This post was originally published on this site
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 (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 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 next FOMC meeting is on June 14, which 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. 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 well above its 200-day moving average. Following this strategy would have kept you in the market since last March. 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, as 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 ~21% 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.
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. Survey respondents have recently gotten more bearish. It’s surprising to see just how quickly sentiment has turned relative to the small size of the correction in U.S. stock market. Data is from the AAII.
The NAAIM Exposure Index measures how bullish or bearish active investment managers are on the S&P. My preferred measure of the index is a four-week average. This metric has recently risen, meaning active managers have gotten more bullish on the S&P. It’s interesting to see the divergence between NAAIM and the AAII survey. Data is from the National Association of Active Investment Managers.
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%. Data is from the St. Louis Federal Reserve Economic Database.
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 typically demand a higher interest rate on high-yield bonds and cause spreads to widen. High-yield spreads are quite low, but it should be noted that this doesn’t necessarily mean spreads will widen out. High-yield spreads stayed extremely low for years between 2004 and 2007. Data is from the St. Louis Federal Reserve Economic Database.
We received new data on industrial production last week. 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 now up 2.2% over the past year, above my cut-off filter of 0%. Data is from the St. Louis Federal Reserve Economic Database.
The unemployment rate is the percentage of the total 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 4.4%, below its 12-month average of 4.8%. Data is from the St. Louis Federal Reserve Economic Database.
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 +9.3% over the past twelve months. Data is from Standard & Poor’s.
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. The trend is up, buyback programs are active, and we’re within the pre-FOMC drift window. It should be noted that the April 30 to November 1 period has historically been weak.
Regarding sentiment, it’s a bit of a mixed bag. NAAIM’s Exposure Index and a very low spot VIX point towards a lot of complacency in the markets. On the other hand, the contraction in SPY shares outstanding and an increase in AAII bears point to fear.
Macro data is extremely strong. The unemployment rate is trending lower and the ISM PMI is north of 50. Industrial production, housing prices, retail sales, and S&P earnings have all risen over the past year.
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.