In a previous article, I outlined both the purpose and construction of the Simple Stock Model. Keep reading for a quick run-down if you're new to the model; otherwise, you can skip down to "Technicals" for the updated data.
Investors are constantly exposed to sound bites and data points presented without any proper context. You might have read an article about how stocks have historically bounced when sentiment has reached a negative extreme. Or that you should be out of the market if it's trading below its 200-day moving average.
When I come across articles like that, I always thought it was shortsighted to base an opinion on the S&P on only one indicator without also considering a wide variety of other inputs.
The goal of the model is to help you form a data-based outlook on the S&P. Additionally, at the end of this article, I showcase a composite model that incorporates all of the indicators I use, so your view can be comprehensive, as opposed to having tunnel vision on only one indicator.
How the Model Works
Each article is broken down into four main sections: technicals, sentiment, rates and macro. Each section includes a number of different indicators. For each indicator, there's a "filter rule" for when to be out of the market. In the spirit of simplicity, the filter rule is always binary, dictating either 100% long exposure to the S&P or a 100% cash position. The S&P is represented by the SPDR S&P 500 Trust ETF (NYSEARCA:SPY). Let's dive into an example graph. All graphs are from Simple Stock Model.
The above data is from Yahoo Finance. The graph shows the price momentum indicator within the technicals section. The bottom portion plots the momentum metric over time and the top portion plots the historical performance of following the filter rule.
For each indicator, new data each weekend is used to generate a long SPY or cash position for the next week. For the above momentum example, SPY's dividend-adjusted close as of Friday is the main input. Using this, I calculate the 12-month total return. For each indicator on this site (except for the macro data), I take a four-week average of the main indicator input.
So, for this example, I'm taking the four-week average of 12-month total return momentum. Why four weeks? To reduce false positives and whipsaws when an indicator is bouncing slightly above or below its filter rule. There's nothing special about a four-week average. You could use two or eight weeks and reach similar results.
Data is compiled as of Friday's close. Buying or selling decisions occur on Monday's close. I do this, as opposed to making trades at Monday's open, simply because I had a more reliable data source for dividend-adjusted close data. It's also important to reflect realistic transaction costs. Each simulated historical performance graph factors in a $10 trade commission and a 0.02% spread on SPY for each buy or sell. Commissions and spreads are lower now, but considering SPY started in 1993, I chose to use these above-average numbers.
Now you understand the methodology behind the model. Each week, I'll cover a handful of indicators, especially those that have changed positioning over the past week. Let's get started with some technicals.
Most people dismiss the saying "sell in May and go away." Surprisingly enough, the strategy has worked well over the past few decades (and over the past few centuries in the UK stock market). March and April are within the historically best six months of the year for the market. If Monday falls between November 1 and April 30, my filter rule says to be in the market. This most recent seasonal period since November 1 has been particularly positive. Data is from Yahoo Finance.
The age-old trend following approach is to have long exposure to the S&P, if the index is above its 200-day moving average. That works, but you get whipsawed with a lot of false signals. That's why I use a 4-week average of SPY's distance relative to its 200-day moving average. It's a bit slower on catching big moves but signals fewer false positives. The S&P is currently above its 200-day moving average. A simple trend following strategy has historically protected you from the majority of bear markets. Data is from Yahoo Finance.
The Chicago Board Options Exchange reports three different put/call ratios: Total, index and equity. The total put/call ratio combines the latter two measures. I choose to analyze the total put/call ratio since it gives the most comprehensive view of options market sentiment. Historically, it's worked out well to cut exposure to the S&P when the ratio is low. A low put/call ratio means that few investors are buying puts, and are therefore not concerned about a sell-off in stocks. The 4-week average of the total put/call ratio is currently 0.903, just slightly above my cut-off filter of 0.9. Data is from the CBOE.
State Street launched SPY, the ETF that this article runs all of its analysis on, in 1993. State Street started providing shares outstanding data for SPY in 2006. The number of SPY shares outstanding grows or shrinks based on the creation and redemption activity of authorized participants. Basically, when SPY is in hot demand and the number of shares outstanding is rapidly increasing, that's typically been a sign of excess optimism. My rule for this indicator is if the 4-week average of the 3-month change in SPY's percentage of shares outstanding is greater than +5%, be out of the market. The 4-week average of the 3-month change is currently +5.8%. Data is from State Street.
