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.
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, meaning it's in an uptrend.
Following this trend strategy would have kept you invested in the market since last March. Data is from Yahoo Finance.
The market just exited the buyback blackout period. During this period, companies suspend their share repurchase programs in advance of earnings announcements.
The bulk of Q2 earnings season is now behind us. Companies typically suspend buybacks (and insider transactions) in the five-week period before their scheduled earnings announcements. It's important to note that this buyback blackout window is different for each company, and my period covers the five-week period before a majority of companies report earnings.
As a percentage of total NYSE volume, corporate buybacks have increased over the past few years. Now that the majority of buyback programs are active, you can interpret that as a bullish sign. Data is from Yahoo Finance.
Growth in margin debt occurs when investors pledge securities to obtain loans from their brokerage firm. The NYSE releases margin debt data on a monthly basis.
It's important to avoid looking at the nominal amount of margin debt outstanding, like most margin debt charts do. 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. Historically, positive annual growth in margin debt has actually been a positive sign for future short-term S&P returns. Margin debt has risen by ~20% over the past year, matching the yearly growth in the S&P. It's nowhere near the excessive highs seen in 2000 and 2007. Data is from the NYSE.
The VIX (VXX) 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 further out in time. The VIX futures curve is typically in contango.
When the curve is downward sloping, the curve 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. This happens when people expect volatility to mean-revert and drop over time.
The VIX futures curve has been in contango since the French election ended. Short vol trades like long XIV have crushed it because realized volatility has constantly been less than implied volatility. Data is from the 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 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 0.85, below my "complacency" cut-off filter of 0.9. Data is from the CBOE.
A weekly sentiment survey has been conducted by the American Association of Individual Investors 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 number of ways to analyze AAII data, and I choose to use the spread between the percentage of bulls and percentage of bears. The number of AAII bears has recently decreased. Data is from the AAII.
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 yield (SHY) and the ten-year yield (IEF) has shifted over the past twelve months.
Historically, a rapidly steepening curve has 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 steepened by 4 basis points over the past twelve months. My cut-off filter is +0.50%. Data is from the U.S. Treasury.
The difference between the interest rate of a high yield (HYG) bond and a Treasury (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 tight, meaning that investors aren't demanding a substantial interest rate premium to lend to high yield borrowers.
High yield spreads are very low now, 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.
We received new data on the unemployment rate last week. 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 negative for economic growth. The current unemployment rate is 4.3%, below its 12-month average of 4.7%. Data is from the St. Louis Federal Reserve Economic Database
We also received new data from the Institute of Supply Management (ISM). 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 new ISM PMI number came in at 56.3, a healthy reading. Data is from the Institute of Supply Management.
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 is up 2.0% over the past year, above my cut-off filter of 0%. Data is from the St. Louis Federal Reserve Economic Database.
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.
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 mostly strong. The trend is up, buyback programs are active, and NYSE margin debt has risen over the past year. We are in a seasonally weak period and outside of the historically positive pre-FOMC drift window.
Sentiment studies point to a lot of optimism in the market. Spot VIX is at multi-decade lows, CBOE's total put/call ratio is extremely low, VIX futures are in steep contango, and NAAIM's Exposure Index is elevated. The number of AAII bears has decreased over the past few weeks.
High yield spreads are tight. The Treasury 2s10s curve has slightly steepened over the past year. At 0.21%, the TED spread is at 18-month lows.
Macro data is extremely strong. Last week's data on the unemployment rate and ISM PMI fell in line with expectations. Industrial production, housing prices, retail sales, and S&P earnings are all up over the past year.
Overall, the composite model is long. This is because the composite score is 0.77, 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.