY and the QQQQ goes, so go the other markets in times of high correlations.

CR:  I liked what you said in your High Probability ETF Trading book where you mentioned a hierarchy of ETFs in terms of trading strategies and performance.  You wrote that there was a preferred hierchy of country ETFs, Sector ETFs, and then commodity ETFs – tell us what you found and what you mean by that.

Connors:  Sure, we’re reversion to the mean traders, so we’re not trend following traders.  Commodities historically have had edges on Trend Following methods, and when you apply reversion to the mean strategies on commodities, it’s so much more difficult to find edges – you’ll find very small wins but then the commodity will trend strongly and it will wipe out a good deal of the wins that you achieved through smaller positions in trying to fade the trend with reversion to the mean strategies.

This is where the “Turtle” type of trend following strategy has historically held out well, but the drawdowns can be horrific.  Commodities do tend to trend, and if your timeframes are five to ten years, you’ll catch trends when oil or gold is moving up – your system will capture it.

Remember that in system testing, you’ll typically show characteristics of the market you’re studying and in commodity ETFs.  ETFs have characteristics of what the commodity is doing.  In doing mean reversion trading, you’ll see the smallest edges in markets that trend.  We’ll still see edges, but not enough to sacrifice a lot of capital in commodity ETFs vs say an equity sector or country ETF.

Country and Sector ETFs tend to revert to the mean better, which is what our strategies try to capture.  There’s a little bit more risk in a Sector ETF than a Country ETF simply because there is individual stock risk in a sector ETF.  For example, if Intel (INTC) comes out with good or bad news, it will have a large impact on a semi-conductor ETF.

Or in the banking industry, when you take a look at the banking ETFs, some of these banks take up a large portion, and sometimes some banks have disappeared.

However, countries don’t go bankrupt. With the exception of Iceland recently, countries don’t go bankrupt and don’t disappear.  What you’re doing by being in a country ETF, one has been rewarded by having that additional safety.  If we have to choose between being in a Country ETF or a Sector ETF, we’ll choose the Country funds all the time.

CR:  Do you do any kind of analysis on Intermarket Relationships, such as might be taught by Martin Pring, John Murphy, and others?  Such as the relation of bonds to stocks, or the dollar to gold or crude oil, etc.

Connors:  Yes, I’m familiar with John’s and Martin’s work and they both do great work.  We actually don’t do work with that yet in our models because our research cue is so large – it’s definitely been in the cue for a long time and as it stands today, we don’t have quantified research on intermarket relationships, but come back in 3 years and we’ll likely have completed some of our own quantified work in this type of analysis.

CR:  That brings up a good point.  You’ve mentioned having a “gut feeling” for knowing when a strategy idea might not work, and so you won’t take up the time and money to test out the idea that would have indeed wound up not producing an edge or failing as a strategy.

How do you filter the ideas you and your research team have to make the most effective use of your time and money in research, and how might the average trader at home make the best use of time in their own strategy testing.

Connors:  That’s a good question, and I’ve seen it for 27 years – as long as I’ve been doing this.  There will be short-term aberrations in markets that will have a very short-term edge.  An edge will be there and then suddenly disappear.  One recent example is that a lot of funds will take the opposite side of Jim Cramer’s recommendations on his TV show.

CR:  At times, I was one of those traders back in 2007!

Connors:  You and I could compare notes!  I know a lot of traders who were doing that at the time.  Then one day it just stopped.  Maybe it was around November, I don’t remember exactly which year, but that strategy just stopped.  What happened was that something that was making money just stopped.

It didn’t necessarily start losing money but it stopped making money and you could feel it.  The profit from each of these short-term trades against Cramer just started getting less and less as the edge dried up and eventually there was no more edge there.

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Stay tuned for Part 3 of our interview!