For long-time subscribers, the current composite model reading is 0.86.
In a previous article, I outlined both the purpose and construction of my 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 think it’s short-sighted to base an opinion 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.
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, meaning it’s either 100% long the S&P 500 or 100% in cash. 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 the Simple Stock Model website:
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 blue line in the top portion plots the historical performance of following the filter rule. The white line shows buy & hold performance for the SPY ETF.
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 at 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 start with technicals.
Technical Data for the S&P 500
We just exited the buyback blackout window. Companies suspend share buybacks in the five-week period leading up to their scheduled earnings announcements. They then restart their buyback programs after they announce earnings. Since most companies have announced Q1 earnings, most buyback programs are active again.
It's important to note that this buyback blackout window is different for each company. My period covers the five-week period before a majority of companies report earnings. I personally think paying attention to buybacks is important this year. Share repurchases were the #2 most commonly mentioned use of repatriated cash. The below graph is from Bessemer Trust.
Corporations have followed through and announced buybacks in February were $153.7 billion, eclipsing the previous monthly record for $133 billion in April 2015. Data for the below graph is from Yahoo Finance. As you can see, the S&P has typically outperformed in periods when most buyback programs were active.
Short-term price momentum is positive for the S&P. The momentum effect is one of the strongest and most pervasive financial phenomena. Researchers have verified its efficacy as an alpha-generating strategy with many different asset classes.
My rule for momentum is as follows: If the four-week average of 12-month total return momentum as of Friday's close is greater than 0%, stay invested in SPY. The four-week average of SPY's 12-month momentum is currently +14.8%. Short-term trend measures have deteriorated more, but overall most trend-based signals still indicate an uptrend for the S&P. Data is from Yahoo Finance.
The next FOMC meeting is June 13. This means we're outside of the historically positive FOMC drift period. The FOMC drift is the tendency of equity prices to rise more often than average in the days leading up to an FOMC meeting. My pre-FOMC period is defined as 20 trading days. Since 1993, only being long during this period has matched the returns of a buy and hold strategy, while being invested in the S&P ~70% of the time.
Click here to read a paper from Federal Reserve staffers on this phenomenon. Data is from Yahoo Finance.
Many people ignore the "Sell in May and go away" rule of thumb. Surprisingly enough, the strategy has worked well over the past few decades in the US (and over the past few centuries in the UK stock market). If Monday falls between November 1 and April 30, my filter rule says to be in the market. We just exited this seasonally strong period, so the seasonal outlook for the S&P isn’t constructive over the next few months. Data is from Yahoo Finance.
Sentiment Data for the S&P 500
The VIX futures curve is made up of prices of individual VIX futures contracts. When the curve is upward sloping from left to right, the curve is said to be in contango. Contango means that market participants expect implied volatility to be higher in the future. The VIX futures curve is typically in contango. When the curve is downward sloping from left to right, it is said to be in backwardation. In this scenario, near-term VIX futures are more expensive than long-term futures, meaning that investors expect volatility in the short term to be very high.
It’s important to note that long volatility products don’t drift lower over time due to contango, instead the main culprit is that implied volatility (as measured by the VIX) typically trades richer than subsequently realized volatility. This article by Vance Harwood does a good job of explaining this dynamic.
The VIX futures curve is currently in slight contango. I should also note that the front part of the VSTOXX futures curve is also in contango. Data is from CBOE.
The Chicago Board Options Exchange reports three different put/call ratios: total, index, and equity. The total put/call ratio combines the latter two. I 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 and traders are complacent and not buying equity protection in the form of puts.
CBOE's total put/call ratio is in the middle of its historical range. At 0.95, the current 4-week average of the total put/call ratio is above my cut-off filter of <0.90. Data is from CBOE.
A weekly sentiment survey has been conducted by the American Association of Individual Investors (AAII) for many years. The AAII asks participants if they are bullish, neutral, or bearish on stocks over the next six months. Survey results are typically used as contrarian indicators, meaning extreme bullishness is perceived as bearish, and vice-versa.
There are a bunch of ways to analyze AAII data. I personally examine the spread between the percentage of bullish respondents and the percentage of bearish respondents. This spread has decreased as survey respondents have grown less bullish and more bearish over the past few months. Data is from the AAII.
Interest Rate Data for the S&P 500
The yield curve is a popular tool used to forecast the direction of the economy. I choose to analyze the movement of the curve rather than its static shape. Specifically, I look at how the difference between the 2-year Treasury yield (NYSEARCA:SHY) and the 10-year yield (NYSEARCA:IEF) has shifted over the past 12 months.
