In a previous article, I outlined both the purpose and construction of the 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 in to an example graph. All graphs are from simplestockmodel.com.
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 4-week average of the main indicator input.
So for this example, I'm taking the 4-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 4-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 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 S&P has positive short-term price momentum in its favor. 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. Data is from Yahoo Finance.
We also just entered the corporate buyback blackout period before Q3 earnings get announced. As a percentage of total NYSE volume, corporate buybacks have increased over the past few years. Most companies suspend both insider transactions, and share repurchases during the five weeks leading up to their quarterly earnings announcements. Data is from Yahoo Finance.
The Chicago Board Options Exchange reports three different put/call ratios: total, index, and equity. The index ratio calculates the volume of index puts traded relative to index calls. The equity ratio calculates the same ratio but for individual stocks. The total put/call ratio combines both measures. I choose to analyze the total put/call ratio since it gives the most comprehensive view. Historically, it's actually paid to have exposure to the S&P when the put/call ratio is high. Currently, the total put/call ratio is elevated. Data is from the CBOE.
State Street launched SPY, the ETF that this site 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 hot 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. Currently, interest in SPY has been increasing as shown by the number of shares outstanding slowly creeping up. Data is from State Street.
The yield curve is a popular tool that is used to try 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. Specially, I look at the difference between the 2-year yield (NYSEARCA:SHY) and the 10-year yield (NYSEARCA:IEF).
Historically, a rapidly steepening curve has actually been more detrimental for stocks than a flat or inverted curve. In a steepening yield 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.
Currently, the curve has been steepening a tad as long-term rates have risen quicker than short-term rates. Data is from the U.S. Treasury.
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 current ISM PMI is 49.4. Data is from the Institute of Supply Management.
Over the past twelve months, the growth in nominal S&P 500 earnings-per-share has been negative. Historically, this has been a bad sign for future short-term returns. 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 for 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.
For each category of indicators (technicals, macro, etc.), an average is calculated. Currently, the average score for technicals is 0.4. All four category average scores are then 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 2/3rd 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 charts below plot each individual category average score and the overall composite score.
So where do we stand? Technical data is a mixed bag, since on one hand the market does have positive short-term price momentum and the trend is clearly up. On the other hand, we're outside of the FOMC drift window, corporations are starting to pause share buyback programs, and we're in a seasonally weak time of the year.
Some sentiment measures like the NAAIM Exposure Index reflect excess optimism. Others, like the AAII survey and CBOE's total put/call ratio, reflect a fair amount of pessimism in the markets. Also, the yield curve has steepened slightly.
Regarding macro data, the manufacturing sector is notably weak an annual S&P earnings-per-share growth is negative over the past year. There are positive signs, with labor market data coming in strong and the growth in retail sales continuing to chug along at ~2% per year.
Overall, the composite model is still invested. This is because the composite score is 0.63, above the cut-off filter of 0.60. 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 information in this article is for personal use only. Investing involves a great deal of risk, including the loss of all or a portion of your investment. Nothing in this article should be construed as investment advice or as a warranty of investment results. All risks, losses and costs associated with investing, including total loss of principal, are your responsibility.