The difference between the interest rate of a high yield (NYSEARCA:HYG) bond and a Treasury (NYSEARCA:IEF) of comparable maturity is called a high-yield spread. The narrower the spread, the more optimistic investors are about the probability of risky U.S. corporations being able to service their debts.
When investors grow more uncertain, they will demand a higher interest rate on high-yield bonds and cause spreads to widen. High-yield spreads are extremely low, but it should be mentioned that they can stay depressed for a long period of time. Spreads remained under 4% for two years, from June 2005 to July 2007. Data is from the St. Louis Federal Reserve Economic Database.
The TED spread is frequently cited as a measure of credit risk in the overall economy. The spread reflects the difference between two short-term interest rates: 3-month USD LIBOR and the 3-month U.S. Treasury yield (NYSEARCA:BIL). LIBOR reflects the rate at which banks borrow between each other on an unsecured basis. The perceived risk in the banking sector grows as the spread between LIBOR and T-bills widens out. The TED spread is below my cut-off filter of 0.75% and has recently fallen as short-term Treasury yields have risen quicker than LIBOR.
Retail sales reflect the total value of sales at the retail level. It's a primary measure of consumer spending which accounts for the majority of economic activity in the U.S. I like to look at real retail sales data that is adjusted for inflation. While last week's data on retail sales came in below expectations, real retail sales are still up a solid 2.8% over the past year. Data is from the St. Louis Federal Reserve Economic Database.
There are a variety of indices that monitor housing prices, and I choose to use the 10-city index that Karl Case and Robert Shiller developed. The S&P/Case-Shiller 10-City Composite Home Price Index measures the change in value of residential real estate in 10 metropolitan areas of the U.S. House prices have risen by 4.9% over the past year. Data is from Standard & Poor's.
Last week's industrial production data came in below expectations, with a flat MoM change. Industrial production measures the total value of output for all manufacturing, mining, and electric and gas utility facilities located in the United States. It's a key economic indicator and is a good way to quickly gauge how the manufacturing portion of the U.S. economy is doing. With last week's data, industrial production is now up 0.3% over the past year. My cut-off filter is if IP is equal to or below 0% over the past year. Data is from the St. Louis Federal Reserve Economic Database.
That wraps up the weekly update on some of the individual indicators. Now for the composite model.
Think of each indicator as a building block that helps form an overall opinion. One study might say current sentiment has historically been bullish on stocks. Who cares? That's just one data point in isolation. I'm interested in a bigger-picture view with more context. A picture that also factors in what's going on with macro data, interest rates, etc. The composite model does just that.
Here's how it works: Each indicator is given a score of 1 or 0 depending on its current reading relative to its filter rule. If S&P earnings are down over the past year and the filter rule for that metric is to be out of the market if yearly earnings growth is below 0%, then that indicator gets a 0. The table below summarizes data from all the previous sections and assigns a 1 or 0 to each indicator based on its current reading.
All 22 indicators are averaged to form the composite score. If the composite score is greater than 0.6, the model is invested in SPY. Think of 0.6 as the overall filter rule for the composite model.
There's nothing special about 0.6 - it results in being invested in SPY about 80% of the time. I could have used a higher filter rule like 0.75 to only be exposed to the S&P when more indicators are saying to be invested, but this results in less time exposed to the market since it's a "stricter" cut-off. The chart below plots each individual category average score and the overall composite score.
So where do we stand? Technical data is strong but getting weaker. The S&P is trending up, seasonality is strong, the NYSE margin debt has risen over the past year. On the negative side, we're within the buyback blackout window when companies start to suspend their buyback programs and we're outside of the historically positive pre-FOMC drift period.
Regarding sentiment, most indicators reveal a lot of optimism in the stock market. CBOE's total put/call ratio is very low, the number of SPY shares outstanding has quickly expanded, spot VIX is low, and the NAAIM Exposure Index is extremely high. The AAII survey is the most pessimistic sentiment indicator I track.
High yield spreads didn't change last week, although they are still very low when compared to this time last year. The TED spread hasn't done much for months. Real retail sales, housing prices, and industrial production are all up over the past year. These are good signs. Also positive, the ISM PMI is north of 50 and the unemployment rate is below its long-term average.
Overall, the composite model is still long. This is because the composite score is 0.73, above the cut-off filter of 0.60.
I update all of the individual indicators and the composite model each week, so be sure to follow me to track future updates!
I hope this article can help you out in your own investing endeavors. Do let me know in the comments below if you have any questions.