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 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 (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 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.
Seasonality will turn positive in six weeks. 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). If Monday falls between November 1 and April 30, my filter rule says to be in the market.
The last positive six-month period was particularly strong for the S&P, capturing a large portion of the post-election rally. Data is from Yahoo Finance.
The next FOMC meeting is next Wednesday, September 20. This means we're inside 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 a 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 phenomena. Data is from Yahoo Finance.
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%, be invested in SPY. Momentum is very positive for the S&P, with the four-week average of twelve-month momentum currently +15%. Data is from Yahoo Finance.
The NAAIM Exposure Index measures how bullish or bearish active investment managers are on the S&P. Like with my momentum metric, my preferred measure of the index is a four-week average to smooth out weekly readings. This indicator is high, meaning active managers are bullish on stocks. I should note that the index has fallen over the past few weeks.
The following chart goes to show how excessive optimism, by itself, can be a faulty signal. Data is from the National Association of Active Investment Managers.
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.
A low put/call ratio means that few investors are buying puts, and are therefore not worried about a sell-off in stocks. The 4-week average of the total put/call ratio is currently 1.08, above my "complacency" cut-off filter of 0.90. Data is from the CBOE.
The yield curve is a popular tool used to forecast the direction of the economy. More often than not, people talk about how a flat or inverted yield curve is bad for markets. I choose to analyze the movement of the curve rather than its static shape. Specifically, I look at how the difference between the two-year Treasury yield (SHY) and the ten-year yield (IEF) has shifted over the past twelve months.
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 portion of the yield curve has flattened by 9 basis points over the past twelve months. My cut-off filter is steepening of +0.50%.
This indicator might be of less use given the current level of interest rates. Rates would have to substantially drop for my +0.50% filter to trigger. 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. High-yield spreads are tight, meaning that investors aren't demanding a substantial interest rate premium to lend to high yield borrowers.
High yield spreads are indeed extremely low, but it should be noted that they stayed near these levels for years in the mid-2000s. Data is from the St. Louis Federal Reserve Economic Database.
The ISM PMI is a diffusion index based on surveyed purchasing managers in the manufacturing industry. It's a leading indicator of economic health. A PMI reading above 50 indicates expansion in the manufacturing sector, below 50 indicates contraction.
The most recent ISM PMI data came in at 58.8, which is a high reading. The highest ISM PMI number since 2009 was 59.9, so it's interesting to see this indicator accelerating so far along in an economic recovery. Data is from the Institute of Supply Management.
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 steadily risen by ~5% over the past few years. The rate of appreciation has rarely been this stable. Data is from Standard & Poor's.
Earnings growth for the S&P 500 is largely driven by sales growth and profit margin expansion. Additionally, share buybacks are a contributing factor in earnings per share growth as buybacks shrink the number of shares outstanding. People view EPS growth as a sign of the improving profitability of American companies. My rule for EPS is as follows: If the twelve-month change in S&P EPS is greater than 0%, be invested in SPY.
S&P EPS has risen by +16.0% over the past twelve months. Data is from Standard & Poor's.
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. Momentum is positive, we're within the FOMC drift period, margin debt growth isn't excessively high, and buyback programs are active. On the flip side, seasonality is negative.
Sentiment data is definitely optimistic. VIX futures are in steep contango, CBOE's total put/call ratio is low, the NAAIM Exposure Index points to bullishness, and spot VIX is also low.
The Treasury yield curve has flattened over the past year and high yield spreads are historically low. Macro data is very strong. Housing prices, S&P earnings, industrial production, and retail sales have all risen over the past year. The ISM PMI is well above 50 and the unemployment rate has continued to fall.
Overall, the composite model is 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 endeavors. Do let me know in the comments below if you have any questions.
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.