For long-time readers, the current composite model reading is 0.86.
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 500. 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 free Simple Stock Model website:
The above data is from Yahoo Finance. The graph shows the price momentum indicator in 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-and-hold performance for SPY.
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 more reliable data 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 over the past week. Let's start with the technicals.
Technical Data for the S&P 500
Margin debt increases as investors pledge securities to obtain loans from their brokerage firm. FINRA releases margin debt data on a monthly basis. It's important to avoid looking at the nominal amount of margin debt outstanding. Any credit-based indicator will steadily grow over time as the economy expands. Instead, I like to look at the yearly percentage change in margin debt. Most would say to avoid the market if margin debt quickly expands, and there is merit to that. Both previous major market tops were preceded by rapid increases in margin debt.
Overall, though, positive annual growth in margin debt has actually been a positive sign for future short-term S&P returns. My cut-off filter avoids long exposure to the S&P 500 if the year-over-year (YoY) change in margin debt is negative. Margin debt has grown by 10.4% over the past year. Data is from FINRA.
We’re about to enter the buyback blackout window, where some companies suspend share buybacks in the five-week period leading up to their scheduled earnings announcements. In a year where total buybacks are expected to be higher than ever, I think this is worth watching. It's important to note that this buyback blackout window is different for each company. My time window covers the five-week period before a majority of companies report earnings.
Since the first Q2 earnings announcements take place in mid-July, the five week blackout window starts in mid-June. Data is from Yahoo Finance.
We just had a FOMC meeting, therefore we just exited the historically positive pre-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. My model calculations are as of the weekend, so the composite model still flags this indicator as being invested. Data is from Yahoo Finance.
Many people think the "Sell in May and go away" anecdote is a joke. 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 Mondays fall between November 1 and April 30, my filter rule says to be in the market. We’re currently outside of this period, and thus, seasonality isn’t constructive for the S&P. Data is from Yahoo Finance.
The classic 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 four-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, meaning it's in an uptrend.
Following this trend strategy would have kept you invested in the market since March 2016. The main benefit of long-only trend-following strategies is not in higher returns, but instead, through (hopefully) lower volatility. 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 the dynamic.
The VIX futures curve is currently in contango. I should also note that the front part of the VSTOXX futures curve is also in contango. Data is from the Chicago Board Options Exchange (CBOE) and Barchart.com.
CBOE 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 lower end of its historical range. The current 4-week average of the total put/call ratio, 0.94, is just above my cut-off filter of 0.90. Data is from CBOE.
The National Association of Active Investment Managers (NAAIM) asks its members each week what their average level of exposure is to the U.S. stock market. High exposure is an indicator of optimism among active money managers.
The current 4-week average of the NAAIM Exposure Index is 86%, above my cut-off filter of 85%. Data is from NAAIM.
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 demand and the number of shares outstanding is rapidly increasing, that’s typically been a sign of excess optimism. Vice-versa for redemption and pessimism.
My cut-off filter for this metric is if the 4-week average of the 3-month change in the number of SPY shares outstanding is greater than 5%. It’s currently -2.7%, meaning the number of SPY shares outstanding has contracted over the past few months. Data is from State Street.
A weekly sentiment survey has been conducted by the American Association of Individual Investors (AAII) for decades. 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. Survey respondents have recently grown less bullish and more bearish. Data is from the AAII.
Interest Rate Data for the S&P 500
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 (BIL) yield. LIBOR reflects the rate at which banks borrow from each other on an unsecured basis. This article is an interesting look at how interbank lending basically disappeared after 2008.
The perceived risk in the banking sector is said to grow as the spread between LIBOR and T-bills increases. The TED spread has recently decreased as 3-month US Treasury bill rates have increased faster than 3-month USD LIBOR. The TED spread is currently 0.41%, below my cut-off filter of 0.75%. Data is from the St. Louis Federal Reserve Economic Database.
The difference between the interest rate of a high yield (HYG) bond and a Treasury of comparable maturity is called a high yield spread. A narrow spread means investors are demanding less of a yield increase relative to US Treasuries of a comparable maturity. You can infer investor probability of higher-risk U.S. corporations being able to service their debts through high yield spreads. High spreads = higher expected defaults; lower spreads = lower expected defaults.
My cut-off filter is if high yield spreads trade above their 12-month moving average. Currently, US high yield spreads are 3.44% and just below their 12-month average. Data is from the St. Louis Federal Reserve Economic Database.
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 (SHY) and the 10-year yield (IEF) has shifted over the past 12 months. This is also referred to as the “10s2s” spread.
The yield curve has been a popular topic in 2018. The common narrative is that a flattening or inverted curve is ominous for the stock market. It’s true, an inverted curve has historically preceded most US recessions.
That being said, a rapidly steepening curve has actually been more detrimental (at a given point in time) 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 10s2s spread has flattened by 43 basis points over the past 12 months. My cut-off filter is steepening of more than 50 basis points. I’ll be the first to admit that short-term rates are in a weird spot now, and that this indicator might be less useful going forward. Data is from the U.S. Treasury.
Macroeconomic Data for the S&P 500
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. People view EPS growth as a sign of the improving profitability of American companies.
If the yearly percentage change in nominal EPS is equal to or below 0%, my filter rule says to be out of the market. S&P EPS have risen by +16.2% YoY. Data is from Howard Silverblatt of S&P DJI.
There are a variety of indices that monitor housing (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.
The ISM PMI is an index based on surveyed purchasing managers in the manufacturing industry in the United States. It's regarded as a leading indicator of economic health. A PMI reading above 50 is said to indicate expansion in the US manufacturing sector, while below 50 indicates contraction.
The current US ISM PMI is 58.7, above my cut-off filter of 50. Data is from the Institute of Supply Management.
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. The current unemployment rate is 3.8%, below its 12-month average of 4.2%. Data is from the St. Louis Federal Reserve Economic Database.
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.
Real retail sales have risen by 2.2% over the past year. 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 3.5% over the past year, above my cut-off filter of 0%. Data is from the St. Louis Federal Reserve Economic Database.
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. 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's average score and the overall composite score.
I’ll conclude with a summary. Technical data is mixed. Margin debt has risen over the past year, we’re about to enter the buyback blackout window, and we just exited the historically positive pre-FOMC drift period. S&P seasonality is a headwind, and the short-term price trend is still up.
Sentiment data is also mixed. The VIX futures curve is in contango, CBOE’s total put/call ratio is low, but not extremely so, NAAIM’s Exposure Index is high indicating optimism, and the spread between bulls and bears in the AAII survey has recently increased as the number of bulls has grown.
The TED spread has fallen, US high yield spreads are still tight (but they’re not above their 12-month average), and the US 10s2s Treasury yield curve has flattened by 43bps over the past twelve months.
US macro data is strong. S&P EPS, housing prices, real retail sales, and industrial production have all risen over the past year. The US ISM PMI is 58.7, indicating a healthy manufacturing sector. The US unemployment rate is low, but continues to trend lower.
Overall, the composite model is still long. This is because the composite score is 0.86, above the cut-off filter of 0.60.
Be sure to "Follow" me to track future updates. I hope this article can help you out in your own investing process. If you found it useful, please forward it to others investors and traders.
Do let me know if you have any questions in the comments below, I’m happy to help out.
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.