Matt Radtke is Senior Researcher for Connors Research. Mr. Radtke graduated magna cum laude from Michigan State University with a degree in computer science. He has 25 years of software development experience in companies large and small, including Hewlett-Packard and Bell Northern Research.
Mr. Radtke has been actively trading stocks, ETFs, and options since 2008. Over the past several years he has become increasingly involved with the Connors Group family of companies, first as a student, then as a member of Chairman’s Club, and finally as a consultant, researcher, and author.
At a high level, there are a handful of major strategic themes for traders of stocks and ETFs, including but not limited to:
- Trend Following: assumes that once a security begins moving in one direction, the overall movement will continue in that direction for some time. The goal is to enter the trade near the start of the trend, and exit soon after the end of it.
- Momentum Trading: similar to trend following, in that the trader is trying to take advantage of the current direction of the price movement. However, momentum trading strategies emphasize the size and strength of the move in addition to the direction.
- Mean Reversion: assumes that when a security makes a strong, short-term move in one direction that it is likely to reverse direction (revert to the mean) in the near future.
- Arbitrage: takes advantage of market inefficiencies such as mispriced assets. This is becoming increasingly difficult in today’s highly computerized trading environment.
- Event Trading: predicts larger-than-usual price moves based on events such as company earnings announcements, government reports on spending and employment, Federal Reserve actions, etc.
For illustrative purposes, let’s assume that you want to build a mean reversion strategy. Long mean reversion strategies usually try to identify a pullback: a sharp drop in price that is likely to be followed by a price increase. Conversely, short mean reversion strategies try to identify a surge: a sudden rise in price that is likely to be followed by a price decrease. Our research has shown that various mean reversion strategies have performed quite consistently over most periods during the past 12-15 years.
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In and of itself, “mean reversion” is not specific enough to qualify as a central thesis. What we’re looking for is a quantifiable idea that we can test for accuracy. Since mean reversion is strongly linked to the concepts of pullbacks and surges, our central thesis should probably focus on the identification of those states.
There are many ways to identify pullbacks. A few of the more popular ones include:
- A momentum oscillator such as Wilder’s RSI or ConnorsRSI falls below a threshold value
- The price closes near the lower Bollinger Band®
- The price of the security closes X% lower than the previous day’s low price or closing price
- The price of the security closes lower than the previous day for Y days in a row
- The price of the security falls below a short-term moving average, but stays above a longer-term moving average
- The price of the security gaps down, i.e. it opens at a price lower than the previous day’s lowest price
In many cases, we can identify a surge just by reversing the general pullback rule:
- A momentum oscillator such as Wilder’s RSI or ConnorsRSI rises above a threshold value
- • The price closes near the upper Bollinger Band®
- The price of the security closes X% higher than the previous day’s high price or closing price
- The price of the security closes higher than the previous day for Y days in a row
- The price of the security rises above a short-term moving average, but stays below a longer-term moving average
- The price of the security gaps up, i.e. it opens at a price higher than the previous day’s highest price
It should be noted that although you can often use the same indicator for long and short strategies, the inverse of the best indicator value for a long strategy may not be the best value for a short strategy. For example, you may find that RSI(2) < 10 is the best long entry criteria, but that RSI(2) > 90 is not the best short entry criteria.
Let’s say that you believe that when a stock gaps up or down on the open, there’s a higher than average likelihood that the stock will reverse direction and “fill the gap”. Thus, stocks that gap down make good long trade candidates, and stocks that gap up make good short trade candidates. That’s a good subjective description of a central thesis, but it still needs to be quantified.
To quantify the central thesis, we just need to express it in terms that can be objectively tested. For the remainder of this series, we will use the following central thesis for long and short trades:
Long Trades: Buy a stock that opens X% lower than the previous day’s lowest price.
Short Trades: Short a stock that opens X% higher than the previous day’s highest price.
Note that we have not yet defined X, the size of the gap as a percentage. When we start testing, we can look at a broad range of values for X, and then refine that range based on our results. Before we can do any testing, however, we need to decide on a universe to test against, and we’ll discuss that next time in Part 3.