Over the past year, I’ve seen more and more people talk about the yield curve flattening and why that’s a bad sign. Historically, a rapidly steepening curve has actually been more detrimental for stocks than a flat or inverted curve. In a steepening curve, short-term rates fall faster than long-term rates. In the past, steepening yield curves have been associated with the Federal Reserve quickly lowering the Federal Funds rate during a recession.
This 10-2 year portion of the yield curve has flattened by 60 basis points over the past 12 months. My cut-off filter is steepening of more than 50 basis points. Data is from the U.S. Treasury.
The difference between the interest rate of a high yield (NYSEARCA:HYG) bond and a Treasury of comparable maturity is called a high yield spread. The narrower the spread, the more optimistic investors are about the probability of higher-risk 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. This weekend I stumbled across this great high yield and bank loan outlook by Guggenheim.
My cut-off filter is if high yield spreads trade above their 12-month moving average. Currently, US high yield spreads are 3.50% and just below their 12-month average. Data is from the St. Louis Federal Reserve Economic Database.
The TED spread is frequently cited as a measure of credit risk in the banking sector. The spread reflects the difference between two short-term interest rates: 3-month USD LIBOR and the 3-month U.S. Treasury (NYSEARCA:BIL) yield. LIBOR reflects the rate at which banks borrow from each other on an unsecured basis.
The perceived risk in the banking sector grows as the spread between LIBOR and T-bills increases. The TED spread has increased as the rise in LIBOR has outpaced the rise in 3-month US Treasury rates. The TED spread is currently 0.55% and below my cut-off filter of 0.75%. Data is from the St. Louis Federal Reserve Economic Database.
Macroeconomic Data for the S&P 500
The unemployment rate is the percentage of the total U.S. workforce that is unemployed and actively seeking employment during the previous month. It is a lagging economic indicator, but a persistently rising unemployment rate indicates a weak labor market and thus potentially weak consumer spending. Since our economy is heavily dependent on consumer spending, a rising unemployment rate is typically negative for future economic growth.
Last week’s unemployment rate data came in at a 17-year low of 3.9%. This is below its 12-month average of 4.2%. Data is from the St. Louis Federal Reserve Economic Database.
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.
Industrial production has risen by 4.3% over the past year, a historically strong reading and above my cut-off filter of 0%. Data is from the St. Louis Federal Reserve Economic Database.
There are a variety of indices that monitor housing (NYSEARCA:VNQ) 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.
According to this metric, US housing prices have risen by 6.4% over the past twelve months. Data is from Standard & Poor's.
We also received new ISM PMI data last week. The ISM PMI is an index based on surveyed purchasing managers in the manufacturing industry in the United States. It's a leading indicator of economic health. A PMI reading above 50 indicates expansion in the manufacturing sector, below 50 indicates contraction.
Last week’s ISM PMI of 57.3 was slightly below expectations, but it’s still a very strong reading and above my cut-off filter of 50. Data is from the Institute of Supply Management.
Composite Model for the S&P 500
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 21 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 let's summarize everything. Technical data is mixed. Corporate buybacks will likely increase over the next few weeks, the short-term trend is still higher, margin debt has increased by 11.7% over the past year, and S&P seasonality is weak heading into the summer, and we’re outside of the historically positive pre-FOMC drift period.
Sentiment data is leaning pessimistic. Both the AAII survey and NAAIM’s Exposure Index reveal increased investor anxiety, CBOE's total put/call ratio is in the middle of its historical range, spot VIX has come down a bit, and the VIX futures curve is back in contango.
The US 2/10 yield curve has flattened by 60 basis points over the past year. US high yield spreads are very low but still below their 12-month average. The TED spread has recently risen.
US macroeconomic data is still very strong. Industrial production, real retail sales, housing prices, and S&P EPS are all up YoY. The US ISM PMI of 57.3 indicates strength in the nation’s manufacturing sector and the US unemployment rate of 3.9% is low and below its 12-month average.
Overall, the composite model is still long. This is because the composite score is 0.86, 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 process. Do let me know if you have any questions in the comments below.
Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.
Additional disclosure: The author does not make any representations or warranties as to the accuracy, timeliness, suitability, completeness, or relevance of any information prepared by any unaffiliated third party, whether linked in this article or incorporated herein. This article is provided for guidance and information purposes only. Investments involve risk are not guaranteed. This article is not intended to provide investment, tax, or legal advice. Performance shown for each indicator is of a simulated hypothetical model. Performance is simulated and hypothetical and was not realized in an actual investment account. Performance includes reinvestment of all dividends. All risks, losses and costs associated with investing, including total loss of principal, are your responsibility.